Data teams guide on how to survive mergers and acquisitions

2023 will be a M&A year. First, I think I need to state that this is not financial advice. (I mostly say that because it sounds like it gives me some credits). M&A stands for Mergers & Acquisitions. It's the process by which companies buy others or merge with others.

Higher interest rates and lower than expected market growth might boost the need to acquire external growth. On the other side, some companies were on the hyper growth path, burning (a lot of) cash and expecting to raise money in 2023. As it might get more complex to raise, they'll first try to reduce their costs. Sometimes it won't be enough and they will prefer to sell the company while in a position they are comfortable with.

Very often, data teams are involved when the process is already in motion but right before closing.

C-levels come for help asking high-level questions: "Can the data be merged together?" "Will there be revenue opportunities? "Will there be cost reductions?" Very often the answer is yes but it will take time and discipline.

Let's take a look at what it could mean for your data team and what you can expect.

Step 0 — Where are you at? Where are they at?

There are a few things that matter particularly at this stage. The key here is to be able to engage the conversation but also to identify potential blockers down the road. It will be fairly different when data is your actual product but for most companies this could come down to properly define and compare those few elements.

• Company level KPIs definition — are you aligned on the definition of a customer, ARR, Revenue Pipeline or number of Active Users. In order to move on, you need to be aligned on the meaning of things. It will help build your first reporting. Do not change how you compute things (not yet) but make sure you know how things are computed.
• CRM data — yep, that comes early. CRM data is one of the most leading indicator of a company's health. It contains the information about the future deal pipeline so the sooner those are aligned the better. Discussing about CRM early and being able to align them is fundamental.
• Data sources — you won't change sources of data anytime soon. However, talking about the sources, quickly opens up the discussion towards an inventory of any piece of data available. There, you'll also start seeing all the effort previously made by the data engineering team.
• Data warehouse, BI tools, transformation, manual processes, ... discussions are not mandatory at this stage. You might want to discuss about it. It might even be a great way to bond a relationship loving the same product (or hating it).

Step 1 — Build a C-level dashboard aggregating company-level KPIs

Yep that's it. That's the task. Build a dashboard. The complexity hides in this apparent plainness of the task.

This will require a strong understanding of definitions so you can sum up revenue or costs that can be summed up. Then you'd need to pick the platform to store the data and the tool to visualize it. It can start with a GSheet or you can pick one of the tool you already have at your disposal. You don't have to move all the data around. Only top-level aggregates.

During that time you should not disrupt Business Users workflows. M&A times can sometimes be tough for some teams and sometimes for the Business itself. You want to make sure that you disrupt their work as little as possible. There is no need for the data team to rush for the big bang migration. Not yet. Not before you're ready.

💡If you haven't yet, now is the perfect time to ask for Data Engineering resources. Not only because you'll need it but because you have leverage. You just gave management a great way to monitor the merger process!

Step 2 — Find the data low-hanging fruit

There is one. Always. Sometimes, it's clear, sometimes you might need to look deeper. And if I need to simplify the problem, you have 2 main low-hanging fruit categories: cost reductions or revenue boosters.

You can imagine migrating databases to the same data warehouse to achieve economies of scale. Another easier option would be sharing the same marketing platform to have a better customer tracking and a lower customer acquisition costs.

Or you can imagine using the brand new recommandation algorithm over the entire dataset. Running the algorithm from one team with the data to the other can be a great way to leverage assets from both sides.

Step X — And after that?

After some time, M&A stories can branch into dramatically different pathways that are hard to predict. For the transition to succeed you'll need support from your Business Users. Very often it means over-communicating about the changes that you'll force upon them.

Should you progressively roll-out a change or should you just rip off the band-aid? This will be my last suggestion. Go deep, not wide.

You should disrupt drastically a small group of users (and make sure they have everything they need) rather than slowly bringing in change to all of them.

While this short intro to M&A for data team might sound naive and only painting the best case scenario I guess it can be of great help to stay focused at any step.