But the advantage given by data isn’t free— it takes work to get the most out of data while avoiding the regulatory pitfalls. Digital transformation must happen for companies to truly use data to their advantage. Companies need the right frameworks, policies, and tools to maximize their data’s value.

Digital transformation starts with understanding customer needs. Companies must ensure that their customers’ data is collected and utilized ethically and securely.

That’s where Data Lifecycle Management (DLM) comes in. 

Data Lifecycle Management is businesses’ most widely used approach to handling data.

Praised for its simplicity, Data Lifecycle Management makes it easy to collect, store, use, and dispose of data. It helps businesses mitigate the risks associated with carrying data and ultimately leads to a better understanding of what it takes to leverage data successfully.

We’ve put together this guide on everything you need about Data Lifecycle Management. Armed with this knowledge, we’ll prepare you to take your business to the next level and tap into the benefits of data.

What Is Data Lifecycle Management (DLM)? 

The given definition for DLM is “the application of a set of principles, processes, and technologies for data management and storage.”

But what does that mean?

Put simply, DLM helps businesses get the most out of data. It’s a comprehensive set of practices that not only help enterprises to capture better data but also ensures they’re handling that data correctly.

DLM covers many essential data management areas, including data governance and security, storage, archiving and disposal, backup/recovery, scalability/performance tuning, analytics, and reporting. It also includes several additional practices that can help optimize how businesses handle their data. 

DLM aims to create a more efficient and cost-effective data storage system that allows businesses to get the most out of their data. DLM helps organizations develop faster, more accurate business insights, reduce operational costs, and support agile decision-making.

Understanding The Importance of Data Lifecycle Management 

In an increasingly data-driven world, large amounts of data are unwieldy and difficult to manage. Without DLM, a data scientist looking for the proper subset of data is like looking for a needle in a haystack, but all of the hay is just numbers and letters on a screen.

DLM helps ensure the right data is in the right place at the right time. This makes it easier for data scientists or analysts to draw meaningful, relevant, and timely conclusions. It helps analysts generate more value for businesses more quickly.

Understanding The Chief Data & Analytics Officer Roles In Future Enterprises is critical to implementing an effective DLM process. A CDO is responsible for delivering data and analytics solutions and services that enable organizations to achieve business objectives through data assets.

Furthermore, if data is valuable to a business, it benefits competitors and malefactors. Companies must protect data. They must handle all data in adherence to internal and external regulations— the consequences for failing to do so are dire.

Data lifecycle management streamlines the process of protecting and managing data. 

Neglecting to leverage data lifecycle management is a dangerous game with little return.

The Three Primary Objectives Of Data Lifecycle Management 

1. Data Security & Confidentiality 

For human-focused companies, data often comes from information about people, whether customers or employees.

Employee or customer data is often personal and sensitive. Collecting it requires permission, which comes with a responsibility to protect the data from unauthorized access. That’s why data security should be a top priority for businesses.

DLM provides guidance on how to collect data legally and share data securely. The framework has been developed with data protection policies at the core. 

It helps maintain confidentiality by ensuring businesses delete data thoroughly.

2. Data Integrity 

Data is only useful if you trust it, which is why data maintenance is critical to businesses. When using data to inform major business decisions, you must be able to count on the data being reliable, precise, and accurate.

Poor data integrity can be dangerous, leading you to draw incorrect conclusions with confidence.

DLM helps you maintain the integrity of any collected data by outlining how that data should be handled, copied, and stored.

3. Data Availability 

Data by itself does nothing. Teams of people need to analyze and manipulate it. While one objective of DLM is to protect data from those who shouldn’t be able to access it, another objective is to keep it readily available to those who can access it. 

Data lifecycle management aims to ensure the teams who need to access data can, exactly when they need to, with minimal friction.

Notably, DLM also aims to make data archiving more effective. By following processes for data archival, businesses are less likely to delete data and regret it because they need it later.

The Five Stages Of The Data Management Lifecycle 

According to the data lifecycle management framework, these five stages are designed to break down the data life cycle into manageable chunks. 

They’re written to apply to all businesses, which means you might have additional business rules for data processing, which you may want to implement at the appropriate stage.

1. Collect, Generate & Evaluate 

Data collection methods are varied and abundant. 

Enterprise applications, IT infrastructure, and internet of things (IoT) devices are just a few examples of quantitative data collection.

Surveys, feedback forms, and opinion polls are examples of qualitative data collection.

The general idea with data creation is “more is better.” But don’t be afraid to use intuition to judge whether a particular data creation initiative wastes resources.

After data collection comes data classification, where you should evaluate data for potential uses and classify it so that it’s easier to find and refer to later.

If you gather data but believe it won’t be helpful, you should discard it at this stage— it’s an early opportunity to carry out some data cleaning.

2. Store, Manage & Secure 

Once collected, you need to store data somewhere. But the best solution for storing data depends on the kind of data.

Quantitative data is usually considered “structured data.” Cold hard metrics, numbers, and objective values are relational. That means it’s easy to relate and compare to other relational data sets.

It’s common to store such data in a relational database system.

Conversely, qualitative data is usually “unstructured data.” It’s text-heavy and not relational, so storing it in a non-relational database (like NoSQL) is more common.

Data protection is also critical at this stage. You should encrypt sensitive data for the sake of data protection. Data storage is a tricky business, and there are governmental policies in place, like GDPR, which mandate good data protection practices from companies.

Now that you’ve protected the data from external threats, you must also protect it from internal threats. Backing data up provides redundancy, which makes it trivial to recover should any data-loss event occur.

3. Use, Share & Distribute 

Outages aren’t the only internal threat to data. This stage of DLM defines who within an organization can access data and what they can use it for.

Typically, data undergoes a process called “data munging” before it’s usable for humans. This is transforming or manipulating data to make it relevant and readable.

Generally, this is the time when data handling is most sensitive. You should have audit trails of who accessed which data and when. You should only give access to sensitive data if there’s sound business justification.

In some cases, companies use data collected by external companies, especially for marketing and advertising purposes. 

4. Archive, Register & Record

Once data has outlived its usefulness, you should archive it. This is an in-between stage, where a business doesn’t think it needs the data but wants to retain the possibility of accessing it just in case. 

Archive locations are usually less accessible than live data storage environments. This makes for more efficient storage.

However, archived data should be accessible without needing IT to restore it from a backup storage device. It’s essential to getting the most out of your data and could be necessary for investigative purposes.

Archived data is typically stored in separate storage space from operational data. The data migration process from the live environment to the archive is a change request.

5. Destroy, Delete, Reuse 

It can be tempting for an organization to skip this step because “the more data, the better, right?”

Wrong. 

You absolutely cannot ignore the data destruction stage. As regulations continue to develop, it’s an increasingly important step. 

It’s important to understand that stored data you don’t need is a waste of resources and exposes you to unnecessary risk. 

Most big businesses have a data retention policy that dictates how long they keep static data. Once the time outlined in this policy has passed, you must perform data deletion.

Some regulations, like GDPR, mandate data deletion under the right circumstances. GDPR states that all citizens have a “right to be forgotten,” so a business must delete their data following a written request from a citizen.

So it’s essential to have a process for data deletion.

How Can Data Lifecycle Management Help SMEs?

Relational databases, archives, and backups… might sound excessive for small to medium-sized enterprises (SMEs), but even small businesses can benefit from implementing data lifecycle management.

In a startup or small business environment where every resource has to stretch further, data can be your silver bullet for guaranteeing efficiency.

A data-driven approach will help you rest a little easier, knowing you’ve backed up your business decisions with high-quality data.

It also stands to reason that the consequences of poor data management are more severe for SMEs and startups.

Mishandling sensitive data can lead to fines but also compromises the trust customers have placed in your business— and that trust is virtually impossible to rebuild.

That said, you can follow the basic principles of data lifecycle management while making it easier for yourself and your business. For example:

• You could collect qualitative data using forms and surveys, which is much easier to manage and understand than quantitative usage data.
• You can use cloud-based solutions for storing your data, which are cost-effective, cheap, and low-hassle.
• There exists a category of tools known as Integration Platform as a Service (iPaaS) which allow you to more easily maintain and apply data, even if you’re not an expert.
• You don’t have to squeeze the maximum value out of your data. SMEs might benefit more from only using data to reap low-hanging fruit. This keeps time and effort to a minimum while still providing value.

What is Hot, Warm, and Cold Data?

We use these temperature descriptors to categorize data based on how easily and quickly practitioners need access. This system helps businesses operate more efficiently by keeping the right data in the right place. 

Hot Data

Hot data could also be called “active data.” It’s frequently accessed data that someone or something is currently working on. Analysts, practitioners, and software systems may require immediate access to it. For example, customer information during online transactions is hot data that your eCommerce system must be able to access quickly.

