data

elements of a good data strategy

Guiding practical choices clearly and concretely, deeply rooted in business strategy

No matter how you are used to organize the strategy process, one thing is certain. This time it will be different. The business environment has changed dramatically during the spring of 2020. There are uncertainties everywhere and digitalization keep on accelerating, faster than ever. You’re going to need to put your data and analytics to work. Here’s how.

The significance of data in understanding and leading businesses has been acknowledged for a long time and achievements in the field have been substantial. Capable teams have surfaced very detailed high quality data which has been systematically collected and stored, creating data assets of unprecedented magnitude. Active use of data has expanded from production, sales and finance centric analytics to virtually all areas of the business world. At the same time, the cycle of observation, analysis and execution has been shortened from days to seconds, much thanks to increased levels of automation and use of embedded analytics. The scale has gone from local to global. A bet could be made that the next significant game-change in the amount and value potential of available data will be driven by collecting real process data far more extensively along with the wider emergence of federated ecosystemic shared or co-owned data assets, turning data into the basis and even means of all transactions.

Organizations are steered by and their targets are set according to their overall business strategy, complemented by a number of more detailed sub-strategies. The role of these sub-strategies is far more operational and practical, but they are nevertheless materially rooted to the company’s central objectives. In a digital operational environment, the highly topical data related capabilities are steered by data strategy which should be in a cardinal role for any successful organization.

Having set the scene, this trajectory in the availability and use cases for data will certainly create significant opportunities for those who are able to make the right choices, focus their efforts and proceed in a decisive manner. Achieving this calls for smooth decision making and impactful implementation. These actions are guided by the data strategy.

What makes a good data strategy will vary from company to company. Different times call for different measures. However, some elements are common to all.

 

Comprehensive and goal oriented

Mountain tp by Ales Krivek from Unsplash

Your data strategy should be comprehensive enough and detailed enough so it can be of significance for the whole organization. It must be deeply rooted to the strategic business choices while at the same time be very concrete in order to steer the everyday work. Too often organizations either stop at the vision level or move directly into detailed planning of technical enablers like data platforms. Understanding the necessary actions and investments requires a lot of detailed thinking. So just mentioning data in the strategy won’t do the trick. Strategy is not a comprehensive list of IT projects, nor is it a technical architecture and it’s definitely not a bunch of generic wishful thinking with little or no connection to what is actually expected to happen. A strategy should always include a clear view of both the current situation and the target state with a plan of how the aspired targets will be achieved. It’s hard to succeed in the execution of your data strategy if you don’t know where you currently are and what are the key blockers your organization faces. The execution roadmap of your data strategy needs to recognize the honest, unbiased starting point for the work and attack the right problems early.

 

Recognizing the incremental and long-spanned nature of building your data asset

Forest road by Jaromir Kavan from Unsplash

The choices you make today on collecting, storing and sharing data will have long-lasting impacts. When dealing with data all decisions should be considered incremental. Yet they will only make a difference after they’re made. This highlights the significance of an actionable data strategy. You have to understand and be able to seize the value of your accessible data as early as possible. After experimenting and validation phases you need to be able to decisively productize and scale the data operations. You also need to able to communicate all this clearly in order to drive impact.

 

Clarity of made choices

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The decisions and made choices should be made as clear as possible in order to ensure their impact. Only made decisions can ever drive action and build a competitive advantage. Making choices will also always exclude certain options. Be clear about this as well. Recognize any interdependences.

 

Concreteness

Map by Matilda Vistbacka from Unsplash

An actionable data strategy crystalizes the data aspects of business objectives, makes a clear statement of data gathering, storage and accessibility policies and guides how the necessary skills and capabilities should be built over time. It sets out a common operating model or provides the necessary means for establishing one. You should identify the most central data areas and their ownership. Map the data assets as well as you can, on a suitable level - what data do you have, what could you easily enough have access to and what may be obtained from elsewhere if needed? How do you address ethics and sustainability in practice? The role and value of data as a means of exchange should be recognized. Lastly, the various matters around data security and compliance in the domain should be covered. 

 

Not a one-off exercise

Like any major business transformation, data transformation needs steering and iteration. Better to have the plan the recognizes this need from the beginning. The clearer your data strategy lays out the objectives and provides means to achieve them, the better it will serve your organization’s development.

If your data strategy needs an update or you would need one in the first place, feel free to contact us for support. 

Henri Engström henri.engstrom@rootsof.ai

AI-Roots-Team-2