A few days ago, I read an article1 about trends for 2022 in data analytics. One of the opinions paid my attention more than the rest. The thesis was that in 2022 we can observe “the death of predefined dashboards” which sounds odd to me.
Maybe it is only some kind of over-interpretation of what is happening in the industries and an attempt to call it controversially. Nevertheless, decision-makers can take it for granted and start a revolution in organisations harming analytical processes, workflows and widely understood data culture.
Let me touch more deeply on why I bare such an opinion.
The case with data literacy
I would love to see legions of employees who are able to read, interpret and work with data fluently at every level of the company’s hierarchy. But we are not there yet, as all surveys of all consulting companies show us.
For years we have been observing how companies have been putting a large focus on data democratization. The main evidence of that is an evolution towards a data-as-a-service direction by using cloud-based solutions to empower different users in data analytics. However, most of that significant potential can be easily lost just because of the immaturity of the organization’s data culture and the data literacy level of each, single employee.
Frankly speaking, too much focus is on the technology side and too less on people. Companies still mainly invest in training improving technical skills or ability to use specific tools. Training which teaches how to use data for a specific purpose is in minority, even on the market is hard to find such offers. We must remember that employees have different backgrounds and different skills. Some of them would always need assistance in data analytics, just because their core skills are allocated somewhere else and there, they bring business value. We shouldn’t require them to waste their time learning how to work with data, while they should master other skills.
Challenge with an approach data as a product
The next point to cover is how those organisations are advanced in digital transformation. Before introducing a new strategy, some basics must be prepared. Many companies would like to be data-driven, however still suffer from a lack of integrated, automated, and accessible databases that provide high-quality data. And it is not a completed wish list.
Efficient and business valuable data sets serve specific business areas. In most cases, it means that different business areas have data prepared differently including data aggregation, hierarchy, and perspectives. The huge challenge for organizations is to provide an environment, structure, and infrastructure to approach data as a product. It requires investment in hiring an adequate number of professionals and changes in existing processes and technology. Apart from that, DaaP is still a fresh concept and companies need time to get familiar with it and step in on this journey.
Underdevelopment of tech-savvy
I’m writing above about too much focusing on tech training. However, some companies don’t have any vision of how to support their employees in their tech-savvy journey while still expecting results.
I was the victim of such an approach gaining access to the tools without any training and vision of employee development and setting a clear learning path. Worse, I was required to figure out how to upskill myself. That was a horrible experience, both for the employee and the organization that ends up in frustration and lack of results.
Mature organizations employ professionals who take care of the technological development of employees in accordance with the company’s long-term strategy and vision. They make sure that the skill set of employees can shift the company from point A to point B. Without them or similar roles, no major changes can take place.
The hell of multi-sources of truth
If you are a fan of Marvel like me, you know what chaos can be brought by having multi universes. The same risk can be a case when we allow separate business units to use databases without supervision. Business units may report the same metrics differently only because they understand or define them differently. From the inside, we can observe that data retrieval is processed in a different manner.
This generates a bunch of problems. Especially in proofing whose numbers are correct ones, and this requires additional time and resources that could be spent on more valuable tasks. Not to mention ruining trust and mutual relations between departments and employees.
As a key conclusion, I would say that giving employees the freedom to create their own dashboard places a huge responsibility on their shoulders and requires them to have various sets of technical skills. Such a strategy may be similar to throwing the baby out with the bathwater if companies do not invest time and money in ensuring that their employees acquire the skills they need, are equipped with the right tools and data sets can be used without worrying about the disinformation.