“The world’s most valuable resource is no longer oil, but data”. This title from an article in The Economist (2017) indicates the importance of data in the creation of company value. Giants like Amazon, Apple, Facebook, Microsoft, etc. understood this only too well and seem to be unstoppable as far as turnover and profit growth goes.
In the SME landscape, the notion of Business Intelligence has also outgrown its infancy. Technological support here has made enormous strides in quality in recent years. Paradoxically enough, businesses monitor a great many processes and data, but there is little effect to be seen on the balance sheet and income statement. The reality is that data progress is going painfully slow for most businesses.
It takes a lot of effort to succeed with data. A business must work on five components: taking care of collecting and accessing quality data, converting data into economic value, developing organisation skills, having technologies, and minimising risk. If one of these component is lacking, then the entire endeavour can be destroyed.

The collection and conversion of data is a real problem for most businesses. The data is spread over silos and departments, systems don’t talk to each other, the quality is poor and the accompanying costs are high. It’s clear that businesses need data that is well-defined, relevant and clearly structured enough to be easy to find and understand The quality of data is of paramount importance so that everyone in the organisation can rely on it. In order to set up such data structures, it’s vitally important to depart from one’s own business logic (e.g., define clear product groups, cost carriers, etc.). That also helps to make certain employees ‘owners’ of some data.

It’s crucial that a plan defines that for which analytics will be used, and what business advantage they could provide. The more data than can be converted into immediately sellable data or derivative services, the more everyone in the business will see its importance. A high-level statement from management about the advantage and importance of data would not do the job. Including everyone in the enterprise in this plan is also important.

Begin with talent. Obviously, a few world-class data scientists are required for going beyond the limits of machine learning. Less obvious is the need for people who can analyse business processes, build predictive models and integrate them with technology. Many businesses rightly see much potential in data-driven decision-making, but in order to strive towards such a goal, people must be taught how to use data effectively. Business leaders must also realise that this demands more than just recruiting one good IT person. Sufficient talent and training AND an organisation structure and culture that value data are crucial.

Businesses need technologies to access data on a large scale and at low cost. This requires storage, processing and communication technologies as well as advanced architectures and analysis instruments that form the motor for income generation. It’s clear that businesses need technology – you simply can’t scale and deliver without it.
But businesses sometimes expect too much of technology, and
are lured into the trap of regarding it as the primary motor for success. Technology is just one component. Understanding one’s own business is equally important.

Lastly, it’s vital to minimalise risks. It’s essential to protect valuable data against loss or theft, and to meet privacy requirements. You probably won’t earn any money here, but poor management of these risks can cost you much time, money and problems.