Business Intelligence

And do you have a data usage strategy?

Graphic shows the databases

When Alice was wandering around Wonderland, she met Cat at one of the crossroads. The following dialogue ensued between them: "Can you tell me which way to go?" - Alice asked. "It depends on where you want to go," replied Cat. "I don't know." - she replied. "Then it doesn't matter which road you take."

I remind my clients of this passage from Lewis Carroll's book when we work together on the topic of building different strategies. It shows us a paradox. Well, in our Insurance Companies we are happy to create financial, sales or marketing strategies. But when we start talking about one of the most important business resources today - the gold of the 21st century, the new oil - data - we suddenly focus only on current problems and the quickest possible ways to solve them. And data, like other key business resources, requires building both a strategy for managing and using it. What does such a strategy consist of? The basis is to describe four elements.


The first element of the aforementioned strategy is to describe the data held by our organization. In the strategy document itself, we cannot detail every data source and every table within it. The key is - first - to catalog the existing data sources by their type, the criticality of the data stored, and a general characterization of what information we may be looking for in each source.

Secondly - it is important to establish a standard for documenting data in the organization: who should describe each of the organization's existing data sources in detail, how and with what tools, and how to manage the updating of this information. In addition to source systems, it is essential to present analytical systems, such as a data warehouse or data lake, in the same standard.


The second element of the strategy is to gather business expectations: how we want to use the data we have. This type of information is best gathered in meetings with representatives of the business departments and the IT department of our insurance company. The key in this step is to pay attention to three specific areas. The first, most obvious, is the area of reporting and data analysis. The second, also obvious, because it has been very fashionable lately, is the area of automating processes with data, using, for example, artificial intelligence.

The third area is the least obvious, yet it can easily make life easier for our employees. It's about simply making data available where it's needed, as part of business processes. For example: how much easier will it be for Customer Service specialists to work if they have all the data they need in a conversation with a customer on one, top two screens, rather than in five different applications?


The penultimate, third element of the strategy is the technology for processing and using data. However, it comes only after gathering information on data sources and business expectations. Why so? Because technology in the data area must be selected not according to prevailing fashion or ill-conceived intuition. The technology must be selected in such a way that the data we have can be optimally turned functionally, efficiently and cost-effectively into the information or decision we need within the framework of our described business needs. In other words - the choice of technology is to serve our business, not to reflect the marketing messages of IT market giants.


Last, but crucial to the data strategy, must be the area of data quality management. This is crucial because any business initiative that is supposed to rely on data depends directly on its quality. It's what contributes to IT projects being prolonged, exceeding their budgets, reducing the effectiveness of the solutions being built or - in extreme cases - even cancelling ongoing projects.

Data quality is based on two elements. The first is the data quality process, which consists of the following steps: evaluation of existing data, determination of the expected level of data quality, implementation of the rules resulting from these considerations, and subsequent monitoring of data quality. The second element here is the division of roles and responsibilities for data - both on the IT side and on the business owners' side.

The right direction

Only a data management and use strategy structured in this way, consisting of a description of source data, business expectations, technology and ways to ensure data quality, becomes the basis for long-term planning and development of our Insurance Company as a data-driven organization. I know from practice that creating such a strategy requires a lot of work and dedication on the part of the organization, as well as the people within it. However, this investment will pay off, as it will ensure that we don't waste valuable resources and time on projects that won't get us any closer to our desired goals at all.

Profile photo of Lukasz Nienartowicz

Lukasz Nienartowicz

Responsible for the development of the Business Intelligence area at Britenet. For more than 12 years he has been involved in building data warehouses and analytical solutions for industries such as banking, insurance, automotive and public sector. He is particularly fascinated by the area of client data processing and analytics. He specializes in advising clients on how to overcome their business challenges and grow their organizations with the help of data.