Another example of hot data is video files, which an editor stitches together.

Hot data is accessed frequently and is actively being used. You should store it in high-performance, low-capacity storage, close to the people or programs who want to use it, for example, on a local SSD drive.

Warm Data

Warm data is pertinent to some active projects that practitioners don’t need to access frequently. For example, if a business were launching a new product, it might keep all market research related to the product launch in warm storage.

Analysts need to access this data daily, but they move or copy it from warm storage to hot storage to work on it. Then move it back once they are done.

Warm storage is usually “capacity-optimized,”— meaning it’s kept on devices that allow a balance between capacity and performance. These might be local HDDs or production file servers.

Cold Data

You need to keep cold data, but people must actively work on it. You could consider it “inactive data.” Once a project manager finishes, they might move all the project data to cold storage. 

Another example of cold data is financial, HR, or legal records, which you might have to keep just in case they’re needed.

The philosophy behind cold data is “cheap and deep.” In other words, high-capacity, low-performance devices like archives and file servers. The purpose of cold storage is to provide the cheapest means to keep data.

The Benefits Of Having A Set Process For Managing Data

Data Compliance 

For most of us, data compliance regulations are a real headache to wrap our heads around. Data lifecycle management can alleviate that headache because compliance considerations are built into the framework’s core, which helps you know when to keep and destroy data.

75% of countries have data localization rules, and companies with customers in different countries must adhere to them.

Collecting customer activity data and leveraging DLM to organize, store and protect them is the best way forward. Effective data lifecycle management allows businesses to easily access and analyze customer data to generate more insightful customer profiles.

Complying with these regulations independent of following a set process is daunting, risky, and probably not worth the work.

Data Usage and Access

Ensuring employees have quick access to relevant data enables them to work more agilely. 

They spend less time trawling through data and more time creating value. They can make faster decisions and react to changes more quickly.

To put it briefly, it accelerates the time-to-value of any business initiative.

Optimized Efficiency 

DLM allows you to “right-size” your data storage solutions, keeping costs low where possible and performance high where necessary.

Furthermore, a robust data-maintenance process helps develop a leaner database of more relevant data. Allowing you to trim the fat, so to speak, with regular data archiving.

Heightened Customer Experience 

Well-managed data can give a business insight into a customer’s experience that wasn’t possible before—allowing analysts to marry traditional quantitative data with qualitative data to develop a deeper understanding of the customer journey.

This has even led to adoption of eXperience Level Agreements (XLAs) in some businesses, which are in place alongside Service Level Agreements (SLAs) to help measure and maintain the level of experience customers receive.

Data Governance 

While data compliance refers to adherence to external regulations, data governance refers to internal standards.

An established process makes it easier for IT teams to govern data effectively and follow internal quality and security protocols. It’s a big help in data cleaning efforts to have transparent processes to follow.

Data Mining & Segmentation 

Data mining and segmentation should be integral to any data lifecycle management strategy. Mining customer data allows a business to identify patterns and trends, which can then be used to refine customer segmentation for targeted marketing campaigns or product recommendations.

Data Lifecycle Management Best Practices 

We’ve covered many of the factors that contribute to good data management. Like how and why you should have a process for data destruction, the importance of data encryption, and so on.

But data sharing isn’t just a bag of risks with no upside.

It’s time to talk about how businesses can get the most value out of data sharing and how to build an organization that uses data as a powerful tool for growth and agility.

Disseminated Data Management 

Data management needs to be a company-wide effort. You should train all employees on handling data— at the very least, security and privacy protocols. Production teams and data analysts should be further trained to:

• Be able to access the data they need without having to raise requests with the IT
• Exercise good data-loss prevention (DLP)
• View data with a scrutinous eye, and assess whether they can move it to colder storage.

Knowing you’ve trained all employees in data management makes data sharing less risky.

Centralized Repository 

Every business has security processes, and there’s no need to reinvent the wheel. 

Keeping data in a centralized repository makes it more easily accessible for all employees while benefitting from internal security by default.

Remember: You should back up business data to a safe and secure environment to improve data redundancy.

Data Stewardship 

Data stewardship helps keep an organization’s data quality high.

Assigning data stewards responsible for overseeing database management processes can be beneficial.

As is often the case with business processes, having a select few champions for data management in a business environment gives the rules more authority. 

It also clarifies to users whom to reach out to with issues or questions. Data stewards aren’t solely responsible for administrative tasks like classifying data but can lend their expertise.

Data Training 

Regulations continue to evolve, as do data management technologies. Continuous staff training ensures you meet regulatory changes with preparation and effectively utilize cutting-edge technology.

Additionally, data analysts should receive ongoing training to better categorize and manipulate data.

Data management is an iterative and ongoing process— there’s always room for improvement.

Data Aggregation

When analyzing data, it’s easy to get caught up in the details. Taking a step back and looking at the bigger picture is often beneficial.

Aggregating data can provide a high-level, macro view of a business’s data. This is just as useful for spotting trends and developing insights as the finer details are.

Data Standardization

Data standardization is trying to keep all data in the correct “types.” Data can be in the form of PDFs, survey results, or database entries. 

Data of the same types are easier to marry or compare, which is a powerful tool. 

You’ll get more out of collected data if you can keep it to the same file types.

Data Lifecycle Management Alternatives 

HSM— The Data Storage Supertool

Hierarchical Storage Management (HSM) is a tool that monitors data usage. HSM dynamically moves data to the appropriate storage system based on how often users access the data.

If data usage is high, it’s considered “active,” and HSM will move it to locations where retrieving data is quicker and easier.

HSM will move less critical data to colder storage media.

This takes a lot of the administration out of data management, as HSM does its job without business users knowing.

HSM takes a lot of the work out of deciding how to store data and can help with decisions around deleting data, as it’s easy to see how long data has been held without being accessed— this identifies suitable candidates for data destruction.

HSM isn’t so much an alternative to DLM as it is an additional option.

ILM 

Information Lifecycle Management (ILM) is similar to DLM, with a few key advantages. Primarily, ILM also encompasses business processes on physically stored information. 

Certificates, letters, contracts. Anything that isn’t digital. Information Lifecycle Management processes provide more in-depth guidance on how to manage data in this format and how it can be stored securely.

ILM also makes finding specific information related to a chosen topic easier. For example: Finding all of the data on one customer to dispose of it following a GDPR request.

DLM focuses more on categorization instead, and it’s easier to search my attributes like file type, size, or date. Which indexing function is more valuable is a matter of debate.

The bottom line is that ILM may be a better alternative if your business deals with digital and non-digital information. DLM is the more streamlined solution if you only deal with digital data.

Looking Ahead: What’s Next For Data Lifecycle Management?  

The future of Data Lifecycle Management (DLM) is most certainly tied to the wider adoption of AI tools in businesses.

A 2021 McKinsey survey found that 57% of businesses have adopted AI in some form. And of those adoptees, 47% are using AI for some form of data analytics.

But businesses still haven’t realized the true power of AI-driven data. Another McKinsey report found that while 75% of companies in the metals and mining industry have implemented some form of AI, less than 15% have realized a meaningful impact.

There’s still a lot of work before AI becomes a must-have part of your data strategy.

DLM might also be affected by the Internet of Things (IoT) and edge computing. As IoT devices grow more powerful, they can process more data, which could change the way data is stored. 

For example, hot or warm data could be stored on IoT-enabled devices, which can also process data— pushing data accessibility to new heights. 

This, in turn, will affect a business’s Agile capabilities. 

Under the Agile philosophy, analysts would be better positioned to make decisions based on the data they process. This leads to faster reactions to changes and lends a competitive edge to organizations that lean into it. 

Finally, it’s essential to recognize that DLM must be iterative. Most importantly, for security purposes. New threats are constantly developing, and DLP technology, processes, and training must be continuously improved to stay ahead of malefactors.

The requirements of data management are only growing more critical and more complicated. Implementing a tried and tested set of practices like DLM is a proactive way to ensure you’re doing data right.

The post Data Lifecycle Management: Everything You Need To Know appeared first on Digital Adoption.

But many sales reps mistakenly use the discovery call to promote a product’s features instead of learning about the prospect.

Let’s discuss the sales discovery process that you can use to optimize your sales team.

What is sales discovery?

Sales discovery is the first step for the sales rep to connect and learn about their prospect. In this phase, they research, ask questions, answer their prospects’ questions, and unearth helpful information about them. 

It helps the sales rep to determine whether the prospects qualify as serious buyers. They can then move them down the sales funnel. 

The steps for the sales discovery process

During the sales discovery process, the reps need to research and tailor their questions to the prospects. 

