Data analytics transformation can get a model to work, create a data asset, or solve organizational inventory questions. The important element that many analytics executives sometimes overlook in this process is change management.

But analytics is really a change management exercise: You identify stakeholders, get them on board, and help guide the transition until delivery and even beyond. The success of an analytics transformation depends on the success not only of the technical solution, but also the change management. In other words, organizations need to both drive the business and understand how to go from hindsight to foresight.

Click to enlarge.

When starting an analytics journey, it’s important to have stakeholders with the necessary business knowledge and to explain explicitly that it’s a change management process. Though no one size fits all in scaling data analytics, a framework for strategic implementation of Big Data in organizations often involves three key phases: pre-scaling, scaling analytics, and establishing analytics as business as usual (see Figure 1).


In pre-scaling, the plan is always to approach the business vision with the organization, department, and team in mind. Based on that, the analytics team needs to create a strategy including potential projects, stakeholders, and the overall analytics vision. For example, if the organization’s strategy is to expand in certain markets, the analytics team would work with stakeholders on having projects that are aligned with the company’s strategy and prioritize those markets.

This is then reflected in the organization design: Are the needed skills present in the team or not? Which data analytics organization design is your company using? Are you using an approach where everything is done centrally, or is the company using a hybrid model where things are done in the functions? Does the company need to do any formal organization restructuring? This drives the company to create and support an analytics-driven culture, one where analytics is embedded in the business. This is where change management kicks in.

The second phase is scaling analytics, which includes creating a team, having the technology set up, and addressing all the related questions. Do we have a data asset? What is the quality? What other skills are needed for the project? Do we need data engineering skills or specific data science skills? Also throughout the scaling phase, clear communication and transparency are important. Data analytics projects are often run using an Agile methodology, which means that changes, successes, and challenges need to be communicated.

The last phase is the proof that analytics became the DNA of the business. At this stage the organization is living and breathing analytics.


Now that we understand the long-term analytics framework, how can we start analytics and work on this change management piece as well as get buy-in? In striving for value-driven analytics, the first question must be, what projects bring the highest value? When companies are doing analytics, it should be value-driven, not just something to be added in the company’s profile.

And this is where the collaboration is really needed. Begin change management with a kickoff that examines what analytics initiatives hold value and drive the business. Create a temporary strategy and road map with quick incremental gains. Start with the teams, and try to understand how to add value quickly. Create a prototype or a proof of concept that allows the team to see the benefit and what the project will look like.

In a nutshell, the analytics change management process can be broken down into three essential ongoing tasks or responsibilities:

  1. Stakeholder management. Be transparent in communication, and introduce your stakeholders to analytics and Agile methodology. (For more on Agile, see “Further Demystification of Agile Project Management,” Strategic Finance, August 2019 and “Agile Project Management in Analytics,” Strategic Finance, May 2019.) There will be good and challenging news when scaling analytics. In Agile when there is a problem, it’s neither an analytics problem nor a business problem, but a project problem that the team solves together. As a team (business and analytics), we need to fix this problem. This is change management. Communication is the key, and being transparent is essential. Everything needs to be shared so that challenges are evaluated.
  2. Identify opportunities. Be efficient in changing course and reprioritizing when needed. If you’re working on a project and a business need surfaces, do a proper analysis for it. This is introducing your team to Agile and getting them to live and breathe it.
  3. The right partners. Start identifying strategic technology partners, and work very closely with them. Have an open communication channel with them, and quickly identify how they can support the project and what tools are needed for the project.

Scaling analytics is a change management process at its core; it looks at strategies, ecosystems, and road maps. Make sure you understand how to scale analytics, the opportunities you have, and the different options.

About the Authors