Way too many Danish companies are immature when it comes to using and exploiting data according to Jens Friis Hjortegaard, one of the four partners in QVARTZ Analytics. Despite the immature approach to data use, Jens Friis Hjortegaard says that the interest in the field has sky-rocketed in recent years. But rather than reaching for the low-hanging fruits, many companies choose an overly complex route through the great data lake.

The customers want to hear about fancy new concepts like machine learning, AI and predictive analytics, but according to Jens Friis Hjortegaard, this is the wrong jetty to choose when you want to sail into the sea of potential offered by data extraction. "Most companies I visit want to hear about the advanced parts of data analytics. But as maturity is often low, my advice is to opt for the easy solutions which are easier to implement and yield value much sooner," Jens Friis Hjortegaard says.

Data analytics has grown up without any control in the companies.

Many people wrongly believe that the advanced data tools related to AI are the holy grail in their quest for better data use. But more often than not, the tools are a step to far for the companies which are still at an early maturity stage, he believes. "The IT departments are spending vast resources on Power BI, Tableau, QlikView or other visualization tools, which – atop a great data lake – can give the employees an overview of all the company's data. That empowers the employees, but the information scope becomes to wide, and in 9 out of 10 cases, no one has actually considered how this data and visualization should come into play in the daily operations or for the individual employee or team", Jens Friis Hjortegaard explains.

"The extraction of knowledge and insights based on the company's data is further complicated by the current lack of people with competences combining business, data and technical understanding"

Many companies have historically not succeeded in linking business understanding and data, and there is thus a need for an overall organization and structure in this field, which has grown uncontrollably in many companies over the years. "I call this concept 'mushrooming'. It means that in many cases, management hasn't actively considered how to use data analytics and what their ambitions are in this respect. They haven't taken the time to figure out how to use data in the organization and where to focus their efforts from a strategic and competitive point of view. Instead, we see a field driven by a couple of enthusiasts in a few departments without any overall plan."

The low-hanging fruits

Instead of initiating a grand, million-dollar plan including machine learning and fancy algorithms, take a step back and approach the field more wisely. The low-hanging fruits are not found up in the clouds, he concludes.

"In many cases, you can pick the low-hanging fruits by basically just drawing up a vision for data analytics and data scientists, sketching the processes across the organization and simply processing data properly to the right employees."

In addition, it is just about intelligent use of the competences which are scarce at the moment. "Resources are in high demand, so it's extremely important to utilize the competences where they create most value and make most sense. But before you do that, you have to define an overall strategy and vision for the area in which you consider what you actually want from the data which – in most cases – is already available in the company. At the same time, it's challenging that the field is still so new that you can't just find the answers in a book. It's different for every company what makes sense, which is why it's crucial to have this dialogue at the top level of the organization."

Three pieces of advice

Jens Friis Hjortegaard points to areas like sales, marketing, HR and production as the areas which will usually result in the highest potential benefits from keen efforts focused on data. In addition, he normally has three pieces of advice for the companies he visits:

  • Don't believe that is has to be extremely complicated. Go for value with low efforts
  • Don't believe that it all depends on big data and the amount of data. That's not always where the value lies. Often, there's a lot of 'small' data with great potential – if it's properly linked and put into play
  • Finally, spend your energy figuring out where to start. It's ineffective to open the gates to the Eldorado of advanced tools You need a structured approach, and you have to eat the elephant one bite at a time