Let’s discuss the steps;

1. Have an up-front strategy and role play

Before making the discovery call, the sales reps should research and know their prospects. 

The best approach is to explore the prospects on social media like Linked In, Twitter, or Facebook. 

The team can:

• Go through their profiles and understand their career aspirations
• Check if they have any history with your organization
• Check if any employee has shown interest in your product.

Then, go through their organization pages to have a glimpse of what they do. They can also rely on Google searches and news to gather more information. 

Once the team has all the information, each rep should ask the following;

• What questions is the prospect likely to ask?
• What challenges are they facing in their business? 
• Do you have examples of your work (or success stories) in case they want to know?

Then the team can role-play to understand the probable situations. You can have a coach to help the team through this. 

2. Ask provocative questions and listen

Thought-provoking questions help the sales reps learn about the prospect’s challenges, how they plan to tackle them, and what it’d take to convince them to adopt your product. 

For instance, let’s say your prospect is struggling with change management in their organization. What specific challenges are they facing? What solutions have they tried? 

Rather than selling your solution, the sales rep should aim for a meeting of minds. 

So, the rep should guide the prospect to analyze their problem and ‘discover’ the solution by offering their industry knowledge and relevant research. 

The most important part for the sales rep is listening to the prospect, following their thought process, and offering valuable insight.

When Gong analyzed sales calls, they found that most of the top sales performers’ talk-to-listen ratio was 43:57. That means they listen more and leave the prospects to do the talking.   

3. Use storytelling tactics

After your sales rep and the prospect are on common ground, the rep can bring in a story. A story grabs the prospect’s attention and makes them relate to your product experientially. 

To get it right, the sales rep can give an example of a challenge that a different organization faced and solved with your solution. But to make their point valid, they can present testimonials and case studies. 

For example,

“When I was working with a certain client, he claimed that his sales team spent only a third of their time selling, and it largely affected his business. But when we trained his team on the importance of selling, they improved by 80 percent and started making sales.”

Follow that with a leading question asking them if they want to overcome the challenges. And if not yet, what would make them want to overcome the challenge?

4. Establish the next steps 

To close the deal successfully, the sales rep will summarize your product’s value in the prospect’s organization (i.e., how does it help the prospect achieve their goals?).

They can approach this in two ways;

• Emphasize how your product will help solve the challenges you have discovered through the call.
• Help them agree that the product is necessary to help solve their problem. You can talk of a demo at this stage. 

These two closing processes will open up another date for a call. It will also bring in other stakeholders who are essential for the sales process.

5. Remember to record the discovery calls

It’s good to record your discovery calls to avoid losing important information and get ideas on how to improve your discovery process. Your team can use call recording software.

The recorded calls will help your team; 

• Analyze the flow of the conversation. Did the rep use the right tone? How did they answer the questions? etc. 
• Improve on your future discovery calls. Analyzing what worked and what didn’t helps the team know what to eliminate or add. 
• Follow up with the prospects. Once they go through the recording, they’ll know whether to continue pursuing a prospect. 

The best sales discovery process questions

The sales team can tailor the questions into four subsections. Questions that set the stage, qualify, disqualify and establish the next steps. 

Examples of questions that set the stage;

When setting the stage, the sales rep takes time to know the prospect’s company. It helps them decide on whether to move forward. 

• Tell me about the company.
• Your role in the company
• The metric you are responsible for. 

Questions that qualify

Qualifying questions help the rep figure out how to help the prospect. 

• Tell me about your goals.
• When do you need to achieve them?
• What areas are you struggling in? 
• What would success [in the problem area] look like?

Questions that disqualify a prospect

These questions will help you have a plan and budget.

• When are you planning to implement a solution? 
• What’s your budget?
• Who’s responsible for paying? 

Questions that determine the next steps

• Who else is involved in [solving the problem]? 
• How can we help you improve [problem area]?
• Would this be the first time you’re purchasing this product?
• Can I call you again on [date & time]?

Discovery call checklist

A discovery call checklist is essential to help sales reps stay on track. The checklist can help sales reps make the right questions and close deals quickly. 

Here’s what it’d look like:

• Research the prospect
• Take time to build rapport and trust
• Understand the prospect’s struggles
• Show them how things would look like if they solved the problem 
• Prove your credibility with a success story/testimonial
• Identify the decision-makers in the prospect’s company
• Get a follow-up meeting

That sums up the sales discovery process. But did you know that transparency could also optimize your sales process? Find out why it’s the emerging priorities for sales leadership in this post.

The post 5 Step Sales Discovery Process to Optimize your Sales Team appeared first on Digital Adoption.

This is where process mapping comes into play. 

As the name suggests, a process map is a diagrammatic representation of all the steps into a business process. It captures the sequence of each step, the people involved, the timeline, etc. 

Processing mapping has numerous benefits, and we will be exploring them soon. 

In this post, we will be examining what a high-level process map is all about, how it works, and how to create one. 

What is a high-level process map?

The essence of process mapping is to give a diagrammatic overview of the steps of a business process. 

Take marketing – one of the most vital business processes – for example. It involves a lot of parts, some of which include lead generation and nurturing, advertising,  sales closure, etc. These parts or steps create a process map when pieced together in a diagrammatic form. 

This map can either be well-detailed, capturing all the nitty-gritty of an operational process. 

Or it can be cursory and concise, and that is what a high-level process map is about. 

A high-level process map is created to give a brief, quick overview of a business process without much detail. It is often used at stakeholders or board meetings to provide concerned parties with a summary of business processes without divulging much information. 

Benefits of high-process mapping

One of the chief purposes for creating a process map is to help businesses spot and eliminate inefficiencies in their operations to boost performance. 

Process maps also come in handy during employee onboarding, enabling recruits to get up to speed with their job roles. 

Additionally, process mapping can help make the life of your employees better. When everyone knows the step involved in a project and who is responsible for what, bottlenecks are eliminated, and things run smoother. 

How does high-level processing mapping work? 

High-level process maps work using flowchart diagrams to illustrate the steps involved in a process, such as procurement, customer onboarding, change management, employee training, etc. 

High-level process maps use a set of shapes such as squares, circles, and diamonds to show the sequence that goes into a business process. It also captures vital information about a process. For example, it can show who is responsible for what, when a task should be completed, the next step to be followed once a task is completed, and lots more. 

High-level process maps make it easy for team members to understand their role in a process and work efficiently. It allows project managers to identify critical areas to improve processes and boost efficiency. 

High-level vs low-level process map

A high-level process map may not be ideal in all cases, hence the need for a low-level process map  

What is a low-level process map, and how is it different from its high-level counterpart?

A low-level process map intuitively zooms in on the minutest detail to give a deeper insight into what is involved. 

Take procurements, for example. A high-level process map might only capture and illustrate the flow between the procurement department on the vendor. A low-level map will further capture how quotation and vendor payment receipts are handled, expected delivery time, and lots more. 

One of the major challenges with low-level process maps is that it often fails to show “who is responsible for what”. Having a mix of high-level and low-level process maps can greatly help a business organization. 

How to create a high-level process map for your business/organization

You can use a couple of techniques to create a high-level process map for your business. The SIPOC technique happens to be one of the most popular. 

SIPOC stands for Suppliers, Input, Process, Output, Customers, and a flowchart shows how all these elements are connected. 

SIPOC  mapping can be done on cardboard, on a whiteboard, etc. 

First, you must first determine the entry and endpoints of your process and what is involved. You don’t necessarily need to go into details. 

When you are done with that, the next thing you will want to do is to identify who your Suppliers are and list them in the supplier section. 

After that, the next step is to determine what you want your final results to be and capture them in the Output section of your map. 

The next step is to populate your map’s customer section. This is where you will list out the people who will benefit from your process’s outcome. 

At this point, you are ready to show the map to your team and get their input. 

Difference between SIPOC and Process Map

Although similar to a huge extent, SIPOC and Process Map aren’t the same. 

The critical difference is that while SIPOC captures the macro details giving a general overview of it, a process map zooms in to capture the minute, refined details. This allows stakeholders to gain deeper insight into a business process. 

For best outcomes, it’s best to mix the two. 

Organizational level process map 

Put simply. An Organizational level process map is a flowchart that makes it easy for business establishments to coordinate their operation and the people involved in it. 

It is also composed of annotations and shapes that help illustrate the processes involved in an operation. 

An Organizational level process map makes it easy for a business organization to spot and fix operational inefficiencies and stop revenue leaks. 

It can also help to boost employee motivation and make your people perform at their best. 

Conclusion

High-level process maps make it easy for you to understand your business operations better and make the needed adjustments to arrive at favorable outcomes. 

Thankfully, creating one isn’t that hard. 

The post What is a high-level process map? appeared first on Digital Adoption.

