Data Science Organization Change

Building a data science culture means different things to different data-driven organizations. It may mean introducing a new data science team to the organization, democratizing the data so everyone has access to the data and the business intelligence (BI) tools to do their jobs, or encouraging the entire organization to develop a data-science mindset.

Whatever the meaning, changing an organization's culture, especially if your organization strongly resists any major change — and many do. To effect a big change, you need some degree of competence in the field of change management— strategies and techniques to prepare, support, and assist individuals, teams, and organizations to adapt to new ideas.

Although change management is a complex topic, in this newsletter I offer several suggestions to overcome common obstacles in implementing any change, including a change in your organization's culture.

Start with a Plan

Changing an organization's culture is an ongoing, often cyclical process, but before you start, draw up a linear step-by-step plan to ensure that you set out in the right direction. Here's a sample plan that you may want to tweak for your own use:

Identify your organization's existing culture. See my previous article "Identifying Your Organization’s Culture." By knowing your existing culture, you have a better idea of the obstacles you're likely to encounter.
Assemble a team of like-minded individuals — proponents of data science. As I explain in a previous newsletter, "Busting Common Myths of Organizational Change,"some people are more receptive to change than others. When recruiting members for your team, look for natural innovators and early adopters.
Find a high-level sponsor, if possible. An executive or someone in senior management would be a good choice. A high-level sponsor can be very helpful in championing your cause. If you can't find a high-level sponsor, however, you can still effect the desired change — you simply need to work with your team to implement the change from the bottom up.
Start with one small team. If you go too big too soon, you may meet with heavy resistance, and any failures will be amplified. A small team can work below the radar until it has achieved some success with legacy data projects.
Celebrate the wins. When the data science team answers a compelling question, helps the organization overcome a challenge or solve a problem, or introduces an innovation, make sure everyone in the organization hears about it.

Having a top-level sponsor to cheer on your team while you do the hard work to effect a change is better than having no top-level support at all. However, any tangible support your top-level sponsor provides adds fuel to the tank and sends a signal to the rest of the organization that people at the top truly support your efforts. Tangible support may be provided in various forms, including the following:

A budget to cover team expenses.
Investment in data science education, training, and resources fundamentally changes an organization's approach.
Space and time for team meetings.
Attendance and participation at team meetings.

Set Reasonable Expectations

Transforming a culture in which status and expertise drive the decision-making process to one in which data drives the process requires a major overhaul in how everyone in the organization thinks. It requires a never-ending process of continuous improvement enabled by big data analytics. If your expectations are too high regarding the level of change and the time in which it occurs, you and others may get discouraged when you don't see quick, dramatic improvements.

To improve your chance of long-term success, manage everyone's expectations, including your own. Prepare your organization for a long and bumpy ride. Steer clear of quick fixes. Slow and steady wins the race. While this approach may sap some of the energy that drives change, it will help to prevent major disappointments, which tend to threaten overall success.

Change Minds, Not Just Infrastructure and Processes

Building a data science culture is about much more than building a data warehouse and rolling out state-of-the-art business intelligence tools. It's about changing the way people think about what they do and how they do it. According to some schools of thought, you can change people’s thinking by changing their behaviors. Others believe that you can change people’s behaviors by changing their thoughts. I recommend doing both:

Change minds. The best way to change minds is through education and results. Start small and celebrate wins to prove the value of data science to others in the organization. When they see the results, they'll quickly become adopters and promoters.
Enable change behaviors using objective metrics.. Provide the infrastructure, tools, and training required to democratize the data, so everyone in the organization benefits from data science and can see the results for themselves. Even prior to democratizing the data, you can set up a question board and encourage everyone in the organization to start asking the data science team questions. See my previous newsletter, "Asking Good Data Analytics Questions," for details.

Listen to the Skeptics

In any organization, you'll find pockets of resistance and even vocal critics of any proposed change. Don't ignore this resistance or merely try to steamroll a change over or past your critics. Listen to them and engage them in discussion, enabling them to contribute effectively. If data science truly holds value for your organization, you should have no trouble convincing skeptics. In addition, your critics may point out real weaknesses in your plan that you need to address for a successful implementation.

Don't Rely Solely on Outside Consultants to Drive Change

Many organizations hire outside consultants to implement a desired change in the organization. Some even treat consultants as disposable change agents — hiring a consultant to drive the change and then firing her when it fails. This practice gives management a convenient scapegoat.

A better approach is to choose a well-respected and longtime employee to drive the change internally with the mindset that the change is inevitable — failure is not an option. One or more consultants can then be brought in to provide expert knowledge and insight on how to more effectively implement a data science team. A charismatic insider can more effectively lead the charge by having some skin in the game and communicating in a language that the rest of the organization understands using examples that resonate with the organization's existing culture.

Frequently asked questions

What is data-driven change management?

Data-driven change management means using data to guide and help with changes in a company. You collect, look at, and use data to make smart choices. You track progress and make sure changes work across the organization.

How can organizations leverage data in change management?

Organizations can leverage data in change management by:

Using dashboards to track key performance indicators
Conducting surveys to gather stakeholder feedback
Analyzing historical data to predict potential barriers.

This makes it easier to create targeted and effective change strategies.

Why is it important to collect and analyze change management data?

Collecting and studying change management data is important. This data shows how change plans are working and progressing. It helps managers to:

Check engagement
Lets them change plans if needed.
Make sure everyone aims for the same goals.

Doing this helps to raise profits and improve work results.

What role does data literacy play in achieving successful change management?

Data literacy is essential for successful change management. Understanding data is very important when you're making changes in a company. It helps people and groups to read and use data effectively.

Having a high level of data literacy can create a data-friendly environment in the company. This makes it easier to start and keep up changes based on data.

How can data science tools aid in change management?

Data science tools can help with change management. They can gather data automatically and it lets you watch what's happening in real time. They also give you smart ways to make decisions.

These tools help centralize change management data and integrate various data sources, making the process more efficient.

Can historical data be utilized in change management strategies?

Yes, historical data can be highly valuable in change management strategies. Analyzing historical data helps organizations to understand past trends, identify what’s worked and what hasn’t, and predict future scenarios.

This information is key for planning and executing successful change plans.

What are some common barriers to data-driven change management?

Common barriers to data-driven change management include:

A lack of data literacy,
resistance to change from individuals and teams
inadequate data integration across the organization

Overcoming these barriers involves taking care of a data culture, providing training, and utilizing effective data management tools.

How do dashboards and real-time data tracking contribute to effective change management?

Dashboards and real-time data tracking contribute to effective change management by providing up-to-date information on key metrics.

This allows managers to monitor progress, make informed decisions, and adjust strategies as needed to ensure the sustainability and success of change initiatives.

What is the impact of data-driven change management on ROI and productivity?

Data-driven change management can impact ROI and productivity. This can be done by making sure that change initiatives are aligned with future state goals and are executed efficiently.

By using data to inform and guide the process, companies can get measurable impacts, reduce costs, and improve overall performance.

This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or LLMs. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and ethics.

This newsletter is 100% human written 💪 (* aside from a quick run through grammar and spell check).