In this installment of his series on “Delivering Value Through Data”, Rich Peters focuses on change management which, he says, is really all about people.

We all call it change management, but it really addresses the people component of a strategy. When it comes to a data strategy, I often refer to change management as the keystone. Why? It’s simple. Because people are the key for each of the other components of your strategy.

  • People choose and implement your technology.
  • People create, document and follow (or don't follow) your processes.
  • People create, modify, integrate, analyze and use your data.
  • People set your organization's strategy and correlating data strategy.

Even in a world of growing reliance on artificial intelligence, it still comes down to people creating, managing and using our systems.

So, how do you get people to believe in your strategy and ultimately use your data to drive value? This is where change management becomes critical. Done correctly, it will ensure your data strategy is successful in both the short and long term.

Your data strategy requires change in your organization. The key to successful change is getting all the individuals in your organization to understand and, ultimately, take ownership and responsibility for your data and information. This will also extend out to external stakeholders so that you have a healthy data ecosystem.

You are already starting the first part of the change by recognizing the potential value of your data and investing the resources to put forward a data strategy. Setting the tone from the top, committing resources and having a personal stake in the results are critical. How do you take your belief and commitment to data and embed that into your organization's culture? Start with “the five E's.”

Changing your culture through the five E's.

  • Engagement
  • Education
  • Empowerment
  • Experience
  • Empathy

It would take a lot to cover all five E’s, so I have split this sixth installment of the series into two parts – 6A and 6B.

This article, 6A, will address the first three E’s, and 6B will address the two remaining E’s, along with some tips on to be successful at integrating change management throughout your data strategy process.


Other articles in this series, by Rich Peters:

  1. Delivering Value Through Data
  2. How to Write Your Data Vision and Mission Statements
  3. How to Build a Data Strategy - Part 1: Key objectives and capabilities
  4. How to Build a Data Strategy - Part 2: The assessment
  5. How to Build a Data Strategy - Part 3: The roadmap


Connecting People to Your Data Strategy

1. Engagement

Simply put, if you can't engage your organization, then the rest of the E's will have far less impact. Engaging your organization goes far beyond email blasts, slogans, lunch-n-learns, or organization-wide presentations. It is about helping every person feel connected to the data strategy, its implementation, and its integration into the organization's culture. While this may seem optimistic or unrealistic, it starts with a simple approach using stakeholder analysis. 

The stakeholder analysis sorts individuals and groups into small units where you can "personalize" the messaging about why the data strategy is essential, how it benefits them, and its impact on how they work. Separate the stakeholder analysis into individuals and groups to make the process manageable, yet effective. The individuals on the stakeholder analysis are the leaders of the organization and the key influencers. They are critical to the strategy's success, and you can use an enhanced stakeholder matrix that adds a RACI component (responsible, accountable, consulted, and informed) for crucial decisions and communications.

It is vital to define groups using the right level of granularity. If they are at too high a level, you will lose the "personal" feel of the messaging, which hinders the connection to the strategy. However, for large organizations too many groups may become burdensome.

One key question to ask when you build your groups is, "Do they interact with the data differently than other people in their group?" If they do, it may be a good idea to break the group into smaller groups. I tend to prefer smaller groups to ensure the messaging is more personal. While building the matrix and corresponding communication plan will require more effort, due to the larger number of groups, the added effectiveness and engagement will make it worthwhile. It is also essential to include key external stakeholders such as customers, vendors, and even regulatory bodies in this process.

The communication plan tells how you activate the stakeholder matrix. The communication plan ensures that you are consistent with your messaging across the organization. Consistency is critical to changing your culture. If you have consistent messaging in ideas, tone, and timing, the recipients will absorb the messages more quickly. If the messaging is inconsistent, then the recipients have more leeway to reject the message.

A successful communication plan includes:

  • Audience – use the stakeholder matrix.
  • Messages – reinforce and build upon past statements; help educate; based in reality; communicate challenges and successes.
  • Senders – organization leadership, data strategy leaders and team members, key influencers, quick win participants.
  • Cadence – organizational leaders less frequent; higher impact, data strategy leaders, team members more frequent, and quick wins soon after they happen.

Communication plans tends to focus on a "push" method of communicating to the organization. It is also important to have an online presence -- a “publish method” -- where everyone in the organization can access further information on the data strategy and why it is important. It should also offer educational materials and provide up to date status information. This is also a great way to see if the organization is getting engaged. Growing activity on the "publish" side is an excellent sign.

Show People Why Change Matters

2. Education

As you are working through your engagement plan, one of the first questions bound to come up is, "What are we communicating?" This is where your education approach is vital.

Real change comes from knowledge, understanding, experience and commitment. Telling someone to change can be helpful, but showing them why changing can benefit them is far more impactful. However, it is difficult to show someone the benefit if they don't understand why it matters, and how they are involved.

