Data As A Product, Redefining Our Approach To Producing Value From Data

AI Data As A Product, Redefining Our Approach To Producing Value From Data Mike Bugembe Contributor Opinions expressed by Forbes Contributors are their own. I am passionate about all things eCommerce, tech, data and AI New! Follow this author to stay notified about their latest stories. Got it! Sep 1, 2022, 05:00am EDT | New! Click on the conversation bubble to join the conversation Got it! Share to Facebook Share to Twitter Share to Linkedin According to a recent survey , the typical adoption rate of analytics is 26%.

This means that when nine managers gather together in a room to make enormous strategic and operational decisions, seven of them will make critical decisions based entirely on their gut. How scary is that reality? Only two of those nine managers would consult data. The last thing we need in today’s uncertain business climate is more guesswork from our leaders.

Even in the face of so much change and uncertainty, decision-makers continue to invest in data analysis. Yet despite this, companies still fail to leverage their data to its fullest potential—or get anywhere close. We need to ask ourselves why today’s approaches don’t work.

Let us look at three of the most popular methods of extracting value from data and discuss some of their key challenges. The Chief Data Officer Hiring a Chief Data Officer, or CDO is not a mistake. It indicates that the company is ready to take data seriously.

In most cases, however, CDOs’ roles are poorly defined with unclear expectations. Tom Davenport describes the CDO as the most unstable job in the C-suite. Their tenure is short, turnover is high, and the role is generally unclear.

In addition, CDOs typically come from technical backgrounds. As a result, their immediate focus tends to be on defensive data strategies such as creating ideal data environments, cleaning data pipelines, establishing data governance policies, and creating enterprise data warehouses. Although all of these tasks are important, they are very expensive, and their defensive nature makes the value they create hard to measure in business terms and invisible to most internal users.

It’s not the best way to test the patience of business executives looking for immediate ROI when your team is stuck in defensive, downstream activities for up to 18 months – unsurprisingly, about the same length of time as the average tenure for a chief data officer. Instead, the focus needs to shift to demonstrating value quickly. Asking business users what data they want Commonly referred to as requirements gathering.

Those who have recognised the inherent flaws in an over-investment in defensive strategies often attempt to follow a needs-first approach to generating value from data. Using this approach, companies first try to understand the business users’ needs, figure out which are unmet, and devise a strategy that addresses those unmet needs. Not a bad idea, but the traditional approach for doing this is doomed because users cannot typically articulate the solutions they want.

In most cases, the user is not a scientist, an engineer or an analyst. They do not know what solutions are possible, but why should they? Why hire a customer to do the job of the marketing, development, and product planning team? Coming up with the winning data solution is not the user’s responsibility. It is the responsibility of the data team.

MORE FOR YOU Black Google Product Manager Stopped By Security Because They Didn’t Believe He Was An Employee Vendor Management Is The New Customer Management, And AI Is Transforming The Sector Already What Are The Ethical Boundaries Of Digital Life Forever? In their 1991 bestseller, Competing for the Future, Gary Hamel and C. K. Prahalad warn companies of the risk they run if they cannot get a view of the needs customers cannot articulate.

The same goes for gathering requirements for data. Despite the available needs-gathering methods, data teams fail to uncover all or even most of the customer’s needs. Starting a data literacy program Sending your business users on a data literacy course is well-intentioned and reasoned.

However, it’s a huge ask and often doesn’t work out for several reasons. Some data literacy programs are not incorporated into enterprise strategy, while others are treated as side projects that are not given the attention they deserve. However, two of the most common reasons for failure are the one-size-fits-all approach and the failure to reinforce learning.

Not all employees require the same standard level of data literacy. For example, those who use data tactically have different literacy requirements to those that use data strategically. To keep the training relevant, individuals only need sufficient literacy to do their particular jobs — which means that extensive, enterprise-wide data literacy programs can result in overtraining and a lack of buy-in from employees who find the training irrelevant.

Then, there is what happens after training. We have all been on training courses that we have forgotten within a week. One of the main reasons for this is simply a lack of on-the-job practice.

Learning needs reinforcement with practical application on the job, which keeps things relevant to the learner and improves the chance of retaining knowledge. We need a new approach Hiring a CDO, gathering user needs, or sending your staff on data literacy training is not wrong, but each approach has many nuanced challenges that still hinder data adoption. The harsh reality is that data is still ignored by most who need it.

For most people, the alternative is still the preferred approach to decision-making. We must redefine our relationship with data and produce insights that are more accessible to users than gut instincts alone, helping them make better and faster decisions. To do that, we cannot continue to treat data as a project.

Instead, we must shift our perspective and treat data as a product that is accessible, visible and usable for everyone, no matter their discipline or desire. Managing data like a product A product is any item or service you offer to serve real customer needs better than the alternatives. To build a successful product, it is essential to be specific about the following details; who will use it, how much and how it will be used, what jobs it will help accomplish, what pain points it will address, and how it will generate revenue.

Companies can unlock the full value of their data by applying the principles of product thinking to create data products. When you have built a great product, you have achieved what is commonly known as product market fit, where your product meets the user’s needs better than the alternatives. When you have a good product market fit, your target customers are buying, using, and recommending the product in sufficient numbers to sustain that product’s growth and profitability.

One of the most notable metrics used to evaluate product/market fit is the Sean Ellis test (also known as The 40% Test ). The rule is simple; if 40% of surveyed customers say that they would be “very disappointed” if they could no longer access the given product, then the product is on the winning side. Let’s return to our nine managers in a meeting room making massive strategic and operational decisions.

All of them have access to the data and have been on a data literacy course. Only two may be disappointed if they no longer have access. The others who do not even bother to access the data will not miss it.

Simply put, at an average of 26% adoption, the data we are producing is already on the losing side. It doesn’t have product market fit. The data team needs to shift from treating data as a project to data as a product.

This means understanding the job the analytics helps get done, desired outcomes and the user experience. Delivering analytics with a clear set of features, user experience, and value proposition that meets the target customers’ needs will get more of them to adopt the product. This will achieve product-market fit and result in a complete turnaround in the adoption, attitudes, perspectives, and behaviours of staff around data.

Back to the room with the nine managers, if the data is designed as an intuitive product with a strong product market fit, we will go from only two managers using data to more than four managers incorporating data into their decision-making. A much-needed change in behaviour for the uncertain future that we are all facing. Follow me on Twitter or LinkedIn .

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