With my 2 Co-Founders, we always several filled organizational gaps. As a "jack of all trade" or "swiss army knife" consultants during our enterprise life or later as product managers in an early-stage startup.
This means we had to cover a wide spectrum of tasks or jobs. From designing the technical architecture to writing code, building the MVP, learning deep domain expertise while doing support, sales, and marketing work. This gave us the feeling that data was an easy topic that a single person could cover on their own.
But we were wrong...
It took us 20 customers to get this straight but here is what we learnt:
First thing, data is one of the most intimate things of any business, it's the DNA of the organization. From your marketing to your sales, your product, your finance, and your engineering, everything happening is written in the data.
Each process, customization, everything that is making your business unique is also making your data unique. So, to analyze data, you have to understand the organization, the domain, the context, and even the technical stack around it.
It's a complex job, and when your business is growing, this work gets heavier and heavier, and soon, it has to be distributed across an organization.
When talking about data, different roles emerge, often concentrated into a few people at the start but assigned to many people later on. For instance:
And, like most things in life, the more stakeholders, the more complex things are. All those roles mean a lot of interfaces, processes, and tools to be put in place.
To make everything even more interesting, while many organizations try to map each of those roles to a single job description (usual suspects: business user, data analyst, analytics engineer, data engineer, head of data, ...), the reality is way more nuanced.
Whether it is because different roles are concentrated within a single person (ex. the Product Manager or the Consultant that we were), or because the role can be exchanged (ex. the CEO want to get a data insight by himself before the board meeting), it means that the profile and skills of the person impersonating those roles will vary.
Some common examples that are underserved by most tools:
So tools and workflows must adapt to those realities while still serving the common case.
For data to play an impactful part in the core life system of any business, it should be:
And maintaining those characteristics is quite a bit of work, especially when doing it with a large number of stakeholders. It implies a lot of consensuses and total maintenance costs.
This is what we learned at Whaly, data should be managed as a team effort where everyone can contribute with their own skills so that the entire organization can benefit from it.
It means that a set of new, collaborative tools has to emerge to provide plural ways of interacting with data.
Those have to reconcile both engineers and business people around the data journey. This also means bringing hybrid experience, from non tech to high tech, at each stage of a data system, from the collection, modelling, and analysis & sharing of data insights.
This is what we're now building at Whaly! And we're really excited by this opportunity 🐳