For something that is everywhere and always present, it often gets remarkably little attention. It is, of course, data. The remarkable thing about data is that everyone who spends most of their working days behind the computer is amassing mountains of it. Everything you look up, type in, process or forward. Data is the modern business equivalent of blood flowing through a body. But for what purpose, really?
A simple answer
A good data strategy knows exactly how to answer that question. And often it is very simple. For example, that the goal of data (processing) is that it causes you to sell more of product or service x. At the same time, the implications of setting such a (simple) goal can be very large. And then especially for:
- The amount of data you process;
- The data quality;
- The way you process data;
- The importance you place on data as a company and how it is handled.
The amount of data
Let's go through all four. First, the amount of data you use. In many cases, data now flows through a company like a waterfall. Marketing, sales, service. Every department is adding data. If that happens unchecked, it is like a waterfall that splits into lots of little streams and dries up completely in no time. The data then serves no purpose and very quickly loses its usefulness within the company.
However, if you know what data you really need to achieve your goal, then you can start regulating the flow of data through your business. Only relevant data is then added and enters the same stream, so to speak. It therefore keeps its momentum and speed and achieves the right goal much faster: more and better business for your company.
An important guideline to get this done is to have the "next" department tell what they minimally need to do their job. In that case, Sales tells Marketing what they minimally need. And Customer Success or Finance does the same towards Sales. On top of that, as a board you can add things you need for certain reports, for example. In this way, you can quickly discover a lot of unnecessary data and remove it from your system.
The quality of your data
At the same time, you're going to find out exactly what data you're missing. To give an example. Many Marketers can tell you what route a lead took to get to Sales. Which web pages they viewed, which form they filled out and which emails they opened. But chances are Sales would much rather have information about the company they work for. How many people work there, what is their annual turnover, and what industry are they in?
Your data processes
So the really important data within your company should flow through it, so to speak. Every relevant person should have access to it and be able to add to it or improve it easily. All with the same common goal. Of course, there is also a lot of data that is specifically important for one department, but not for the rest. And yet other data is only important to one or two people. To keep the momentum going, it is important that you only disclose data to the people for whom it is needed and that you store and manage it as centrally as possible. This is also called your data management.
Good data management makes work better for everyone. For one thing, the less information and systems you need to do your job, the easier and nicer you can work. It saves time and, for the less digitally proficient (and there are often more of them than you think), a lot of frustration.
Second, by doing so, you ensure that there is a smaller chance that people will (accidentally), discard, mismatch or misplace data. And by centering data, you create one truth that is the same for everyone. Finally, it also makes the work for the administrators of your data systems a lot easier.
Thus, a good data strategy should always assume the 'less is more’ principle. Rather fewer good quality data and systems that serve a clear purpose, than lots of data and systems with no purpose. Once you have that in place, the challenge really begins. Because putting something in place is one thing, keeping it that way is a challenging second.
The safeguarding of data
The success of your new data strategy depends on the adoption of your employees. In other words, their willingness to add the data they really need to add all the time. Here you can run up against a corporate culture that is not particularly flexible. A good example is the tax authorities, where consultants found that "employees have been doing their jobs according to ingrained patterns for years, and where ict workers engaged in innovation have serious doubts about the willingness of their colleagues to adapt it." NRC - 27/06/23
If that is the case, you can spend billions on new ICT, but if no one wants to work with it, it is an extremely costly waste. So a good data strategy is certainly not just determined in the boardroom, but always includes key stakeholders from the shop floor. The quality of your data and its assurance is one hundred percent dependent on the people working with it. But if you know how to get all this right, you won't recognize your company in a positive way.