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The Lightdash Way: Data Access

Our mission at Lightdash is:

To enable everybody in your company to answer their own questions using data.

Lightdash is where the data team (👷‍♀️ the builders) and the rest of the business (👩‍🌾 the consumers) come together to make better data-driven decisions.

We have some pretty strong opinions about how this relationship between data builders and data consumers should work. So, we thought it would be useful to explain the principles that make up The Lightdash Way.

The Lightdash Way isn't just semantics (😏) - it's about the entire experience of enabling everybody in your company to answer their own data questions. But, we'll start off slow and just go through one of the principles for now:

How should you serve data to people who want to answer their own data questions?

⚡️ The Lightdash Way:

Give end users meaningful building blocks to answer their own data questions.

Let me explain what we mean...

We've come across three ways to let people answer their questions using data:​

I hope it's obvious which one is our fave 😇

  1. You give everyone access to the raw data.
  2. If you have a data question, you ask the data builders, and they just give you complete answers.

⚡ The Lightdash Way:

  1. The data builders give you meaningful building blocks to answer your own data questions.

1. You give everyone access to the raw data. 🤪​

If everybody in your organization knows SQL and everyone has the context they need to query the raw data (...and unicorns were real 🦄...), then giving everyone access to the raw data wouldn't be that bad.

But, if a single person doesn't know SQL then they're totally dependent on someone helping them with a data question...no bueno.

  • 👍: very flexible, totally self-serve (as long as everyone knows SQL)
  • 👎: everybody has to know sql, everybody needs context of raw data, duplicated work, bound to be errors

2. If you have a data question, you ask the data builders, and they just give you complete answers. 🎁​

If nobody knows SQL or they aren't willing to explore data themselves, then dedicated team members (probably data analysts) will be completely responsible for exploring data. Everybody relies on the data team for answer questions.

  • 👍: only analysts have to have data skills, fewer errors (since only data experts are answering data questions)
  • 👎: doesn't scale, analysts become a bottleneck, this kind of work sort of sucks :(

3. The data builders give you meaningful building blocks to answer your own data questions. 🧱​

⚡ The Lightdash Way: We give your data team the tools they need to build metrics + dimensions that everyone else can use in a user-friendly interface.

With this approach, you leave the SQL to the experts: your data team. They become force-multipliers when they create these pre-defined metrics + dimensions in SQL because anybody in the business can combine, segment, and filter them to answer their own questions.

The downside is that the data team need to spend some time defining and maintaining the library of metrics. But, a small set of metrics can power a huge amount of different analyses, enabling the rest of your team to answer their own data questions.

  • 👍: you only need a few SQL experts, anyone can ask questions using the data, fewer errors, scaleable
  • 👎: you need a data team, this system needs maintenance

When we aggregate data, we add value.​

Giving data consumers useful building blocks makes it easier for them to self-serve and answer their own questions.

We believe that this way of sharing data insights is a core part of the experience of everyone being able to answer their own data questions. It's the foundation of a healthy, happy relationship between data builders and data consumers 👯‍♀️.