Lean Six Sigma’s philosophical methodology is a combination of Lean manufacturing and Six Sigma. Lean manufacturing is a method principally focused on minimizing production time and increasing agility across operations. Six Sigma aims to eliminate variables within manufacturing and organizational processes, with Six Sigma standards aiming to produce no further than 3.4 defects per one million opportunities (DPMO). 

When combined, Lean Six Sigma is born. The unification of two highly effective improvement methodologies, Lean manufacturing, and Six Sigma’s combined effectiveness can cultivate and support a robust and reliable production model. With LSS processes aiming for near-absolute perfection – 99.99966 percent, in fact, knowing exactly what talents and skills are required and where they’ll be most effective is of major importance when employing talent to oversee and conduct LSS projects. That’s where the LSS Belt certifications come in. 

What Are The Lean Six Sigma Belts?

The Lean Six Sigma certification scheme is focused on training and educating individuals on the different levels of Six Sigma practices – giving them the specified tools and qualifications required in navigating and completing Six Sigma level projects. There are five main LSS belts – White, Yellow, Green, Black, and Master Black Belt. Following a color-coded hierarchy that covers the entirety of Six Sigma knowledge, those qualified in one or more Six Sigma Belt certifications can deliver (to differing degrees) skills that implement change and encourage process improvement. In this article, we’re going to break down the different echelons of the LSS certification scheme and help you identify and choose which one is right for you.

White Belt

Those with a White Belt qualification have an introductory understanding of Six Sigma processes – possessing essential skills that support essential tasks. White Belts are versed in the core basics of Six Sigma and can participate and assist alongside Green and Black Belts in Six Sigma projects. White Belts should be able to communicate effectively, understand processes, compose solutions, strive for improvement, and be versed in DMAIC standards. There are no criteria for pursuing White Belt status and no limitations in ascending Belt status. 

White Belts are ideal for any professional looking to enter the Six Sigma arena. The spectrum is diverse for those seeking a White Belt, with most professionals in organizations adopting LSS methods being able to achieve this feat. White Belts are perfect for those interested in knowing more about improvement and quality management. 

Yellow Belt

Pursuing Yellow Belt status is for those already possessing the essential skills needed to support and navigate Six Sigma projects and for those exceeding the limitations of White Belt capabilities. Yellow Belts look to shed their beginner knowledge and embrace higher knowledge on LSS processes. Yellow Belts are proactive contributors that can assist Green and Black Belts in process improvement projects. Yellow Belts should be able to easily execute minor tasks, initiating methodologies such as Define, Measure, and Control (DMC) and Plan, Do, Check & Act (PDCA).

Yellow Belt certifications are perfect for those with a desire to expand their knowledge on Lean Six Sigma of those seriously considering a career in quality management. Prospective Yellow Belts can expect to enter the Six Sigma periphery and act as an instrumental member that underpins and abides by the methods of quality improvement, e.g. DMAIC.

Green Belt

Those of Green Belt status adopt a more hands-on approach when undertaking Six Sigma projects, usually initiating and overseeing key elements including, enhancing processes, data analysis, project management and to support and reaffirm objectives appointed by Black Belts. The role of a Green Belt also includes navigating and orchestrating ground operations, deploying mid to large-scale improvement projects (depending on the level of experience), and observing process operations to detect and correct anomalies within a process. This Six Sigma level will expand your understanding of LSS methodology and can be used as leverage when looking for prospective work. 

Pursuing a Green Belt qualification is for those looking to gain a comprehensive understanding of Lean Six Sigma and quality management practices like DMAIC. To become a Green Belt practitioner, you’ll need at least 3 years of work experience within the field of quality management. Once you have Green Belt status under your belt (no pun intended), employers will be keen to hire you – potentially earning you up to $75,000 annually.

Black Belt

Now we’re entering the top tier of the Belt scheme. Lean Six Sigma Black Belt specialists are expertly qualified in orchestrating and conducting large-scale quality management processes and hold a confident understanding of Lean Six Sigma’s principles and methodology. Colloquially known as agents of change, Black Belt practitioners possess strong leadership skills and can manage staff, assign roles and manage multiple cross-functional workloads and objectives. Black Belts’ responsibilities also include overseeing and mentoring Green Belts.

Armed with a complete and comprehensive understanding of Six Sigma methods (DMAIC/DFSS), systems, and tools, Black Belts are tasked with validating and analyzing data to explore and improve process improvements. If improvements aren’t ideal, the new data is studied and new solutions are implemented in accordance with quality standards. Black Belts aim to drive innovation and profitability for process owners and industry alike, harnessing their expert skills to deliver the exceptional standards of Lean Six Sigma methodology and quality process management.

Black Belt status is usually a stepping stone for those graduating Green Belt status, though the characteristics that define a Black Belt are of a different calibre. Black Belt candidates need at least 3 years of full-time experience practicing LSS methodology, and verification of two successfully completed LSS projects. Black Belt courses can be achieved within 1-4 months, with no prerequisite for accessing the course. So where can you access these courses? ASQ and IASSC are both globally recognized LSS Black Belt certifiers and are a superb choice offering the highest quality education.

Master Black Belt

At the top of this hierarchy sits Master Black Belts (MBBs). With a complete and total understanding of LSS philosophical principles, Master Black Belts assume expert, leader, innovator, and mentor roles. Holding the highest LSS Belt certification, MBBs are almost as foolproof as the processes they’re improving, equipped with excellent data-driven statistical skills and unmatched knowledge of process improvement methodologies. A huge part of a Master Black Belts responsibility is passing on knowledge to lower-tiered Belts, educating and guiding staff so that the LSS methodology and practices trickle down through the workforce – which in turn cultivates productivity and improvement across sectors. 

A major part of a Master Black Belts role is harnessing their interpersonal skills to create synchronicity within and around project activity. This means knowing how to utilize both their soft and hard skills where appropriate. Establishing project objectives and deadlines, easing cross-level communications, understanding conflicting viewpoints, and eliminating major errors are all within the responsibility of an MBB. 

To seek higher education and qualify as a Master Black Belt, you’ll need a minimum of 2 years of full-time experience working on Black Belt level LSS projects as well as over 5 years of business experience. You’ll need proof of at least 5 successfully completed Lean Six Sigma projects with verifiable results, strong change management skills, and exceptional verbal and written communication skills. 

Becoming a Master Black Belts is not easy, you’ll need years of experience and education within LSS. You’ll need to be a master in quality process improvement tools such as DMAIC and Lean practices, holding a confident knowledge on various LSS improvement methods that include, rectilinear regression, material-and-information flow mapping, and root cause analysis. When it comes to seeking a Master Black Belt certification, The American Society for Quality (ASQ) is the only institute exclusively offering MBBs. 

Conclusion

Whether you study with the American Society for Quality (ASQ) or the International Association for Six Sigma Certification (IASSC), or the Council for Six Sigma Certification (CSSC), striving to become a Six Sigma practitioner is an invaluable skill that will gain the attention of employers and set you apart from others. Along with increasing your credibility, certified LSS practitioners versed in identifying and eliminating risks have a greater potential to affect change within the field of quality process improvement.

The post Lean Six Sigma Belts Explained: Which Belt is Right For You? appeared first on Digital Adoption.

This calculation considers various financial aspects to give a more accurate representation of the company’s value.

This metric or KPI is pivotal for assessing a company over time, establishing fiscal targets, making informed decisions, and evaluating potential acquisitions.

The enterprise value formula is also used when considering alliances and joint ventures, corporate bond issuance, international public offerings (IPOs), banking credit, portfolio management, risk evaluation, and occasionally top-management bonuses. 

This formula can help create a fair company valuation that aligns with well-established and widely accepted systematic appraisal methods.

But why factor in debt and cash in a company’s valuation? 

When acquiring a company, the buyer pays the equity value, often set above the market rate in mergers and acquisitions. Additionally, the buyer assumes the company’s debts. 

However, they also inherit the company’s cash, justifying its subtraction from the valuation. 

This is why it is important to include debt and cash in a company’s valuation to ensure fairness.

By the end of this article, you will know:

• What an enterprise value and its associated formula
• An enterprise value formula example
• What does an enterprise value formula tell you
• Why an enterprise value matters for your business
• The difference between an enterprise value, market cap, and a P/E ratio
• How to use enterprise value as a valuation multiple
• The limitations of enterprise value
• How you can demonstrate that you understand enterprise value on your resume 

What Is Enterprise Value (EV)?

Enterprise value (EV) captures the holistic worth of a company in financial terms. This value encompasses the market capitalization, which is the going rate of its shares, and its net debt, which factors in its liabilities and cash on hand. 

By amalgamating these numbers, the enterprise value paints a picture of the approximate amount one would need to acquire the company.

