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Data has become one of the most important business assets for any size and activity company. At the moment, there are many data use cases. However, each company must choose its own option that will bring the greatest benefit. To be successful today all companies regardless of size or industry must have a data strategy. It is important to understand exactly what data is needed to achieve goals and improve business results.

With so many potential data use cases it’s easy to get confused. But the task of choosing a scenario should not be left to chance. Below are some tips to help leaders prioritize and create an effective enterprise data strategy.

1. Brainstorm

First of all, it is necessary to outline the desired result: «What exactly does the business want to achieve?», «What are the key business tasks and goals?.

There are 4 main uses for data:

• Understanding your customers: build a customer profile, identify their needs and track their purchases, identify market trends;
• Product or service improvement: an individual approach to the client, offering an improved product or service creates company value and allows to increase customer loyalty, as well as attracting new ones;
• Internal business processes improvement: business operations optimization, business management improvement, marketing improvement, production processes optimization, etc.;
• Monetization: data sale or lease. For example, the multinational financial corporation Mastercard collects data on the shopping habits and activities of its users, and then sells it to other companies.

2. Detailing use cases

Every data project that has been brainstormed needs to be drilled down:

• The strategic goal definition. What business problem needs to be solved?
• Success measurement. How will be determined the success of data use? In order to understand how data helps to increase productivity, increase sales, improve company efficiency, etc., it is necessary to define KPIs;
• Determining the owner of the data use case (data client): marketing department, operations department, sales department or other users;
• Data source determination: where it will come from and whether there are any rules/requirements for their use (legal requirements, GDPR, etc.);
• Understanding the process of transforming data into ideas: analytical approaches, identification of implementation problems, the need for certain skills for development.

3. Define the use case

Having passed the previous stages, it is necessary to determine the most suitable for a particular business. This approach will identify data priorities, cross-cutting issues, requirements and goals. You should start with the current business strategy, the most significant challenges and opportunities. Depending on the ambition and scale of the data processing strategy, it is necessary to choose from 1 to 5 use cases. They will also be the most strategic and long-term ones.

After that, it is worth identifying 1 – 3 «quick goals» that can be achieved quickly and can show value. A quick result will demonstrate the effectiveness of the data processing strategy and allow to get team members’ support.