Why Data Collaboration is Broken

August 25th, 2020
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From the beginning, data collaboration has been broken. It all started with legacy SQL tools.

Legacy tools

Legacy SQL tools have two foundational flaws: they trap your team in silos and fragment your workflows across numerous tools.

Siloed teams

Setup: Every user of a legacy SQL editor must first hunt down and manually re-enter database credentials. These credentials are the "keys to the kingdom"; they must be securely distributed (a challenge in its own right). Often, less technical users struggle to complete this step or are denied permission outright.

Wasteful rewriting of queries: Imagine if every email from your support or sales team had to be written from scratch. How much time would be wasted? With legacy SQL editors, that is the status quo for SQL. The perfect query may be sitting in a teammate's editor... but instead the query is rewritten for the umpteenth time.

Wasteful reruns: It's a common joke: start your query and then go get a coffee ☕️. Once the query completes, you need to share the results with a teammate. The choices? Either send that 2GB CSV via email or Slack a .sql snippet to your teammate so they can rerun it, and get their coffee too! And if the query runs against a cloud data warehouse? You've just paid twice for the same insight.

Fragmented workflows

Data knowledge: Many poor souls have started a Google Spreadsheet data dictionary, then struggled to maintain it. Just as many data dictionary products flood the market, but they don't integrate with your editor. Wouldn't it be nice to see in-editor which tables/columns your teammates use most often in their queries?

Visualize data: It's hard to spot trends in raw data outputs. Legacy SQL editors make you export data to Excel, Sheets, etc just to build a basic chart. That wastes time and breaks the linkage between the data source and the visualization.

Sharing a query: As mentioned above, sharing a query involves email, Slack, and coffee breaks. Of course, the recipient must also have the right credentials to run the query.

Standardize definitions: In most teams, the question of "How do we define an active user?" is answered: "12 different ways!" With legacy editors, the process of standardization requires manual upkeep in additional tools.

Seeing the pain of legacy tools

These pain points snowball. Let's look at a common use case, a new feature launch:

Business intelligence tools to the rescue?

Weren’t BI tools supposed to fix all this? They've tried, but they've swung way too far.

They've become rigid in an attempt to standardize. Now data analysts spend more time instrumenting data than exploring it.

They've become overly report-centric in an attempt to provide visualizations. They push you into dashboards when sometimes you need to rapidly iterate and explore.

They've abstracted away SQL to give access to all users. But as a result, they create endless dependency on analysts and set rigid boundaries on what can be explored.

Seeing the pain of BI tools

Again we'll look at a feature launch. With BI tools, the endless rework of legacy SQL tools is replaced by painful delays:

There has to be a better way

Yes! A collaborative SQL editor like PopSQL fixes the foundational flaws of legacy tools without going overboard.

There are no silos in a collaborative SQL editor. New members of your team are greeted with a shared connection, not a quest for credentials. Teams organize their queries into shared folders: reducing, reusing, and recycling those old queries! ♻️. And instead of time wasted on reruns, teams can securely share results.

A collaborative SQL editor is not fragmented, but holistic. Need to find data quickly? Learn from teammates’ queries. Need to visualize? Find all the charts you need without ever obscuring the source. Stop toggling between your query and its results with a scrolling, iterative UI, and explore your schema just as easily. You can automate query runs and push your data where your team will see it: Slack.

And data exploration remains blissfully unbounded, not rigid and report-centric. Not every question can be answered by a dashboard (although PopSQL does have dashboards). There is no data instrumentation, yet less technical users can still pull reports thanks to query variables.

Seeing the ease of collaborative SQL

One last time, a feature launch can be as simple as:

It's so easy, we even wrote a template for feature launches.

See the difference for yourself

Collaborative SQL editors allow users to write SQL as a team. Data collaboration doesn't have to be broken:

Ready for a modern SQL editor?