In essence, when considering enterprise value, you’re looking at the sum needed to satisfy all the stakeholders with a financial claim in the company. 

This includes compensating equity holders or shareholders and settling any outstanding debts to lenders. If you were to purchase the company, you would need to cover the cost of its shares and be obligated to clear its debts. 

Upon acquisition, the company’s cash reserves come into your hands. This accessible cash effectively reduces your acquisition cost, explaining why, in determining the value of a company, one adds the debt but deducts the cash.

What Is An Enterprise Value Formula

A company’s enterprise value (EV) provides a comprehensive snapshot of what it would cost an investor to buy the entire company outright. Essentially, it represents the complete takeover price. To determine this, one can use the enterprise value formula:

Enterprise Value (EV)=Market Capitalization+Value of Debt+Preferred Stock+Minority Interest−Cash and Cash Equivalents

The components involved in the EV calculation are:

Market Capitalization

This is the aggregate value of a company’s outstanding common and preferred shares.

Value of Debt

This encompasses both long-term debt and short-term debt obligations.

Preferred Stock

These shares have a higher dividend claim than common stock.

Minority Interest

This accounts for the value of a subsidiary in which the company owns less than 50% stake.

Unfunded Pension Liabilities (if applicable)

This represents the amount a company is short of to meet pension commitments or the funds that need to be set aside for pension disbursements in a plan that isn’t pre-funded. If this figure is available, it can be added to the market capitalization.

Cash and Cash Equivalents

These are liquid assets like money market funds, short-term government bonds, commercial paper, drafts, and others that can be quickly converted into cash. Since acquiring a company means gaining its cash reserves, this amount is subtracted to reflect the net cost to the buyer.

Enterprise Value Formula Example

Consider a hypothetical company, ABC Limited, with the following financial details:

Shares Outstanding: 3,000,000

Current Share Price: $4

Total Debt: $2,000,000

Total Cash: $1,000,000

From the information provided:

Market Capitalization = Shares Outstanding × Current Share Price

= 3,000,000 × $4

= $7,000,000

With no Preferred Stock and no Minority Interest stated, their values are $0.

Outstanding Debt = $5,000,000

Cash and Cash Equivalents = $1,000,000

To calculate enterprise value, use the EV formula:

EV=MarketCapitalization+PreferredStock+OutstandingDebt+MinorityInterest–CashandCashEquivalents

Enterprise Value = $7,000,000 + $5,000,000 – $1,000,000

= $11,000,000 or $11 million.

Who Can Use An Enterprise Value Formula?

In mergers and acquisitions, financial experts, especially investment bankers, can use an enterprise value as a yardstick to evaluate companies, assisting their clients in making informed decisions about potential acquisitions or mergers.

Beyond the M&A landscape, EV is also a cornerstone metric in various other financial and accounting calculations. 

For instance, it can be paired with indicators such as EBITDA to derive ratios that offer a nuanced perspective on company value, facilitating more comprehensive business comparisons.

Why Does Enterprise Value Matter For Businesses?

An enterprise value is important for businesses and is an invaluable instrument for gauging a company’s size and comprehending how it leverages debt. 

For instance, certain burgeoning tech stocks might appear excessively valued when solely assessed through market capitalization. 

However, accounting for minimal or non-existent debt and subtracting a substantial cash reserve may reveal that the enterprise value provides a starkly distinct valuation compared to mere market cap.

Enterprise Value Vs. Market Cap

Market capitalization doesn’t wholly capture a company’s worth because it overlooks key components like debt and available cash.

By accounting for these factors, enterprise value offers a more holistic view, thus presenting a clearer picture of a company’s value.

Although they seemed identical when evaluated solely on market cap, their enterprise values reveal a distinct disparity in their overall worth.

Enterprise Value Vs. P/E Ratio

The price-to-earnings ratio (P/E ratio) is a valuation metric that relates a company’s current share price to its earnings per share (EPS). Sometimes known as the earnings or price multiple, the P/E ratio doesn’t account for a company’s debt. 

In contrast, the Enterprise Value (EV) encompasses a company’s debt, providing a more holistic valuation. To obtain a well-rounded assessment of a company’s worth, the P/E ratio and enterprise value are often considered together.

Using Enterprise Value As A Valuation Multiple

Enterprise value (EV) is a foundation for various financial metrics that gauge a company’s performance. 

One such metric is the enterprise multiple, which connects the company’s entire valuation from all funding sources to its operational earnings, represented by earnings before interest, taxes, depreciation, and amortization (EBITDA).

EBITDA gives insights into the revenue drivers of a company as an alternative to straightforward earnings or net income in specific scenarios:

EBITDA = Net Income + Interest Expense + Taxes + Depreciation + Amortization

However, EBITDA might not always provide the complete picture since it omits the capital costs related to assets like property and equipment. 

When working capital inflates, EBITDA might exaggerate cash flows from operations. Additionally, it overlooks the impact of different revenue recognition policies on a company’s operational cash flow.

Another popular valuation metric is the EV-to-sales ratio (EV/Sales). This ratio is more precise than the Price/Sales metric because it accounts for a company’s value, including the debt it must repay.

A lower EV/Sales ratio suggests a company might be undervalued. In unique cases where a company’s cash surpasses its market cap and debt, the EV/Sales ratio can be negative, signifying it can settle all its obligations.

Enterprise Value Limitations

Enterprise value (EV) has limitations, especially when comparing companies operating in different sectors or growth stages. 

While EV gives a comprehensive view of the overall cost to acquire a company, considering factors beyond just market capitalization, it may not always provide a complete perspective on a company’s financial performance or health.

For instance, two companies might have identical market caps, but if one is laden with debt. At the same time, the other boasts substantial cash reserves; the latter would naturally be more economical to acquire. But this only tells part of the story.

Debt utilization varies across industries. A software firm heavily in debt with limited cash might seem less appealing than a debt-free firm with a similar market cap. 

However, the narrative changes when you compare companies across industries. Capital-intensive sectors like utilities or automobile manufacturing often require sizable debt to fund the necessary assets for revenue generation.

Furthermore, the growth stage of a company affects its EV. Startups or companies experiencing rapid growth might not have accumulated as much debt as their mature counterparts. 

Therefore, although the enterprise value formula provides valuable insights into a company’s worth, it is crucial to consider the industry landscape, growth stage, and debt utilization when making investment choices.

How To Show You Understand Enterprise Value On A Resume

When showcasing a knowledge of calculating enterprise value in your resume, consider the following points:

• Skills Section: Under your skills or expertise area, highlight your proficiency in business valuation techniques. You might list “Enterprise Value (EV) Assessment” to showcase your knowledge.
• Work or Internship Experiences: Detail your practical experience with EV in the descriptions of relevant positions. For instance: “Conducted comprehensive business valuations for multiple firms, employing techniques such as Enterprise Value.”

Next Steps For Enterprise Value

Enterprise value (EV) provides a fuller assessment of a company’s total worth and is often utilized by businesses contemplating mergers or acquisitions. 

Enterprise value offers a broader perspective on a company’s value than merely its market capitalization (market cap), which solely reflects the total market value of all outstanding shares. 

Market cap might give an idea of how the stock market values a company, but it doesn’t encapsulate the entire picture. This is because market cap omits crucial details like a company’s outstanding debts and cash reserves. 

While investors might use the enterprise value calculation to gauge a company’s scale and valuation, helping them make informed stock selections, combining an enterprise value formula with other valuation metrics is essential for a holistic view. 

Two commonly used ratios are EV/Sales and EV/EBITDA. The former indicates a company’s cash flow, while the latter suggests its earnings before interest and taxes.

Frequently Asked Questions

What Is The Difference Between Enterprise Value And Equity Value?

Enterprise value represents a third party’s total price to acquire a company’s stock and assets.

For public companies, determining the enterprise value involves multiplying the current share price by the total number of outstanding shares and adding any outstanding debt. However, calculating the enterprise value for privately held firms is more challenging as the value of their stock is not readily apparent.

On the other hand, equity value represents the portion of a company’s overall value, known as the ‘enterprise value,’ that belongs to its shareholders. To calculate the equity value, start with the enterprise value, which is essentially the cost for a third party to purchase the company’s stock and assets. 

From this, subtract any debt or equivalents, such as capitalized leases. Then, add back any excess cash or liquid assets the company possesses.

The resulting figure represents the total value available to the company’s shareholders, known as its ‘equity value.’

The post Enterprise Value Formula: What Is It And How To Calculate It appeared first on Digital Adoption.

And when the changes begin, businesses risk losing their best employees.

To materialize the potential synergies and efficiency, set up the integration’s direction upfront, open two-way communication lines, and manage the integration separate from the day-to-day business operations. 

Most importantly, focus on driving value from the deal soon after Day 1.

