20.03.2017Blog

Data-driven culture, phase 2: Recognize opportunities

It may seem difficult to begin creating a data-driven culture if you need to start with changing people’s ways of thinking. You may not know where to begin. In this series of blog posts, I tell you about the six phases that will help your organisation’s culture to become a data-driven one, step by step.

In the first post, I explained why a data-driven culture should be your goal, and what phases are necessary to achieve it. In the first phase, we discussed how you can recognize your analytics maturity level. That is a good starting point for the second phase discussed in this blog post: how to find and recognize the opportunities for applying your organization’s data.

When you have completed the first phase and identified the analytics maturity level of your organization, you should take a moment to look in the mirror and consider your discoveries. If you have not yet gathered your colleagues to participate in the effort, this is a good time to get your team to help you. Take the current state survey you completed in the previous phase, find new viewpoints to complement it, and use the following exercise to challenge your team and to achieve top results.

Survey of future opportunities: A + B = C

Image: a + b = c

To find the best opportunities, you need to assess your current successes and failed projects. Ideas rising from these two extremes help us list future opportunities. To put it simply, it’s about summing up and analysing past experience.
The same process is also known as the classic SWOT (strengths, weaknesses, opportunities, threats) analysis, without the T. In the exercise described here, threats are not examined until the third phase, where ideas are prioritized. This way, we will be able to focus on the inspired process of creating ideas and avoid being blinded by risks.

A. Successes: Pat yourself and your colleagues on the back

List the successful analytics operations you see around you. Success does not always mean advanced artificial intelligence analytics using automated processes; one good old-fashioned bar chart may steer business operations towards better decisions every day. Flashy gear and gadgets are of course fun to have, but in the end, it’s the results that count – in sports and business alike.

Successful analytics respond to the need for data in business operations and have a direct impact on decisions.

At best, your client will receive personalized service in any channel that they choose. More modest indicators of successful analytics include the number of years when a correctly compiled financial statement has helped you avoid court sessions involving financial law.

B. Weaknesses: Where could we have performed better?

Now, list wasted opportunities. Try to dig out the situations where you needed customer data but failed to get it in time – for example, manually compiled weekly Excel reports. Question your earlier analytics projects by considering how and to what degree the information they produced has led to concrete measures.

Don’t get dazzled by big data MPP databases, real-time data discovery dashboards or artificial intelligence applications produced by advanced analytics. If the analysis never leads to the final step – the decisions and actions – the analysis project has failed.

Of all failed projects, the bleakest is the project that used plenty of resources, but in which the processed data was never used as the basis for concrete actions.

Discuss ways of measuring the concrete actions that were completed based on the analysed data. What business results did these actions produce? If you cannot answer these questions in the time it takes to reach your floor in the lift, you may find it difficult to justify your future analytics resource requirements to those in charge of budgets.

C. Future opportunities

Next, it is time to start applying analytics of all sorts to find new business opportunities. Start with the successes and targets for improvement you identified above.

Consider questions such as the following:

  • How can you combine data from various operations to find new opportunities?
  • After looking at the issue from all angles, what new thoughts rise to your mind?
  • Do you measure your operations?
  • Do you see the big picture behind your charts and trend diagrams?
  • Do you steer your operations proactively based on your outlook?
  • Could you get a recommended response, together with justification, automatically to support all decisions?
  • Could the decision itself be automated?

Below is a list of thought-provoking stories of opportunities already realized by Solita, for example. Read them to find inspiration:

Case Sanoma: Improvement of the online experience based on real-time data
Case Vaasan: Improvement of the predictability of business 
Case Finnish Transport Agency: Monitor and optimise concrete field work

What next?

In the next blog post, we will focus on prioritizing the opportunities you have discovered. We will tackle one of the most important questions on your way to a data-driven organisation: how do you know that you have made a good start towards a data-driven approach?

Before the next blog post is published, I encourage you to apply this perspective to the opportunities you have listed. Do you believe that you have already found the right path?
If you need help doing the exercise described above, or want to move on to prioritizing your opportunities right away, don’t hesitate to contact us today!

Olli Lindroos works for Solita’s Agile Data team. He is a passionate student and proponent of data-driven culture, with an interest in technology in all of its forms, whether it’s about the user experience, technical implementation or business strategy opportunities. Olli describes himself as a dad and a nerd, as well as a food and drink aficionado. In his free time, Olli likes to try out all the newest trends as a consumer and building the IoT equipment he needs by himself. Olli is on Twitter as @ollilind.