Hey everybody, I'd like to talk to you a bit today about data platforms, and what we
mean by a data platform. Some of the common terms you might run into, when
it comes to describing a data platform, are things like a 'data warehouse',
a 'data mart', a 'data lake' - obviously growing increasingly in popularity at the
moment. You've also got things like a 'data management platform'; that's
something that can have really specific connotations, if you're in a marketing space, for example.
Hadoop gets thrown in there; it's often we hear tools
substituted in for that - so, maybe you're a Tableau user, your users think of
Tableau as the 'data platform', and they're using that to mean all the different
bits of toolsets that live behind the scenes of that. This can be a really
big problem for organisations - it can be really difficult when trying to
decide what it is you're actually trying to achieve, because some of these words
have really specific relationships to things like architectural styles, so when
people say a data warehouse, they're normally thinking it's facts and dimensions in a
Kimball-style model, for example. When they say data lake, that's a bit more of a
murky one because it's a slightly less mature term, but it immediately jumps to
things like unstructured data - people often think poor data quality, and so on.
So, you've got to be really careful about how you choose these words.
When we going to talk to people about this kind of thing, we like to talk in the really
simple terminology of 'data platform'. We break that platform down really simply
to try and call out the key components that go into a data platform
for people. In some of the other videos I'm going to be putting out, I'm going
to talk about some of these things, like the Lab and Factory model, or the
different layers to structure in the storage there. But, in a nutshell,
everyone's got sources of data in their business, whatever those are, whatever
those look like, and they need to get those in. So, somewhere in your platform
you're going to need the technology to ingest data into the platform. We've got
our storage layer; so that data has to live somewhere once it's in, and whether
that's in its raw format, or whether that's once it's been transformed into a
particular data model, or cleaned up, or whatever is that you've needed to do to
that data to prepare it for your business needs. That's got to live somewhere.
Wrapping all of those things, at the bottom of that image, you can see governance -
so there's a whole load of things in a data platform that need management, that need
governance, whether that's things like security, whether that's configurations
of your environment. It's a whole load of things that live in that governance box,
when it comes to managing a data platform, and depending on normally your
attitude to risk, and how quickly you want to move, you're going to do
different things in that space. And then finally, on the right hand side, you've
got all of the tools, and the ways of getting data out for platform.
Integrations into other platforms; so maybe you've got a CRM platform, and you
want to process some data in the platform and share it out to that CRM
platform, you need to integrate it with that - maybe through APIs, for example.
There's the standard classic reporting; so, building BI reports, measuring KPIs,
giving people dashboards and visualisations in the organisation, and
more complex analysis - so that might be data science, that might be machine learning,
that might be complex statistical models. Finding a common
language - and a simple language that everyone can align behind - massively
helps the conversation, makes it so much easier and simpler to get on the same
page about what you're talking about, and focus the conversation on the right area,
that I highly recommend you take a look at how you use language internally, what
you call stuff, and what hidden meaning and connotation might come with that,
that you've not realised before, and think about if you can simplify that
language down, make it easier for everyone in your organisation to better
understand what you mean by your data platform.