If you’re looking for best practices for a successful post-merger integration, read on!

This article has important PMI tips for CEOs.

Let’s dive in.

1. Set the direction of the integration early and upfront

Successful post-merger integration depends on the solid foundation built before the merger because all the planning needs to start pre-merger. Additionally, a well-laid PMI plan ensures that the integration happens quickly.

A solid foundation typically includes the business processes, configuration, and systems’ application data.

So, the PMI lead, functional team leads, the team members, and the executives need to know why the businesses are doing the merger to get everyone in the same thought that “we’re in this together.”

Getting all the stakeholders in the same boat depends on the activities done before closing the deal.

Here are our tips for setting the direction of a PMI

• Clearly define the objectives of the merger, the philosophy “narrative,” and the PMI plan. Then communicate these to all the affected stakeholders to achieve the desired post-merger synchronization. 

Departments that do silo tasks without understanding the complete picture may prioritize the wrong tasks, assign resources to the wrong task, or even work on the wrong project. 

It results in integration delay, which is why some mergers fail to extract the merger’s value. 

• Design a clear picture of the future state operations of the combined business during the due diligence phase. The end state will guide all the actions post-merger.
• Articulate the right PMI plan with specific tasks and timing. The right plan is easily explained and communicated to all the stakeholders.
• Create a roadmap that clearly defines the specific outcomes (results) that must happen in every milestone with clear financial or other deliverables. The focus is not on the steps but rather on the end goals.
• Based on the due diligence data, define ballpark figures of the potential cost, revenue, and financial synergies. 

The figures will depend on your business and the target business regarding existing processes, proficiencies, and capabilities. You may also do a SWOT analysis against the industry’s best practices to determine capabilities.

Have a proforma on the expected financial situation of the organization. It will become the business case that the integration management office (IMO) will test in the first 100 days.

• Articulate the key performance indicators (success factors) that will mark the integration and the combined organization. 

These could be:

• The organization’s stability
• Employee retention and wellbeing
• Maintaining customer focus
• Profitability
• Integrating the businesses’ cultures
• Maintaining and increasing value

• Communicate everyone’s role and responsibilities and make sure they understand these in addition to the integration’s mission.
• Address concerns about job security on the get-go. So you’ll need to identify the best performers you want to keep (these are usually the first to leave). Then make sure that they know how you value them and their future career path after the integration. 
• Get all the key stakeholders excited about the integration. You can only do this if you involve them all through the integration planning. You could also throw a kick-off party.
• Identify key risks and develop mitigation strategies. For example, the IMO may realize that the integration could distract resources from the daily operations of the two businesses. In that case, they’ll have to communicate how the functional teams should prioritize duties.

Set up the governance aligned around the goals of the integration 

At the beginning of the integration, assign management roles to clarify who the people should follow and specify who’s accountable for what. Selecting the leaders early also prevents the uncertainty of mergers and acquisitions.

The most important element of the PMI is leadership. You need a disciplined, focused, organized, and decisive PMI leader. One with the ability to make decisions and have people follow him/her through the challenging time.

But because of politics, you can’t pick a PMI leader from either company. It’ll be challenging to get everyone on board if you do so. 

Instead, you may hire a PMI consultant who will focus on the core business operations and a transaction/private equity firm to handle the financial and legal matters.

2.  Have two-way communication systems with the internal and external stakeholders

Consistent and clear communication before, during and after the post-merger integration maintains stability. So, the best practise is to set up a dedicated communications team. And communicate a lot.

Mckinsey clearly depicts what the level of communication would look like throughout the integration (image below).  Besides the announcement day, there’s a spike in Day 1 and never-ending consistency after the deal is closed.

Source

On Day 1, redeployed employees need to know their roles and responsibilities. They want to know who their new bosses are and the new business processes. 

And son after close, you need to gather feedback as you test and refine the business case.

Besides announcements and formal messages, you also need to keep the employees motivated and energized.

Read through these tips for effective post-merger communication

• Besides the general story, address the questions that directly affect the stakeholders personally. For example, is there a new boss to report to?
• Use all available channels to address all the key stakeholders and ensure that the message is reinforced. Besides project management tools, you could use social media to reach customers and employees.
• Recruit ‘influencers” in the organization to gather feedback and even communicate it. Well-respected employees could easily get raw feedback from the employees and the rumors of, for example, key personnel thinking of leaving the organization.
• Use feedback collection tools like surveys, focus groups, town halls, email, etc., to get feedback on the employees’ states. After receiving this feedback, the communication and IMO will analyze and act on it. 
• Supervisors should have honest conversations with the employees to minimize friction. They also need to be more personable, inviting, and responsive.

Need more information on leadership?

Check out the role that leadership plays in organizational change.

3. Manage the integration separately from the day-to-day running of the organization

You may consider outsourcing the back-office integration so that your employees focus on their jobs, and at the same time, you’ll give full focus on the integration. Doing this will allow the business to derive the value of the deal.

Though outsourcing integration will require a cash outlay, your business will realize the following benefits, which offset the initial costs.

Benefits of outsourcing integration

• You’ll leverage the third party’s expertise in integration. Though your internal teams can achieve some synergies, leaving cash on the table is easy because of unrealized opportunities. 

A PMI expert is more experienced in realizing synergies and responding to the inevitable obstacles that will come up.

• The organization will give the integration the laser focus it demands by hiring someone who is managing the integration full-time. 
• Avoid prolonging the integration, which may lead to unrealized synergies.
• A third party will ask the tough questions that people are afraid to ask. 
• The expert will help determine realistic timelines for realizing synergies and identify risks and dis-synergies.

If the organization chooses to hire an integration director, they should be involved right from the beginning of the deal. 

And you should support them to be the best set of eyes and ears for the integration.

How to support the integration manager

• Trust the manager and give them access to executive-level information.
• The integration manager should have the authority to lead discussions and enforce decision-making.
• He/she should have the authority to assemble resources as need be. The CEO can clarify that the integration manager is authorized and capable of making critical decisions in the IMO.
• The CEO can encourage the integration team to support the manager’s decisions. 

4. Focus on driving value from the deal 

At the end of the first 100 days, the business should operate effectively. The priority here is achieving the synergies. And the driving factors are the key performance indicators.

Here are some high-level milestones to drive value from the integration

Between day 1- day 30

• Complete employee onboarding and training
• Send out the first post-onboard employee survey
• Send out the first post-close customer survey
• Conduct your first Record to report (R2R) cycle
• Kick off the sales transition calls

Between day 31 and day 60

• Conduct the second R2R cycle
• Analyze the post-close customer survey
• Analyze the post-onboard employee survey
• Have the most-promising attendees in the sales transition calls

Between day 61 to day 100

• Conduct the third R2R cycle
• Send out the Day 100 post-close customer survey
• Send out the Day 100 post-onboard employee survey
• Finish migrating data from all the retiring systems

Check out these important tips for deriving value from the merger

• Focus on the most promising customers and reallocate resources to profitable accounts
• You may major defer changes in support functions like sales to avoid disrupting income flow
• Focus on winning big transactions that will act as “proof of concept” to drive momentum and enthusiasm.
• Focus on selling easy-to-sell products from both companies.
• Constantly revisit the explicitly defined cost and revenue synergy targets, refine them and track against them.
• Remove redundancies to maximize cost synergies.
• Involve customers in the post-merger integration process to retain them
• The businesses may operate separately until all systems are ready to achieve the synergies fast.

Once you know where the big value areas are, you can then organize the PMI teams to mirror those value drivers.

5. Spend time and effort in building the organization

The new organization should be built from a people, process, and technology standpoint. Overall focus on the customer and employee experience. Then use technology to enable an efficient business architecture.

The organization will not be efficient if you fail to integrate the culture, maximize cost synergies, and retain customer value.

So, the organization needs to allocate time and resources for post-merger culture integration.

Our tips for building the organization post-merger

• Track and report synergies daily. It would be nice to have a dashboard that shows you where the organization is every day.
• Have weekly synergy meetings and establish a rhythm of regular reviews of the progress of each key workstream
• Select, retain and develop the best talent, i.e., people who do the best job while supporting the organization’s mission and culture.
• Clearly define the desired behavior and the key role models (ideally, the leaders should be the role models). For those who achieve the desired behavior, recognize their efforts with incentives (monetary and non-monetary).

The Human Resource department will play a key role in cultural integration. The gap between the starting point and the target culture helps identify the practical things to do.

• Finally, overcommunicate and keep the feedback lines open.

6. Support employees to create value from the change

Change management is important to align and motivate people to deliver the integration’s objectives after day 100. But integrations might fall short of expectations with a fragmented approach, lacking concrete and actionable items, or addressing some but not all drivers.

