Issue #6 - Hey Data, Show Me The Money!!!

How do you approach getting data investment?

Read time: 7 minutes

Want to waste a ton of money? Just set up a data team!!!!

Or at least, that is how a ton of executives feel.

From growing cloud costs, high people salaries, expensive tools, and constant investment requests, where data is mentioned, so is money.

This line will be in the Jerry Maguire reboot focused on the Data & AI industry

The problem is, you either don’t invest and you miss out on a ton of lost opportunity, or you invest and you risk burning through your cash chasing a pipedream of better business decisions and operational efficiencies (to use the buzz words that are so well known). The underlying theme here is whether to invest.

It’s 2024. Data is in a bit of a slump, but it is starting to roar back. We just finished the era where data was often heralded (really naively) as the new oil. During this era, every company was picking up a pickaxe and trying to strike it rich. Companies recruited expensive data scientists, made massive investments into new tech, and began talking like they would be the next Amazon or Netflix.

Safe to say, most expectations didn’t quite pan out, the value just wasn’t there.

Blame was cast. Budgets got slashed. And trust was diminished.

All this led to the most recent datapocolypse where data teams lost significant resources and roles (funnily enough this partially coincided with the AI boom, which is another partial bubble that created more investment, but I won’t go into that here).

These figures are from 2019 about how hard it is to prove ROI in data, but, from my experience, these numbers are still accurate today. Source: Consultancy UK

So let’s talk investment decision-making and approaches. As we’ve seen in the last few newsletter editions, navigating the data ecosystem, and the investment that goes into it, requires a nuanced understanding of its principles, trends, and outcomes. In this piece, we delve into the essence of data investment, aligning it with overarching business goals, and uncovering strategies for maximizing its return on investment (ROI).

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Approaching Investment Strategically

Data investment isn't merely a financial commitment; it's a strategic endeavour. FULL STOP!

Let that sink in. Every data investment that starts with “let’s hire a data scientist so they can build us the latest Machine Learning model” ultimately fails. And this approach is why trust between organisational leadership and data was broken.

But I don’t blame leadership. This isn’t their area of expertise; they are business leaders, not data experts. Instead, they hear whispers from the snake charmers (SaaS companies) and false prophets (click-bait Forbes articles) of the world, and don’t have a reliable source to turn to that speaks their language.

At the same time, they do recognise the need to rush in and do something because everywhere you turn it seems like another company has got data figured out. And in the end, they want one thing: tangible value—something they can point to and say, “data delivered that benefit.”

Investment requires a holistic approach. It starts with the business identifying the problems they want to solve with data. This can be done in different ways:

• Business strategy informing a data strategy

• Business stakeholders identify the need

• Creation of a data vision of where the company wants to go and do with data

All of these things have one area in common, they start with the organisation’s strategy and direction. This creates alignment between executive leadership and data teams about what is being done and why money is being put against it. Out of that comes a shared understanding of objectives and a roadmap that marries data projects with business milestones.

Consider Amazon's foray into big data analytics. Their investment centred around enhancing the customer experience through streamlined operations. This wasn't just an investment in technology or a one-off initiative; it was an investment in data initiatives that aligned with their business model, company mission, and overall strategic direction.

A simplified view of how Amazon’s business strategy informed its data investment

So as a Data Leader, what’s my pitch?

The simplest way to get investment is to pitch the value that investment will create. This isn’t rocket science, but it isn’t easy either. Most investments are made on platitudes and aspirational promises, which has led to high expectations and lost trust.

So how can you do it? Here are five steps to take:

1. Determine The Path

   1. Understand what business domains actually want from data rather than forcing solutions/ products on unappreciative candidates.

   2. Conduct thorough stakeholder interviews to uncover their highest priority business needs within those domains.

   3. Match up those business needs with potential data solutions (hint – don’t ask business stakeholders for data solutions as your end products will be wayyyy too specific and not scalable).

   4. Understand current data capabilities against those desired outcomes or solutions to determine gaps and steps required to deliver.

   5. Prioritize areas with the most significant impact on business objectives.

2. Articulate The Data Benefits/ Value

   1. Re-engage with business stakeholders to quantify the direct (e.g., increased revenue, cost savings) and indirect (e.g., better decision making, reduced time spent, customer satisfaction improvements) benefits that your initial data solutions can deliver (hint – try to get solid numbers and financial figures but reassure stakeholders you will never hold them to those numbers).

   2. Create the storyline/ narrative about how investing in these solutions will help the organisation realise these quantified benefits.

3. Understand The Effort Required

   1. Map out the business (e.g., people, process, technology) and data (e.g., data acquisition, engineering, analytics) effort required to deliver each solution.

   2. Based on these requirements, understand the financial and people resources, time to deliver, and prerequisites that underpin each of the areas above.

   3. Identify potential risks and barriers to implementation, including timeframes and dependencies.

   4. Consider ways to streamline effort (e.g., building engineering pipelines that might feed into multiple use cases).

4. Build The Business Case

   1. Compile a comprehensive document outlining the business need, proposed solution, expected benefits, resources required, timelines, and projected ROI.

   2. Use quotes, case studies and a compelling narrative to illustrate why the company should invest and how the investment aligns with broader business strategies and goals.

   3. Create an executive summary or elevator pitch to sell it in a concise way.

5. Plan to Measure ROI Through KPIs

   1. Determine what KPIs should be measured and map out a process to track the ROI.

   2. Most KPIs will be company-specific and numeric, but a few interesting project delivery KPIs include:

      1. Time to Value (external) – How long it takes for a customer to realise value from the product/ service provided?

      2. Time to Insight (internal) – How long will it take to turn the data into insight?

      3. Time to Deployment – How long does it take to release the product/ solution?

   3. Assign those targets to timeframes or stage gates for the data initiative.

   4. Follow up with business stakeholders with interviews or surveys to understand the value of the data solution and their reactions.

Now that we have broken it all down, data investment isn’t actually that scary, right?

Given it is a necessary pre-requisite for most data activities and initiatives, it is important to think about how to approach it. Plus, chances are you will ask for money again. And when you do, you can leverage this process, your past business cases and any positive data results.

For that reason, measuring the success of data investments through well-defined KPIs and an effective, well-thought out system is crucial

The next two weeks will focus on the first two parts of the Data Investment journey—The Business Strategy and Understanding the Business Needs—so you won’t want to miss those.

Also, I do plan on doing another piece on Data Investment, going deeper into the reoccurring nature of it, navigating budgetary requirements to fulfil data requests, and how to approach foundational (e.g. data platform, governance, engineering) vs. value-add data initiatives (e.g., data science, AI, analytics).

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Note: I read this article right before posting and wanted to recommend

Sol Rashidi

’s piece on doing use cases and thinking beyond business value - https://solrashidi.substack.com/p/never-select-a-use-case-based-on

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