Data Strategy or Data Tragedy? Only a few letters in English differentiate these conditions, but there is a major difference in how organizations on each side live their daily lives, plan for the future, and adapt to constant changes they are facing.
Organizations that are living with Data Tragedy status are easy to recognize. They don’t have any means to communicate internally (or externally!) their current state of architecture, future development roadmaps, ownership of data or systems, and all change requests to the current setup (new source systems, new applications…) initiate an extensive investigation project, again and again.
People are stressed about change and there is no easy way to communicate internally with different stakeholders about IT and process development. Eagerness to develop businesses further is limited.
You don’t need leadership or plans to develop Data Tragedy, you just let things evolve over the years.
So, what is Data Strategy all about?
Data Strategy is the daughter of Business Strategy. It is vital to understand what the business wants to achieve now and in the future, and how data can help with these business targets.
Data Strategy is not about quick wins, it is about creating a long-term competitive advantage. Data Strategy includes also quick wins.
A comprehensive Data Strategy covers areas like improving decision-making, operational efficiency and data monetization. Collecting use and business cases is a necessary step when starting to formulate the Data Strategy.
It also consists of a longer-term architecture plan that can adapt to changes. AS-IS, transition and TO-BE architectures with source systems, integrations, operational and analytics systems and dependencies between different parts are needed. Architecture principles are created to guide the future development.
In order to facilitate the discussions between internal and external stakeholders, there might also be a need to create common and shared vocabularies and notions. “A truck” can mean different things to different persons.
In many organizations, the ownership of IT systems and data is not clear. A name on an Excel sheet listing IT systems is not equal to ownership. The concept of ownership (like owning a house or car) means, at least to me, that you take an active role in maintaining the quality of your asset. Data Strategy should also include this aspect.
When you want to harness new technological innovations to solve your business problems, competencies and skills start to play an important role. Technology itself is only 30-40 % of the whole business solution. The rest is about people. You can do magic with MS Excel if you have the right competencies in place. Understanding the needs of the future and the skill gap with the current knowledge base may lead to a need to hire new people, establish new roles and look outside the competencies needed.
When you know where you should go and what is needed, it is possible to draw a roadmap that includes both quick wins and longer-term opportunities. There will be, most probably, needs to verify the direction on a regular basis when the business environment changes. But when you have done your homework, it is business as usual – not an overwhelming task.
To conclude, creating a Data Strategy is not a “piece of cake” nor “walk in the park”. It will not happen by accident.
Creating Data Strategy is a serious exercise that needs strong commitment and leadership from the management board.
But as always, where there’s a will there’s a way.