The four categories of data value

Let’s look at 4 key categories of data valuation. The reason to do this is because we use each of them to identify possible data sets and data use cases that can add value to the business. Once we’ve calculated that value, we need to know which category they fall under so we can translate that back to a number your execs are going to care about.

The 4 Categories of data valuation

What are they? Well, let’s take a look.

1. We’re going to improve free cash flow and by unlocking free cash flow. As we said, we enable our company to pay down its debts more quickly and operate more efficiently.
2. We can reduce the costs to the business, and every dollar of cost that we take out is a dollar that can be reinvested into the business to generate more returns. That’s going to drive up our EBITDA number.
3. We can increase revenues if we sold our data, for example. The more money we’re able to bring in, the better.
4. We reduce risk because the risk of fines or regulatory penalties can be mitigated with better data management practices, and we can calculate the likely fine and add that back as a dollar saving.

The two sides of data valuation

In many ways, data valuations have two sides. Often, data practitioners forget that these are complementary to one another, like yin and yang.

Now, duplicating our data, making it more accessible to more teams for analytics and for data product managers to license can increase the revenues we’re going to generate for the business. But not all data duplicates lead to success.

The dark side – Yin

On the Yin or dark side of our data is the fact that duplication can lead to errors. The more copies of the same data we have, the less likely they are to be synchronised.

And if you’ve got multiple systems that all have a customer record, for example, there’s an increased chance that those records will be out of sync, which lead us to master data management techniques to solve these problems. It leads to silos when different teams have different views of the same data record.

The more locations your data is housed, the greater the risk that they can be accessed maliciously or accidentally shared by a user. So by reducing the number of duplicate data sources we have, we hope to reduce the costs and reduce risks.

Ambidextrous data valuation

So when you’re thinking about ways to value data, don’t get stuck in a rut. And that’s where our ambidextrous data strategy work comes in, where you can see below the line you’ve got customer and this is your data consumer here, not necessarily your physical customer. Their expectations are unfulfilled by the data because it’s got errors in it and it’s costing them time and money to rectify that. And this is where you have problem led data innovation and you focus on the Yin side.

On the other side, you’ve got to try and delight those customers and deliver them new revenue sources and new valuations. That’s where we focus on the Yang side of the equation with opportunity led data innovation, where we create new revenue streams for the business that weren’t there previously.

Let’s go back and have a quick look at the 4 ways that you can value data. Then we’ll move on to each of them and talk about them in turn.

So how does the Ambidextrous Data Strategy deal with data valuation? Well above the line – delighting and exceeding customer expectations are obviously improving our free cash flow and increasing our revenues. Below the line, helping our customers to feel like their expectations are fulfilled and reducing customer dissatisfaction. We’re going to get rid of costs and reduce risks in our business.

Let’s take a look at each of these in turn and break out some example use cases that you might find in your firm.