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Data is Oil but Data Democratization is the Real Fuel

Data is Oil but Data Democratization is the Real Fuel
September 14, 2020

Information is no power unless it is equally accessible to all. Data can be the new age oil, but it cannot be used to fuel the world unless it is equally available, accessible, and affordable to all the stakeholders. More so in a day and age where, under the weight of the global pandemic, enterprises worldwide have embraced virtual agile delivery as the new accepted norm of working.

Enterprises worldwide have embraced virtual agile delivery as the new accepted norm of working.

Surveys have shown that employees on an average end up spending ~40 minutes a day in trying to discover a document. While 71% of the people keep asking around, around 46% of them choose to use the company directory, roughly 34% use the intranet and 30% of the people eventually send a company wide mail to find the information they need.

While communication and collaboration tools such as Microsoft Teams, Slack, Jira, Zoom etc., ensure a smooth remote coordination, what they do lack is an enterprise level storage and accessibility of information.

For example, if person A sitting in Dallas coordinates with person B sitting in Mumbai on a use case and upon collective brainstorming, they come out with good inferences and insights, those essential nuggets of truth remain limited to that conversation between person A and B while the rest of the enterprise remains oblivious to the existence of such an use case and such information. Ten days later, Person C and  D spend another 2-3 hours discussing a similar use case rather than using inferences already available but sadly inaccessible and unknown to the majority of the enterprise. There goes data democratization.

Ensuring all material information is made available to all kinds of users – technical and non-technical in equal measure is what can truly empower, energize, and engineer the new age growth of an organization where every individual is able to access the data, interpret the same in the context of his/her requirements and use it to serve the customer in the best manner possible.

Data democratization will be the new substratum that would engine the growth of enterprises.

With the positives of data democratization well known, how should enterprises approach to institutionalize this in their respective organizations?

The Four Pillars of Data Democratization:

Data democratization shouldn’t be limited to blind accessibility and non holds barred usage bereft of any regulation and governance. Remember, data is the new oil and when handled roughly has the potential to burn down enterprises. Hence, there ought to be a method to this madness, a system to this spectacle.

This is where the below data democratization chain has to be looked at and considered carefully before an organization can roll out unfettered access to its data.


Figure: The Data Democratization Implementation Chain

An organization will have to ensure that all the data sitting its database meets integrity requirements.  Simply rolling out the data won’t cut ice, training people how to interpret and use them will make the entire initiative successful. It also needs to be considered that serving a CMO insights that a marketing analyst would need and vice versa will not just be an exercise in futility but an exercise that’s potentially dangerous. Therefore, the data insights strategy of an organization needs to be tailored and customized. Not to forget data security is the most important component in the implementation chain and an organization has to be doubly compliant to ensure that unfettered access of its data doesn’t end up being exploited in an unauthorized manner.

The below illustration breaks open the chain and dwells on the core pillars of a robust data democratization initiative:


Figure: Pillars of Data Democratization

Pillar 1: Build Data Sanctity:

Data sanctity means obtaining accurate data that’s seeped in integrity from clean sources. Blind lift and shift of data from source A (company database) to destination B (users SharePoint) is not only impractical but will also add to latency. The solution is incremental loading of data, only transferring that which the users do not have already. Only new data should be transferred, thus reducing the volume and the latency as well. This will also result in far less data to monitor for accuracy.

Having a clear data model, whereby only that much data is exposed to the end user as is relevant to him/her basis their role and having an all inclusive data dictionary in place that explains people the meaning and relevance of the data goes a long way in establishing data transparency. Likewise, leveraging ELT (extract, load, and transform) instead of the old ETL (extract, transform, and load) helps in improved data agility.

Pillar 2: Foster Data Usability:

Once the data is sanctified, making it usable becomes very important to the success of data democratization implementation. Leveraging cloud data warehousing to store data allows for easy discoverability, irretrievability, faster query resolution, and improved data visualization that helps the cause of technical and non-technical users alike. While storage is an important component, tailoring the answers of the query to meet the larger business needs of the enterprise will make the data more targeted and useful. Showing empathy toward the non-technical users and training them on how to interpret the cloud data warehousing and contextualize them to suite their business needs is another string which will help in making the initiative user friendly.

Pillar 3: Drive Data Insights:

So, the data is accurate and is served in a usable manner but what good is the carbon which can’t lead us to meaningful diamonds! Data is as good as the insights it helps generate. As a part of the data democratization initiative, the organization should endeavor to cull out meaningful insights from the same data set and serve it to the wider user based on their roles. Thus, the dataset could be the same but the interpretation from the same for a CIO could very well vary from the interpretation that’s meaningful for the business analyst. Likewise, empowering the user base on how to use basic BI tools goes a long way in making the initiative successful.

Pillar 4: Maintain Data Security:

Ensuring that the enterprise’s data confirms to global data regulations such as GDPR, CCPA, and PDPA will be crucial if the enterprise deals with customers from any of these data sensitive geographies. Having the workforce aware of their responsibilities toward confidential data, internal data, and general data etc., and training them regularly on these aspects is important to maintain internal data security. Likewise, robust data vulnerability scanning, patch management, and monitoring data security on a daily basis will be key to the success of the initiative.


There you have it. Data democratization will separate the best from the rest in the future and the best way to make the most of an enterprise data is to make it accessible to all relevant so that they can interpret it in all relevant ways possible and use it in the best way that would help the customers. But while doing so, the four pillars discussed would really be the go to guide that an enterprise could abide by to make the initiative successful.