Data democracy or data anarchy?

The importance of data in decision-making has never been greater. There is more of it being created than ever before, so the silos between those who need answers and those who look after it have to be broken down. If we move from the traditional model to something more democratic, we can level the playing field.


Data Monarchy
Data Aristocracy
Data Anarchy
Data Democracy
Data Citizenship
Further Reading


Data Monarchy

There’s nothing new about data-driven decision-making. Due to increased volumes of data generated and decision-makers need for information, the term Business Intelligence (BI) gained traction in the 1980s.

A surge of data and the need for answers resulted in the increasing usage of data warehouses, ETL processes, and visualisation. Data analysts were hired to support these processes and provide answers to those who sought them.

Due to this traditional model, information was difficult to get to decision-makers. The development of custom static reports involved a lot of business logic and long lead times.


Data Aristocracy

An organisation that has recognised the benefits of eliminating its data monarchy may have a self-service business intelligence tool. It is a good step forward, but just learning the tool presents a steep learning curve.

A few organisations have broken down the walls and embedded data analysts in teams that require the most data. The analyst reports to the functional manager and has a dotted line to the centralised data or business intelligence function. Analysts who are embedded gain an understanding of the stakeholders they serve and the subject matter they handle.

The tradeoff in this model is that the tools remain in the hands of a few. Despite being empowered to provide excellent service, analysts may become a bottleneck with increasingly complex requests.


Data Anarchy

In a work environment where the analyst, functional manager, and business intelligence manager are in constant communication, work should flow smoothly. By working together with their peers, analysts can form a community while solving problems and becoming subject matter experts.

Communication ultimately poses the greatest risk. If this communication fails, there will be data anarchy. There could be a situation where existing or new teams are hired to handle the increased number of requests. Without an established relationship with the corporate business intelligence team, bad practices might creep into the organisation. Analysts might be moving data out of approved systems, releasing statistics without approval, or becoming a “shadow BI” function.

An organisation’s management or structure may also pose problems if it does not provide analysts with the tools they need. Training or tools that are restrictive could lead to workarounds if neither are provided. At best, this may be bad practice, and at worst, it could compromise security. Complying with privacy legislation is more important than ever, so getting this right needs to be a priority.


Data Democracy

The aim of a data democracy is to get insights to those who need them quickly, while ensuring security and governance. Training, tools, and trust make this possible. Instead of simply serving as a ‘data vending machine’ or resource, a data analyst should serve as an educator.

It does not mean that everyone should have access to everything. To play by the rules, teams must judge when to share results and when to seek guidance.


Data Citizenship

Start by creating governed datasets and training power users in best practices. This will help the teams find creative solutions while giving them an appreciation for where their data comes from.

While on-the-spot training can help new users get through the first few months, it is not scalable. It is much more scalable to train power users to champion the use of data in their teams.

  • Grouping datasets so teams can access only what they need.
  • Prepare short videos with clear instructions on how to get started and use the datasets.
  • Making use of a data dictionary that describes the dataset and provides examples.
  • Train power users together and provide them with new information regularly.
  • Data visualisations that encourage exploration and can be repurposed.
  • Clear expectations about data governance responsibilities and a framework in place.

Insight-seeking organisations should prioritise this shift in culture. Although it may not be a quick process, the move toward data democracy will be worthwhile.


Further reading

It is possible to achieve data democracy without sliding into anarchy. When data silos are broken down, teams can better understand their customers, products, and industry.


Photo by Roy Reyna from Pexels

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