What is the Point: Our Big Mission

Hi again 👋 I am Erik - still one of the co-founders of Infer - and welcome to our second blog post. This is a follow on from the first one (Hello World 👋), where we talked about how we are building Infer as well as touched on the motivations behind it.

In this post we will dig a little bit deeper into the motivation and mission of Infer - the wrong we are trying to right, as they say 🤗

As with all things startup this is very much WIP - if there is something you think we should do differently or better, please drop me a line 🙏

Being a bit pretentious 🙊, perhaps the easiest way to describe our mission is to state what the thing is that we believe is wrong with the world today and that we aim to fix. That thing is the lack, or misuse, of the scientific method when it comes to making business critical decisions. Often people use the term "data-driven", but it isn't just about using the data you have. More importantly, it is about using it correctly.

Too often decisions are based on no, invalid, or biased analysis and, more recently, on an over reliance on machines and automated analysis. Using data and analytics wrong is often more damaging than not using any data at all, since it instills a false sense of confidence in the decisions and recommendations made from it. The key to applying the scientific method, and to performing “good” , rigorous analysis, is to combine automated analysis with human insights at every step of the process.

(Another common misconception about being "data-driven" is that it is mostly about the technologies you use. Technologies are important, but to be "data-driven" is mostly about your process.)

To achieve this we should build systems that facilitate the interaction between users and automated analysis. Systems that assist users in exploring the facts and data available to them and guides them to propose, analyse and test new hypotheses. That is our ultimate "moon shot" mission 🌟

Clearly, we won’t be able to solve the entirety of this enormous challenge any time soon 😊 Instead we are focusing on a subset of it, the part we know well and where we have felt the pain most intimately and repeatedly: improving the way data is used and analysed internally within businesses to enable better, faster, repeatable and more reliable decision making.

This is a challenge that we are intimately familiar with and have encountered many times during our careers in data science and analytics – both from the leadership side, seeing bad decisions being made, as well as from the practitioners side, seeing bad analysis being done. Preventing this from continuing to happen was our motivation for starting Infer.

However, there are many barriers to this, primarily the lack of easy-to-use and accessible tools that allow anyone with a modest understanding of data analysis and machine learning to perform advanced analysis.

To overcome this we are building a platform to enable analysts to use advanced machine learning and analytics without the need for expert data science tooling or knowledge.

Our ambition isn’t to build a full end-to-end platform to achieve this, but instead to build a layer - sitting between the data layer(eg your database) and the data consumer layer(eg your BI or other downstream tool) - which enables the user to easily analyse, build and test their hypotheses within their tool of choice and without having to learn new, cumbersome technologies.

We call this the “inference layer” and it fits nicely into the data stack, without the need for users to learn new technologies or platforms.

And that is what we are building at Infer 🤩

That was it for our second blog post and Part 2 of "The Beginning of Infer"! Thank you for reading this far 🙏

In the next post we will take you inside the actual product and platform, show you what it looks like now and what it will become soon. Lots going on, so stay tuned!