Without clearly defined processes and effective communication, speculation and rumors could result in high turnover. So, HR might be the most fragile function in an integration. 

Additionally, frustration with the new systems (due to limited training) and technology resistance could make your business lose high-value employees.

The first step, which should happen pre-merger, is creating the change management plan and integrating it into the merger process. You may also consider having the change management leader instead of leaving it all to HR.

Check out these change management tips after closing the merger

• Deploy resources for employee training. Identify all the areas that will change (and need integration training), then develop the programs to train the affected employees.
• Have a clearly defined future state to put an end to speculation. And have a process in place to address any concerns among your teams.
• Assign your functional teams their responsibilities unambiguously. Let them know what decisions they own and those they’ll need to share with other leaders.
• Clearly define the new policies and processes that will guide the combined company’s new way of doing business. Then communicate these to the employees to ensure that everyone understands fully.
• Encourage two-way communication to keep up with how the changes affect the employees and determine the best corrective actions.
• Track the progress of executing the change management program. For example, pulse surveys and one-on-one interviews can help gather employees’ views on the changes. 

But to track progress properly, use organizational-health indicators like absenteeism, attrition, inbound job applications, and recruiting referrals. 

• Organize town hall meetings 6 months after closing to discuss unresolved issues.
• Have an intranet site to share announcements and FAQs and gather employee feedback.
• Send out a 1-year employee survey to determine the success of the merger. Then discuss any lessons and the way forward. And document these (on the intranet), including best practices. 
• Remember to celebrate successes.

Find out more ways to involve employees in the change management plan in this guide.

The post 6 Critical Tips for Post Merger Integration appeared first on Digital Adoption.

In this post, we’ll explore the answer to that question, and we’ll also learn the pros and cons of each, as well as other alternatives to these process improvement methodologies.

Lean vs. Six Sigma

Let’s begin by looking at lean.

What is lean?

Lean is business methodology aimed at achieving aims such as:

• Improving quality
• Reducing waste
• Increasing customer value

The methodology began in manufacturing, but it is since spread to many other disciplines from product development and product management to supply chain management.

It is founded on principles such as continual improvement, which is the idea that performance improvement should be an embedded feature of the business.

Lean is a relatively easy to understand system and it usually generates positive results, which is perhaps why it has become so popular in recent years.

Using lean, for instance, businesses will often realize benefits such as:

• Improved business efficiency
• Increased customer satisfaction
• Lower business costs
• Reduced time-to-market

Reducing waste, such as useless labor or transportation, is one of the key means by which lean can achieve these goals – and, as mentioned, this is one of the primary focuses of the methodology.

Yet there are other ways to achieve the same benefits without focusing so heavily on waste reduction. 

Six Sigma offers a different approach. 

What is Six Sigma?

Six Sigma is another process improvement methodology that aims to systematically and continually optimize processes. Like lean, many of its benefits include:

• Increased business efficiency
• Reduced costs
• Increased customer satisfaction
• Better Business outcomes

However, whereas lean focuses on waste reduction, Six Sigma focuses on reducing variation and defects.

To this end, Six Sigma relies heavily on data-driven processes, statistics, and scientific approaches to solving business problems. Also, since the focus of Six Sigma does differ from lean, the outcomes will also differ to a certain extent. 

For that reason, some use a hybrid approach that combines elements from both lean and Six Sigma.

What is Lean Six Sigma?

Lean Six Sigma, unsurprisingly, attempts to combine the best of both lean and Six Sigma into a single methodology.

For instance, goals for Lean Six Sigma can include:

• Reducing waste
• Reducing product defects
• Minimizing variation within systems
• Maximizing customer satisfaction

Lean Six Sigma has in fact become so popular that there are a number of certifications in this method. However, even these certifications have their own emphases and structure.

Which Is Best – Lean, Six Sigma, or Lean Six Sigma?

Each approach has its pros, cons and use cases – we cannot say that one is “better” than any other. Instead, every business should define its own goals, values, and culture, then choose a system that aligns with those.

For instance, a company that is very data-driven and methodical may prefer Six Sigma.

A company, on the other hand, that wants a minimal system built around continual improvement may prefer lean.

A company that wants the best of both worlds may choose Lean Six Sigma.

Finally, a company that wants yet another approach may choose something else entirely.

Agile vs. Lean vs. Six Sigma

Lean and Six Sigma generate positive outcomes for the most part. Yet they are not without criticism. 

One common criticism of process improvement methodologies such as these is that they are too narrow. Six Sigma, For instance, focuses exclusively on reducing variation. It cannot therefore be useful for innovation or adaptation.

For that, a different approach is needed.

Agile is not a methodology.

Instead, it is a set of values. 

Those values revolve around:

• Responsiveness and adaptability
• Continual collaboration with stakeholders
• Functionality over static processes and documentation

While lean and Six Sigma remain focused on improving processes, agile focuses on adaptation, change, change management, and staying nimble in the face of disruption.

For example, during the COVID-19 pandemic, agile businesses were willing to restructure, build new processes in order to stay relevant and effective. This contrasts with the two approaches covered above, which focus on a continual gradual optimization rather than rapid change and adaptation.

Despite the differences between agile and the other systems covered above, these systems are not mutually exclusive. 

In fact, one could adopt all of these approaches – lean, six sigma, and agile.

This would result in what is also called a hybrid approach, often used in software development. In this approach, businesses will selectively adopt and combine principles to form an approach that is part agile and part linear. The same tactic can be used to combine lean with agile or any of the other methods mentioned above.

Final Thoughts

Process improvement methodologies have become normal in today’s business environment, and for good reason. They can be used to increase a number of business metrics, from costs to customer satisfaction.

Without being said, every business methodology has its own area of focus. It cannot therefore be relied upon to improve areas outside that scope.

To overcome these deficiencies, it is often best to survey a number of business methodologies and process improvement methodologies, then adopt a process that combines elements from each.

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In this post, we’ll take a look at automated business analysis and process mining in order to understand how they can generate a competitive advantage in today’s marketplace.

Automated Business Analysis vs. Process Mining

Business analysis is a practice aimed at identifying and driving needed changes within an organization.

Naturally, business analysis requires high-level strategic thinking and deep insights into and organizations’ processes and its performance.

Among other things, business analysis involves:

• Identifying and quantifying an organization’s problems
• Defining solutions to those problems 
• Developing organizational change plans
• Leveraging data, metrics, OKRs, and KPIs to better execute change plans
• Working with stakeholders across the organization before, during, and after a change program
• Using techniques and practices such as business process management (BPM)

The role of the business analyst is to guide an organization towards better business processes, practices, and models.

Similarly, process mining is also aimed at improving an organization’s processes. The method, however, is different.

Process mining specifically refers to:

• Extracting data on processes within the organization
• Comparing processes as they are to processes as they should be
• Using the information learned to enhance processes

Process mining relies upon the use of software to analyze processes, mine data, learn from that data, and help analysts optimize processes based on that information.

Since process mining tools operate continuously and automatically, they are ideal for automated business analysis.

Automate Analysis to Gain an Edge

Certain aspects of business analysis cannot be automated. A great deal, in fact, requires human insight and input.

However, process mining tools have exploded in popularity because they automate key aspects of business analysis.

A few advantages of automating business analysis through tools such as process mining platforms include:

• Cost savings. Process mining tools prevent professionals from having to perform analysis themselves. This drives down the cost of business analysis, especially for larger organizations.
• Increased process efficiency. One of the core benefits of any type of business analysis, including automated business analysis, is increased efficiency. Once processes are analyzed an improved upon, they are invariably more efficient, less costly, and more productive.
• Deeper insights. Process mining platforms use analytics, such as software usage analytics, in order to offer deeper insights into business performance. The use of such analytics offers data that would otherwise be impossible to see. Digital adoption solutions, for instance, can monitor and analyze workflows and processes automatically.
• Improved business performance. Ultimately, process mining and automated business analysis can enhance business performance. Decreased costs, improved output, increased employee engagement, and other benefits all combine to generate better outcomes for the organization.

Naturally, the benefits derived from a process mining platform or business analysis function will depend on many factors.

These include everything from the size of the business to the process mining platform being used to the maturity of the business analysis function. In the next section, we will look at a few tips that can help you improve your business analyses.

Getting Started with Automated Business Analysis

Here is a quick set of tips that can help those who are just getting started with business analysis and automated business analysis.

