There’s a glaring problem in the data space that no one seems to be able to solve– nor do they have the bandwidth to do so. While there’s been a boom in data usage, organization, and recording in the last 10 years, there’s been little to no adoption among non-data folks (referred to as business and product teams in this article moving forward). There’s a bit of a self-fulfilling prophecy that contributes to this problem: data teams are often too busy balancing multiple priorities, so they’re not able to create an environment or leverage tooling to help business and product teams utilize data on their own. As a result, business and product teams are reliant on data teams to leverage data insights in their day-to-day decision making. The problem only becomes bigger as companies begin to scale.
We’ll be digging into…
- Why you should care about the data value gap on your team
- What the current landscape for data solutions looks like for growing companies
- Why this landscape fails to serve data teams
- How to solve the lack of self-serve data discovery options for business and product teams
Why is Data Essential for Growing Companies?
It’s no secret that in a competitive landscape, businesses need to use data to make important decisions and to be proactive in how they approach strategy. There are so many moving parts to companies that each team needs to trust that the other is using data to drive their decisions, and for startups, using this data is important in building the narrative for investors and advisors.
There has been an endless number of tools to help make these data insights more accessible for everyone involved– however, a recent Accenture report reveals that “Only ⅓ of firms trust their data enough to use it effectively and derive value from it”. So, despite the growing demand for better, faster data insights, and the tooling emerging to meet this demand, there is still little functionality in using data. Why is this happening, and why is the current offering of tools inadequate in meeting these needs?
The Current State of Data Discovery Tools
Understanding this problem begins with addressing where it comes from– data is technical, and data tools are even more technical. This means that usually, only a small subset of people within a company have the skillset to work with data and data tools (AKA the data team). This data team is responsible for building the systems that ingest and organize data, maintaining them, and responding to data requests from other team members (usually business and product teams). This is a lot for any one team to take on, and is especially difficult as a company begins to scale. Unlike hiring a new engineer or a new sales person, onboarding a new data hire requires a big lift from the entire team. You can’t necessarily shadow an existing team member for a few weeks and have an understanding of the data stack or knowledge, and because data teams are inundated with tasks and requests, documentation is typically dated or non-existent.
The Vicious Cycle of Inadequate Data Tools
As a result of trying to stay on top of the data set up, data teams have adopted many different tools in an attempt to lessen their mental load and hopefully empower business and product teams to explore the data themselves. These tools include things like business intelligence platforms and data visualization tools (Mode, Looker, Tableau). The issue with these is that they’re almost as technical as working with the data itself, rendering them useless as a self-serve solution for anyone who’s not on the data team.
Companies with larger data teams might even consider building a solution from scratch– but oftentimes the tools we’ve already mentioned are expensive, too technical, or are simply unpleasant to use for both data and non-data teams. Building a solution can be equally expensive after considering the cost of engineers this requires, and the time they’ll have to invest in maintaining the from scratch solution.
This is where that vicious cycle of inadequate tooling and an overloaded data team comes in– data teams pay a lot of money for access to tools that have low adoption across their team and only adds to their workload. More tooling = more siloed data and inconsistent documentation, which means more work from the data team in maintaining these tools.
Democratizing Data for Entire Companies
There’s no beating around the bush when it comes to data discovery or data “enablement”; the term we use to describe giving data teams the power to better serve their non-data team members, and the resulting accessibility to data insights that business and data teams reap the rewards of. Truly democratizing data across the company begins with the data team. This initially will require a bit of a lift in getting things set up, but if you choose the right tool, maintaining this repository of data and data knowledge should be a minimal time investment. The right tool should also work with your current data stack and integrate with these tools, not further fragment this.
Secoda’s data solution aims to push companies to true data democratization. We do this by:
Working with your current data stack, not adding to it.
- Secoda integrates with all common data tools, from your data warehouse, ETL tools, reverse ETL tools, metrics layer, and BI tools. This means that as you update your resources in these other tools, Secoda updates as well. Documentation, collaboration, and questions on these data resources can live right alongside the data itself. So, no more dated documentation, inconsistency across tools, and toggling between multiple platforms. Better yet, it means one tool that’s intuitive and comprehensive enough that business and product teams are able to use it without technical experience.
Providing a central place for data questions and requests, and creating a searchable repository from this.
- Because Secoda works with all of your other data tools, it can act as one central place for all things data, especially for non-data team members. This means that data requests and questions can live in one platform, and that past questions/answers are searchable. Business and product teams can not only search for the answers to their questions, but can use Secoda to search for data resources or documentation that might answer their question before they have to reach out directly to the data team for help. This is all done in plain language, so it’s data discovery without writing a single SQL query.
Making collaboration easy.
- Protect your data team's bandwidth and time by pushing out a process for making data asks. Empower the data team to better serve the business and product teams– commonly asked questions and most-frequented data resources should signal to the data team that it’s time to update documentation or collections around a particular topic. Secoda also provides the opportunity for data teams to build curated collections of documentation, catalogs, data dictionary terms, and data resources that are relevant to a particular group of users. For example, data teams can build a “Finance” collection that will consist of relevant resources for the Finance team, giving these non-data users the ability to explore and derive their own insights.
Being affordable and effective.
- Secoda templates and automates things like data documentation and user analytics, meaning the time investment from your data team is minimal after the initial set up. The cost of using Secoda is a lot less than other BI or data discovery tools, often at a fraction of the cost of these tools per user. Users don’t have to worry about technically maintaining the platform like they would should they build a custom solution from scratch.
Choosing the best Data Discovery tool for your team
The modern data team is balancing so many different priorities and projects– it makes sense that finding a tool to make their jobs (and the jobs of their business and product counterparts) easier falls to the wayside. After all, they’re responsible for keeping the entire data stack running. However, by choosing a data discovery tool that truly results in self-serve data exploration, business and product teams will start to trust the data they’re seeing, and better yet, understand and utilize it efficiently with minimal disruption to the data team.
It’s a lot of responsibility to take on the success of data usage for the entire company, but by practicing good documentation practices, creating a process around data requests, and leaving enough context so that team members can come to their own conclusions, there will be a shift in how the entire company interacts with the data. Instead of bombarding the data team with questions and requests towards the end of the month or end of the quarter, they can equip themselves with these insights on their own terms.