Your data team are mercenaries, define your ways of working based on this
Modern data teams are transient, often staying less than 5 years, unlike past decades of long-term loyalty.
Companies should adapt by defining robust Ways of Working (WoW) that endure beyond individual tenures.
Balancing in-house teams with reliable data vendors for continuity and efficiency may also be a useful pattern as part of your WoW.
Your data team are mercenaries
Your data team is fickle, they will leave you at some stage, your data vendor probably isn’t and probably won’t.
People stay with companies for less than 5 years
I remember when I first started my working career over 3 decades ago, it was common to see people who had worked for a company for 25 years or more and had no plan to leave that company until they retired.
These days you’re lucky if a person works for a company for 5 years, they will typically leave before then, often by their own choice or sometimes not.
Define your Way of Working to absorb team change
It intrigues me when an organisation builds an internal data team, but doesn’t implement Ways of Working that are designed to deal with the period when the data team decides to move on.
Yet when I talk to the leaders of those organisations they seem to be more worried about the risk of using a vendor and what happens if that vendor moves on.
In my experience no vendor wants to move on from your company, their entire reason for being is to work for your company, and charge for that work. That is literally what the company is designed to do.
And unlike permanent data team members they are designing these companies to enable them to scale, they design their companies and their ways of working to enable them to work with multiple customers at once. They want to charge for outcomes delivered more than they want to charge for hours worked. That way they can leverage their experience and intellectual property and earn more than they can compared to providing a bum on seat and charging for the limited working hours in the day.
Whereas permanent team members can only work for one company at a time, they can only “charge” for 40 hours a week, they are paid for hours worked and effort expended not outcomes or efficiency.
And then there is the pattern where the data leader is effectively running a consulting team, but it’s just not obvious. You know the pattern, a new head of data or head of analytics turns up in your company, and all of a sudden the permanent team members they worked with at their old company suddenly start turning up in new roles in your company.
Don’t get me wrong, I am a big fan of the benefits of working with people you know and trust over working with people you don’t. When you have worked with a person or a team for a while you create a shared language, you have discovered shared patterns which add value in the data space, and the team all know and love those patterns, making adopting them much quicker and easier.
But the thing I wonder about is this
Did the data leader set up the ways of working at their role company in a way that enables this transition to happen without damaging the Information Value Stream at the old Company?
If you hire a vendor to do the work, you can be pretty sure they are not setting things up so that transitioning to another vendor is simple and easy, unless of course you are baking that into your contracts and into your ways of working. But when you hire a vendor you are probably aware of that.
When you hire a head of data, are you working on the basis that they will eventually leave and are you incenting them to build a way of working that will survive in your organisation without them?
When they start hiring their trusted people from their old company, are you incenting those people to build a way of working that will survive when they eventually follow their trusted leader to their next gig?
Keep making data simply magical
The AgileData App and AgileData Platform is designed to enable data consultants to help customers manage the complexity of their data, by becoming fractional members of their data teams.
This allows organisatons to define ways of working that are based on blended data teams, combining permanenent data team members with fracitonal consultant team members, and delivering the best of both worlds.