Education starts when you are building out your data vision. The data vision and data mission are focal points for all of your stakeholders. Well-written data vision and mission statements enable stakeholders to see how they fit into the strategy. It gives them a sense of why the strategy is vital for the organization. It builds from there.

Many people don't understand how a single piece of data is used across the organization and how it ends up in different metrics and analytics. Nor do they know how much data it takes to support a single transaction with a customer or vendor, or to support analytics used in forecasting. Creating messaging and tools that show how data flows helps people understand how the data they touch is used and how the ways that it is created and maintained can be very impactful. It can also develop a sense of community which drives higher data ownership and quality.

Education is most impactful when you use actual data from your organization. Creating custom examples for various groups in your stakeholder matrix may take extra work, but it will drive better understanding and higher adoption. As you get into the implementation, using your quick wins as an educational tool reinforces the value proposition of the strategy. Real-life examples are an effective way to show how data helps the organization.

An education plan is like a communication plan or a training plan. You base it on the stakeholder matrix, and it precedes your training. It is also broader than a training plan since everyone in the organization will benefit from education and not everyone will need training for changes. Part of your education plan is some basic information on data types, like the primer below. It is beneficial to increase the overall level of data maturity in your organization.


A data primer for your organization

Understanding the three “M’s” – Master Data, Metadata, and Metrics.

Master Data – This is the structure of how we look at our data – Customer, Products (services), Vendors and our organization as the main elements. It includes attributes like names, locations, colors or sizes and may include a hierarchy that shows us a customer and its subsidiaries or one of our product lines.

Metadata – This shows how we define our data and how to find our information, how we use it, who owns it and why it is important. An example is who are our top ten customers by profitability? First, define customer. Does it include all customers rolled up to the parent or even affiliated groups? Next, define profitability. Is it defined as initial profitability, long-term profitability that includes returns and rebates or does it include cost of selling, customer support or other costs? Finally define the time horizon. Is it quarterly, annually, over multiple years and does the trend matter and why? Metadata gives us the contest to know how to use our data.

Metrics – This shows how we analyze our operations and drive the business. Includes high level Key Performance Indicators (KPI) all the way down to monthly, weekly, daily, hourly or even real time operational metrics. Each industry has its own KPI’s but some common ones are Profit, Costs, Sales, Customer Lifetime Value, Customer Satisfaction, Product Quality, Employee Satisfaction and Employee Turnover Rate. Some operational metrics are products manufactured, current inventory, products shipped, service hours logged, product or service backlog, number of defects, tickets closed, customer wait times or employee absentee rate. Clearly there are thousands of different operational metrics and many organizations tweak standard industry KPI’s to meet their needs but understanding the concepts is critical.


Data Access and Ownership

3. Empowerment

If your people do not feel empowered, it will be hard for them to fully engage in the process and take ownership of the data. Empowerment comes from access to data, usage of data, ownership of data, and trust. However, along with empowerment comes accountability. Your people are accountable for data quality and the "appropriate" use of the data. Shadow data, where people work outside the system, will no longer be accepted.

One way to empower your people is to foster their inquisitiveness about the data. The more questions they ask about why you use specific data and how it is created or used in other areas, or why a certain person is the data owner, the better! It drives their education and empowerment. The opposite is also true – if you ignore or disregard their questions, they lose the connection with the data and ownership of it.

Inquisitiveness about data will also lead to questions about access to data and, many times, the desire to use third-party data to enhance your internal data. This type of curiosity will help drive data maturity and reinforce understanding how much data you need to make an informed decision. Organizations that unduly restrict their users' access to data slow down data maturity.

Empowering people with data requires responsibility for making sure the data has context to tell the whole story. Incomplete data or data with the wrong context can misrepresent reality, and drive mistrust of the data. There are times where you don't have all the requested data. Understanding when you have enough of the critical data to decide is a sign of maturity and leadership. Make sure you acknowledge the gaps and assumptions that you are making so everyone understands the risk. Misrepresenting the data or completeness of the data is a red flag and must be addressed.

The last and perhaps most critical part of empowerment is to let the people use the data to make decisions. Once the decision is made, track the results so they can see how well it worked. Was the hypothesis correct? Were the assumptions accurate? Did you have enough data, or too much data? Did the data quality support the decision? What can you learn to accelerate this decision, or do you need a course correction?


  • Give responsibility and increase accountability
  • Foster inquisitiveness which drives ownership and education
  • Ensure the data has the context to be used appropriately
  • Appropriate access to data will help increase data maturity
  • Track and enhance your decision process

The first three of the five “E’s” give you a framework for helping the people in your organization through change. In the next article on change management and data strategy, part 6B, we will look at the last two E’s: Experience and Empathy, which address how to help people absorb, accept and embrace the changes.

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