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May 24, 2021
Data Intelligence

Are you overcomplicating data intelligence?

When business leaders hear, “data is paramount to the future of business,” the response, of course, is head nodding.

Harvard Business Review recently reported that 83% of companies stress the importance of turning data into actionable insight. So why do only 22% of that same group feel they are successful at doing so? The answer lies in an overcomplication of data intelligence as a whole.

Data intelligence is simply about driving the right insights, to the right people, at the right time — and companies have an advantage if they can move fast on artificial intelligence (AI) and machine learning (ML). The value proposition of AI/ML is not about replacing your teams but about taking what your teams already accomplish and layering additional computing power and insights on top. The partnership between your people and your data intelligence leads to increased scale and speed around decision-making that can move your organization to the next level of market share, improved customer satisfaction, and more.

Easier said than done, right? Where organizations struggle is in the gap between understanding their need and moving on them. Most companies have a backlog of known issues relating to data — cleaning it up, formatting it, or simply finding it. Doing this type of work is demotivating to teams because the return could be months, quarters, or years down the road. Starting here also assumes you have control over all your data and doesn’t exist in silos in other parts of the organization. It’s a huge challenge to tackle.

For most businesses, progress comes through micro-use cases with attainable, smaller wins that can generate buy-in and momentum across teams. View this as a proof point you can sell internally to showcase what you can do with the power of AI/ML.

Rather than tackle, say, applying ML to all of your inventory intelligence, consider partnering with finance on mapping the data journey of a single SKU from procurement to pay. Even better if the product is a known problem child for finance that is difficult to track. By being willing to jump in and do the dirty work of mapping the product journey through your organization from procurement to paying the vendor, you stand to gain an ally — an ally who happens to own the purse strings. You have simplified the problem with data projects to a single product, and if successfully mapped, you present a useful win for your organization. Last but not least, you are building champions for when the initiative needs support at scale.

So, where do leaders start? Start with a plan. If AI/ML are intimidating as an organizational initiative, here’s a list of simple steps that will help you transcend your understanding of data intelligence that can support you in conversations with anyone from data scientists to the CEO.

  1. Identify the why. Why would you be applying AI/ML? What are you trying to solve? What are you trying to bring advanced computing power to?
  2. Once you identify your why you need to know what the end state is. What does good look like? Is there a specific metric you can track for “before” and “after” improvement?
  3. Where are you today? Have you taken a data inventory and uncovered workarounds?
  4. How are you going to get there? Will you leverage tools like user mapping, technical architecture, road mapping?

In our experience, mapping out data flows allows you to visually see where data is siloed because often, you don’t even know where you’re hitting bottlenecks. The most important part of this process is spending time understanding what internal users want and need from data and then figuring out how to query it so teams can find insights (or your AI model can surface it for them). Once you start taking these steps using these methodologies, it’s an agile and continual process to make progress and uncover quick wins.

By approaching data from a pragmatic perspective, you can leverage micro wins that showcase the value of AI/ML and drive impact into the organization. If you are a data pro, this article may have been so simple for you it was eye-rolling. However, If this plan seems overwhelming, don’t be afraid to leverage a partner that has done it before — let’s grab a cold one and chat. Data and beer are like peas and carrots, so I have heard!

This article was originally published on LinkedIn. Click here to view it and join the conversation.

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Danny Estavillo
Danny Estavillo, RevUnit's Chief Revenue Officer, loves generating new ideas to implement creative solutions to real-world problems. With a background in sales, client partnership, entrepreneurship, IT service management, and non-profit management, Danny is driven by building strong teams that can rally around a unified mission. He is a Marine veteran and has worked for organizations like RBC, Guaranteed Rate, Target, and Verizon.

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