4 Aug 2017Blog

Data-driven culture, phase 6: Share and start again

Congratulations! You will soon be completing your first phase towards a data-driven organisation! You have come a long way by recognising your analytics maturity level (phase 1), recognising (phase 2) and prioritising (phase 3) opportunities, acquiring the necessary expertise (phase 4) and starting implementation (phase 5). Now is the time to stop for a moment and effectively communicate to others what you have learned.

In the last article in this series, I will discuss how you can share within your organisation what you have learned during the implementation of your data project. Competence and culture live inside people and are shared through interactions in daily life. For this reason, it is important that you inform others about what you have learned and the results you have achieved, as communication of this type attracts interest and inspires the people around you.

4 phases of spreading a data-driven culture

The SECI model is a process model for creating new information. It encourages people to combine tacit knowledge (information based on experience gained in day-to-day work) and explicit knowledge (information that can easily be given as instructions) and convert them into organisational knowledge. As a model, it stresses the importance of sharing experience gained in daily work and documenting this experience as applicable. Successful peer-to-peer training and easily accessible results lower the threshold for colleagues to make use of existing knowledge. This also reduces overlapping work.

According to the SECI model, new knowledge is created cyclically through four phases: socialisation, externalisation, combination and internalisation.

By following these phases, you will ensure that the entire organisation understands and appreciates the effects of a data-driven culture and will support new, data-driven ways of working.

1. Combination

This phase involves selecting information from the knowledge that surrounds you and combining it in a unique way to create new knowledge that is suitable for your particular business environment.

Example: During this series of articles, you have implemented an analytics project that involved collecting data from various sources and combining it in a way that meets your needs. Write down what type of data you combined, what kind of results you expected and achieved, and what new discoveries you made. (Phase 5: implementation, weeks 0–3) You can use images, video clips or 3D models to describe the work you have done.

2. Internalisation

Internalisation means converting the collected data and the information created by processing the data into in-depth knowledge.

Example: After you have tested your observations in practice (phase 5: follow-up phase, weeks 3–5), you have a deeper understanding of the effects that raw data and the real world have on each other, which helps you to better understand your own business environment. Write down the end results. What measures did you take based on your observations and what was the end result of your experiments?

3. Socialisation

Socialisation concerns transferring, through interactions, competence and knowledge which are difficult to share.

Example: Organise a demo or hold a training-like briefing for the people around you and for the end users. Briefly explaining what you did, why and how you did it, what you learned and how others can benefit from your results is the easiest way to lower the threshold for others to make use of your results.

4. Externalisation

In the externalisation phase, the accumulated knowledge is converted into instructions and the collected data is made available to the entire organisation.

Example: Establish a portal or use an existing portal to share software code and information you have produced. By sharing your results with the entire organisation (or the world, as applicable), others can make use of your results in their combination phases.

After sharing what you have learned, it is time to start again from the first phase. Examine how your analytics maturity has changed since your first effort and proceed towards new implementation ideas. A data-driven organisational culture cannot be purchased as a service. It is created gradually, phase by phase, through daily actions and decisions.

I hope this series of articles has helped you and your organisation to get started with creating a data-driven culture. I wish you every success with your data journey!

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.