Data Democratization: Providing Accessible Data to Everyone
Background
In this modern world, where data becomes a goldmine; and is able to generate incredibly valuable assets, every company is trying their best to be data-driven. Using an old-school way such as intuition for making a decision ain’t no better than gambling. Thus, this creates a new role that didn’t exist 20 years ago, sometimes they call it Data Analyst, Business Analyst, BI Analyst, you name it; the expectation for that role typically is to deliver the data to the decision-makers.
It is great to have an analyst that can help them to make a decision, but if we only rely on them to deliver the data, then it will create problems. First, decision-makers have to wait days or even weeks to get their data, which might be irrelevant to the problem anymore, since we are living in an agile world. Second, this will keep the analysts busy with providing raw data instead of high-quality insights. We called these kinds of analysts “query monkeys”. That’s where Data Democratization takes its place and tries to solve the problem.
Definition
According to Amplitude’s definition, Data Democratization is the ongoing process of enabling everybody in an organization, irrespective of their technical know-how, to work with data comfortably, to feel confident talking about it, and, as a result, make data-informed decisions and build customer experiences powered by data. Data Democratization means that the delivery of data doesn’t rely on the analysts. We are opening the access to the users, so it can be consumed by anyone in the organization.
Objective
By implementing Data Democratization, we can solve two problems that have been mentioned in the background section. First, the decision-makers can get their data without any blockers. They don’t have to wait for days/weeks to get their data, so it will be able to improve the data timeliness. Second, the analysts are able to shift their focus from delivering raw data, to higher quality insights.
Speaking of the quality of the insights, we need to break down types of analysis that can be delivered by an analyst. In general, it divides into four types:
- Descriptive Analytics: tells you what happened in the past.
- Diagnostic Analytics: helps you understand why something happened in the past.
- Predictive Analytics: predicts what is most likely to happen in the future.
- Prescriptive Analytics: recommends actions you can take to affect those outcomes.
By implementing Data Democratization, we expect decision-makers to implement descriptive or even diagnostic analysis on their own. While the analysts can focus on predictive, and also prescriptive; which enable them to provide recommendations to the decision-makers.
How to Achieve?
If you ask me how technically we should implement data democratization, I’d say it depends on the situation in your organization, but the key is, to open the access of the data to your stakeholders. So, if currently you are accessing your data solely using SQL query to the data warehouse, you might need to assess if your organization needs to purchase BI tools; while in parallel, you can teach your stakeholders how to write SQL queries. But if your organization is already utilizing some BI tools, you can teach them how to use those tools, and SQL queries as well. There are pros and cons to each option, but it doesn’t mean that it is impossible to implement both of them.
The Allies
A good idea means nothing if you don’t implement it, and when we talk about implementation, we obviously need to include our users throughout the process. Since they are the ones that will use the platform, we need to convince them that this solution will benefit everyone. We need to make them realize what is the current problem you are facing, and how Data Democratization can help to solve it.
It won’t be easy to make all of them understand. That’s why, the most important part at the beginning is to convince the leader of their packs (e.g. VP of Commercials, VP of Products, etc.). Once you get their approval, it will be easier to floor the initiative to the rest of the teams.
Please note that it is very common if some users feel reluctant to accept this change. That’s why it’s important to provide them with a series of training sessions, so they don’t feel like we’re only giving them additional jobs without teaching them how to do it.
Wrap Up
It is really important to implement this solution based on a problem-based approach, which means that you need to assess if your organization actually has problems that are already mentioned in the background section or not. If you decide to implement this initiative, hopefully it can help you and your team to generate more valuable and impactful insights in the future.
