With data getting bigger and bigger, the number of data citizens is continuously increasing, thus leading to a great cultural change in organizations. This change often enables data democratization, which consists of making data available to all employees and stakeholders, regardless of skills. Originally marginal, it is now easier thanks to technologies like virtualization, data federation, cloud storage and self-service BI. This cultural change may seem easy, and its benefits are undeniable, but some mistakes in its execution can lead to failures in data projects and thus a disinterest from business teams. Data democratization must be monitored and supported with certain measures to ensure that the company reaps its benefits.
Data exploitation aims to fuel three strategic goals: revenue increase, productivity increase and product innovation. Its democratization tends to ease this exploitation by each collaborator, as well as making the latter more autonomous.
For business teams, the availability of data in a self-service context leads to two major advantages in their daily work. Through data analysis, it allows for the betterment of the quality of their tasks or the acceleration of their execution. For example, a CFO is going to increase the quality of their previsions or recommendations if they can access a maximum volume of reliable financial data. Likewise, a marketing officer will be in a better position to recommend a new market segment or the release of a new product if they have solid data on said market, on the competition and on the growth potential of a new product.
On the other hand, a salesperson who gets their clients’ history every day will be able to advise them more quickly during his round.
Lastly, a chief production officer will be able to be more efficient in the creation/optimization of their production plans if they get the sales previsions in real time.
Data analysis is now an essential element regarding the quality of the daily work throughout the company, but also regarding the company actors’ capacity to execute their tasks rapidly.
If data democratization has a lot of benefits, it still requires attention on some points.
The first point of attention is the respect of norms and regulations. Indeed, the increase in the number of people who can access data naturally increases the risk of infringement on regulations like the GDPR. It can also lead to leaks.
Moreover, generalized access to data might paradoxically complicate its access if the data isn’t funneled. It is important to think about who should have access to which data. Beyond the confidentiality/sensitivity aspect, a reflection on the actor/relevance intersection is necessary.
Finally, data democratization is an important change to which business teams must get accustomed. The first implementations can lead to some paralysis or errors related to a lack of proficiency. The most important thing is to make sure that this type of issue doesn’t hinder the data democratization strategy because of a drop in motivation or a failure. That’s why change management must include a good training plan on the goal associated with the data democratization and the new tools implemented.
A good data democratization relies on data of “good” quality that is auditable and traceable, to secure the trust of the final users in its exploitation. The quality standards of the data must be defined by the different actors in place.
In conclusion, data democratization is a cultural change that greatly impacts the company. It requires not only the establishment of processes to facilitate its adoption, but also the contribution of emerging technologies and methods which ease democratization.
Use cases with scenarios corresponding to the company’s reality and strategy not only allows for a better understanding of the democratization by business teams, but also a better adaptation.
All of that to work towards a data-driven business.