Do you need a data strategy?
The importance of data strategy is often underestimated. In the past, data was just a byproduct of processes and business activities. It’s different now. Data is the most valuable resource that allows companies to gain competitive advantage and come up with new ways of improving their operations. Many companies now appreciate the value of data – they use analytics, study trends, excel at reporting. However, fewer have adjusted to a more data-centered approach focused on capturing and managing data assets. Organizations need data strategies that cover the current state of business and technology but also future objectives.
As defined by SAS:
A data strategy ensures that the data collected by the company is actually managed like an asset. Data strategy includes elements of business strategy, goals for the project, data requirements, KPIs. Each data strategy may be different and consist of various elements, adjusted to the organization’s needs.
The creation of a data strategy usually requires the analysis and planning of the following:
Your data strategy cannot work separate from your business strategy. You want to use data to drive business results, so the first step is to look at the business objectives and priorities. Then, you make a business case where you identify ways to use data to address these priorities. Your data strategy doesn’t have to cover all of the possible use cases you come up with. Focus on what’s doable. Select a few use cases to start with.
Objectives and quick wins
What’s the long-term goal of your data science project? You probably know it already, and it’s a very important element of your strategy, but you also need quick wins – shorter-term goals. These should be fast and rather inexpensive, and they deliver value right away.
It’s time to answer some questions considering data.
- What data do you need? Where will this data come from?
- Is internal data enough or do you also need external data (e.g. from social media)?
- What data do you already have?
There are also questions related to data governance:
- How to ensure data is stored in a secure way?
- Who’s responsible for data-handling?
- How to make sure your use of data is GDPR-compliant as well as ethical?
And you should consider these issues, too:
- How is data collected, stored, and organized?
- Do you have an efficient data pipeline?
- What technologies are you considering for your project and what are the technical requirements (like hardware, software)?
- How will the results provided by the model be interpreted?
Skills and know-how
Apart from the technological aspects, you should also consider your team composition. Do you have the skills you need to deliver the project? Do you want to train your staff? Hire an in-house data science team? Do you want to partner with another company?
With the business cases selected, tech and staff requirements analyzed, you can outline the activities that have to be performed during the process. You don’t have to design a very detailed project roadmap, but identifying core activities will go together with identifying the skills and know-how you need in the project.
KPIs and metrics
Identify appropriate KPIs to verify whether your project is on track. Check these on a short-term and long-term basis, and adjust if necessary. No strategy can be pursued without KPIs – a strategic approach cannot be lacking information about progress, success or failure, and relevant metrics.
Your staff will have to learn how to work with AI solutions and how to use the insights in their everyday work. You need to make sure it becomes their habit to make data-driven decisions. Data-driven organizations have processes that enable employees to acquire the information they need, but they also have clear rules on data access and governance.