Data democratization is a game-changer for modern organizations. By giving everyone the right tools and the right training, teams can make sense of the data available to them.
In most organizations, data sits in databases, data warehouses, and distributed systems. Data democratization is a culture of sharing the knowledge which comes from that data. This movement is driven by those passionate about sharing knowledge and empowering those who aren't in traditional analyst roles. By empowering those who need the data to query the data source itself, decisions can be made faster, and analysts can focus on bigger projects.
This is not to say that everyone should have access to everything. Governance guidelines still apply and security should still be in place. Decisions should not be made simply on a misguided one-off query either. Simply opening up the door to the data world doesn't make everyone an expert right away. But when empowered to share queries used on governed datasets, silos are broken down between data analysts and those who rely on them.
This post describes the current state most organizations are in, and how to move forward on a data democratization journey.
Organizations are embracing different options to reduce storage costs and avoid the work involved in maintaining data pipelines. The modern data stack may include the traditional data warehouse, data stored in S3, product databases, CRM systems, and more.
Data Lakes and querying data 'in place' are also part of the modern organization’s strategy to use the right tool for the right application. This doesn't mean that the data warehouse is dead. But storage options are changing to reflect the move to a culture of open data. Data democratization should be considered as part of this strategy so all teams benefit from a database modernization program.
The traditional strategy of leaving the data tools, data knowledge, and known business logic in the hands of a few people on the data team can cause problems. The teams requesting data and insights may find their requests sit in a queue and take longer than expected. The delivered dashboard or dataset may not answer the question, and the back-and-forth between the analyst and the requester can take time and effort.
While the team requesting the data is getting frustrated they can't get their answers, the analyst is getting frustrated too. They don't have the background information about the problem they are trying to solve. There may be another dataset the team is trying to compare to, and the pressure is on when there are multiple stakeholders to keep happy.
While embedding analysts in teams goes a long way to help break down the silos, what works best is to empower the end-user and give them tools to surface their insights.
Training power users on each team to access the data they need, and what to look out for on their journey can break down the silos. These teams can use data to learn more about their customers, their industry, and deliver faster.
Sales - account managers can learn more about their clients using the data the organization has, add their own, and share findings with the team.
Marketing - campaign managers can segment users by relevant data points, pull their own lists for email campaigns, then measure the results when the campaign has finished.
Product - product managers can learn more about product usage by tracking customer journeys, evaluating the success of new features, and use data alongside customer interviews to prioritise feature requests.
With the right support, teams will be able to ask better questions when approaching data and get to the bottom of all the tricky problems they've been faced with. The added benefit is a true appreciation for data and how to use it, that goes a long way to bring teams closer to analysts.
Choosing the right tool is just as important as training. The teams requesting data will be able to train others on their team, ask smarter questions of the data, and break down the data silos once and for all.
While traditionally the tools and expertise have sat with the data team, this places pressure on analysts and frustrates those who need the data most. Empowering those on other teams to use the right tool for their simple queries lets the data team focus on bigger projects.
Sharing queries and insights with a tool like PopSQL reduces effort by all teams who can leverage what has been worked on in an insights library.
I was able to create an account, connect to a database, and create a quick dashboard when I took PopSQL for a test drive.
Using the Slack integration I can then share a preview of the dashboard and start to build out an analytics library that’s searchable by my team and any other team members.
With the right tools and education, teams who need data can uncover insights faster, and data teams can focus on innovation. By making small steps on a data democratization journey all teams can benefit from data and insights.
If you’d like to see PopSQL in action and read more check out the links below: