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Data strategy is different for every company, but usually challenges we face are similar. To better illustrate these challenges and data strategy as a solution let’s use a container logistics analogue.

If you think each of your data assets as a container, then the (possibly massive) container ship including all your data asset containers is your data capital. Harbours are locations where your data assets are utilized to create business benefits. In a perfect world your data capital (ship) is perfectly organized, all data assets easily accessible and fully compliant with all the needs in each harbour. In real world the situation is usually less perfect, and it will take many iterations and constant learning to achieve even a close to perfect situation. We call this journey a data odyssey. Odyssey being a long journey full of adventures; a series of experiences that give knowledge or understanding.

Our data capital requires constant attention, new harbours are appearing making our routing more complex, there are interests to expand our data asset utilization by sharing the assets with some external harbours in unknown waters, there’s even an ever-growing risk of piracy.

The only way to manage this increasing complexity is to create a baseline where we are, define a target state and steer all activities towards achieving this common goal. In data context we are of course talking about data strategy which should be the main tool in navigating though the sea of constant change.

Data strategies have been around for a while, but we feel that one crucial success factor is too often missed. One goal for a good data strategy and essential to extract real business value out of data is to form a common understanding of capabilities and opportunities between business and data organisations. Thus, a data strategy should always be formed by a close cooperation between business and data organisations serving equally both parties. Consider a ship captain with a shiny, newly painted ship having piles of detailed ship driveshaft and crane maintenance manuals, going into the sea with only a vague understanding of the direction the ship should be heading.

Due to the different business environments and data “maturity” of the organization each data strategy has a bit unique emphasis and structure. However, one should never lose sight of the aim of creating a shared target state between business and data organisations. For that reason, data strategy needs to start with business-related topics like

  • Data value proposition: Define how data is driving current and potential sources of competitive advantage
  • Data capital: Identify business critical data assets and activities to maintain & enhance their business value
  • Data collaboration: Define how collaboration with partners can generate additional value from data assets
  • Data business architecture: Create a business-related view of the data landscape within the organisation
  • Data operating model: Define how business, data teams and IT work together to achieve business goals

If you want to be the Captain of Data Odyssey we can together dive deeper dive into the above and many other key elements of a data strategy and how to create a best fit for your organization’s needs.

Picture Juhana Jutila

Author Juhana Jutila

Juhana Jutila is Head of Strategy and Business Consulting at adesso Nordics. His key focus is to help companies in their transformation journeys across digital, data and sustainability dimensions. Juhana has over 30 years of experience in business and technology consulting from multiple industries such as telecom & media, finance, retail, healthcare, manufacturing, construction and public sector.

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