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3 Reasons Your Company Shouldn't Invest Into Data
It’s an investment, and you must treat it like any other opportunity.
This is the Three Data Point Thursday, making your business smarter with data & AI.
Let’s dive in!
Data is the new oil, right? Just like turning oil into money, turning data into money is a freakishly hard task only a few companies have really figured out how to do.
It's a little like becoming a YouTuber; millions of people think success is right around the corner, wake up at 6 a.m. to make videos - the reality is, only 0.0001% of them will make any money.
I've been in the data space for the past decade, and this picture only solidified.
Our shiny YouTube stars are the Netflixes and Amazons of this world, the Googles, the databricks & Snowflakes. But in the end, we pump years of money into data teams, data technology, organizational changes, data meshes, data scientists, and machine learners - only to get very little in return - besides dark circles under our eyes and a feeling of resignation.
I'm not trying to depress you, however. Getting your company into data is a long-term investment decision, and you should consider it like any other investment decision.
1. Do you really understand the cost?
To understand your investment, you need to have a rough understanding of the cost. I’m claiming almost no company understands the cost because they did not ask the most important question first:
Why are you investing in data? What specifically do you think you’re getting out of it? What is the scope of your data project initiative? Surely, you do not plan on hiring a team and then firing it in 2 years.
So once you answer the why, you get a rough understanding of the time horizon to see the first results and the resources you truly need.
Really getting into data means building up:
Teams that can deliver (the results you’re looking for)
Leaders that can ship results
Infrastructure might be the smallest part of this. Building teams requires time and teams that deliver within your company even more. Finally, finding leaders who can ship results in line with your business will probably be your biggest cost factor.
Data people are notoriously expensive to hire; Netflix still pays $200k-$700k for AI/DS/ML PMs for data leaders that can ship. Your cost isn’t going to be less than that; you might just need to rehire or retrain a couple of times over months and years.
2. What are your opportunity costs?
Yes, data is hot, but so are your products! Every $$$ you can pour into data, you can also pour into product management and software engineering teams.
For some reason, companies seem to miss this trade-off. If you're about to invest 10% of your budget into data yearly, you're not doing a 260% investment over 10 years into your product department.
3. Can your business pull it off?
If you got the cost and what you think the value is, once you decide the opportunity is net positive, the final question is, is it possible?
Not all investment opportunities are available to you, and this one might be one that’s not attainable for your company.
It boils down to one question: Is your company able and willing to change? To change:
Part of its culture
Part of its business strategy (or is your strategy already aligned with data?)
Those are big changes; only top management (or at least top management support) can pull them off.
If the answer is no, you're not getting into data, at least not with any material results.
Many companies think about one or two of these points but rarely about the three. But only the three of them together, cost + opportunity cost + the constraints on the potential value from the investment, make up for a sound decision.
Goodie Time! Exclusive Gifts
Here are some special goodies for my readers:
👉 The Data-Heavy Product Idea Checklist - Got a new product idea for a data-heavy product? Then, use this 26-point checklist to see whether it’s good!
The Practical Alternative Data Brief - The exec. Summary on (our perspective on) alternative data - filled with exercises to play around with.
The Alt Data Inspiration List - Dozens of ideas on alternative data sources to get you inspired!