There was an infamous article by the Harvard Business Review a couple of years back that called data science the ‘sexiest job of the 21st Century’. It attracted much attention, not least because data management has been traditionally viewed as a numbers-heavy, statistical world dominated by countless spreadsheets, with only rare gurus of maths being able to operate in it. That has, of course, been changed by companies who have made their fortunes by proper implementation of data, like Meta, Google, and almost every free internet service or tool you can think of. But data isn’t just used to improve the upper echelons of tech businesses. It has trickled down to be easily implemented by most businesses, leading to observable improvements in efficiency and efficacy alike. But how exactly does that work? What are common ways that non-fortune-500 businesses actually use data?
You might have heard of the product pipeline. This is a model for product development that compares the product to an item being pushed down a pipe to a destination. What does that imply? A one-way flow, where the receiver of the product (the buyer) waits at the end for whatever comes out of it. Sure, some valves along the way might be turned by product testers and consultants trying to ascertain customer feedback, but it was essentially a one-way street. Data can and has completely changed that model, turning product development into a platform instead of a pipe. By collecting data about how your customers respond to your product, use your product, and ultimately love/hate your product, you can make quick changes to your offering simply through that large-scale relationship between the business and its customers. It’s a much more flexible model that allows you to keep customers before they move on to competitors. However, this data needs to be properly managed, especially to avoid potential fines for misuse of data (like GDPR rules). That requires knowledge management software. The best knowledge management software facilitates the business’s growth while protecting important knowledge, content, data, and documents.
Let’s go back to the inefficiency-spotting model that was inspired by Adam Smith and the industrial revolution. A business owner would check a monthly report, respond with shock at the inefficiency that one particular metric suggested, and proceed to issue an audit or conduct a manual inspection (think slowly walking around the factory floor). This is an exceptionally inefficient process, but it was all that was possible because there weren’t monitoring programs that could show the business owner where exactly the problem was. This is an issue that can be completely solved with adequate monitoring tools and adequate databanks based on the output of those monitoring tools. As long as the data are transformed into digestible graphs (much of which can be done automatically nowadays), the business owner and the management team can instantly see if there is a problem in their business, where that problem might be and then begin the more difficult step of starting to solve it. The speed at which a problem can be solved is a major boon to efficiency, but even more than that, effective data monitoring allows staff to amend a problem before it has even become a proper issue to the company, saving countless downtime and revenues.
Open Up New Revenue Streams
Imagine your company sells a product that is used by a particular type of user. You monitor your data properly, recording, interpreting, and forming a great, detailed understanding of your user, their pain points, and how your solution helps them. This allows you to identify new pain-points. By having such a three-dimensional view of who is using your product, you will also develop an understanding that will allow you to address other parallel pain-points with product expansion. This will allow you to release a new product with a high confidence that it will be used, which will mitigate your risk and make the investment you put into the product a lot less painful.
Alternatively, imagine your company situation is like the company above, but you are either too small to offer a parallel product, or it is not part of your long-term strategy. Guess what – if you have properly identified pain points, that is incredibly valuable information for a company that might want to bring out its own products to offer your customers. As long as you’re sure this won’t cannibalize into your sales, you can sell off parts of your databank that will enable to you to open up an easy new revenue stream that can keep on giving as long as you keep collecting data. Sometimes, this can be more valuable than the product itself, as you can sell the same data to multiple different parties. If your industry is big enough, you can fuel your company’s growth massively using this technique.
A Word on Sensible Data Usage
Did you know that the more films Nicolas Cage appeared in per year, the more people drowned by falling into pools? Or did you know that the lower the divorce rate got in Maine, the fewer margarine people ate per capita? These are examples of spurious correlations, and the world is absolutely full of them. Two things can appear tightly related (the margarine and divorce correlation rate was 99.26%) while being utterly and obviously (to humans) unrelated. As big data develops, data usage can get less than sensible.
Without proper precautions in place (and skilled data scientists), you can collect masses amounts of data that lead to completely false conclusions about your customers and your business. This can destroy the value that the data might bring you, waste your time, and set your company down the wrong path if you’re not very careful.
Part of sensible data usage is, as mentioned earlier, proper security and management of data. It might well be worth hiring a data specialist, especially if a company is thinking of using data to open up new revenue streams. If you do not know what you’re doing, you can get hacked, sued, and even shut down.