How To Create a Data Strategy

Most businesses and organisations use objective data to evaluate their performance and make crucial decisions, but a majority does…

Most businesses and organisations use objective data to evaluate their performance and make crucial decisions, but a majority does it on ad hoc basis, without a strategic approach. This is a serious mistake, as data contains numerous insights that only become apparent if they are explored systematically, with clear goals in mind. Developing a data strategy should be the first step on the road towards data-driven business management, and it can be undertaken with internal resources only if you understand the answers to the following questions:

Why is it important to have a data strategy?

Sheer quantity of data that an average enterprise deals with over the course of its normal operations lends itself to a framework for interpretation, or otherwise any valuable information could be hidden underneath a deep layer of irrelevant noise. In short, having a data strategy helps businesses navigate through the oceans of data they generate and systematise it in a way that facilitates quick and intuitive retrieval and meaningful analysis.  Of course, the strategy should be a reflection of business objectives and take into account the types of data (quantitative, semantic, visual…) that will be most frequently encountered each and every day.

What are the main elements of a data strategy?

Any successful data strategy has to include certain components that are common to all business fields and organisational structures. The first mandatory element is the data identification protocol, which guides how information is collected and interpreted. Documenting all your data sources and outputs is a vital first step.

Secondly, hold sessions with all your data stakeholders within the business such as Finance, Marketing and Web Development teams to determine what data and KPIs are important to them. This will ensure time will not be wasted on collecting, manipulating and visualising data that is not important.

Next, it’s necessary to organise data storage and define levels of access (public, restricted or high security) before formulating the standards for data processing and provisioning.

Finally, it’s necessary to adopt general guidelines regarding ownership of the data and active governance over the data visuals, as well as local and cloud databases. Additional elements could be needed for organisations working in data-intensive fields and those that operate in highly specific environments.

How to build a strategy around problem solving?

Thinking about data strategy in too narrow, technically-minded terms can obscure the real purpose of this practice, which is to improve the problem-solving capacities of the organisation as whole. Many businesses define data strategy in a discriminatory way, that may exclude decision makers who hold less technical roles.

Experienced business professionals are well aware of the key challenges in their daily work, which makes them qualified to shape the procedures in order to maximise practical gains. It’s important to involve all stakeholders into the process and make sure their priorities are respected. On the other hand, top decision makers should keep the big picture in mind and avoid getting tied up with concerns that affect only a small part of the company.

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