In the digital world of today, data is the most valuable asset that companies possess in their efforts to provide customers with tailored offers and experiences.
When detailed data is available and has been put in context, it can be used strategically to ensure that company decisions and actions are relevant and viable.
Data is no longer a mere byproduct of business processes – it has become a critical asset that aids company decision-making and is easily processed. If this data is leveraged properly, it can also provide competitive advantage by signaling when to attack or defend against the competition.
However, in the past, data strategies have all too often been mainly technical exercises developed by companies’ IT organizations. They have addressed data storage, focusing on identifying, accessing, sharing, understanding and utilizing data. The predominant questions have been, for example:
Nowadays we live in an “online world”. The data strategy requires, first and foremost, an understanding of the data needs inherent in the business strategy.
The data strategy will define a “set of choices and decisions” that together will be sufficient to guide “a high-level course of action in order to achieve high-level goals.”
It may ask, for example:
A good data strategy will start by looking at the big picture, to gain a clear vision of:
These questions must then be evaluated against the key questions:
Earlier, the typical comments regarding any kind of development were that “we don’t have enough data, or, some important data is missing.”
This could have been due to the fact that companies never collected it, because they did not clarify what kinds of data they needed in order to reach their goals. On the other hand, companies often already have the data they need to tackle business problems, but their managers simply don’t know how they can use this information to make key decisions.
A good data strategy should allow companies to transform the collected data into capabilities and future opportunities. To achieve this, companies should think “outside-the-box” in order to devise a data enrichment process that identifies correlations between data and evaluates what these correlations could mean in terms of new business opportunities.