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A soft approach to hard facts – time to get ahead with an analytics strategy

How can you bring your analytics strategy to life so that it benefits the whole organization?

Mirva Toivonen / April 20, 2021

A solid analytics strategy is a must if you’re seeking to gain a competitive edge, but implementing one requires a cultural change to ensure analytics isn’t relegated to the sidelines, siloed in individual projects. How can you bring your analytics strategy to life so that it benefits the whole organization?

Roughly speaking, an analytics strategy can be broken down into five main elements:

Usually, leveraging data and analytics has top management support – after all, which C-level manager would say no to the opportunity to increase revenue or optimize processes?

However, the cultural shift required to implement an analytics strategy can lead to challenges.

This is where the softer aspects of an analytics strategy come into play – the ones that are more difficult to implement and measure, such as initiating a significant change process and building a business culture that is favorable to analytics.

Winning hearts and minds: time to break the data silos

Without a change in corporate culture, analytics will remain siloed in individual projects, killing off the prospect of any gain in competitive advantage. The purpose of an analytics-friendly corporate culture is to help create a sustainable competitive advantage by changing the way people in a company think and act.

However, there are a few common challenges on the journey to sustainable change.

Analytics is about identifying and solving pain points in business processes, which requires a deep business understanding and the ability to identify where analytics can be utilized. Usually, data does exist but there is no clear way to use it for business development. For example, it may be unclear to a company what information it holds about customers and their service experiences and how this information could be systematically collected and combined so that it can be used to build stronger customer relationships.

Common reasons for this usually include a lack of resources for data-utilization projects, data literacy not yet being perceived as part of business expertise, or people being unfamiliar with analytics tools.

So how do you make sure your grand plans for an analytics strategy are brought to life rather than merely existing on paper?

5+1 tips for putting an analytics strategy into practice

Implementing and managing analytics requires a change process. Sustainable change is achieved through long-term work that begins with individual pilots and then evolves into a new way of acting and thinking.

Here are some tips to help you implement a sustainable change process:

1) Ensure top management support: Analytics should be represented in top management, have a strategy, and be measured in the same way as other assets, expenses, and strategic functions. Management should have a plan for how the change process will be carried out. Analytics is not an IT project or department – rather, analytics-based thinking and action is everyone's responsibility.

2) Make sure you have the time and resources available: Good data products are needed. The data should be easy to find, in an understandable format, and possible to validate. Data and analytics should be available to everyone and implemented in cross-functional teams instead of in silos.

3) Set out a clear shared vision: Ensure supervisors and project managers understand analytics and engage them in leveraging analytics. Start with identifying some clear goals. Do you want to use the data to a) prioritize tasks, b) increase revenue, c) improve customer satisfaction, or d) something else? And do you know how analytics can help you achieve your goal(s)?

4) Collect data and perform experiments: Building a business culture that is favorable to analytics begins with individual experiments and data collection. Individual experiments provide concrete examples of what can be achieved with data and make it possible to learn from your mistakes. At this point, you may notice that the basics are not in place and need to be adjusted. Some experiments may result in new projects as people realize how the data can be utilized.

5) Develop operations and share information: The company's operations and structures should already be starting to change. Operational systems and processes should systematically measure the right things. This means all analytics are put back into circulation transparently as a part of your processes instead of being stuck in spreadsheets on individual hard drives. Development should not be done in silos but across the entire organization. If one part of the organization is more advanced in utilizing data, people should be transferred from there to other units to share their knowledge and information.

+1) Renew and constantly innovate: Technology and analytics are evolving rapidly, so constant renewal is essential. Leveraging the expertise and vision of outside partners will help you to address this need. If projects do utilize partners, make sure that information and know-how are shared with everyone involved in the project.

Cultural change requires investment, staff training, and pilot projects in which information and learnings are shared across the organization.

The goal is to make the utilization of analytics follow a new, systematic development process.

As Mike Grigsby says in his book Marketing Analytics: A practical guide to real marketing science: "Analytics without application to an actionable strategy is meaningless, much like special effects in a movie without a plot."


Photo by John Ruddock on Unsplash Universal Studios Hollywood, Universal City, United States. The climactic scene from Universal Studios Hollywood’s “Waterworld” show.

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Mirva Toivonen
Data Scientist

Mirva works as a data professional doing hands-on consulting in data activation. Mirva's expertise is to build strong customer relationships and maximize sales through analytics. Currently, she is working in the customer experience analytics domain doing predictive analytics for customer journeys and marketing automation.


Mirva Toivonen

Data Scientist

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