Data Democratization starts with Data Governance

Data Democratization starts with Data Governance

There is no consensus of where the data democratization process begins and where it ends. It is a complex strategy with many proposed steps for data democratization to drive businesses. Each and every organization is unique and should define its own democratization steps according to its own needs, challenges, risks.

For data democratization to be successful, it is mandatory to operate with trustworthy data regardless of the company’s pain points. Therefore, before even considering data democratization, the company needs to ensure that they have already been developing data governance.

 

Data Governance – first steps to Data Democratization

In a sense, it is like pouring the water into a cup when we are thirsty. We do not think much about where it comes from or is it safe for us every time we pour water from the pipe. That is something we had already done before. And we’re not going to do it every time we drink it, on a daily or hourly basis. The same is with data governance. It is something that guarantees the data we consume is valuable. Otherwise, you should put a lot of effort into searching for trustworthy data, which is time inefficient and still doesn’t assure data would ever drive the business.

Once strong data governance is carefully managed, we can talk about the next steps. There are several published propositions of strategic steps to data democratization. Again, it is individual for every company. However, the following tips are common:

  • Define the problem. Get to reality. Acknowledge your pain points. If you admit a problem, you can fix it.
  • Address what is now and not what there should be. Align with enterprise-wide initiatives. Always check if governance is meaningfully monitored. Be sure at any moment you are privacy compliant, and data is protected. Finally, you must be sure you remove silos and process redundancies. Otherwise, democratization is not successful.
  • Empower tools. Keep up with technology for quick access to data. Systematically train your teams. Utilize tools according to business goals. Today’s technology provides tools that allow specialists to make it usable to anyone.
  • Grow gradually. Start small, think big. Data democratization is a transformational project giving access to data and technology to employees that have never seen it before requires special attention. If people using specific data don’t understand why things are being done the way they are, democratization is set to failure.

 

 

Technology that supports Data Democratization – key factors of technological enablement

Technology has become more and more iconic and pop culture in nature. The knowledge base has never been more transparent. Many skills can be learned just by watching YouTube tutorials. Not to mention that a pandemic caused that many educational sites offered open and free lectures available to anyone willing to learn. One of the top software industry goals is tool simplification. Low-code… self-service… AI-powered… echoes everywhere.

Automation and intelligence contribute to the acceleration of data democratization. Low-code development breaks the bottleneck of data science skills. Cloud storage is not something we’re waiting for anymore. It’s our reality. Self-service BI apps and analytics, DataOps methodology, data virtualization software, pre-trained models, data federation software are what technically supports data democratization. AI-driven catalogs are something every company should already have built and empowered to stay competitive. Thinking about appropriate technology, it’s important to keep in mind that data democratization should finally have the ability to deliver insights as quickly as possible, as required. According to Gartner, many machine learning projects will fail because they don’t achieve significant business value. Data literacy rises when machine learning is part of a decision-making process.

It’s important to note that the point of automation is not merely about speeding up the business processes but also about increasing productivity and efficiency. If those processes are still dealing with irrelevant data, the automation process is in vain. Also, the tools we’re going to rely on must be built for the people. Otherwise, democratization efforts will fall to pieces.

Finally, whenever AI systems are used, it should be considered that aligning the values of AI system executors and AI systems for itself is a challenging task.

 

 

The future of Data Democratization

We are on the brink of the golden era of data democracy. The data democratization process will tell a lot about this moment in history when it will be investigated in the future.

There is no one-of-a-kind cookbook for data democratization. It is an ongoing process that every company should adjust according to its needs and nurture it from the bottom. The transformation process should grow gradually, and it is not necessary nor possible to change everything overnight. The process evolves together with employees, technology, and business objectives.

Considering data democracy in a political context, it is a powerful weapon against data tyrants. If wars of the future are about data, democratization is the main point of discussion. Everything else is noise.

So, bearing in mind all of the above, let’s go back to the definition from the previous article, according to which data should be available everywhere and to everyone in every moment and put it this way:
Data Democratization means data should be available to people of interest, either business or technical, in the relevant moment and within an enterprise which is properly regulated, data-driven, and privacy compliant.

Governed data is what drives democratization. Therefore, nurture governance, build a trustworthy data-driven enterprise, acknowledge the power of AI, foster it within the enterprise, break silos. Finally, beat the fear of data violation by democratizing it properly and in a timely manner.

By this article, at least on a high level, you are informed and suitably warned.

Have you considered implementing a data democratization strategy?

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