• Understand the purpose of automated business analysis. In this post, we have already learned what business analysis is and how it benefits organizations. That understanding is crucial to the successful adoption of any business analysis program, especially one that involves automated business analysis.
• Get buy-in from executives. Executive leadership is critical to the success of any change management program. To obtain buy-in from business leaders, make a business case for your proposal and communicate that value in terms executives can understand. 
• Form a partnership with IT. Since automated business analysis tools, such as process mining tools, need a structured digital adoption effort, so support from IT is necessary. Open discussions with IT leaders, such as CIOs, and work with them to develop an adoption program.
• Set achievable goals for your business analysis function. Every business function should have a purpose. Define that purpose clearly in a strategic statement, then elaborate that into a series of goals and measurable objectives.
• Create a change management roadmap. Change management is the discipline dedicated to overseeing organizational change programs. Develop a roadmap, or a plan, that describes the implementation of the business analysis function.
• Evaluate and choose digital tools carefully. Business analysis tools include but are not limited to process mining tools. Start by analyzing the data that you would like to collect, see if any data sources already exist within your organization, then examine the ecosystem of business analysis tools, which can include BPMSs, DAPs, change management software, flowchart software, process mining tools, and more.

Implementing a business analysis function requires an investment of time, energy, and money. Yet that investment can generate a significant ROI, which can then result in major performance gains across the organization.

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Process Modeling Explained

Process modeling, unsurprisingly, refers to the modeling of a business process, usually by mapping the business process on a flowchart, in a diagram, or in documentation. More recently, software can be used to model and improve processes.

Process modeling, also known as business process modeling, is an established practice that takes specific forms such as:

• Business process model and notation, or BPMN. BPMN is a notation used to describe business processes. This notation system is easy to learn and can generate significant business benefits when used properly. BPMN uses graphical elements to describe business processes, such as activities and events.
• Flowcharting. A flowchart is a diagram that represents a process or a workflow. It outlines a series of tasks, events, or activities to undertake in order to complete the process. This is a very straightforward way to enhance one’s understanding of a business process.
• Data flow diagrams. A data flow diagram maps out the information flow within a process. It looks much like a flowchart, but it focuses more on data and information, inputs and outputs, and the path that data takes through an organization.
• Value stream maps. A value stream map is a tool used within lean. This map the steps needed to take to deliver or create value for end customers.
• Event-driven process chains.  An event-driven process chain is similar to BPMN in that it is a language used to describe processes. The language takes a slightly different approach, and while it does include similar elements to BPMN, such as events, it offers more detailed elements, such as logical connectors, control flows, and information flows.
• Formalized administrative notation. Another type of notation, formalized administrative notation focuses on people within the organization, their responsibilities, the sequence of operations, and other elements of interest to administrators.
• Gantt charts.  A Gantt chart is a project management tool that shows business activities on a timeline. At the top of the chart, dates are organized horizontally from left to right. On the left side of the chart, activities are listed. Then in the main grid, each activity spans a certain length of time, and is represented by a bar on the chart.

There are actually dozens of tools that can be used to model processes, and they are often also used with other tools, such as a business reference model or a business model.

When Is Process Modeling Used?

Process modeling can be viewed as a process, technique, or activity, which is often conducted as part of other business disciplines.

A few of these include

• Business process management. Business process management is the discipline dedicated to process design, redesign, and improvement.
• Process improvement methodologies. Certain process improvement methodologies, such as lean and Six Sigma, aim to improve organizational processes and activities. One way they can do this is by re-engineering business processes, using tools such as those covered above.
• Organizational change management. Change management focuses on designing implementing, and optimizing organizational change initiatives. in the course of any organizational change project, business processes will often be redesigned, refined, or created from scratch.
• Business process reengineering. Like business process management, business process reengineering aims to improve processes. The emphasis typically is upon rethinking business processes and applying a repeatable method to improving those processes.

Process modeling, in short, is a well-established and well studied business activity that has been used for a long time to enhance business processes and outcomes.

Why Model Business Processes?

Though we alluded to the benefits of process modeling above, it is useful to understand exactly why process modeling is used in business. 

Here are a few reasons:

• A greater understanding of business processes. As mentioned, analyzing business processes can offer a window into how those processes operate. that information, can then be used to offer insights into what works, what doesn’t, and what needs fixing. 
• Improved process efficiency and performance. One of the main benefits of reengineering processes is performance improvement. By understanding how the process works, process modelers can redesign processes and optimize them to decrease waste, shortened process timelines, improve outcomes, and more.
• Better business outcomes. Better business outcomes is one of the primary reasons to improve processes. Inefficient processes, for example, can miss their objectives, cost more, impact quality, and so forth.
• Process documentation. By analyzing and understanding a process, it is much easier to document and standardize a business process. That standardization, in turn, can help improve key stages in employee workflows. When a process is well documented, for example, employees will have a much easier time learning that process, understanding expectations, and carrying the process out.
• A smoother work environment. The more clearly a process is understood, the easier it will be to onboard and train employees. These types of benefits, in turn, can contribute to a more seamless, less frustrating workplace.

Business process modeling is an essential business technique that can enhance the organization in many ways – not only by improving processes and their outcomes, but also by positively impacting the workplace and the customer experience.

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In this guide, we’ll cover key concepts related to the agile methodology, including:

• A definition of agile software development
• Agile vs. waterfall
• Hybrid approaches to development
• Important concepts related to agile, such as scrum, sprints, and user stories

Agile software development has become very popular in recent years and could even be considered an industry standard for the programming world. 

But why has agile become so widespread – and is it really the best method of software development? 

Below, we’ll find out the answers these questions.

A Byte-Sized Guide to Agile Software Development

Let’s get started by looking at the basics – what agile is and what it’s benefits are.

Agile Software Development: A Definition and Short History

Agile software development is an approach to software development that focuses on principles such as:

• Responding to change instead of following static plans
• Functional products instead of extensive documentation
• Collaboration over contract negotiation
• Individuals and interactions over processes and tools

The ideas underpinning the agile approach have been around for decades. Iterative and incremental software development and adaptive software development, for instance, had similar ideas to those that would later be embodied in agile. 

Agile truly became mainstream, however, when a group of software developers met in Snowbird, Utah, to create and publish the Agile Manifesto. That document “hard-coded” the ideas mentioned in the list above, which principles have now become central to the agile methodology.

Agile vs. Waterfall vs. Hybrid Development

Agile is focused on adapting to the needs of customers, the market, external conditions, and change. This is an ideal approach for adapting to today’s fast-paced digital economy, meeting customers’ shifting expectations, and releasing products quickly and cost-efficiently.

In part, this approach was a reaction to slower, less adaptable approaches to software development, such as the waterfall model.

The waterfall model focuses on:

• Planning a project in advance
• Breaking down the project into a linear roadmap
• Completing each stage in that process before moving to the next

From an agile perspective, the biggest drawbacks to this approach is its lack of flexibility. 

Since project managers must stick to a static plan, they cannot readily make changes if needed. 

For example, assume that the customer experience team finds that customers prefer one feature instead of another – a waterfall development approach would have a difficult time accommodating this shift. An agile approach, on the other hand, would be setup to respond to those changes.

That being said, there are benefits to the waterfall model.

For example:

• Staying on-budget is easier, since project requirements are predefined and predetermined
• It is easier to delegate tasks, since responsibilities are less likely to change during the project
• Progress is more clearly defined and therefore easier to measure

Software developers that want the best of both worlds combine the waterfall and agile methods into a hybrid approach. 

A hybrid agile approach incorporates different development methodologies, such as:

• Agile
• Plan-driven
• Incremental
• Iterative

Hybrid approaches are customized to suit the situation at hand, so there are perhaps as many hybrid models as there are software development projects.

The advantage of hybrid approaches is that they can be designed to suit the needs of the team, the project in question, the business, and the circumstances. 

Components of the Agile Workflow

Here are a few of the most common steps that are included in agile workflows:

• Epics. An epic defines a body of work that must be completed during the project, which can then be further broken down into user stories.
• User stories. User stories define specific features and software components needed, as those features might be described by users.
• Scrums. Scrums are a set of practices, such as meetings and roles, that allow teams to self-organize and stay productive during a project. 
• Sprints. Sprints are time frames within which scrum teams must complete a specified set of tasks.
• Backlogs. A backlog is a list of work items that must be completed by the teams. 
• Kanban. Kanbans are work boards divided into columns, and each column is broken into a category, such as a priority level or a stage in the workflow.
• Roadmaps. The roadmap is the overarching plan of action that will be followed when developing the product.

Importantly, agile workflows should also follow the principles of agility – namely, they should respond to the needs of the team and be flexible when necessary.

Beyond Agile Software Development

Agile methods were popularized thanks to the software development community, but agile has spread into many other business functions.

Today, for instance, we have concepts such as:

• Agile project management
• Organizational agility
• Workforce agility
• Agile change management
• Agile manufacturing
• Agile supply chains

In fact, agile has become so central in today’s business world that major research firms such as McKinsey argue agility is essential to succeeding in the post-COVID next normal.

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