What Is Data Wrapping?
Data wrapping is a strategy utilized by many leading companies to create higher profit margins on data they’ve obtained. Originally coined by a scientist at MIT, it involves “wrapping” tools with relevant data to boost its overall value. These tools can be B2B tools or consumer-facing ones. Examples include user dashboards that already contain a user’s interests or have data that will allow artificial intelligence to more easily determine the characteristics of users.
Only recently has data wrapping begun to be incorporated into commercial products. Though data and tools have certainly been sold independently for decades, this combination is novel. Though the joint use of the two was initially only intended for business to business or “B2B” programs, some consumer-facing portals have ended up using it, as well.
Who Came Up With Data Wrapping?
The MIT Center for Information Systems Research often publishes ideas for people and companies to better monetize their data. A research scientist working for this agency coined the term. Once they had defined it well and come up with a way to explain it to the public, the Center published a blog post about the practice here.
Why Is Data Wrapping Important?
The idea of simply rolling existing consumer data in with existing business analytics tools might seem obvious. After all, these tools are meant to take data in, process it in a meaningful way for a business, and put out reports based on the data. Though it’s likely that some companies already were informally performing this to further monetize products, the fact that a prominent institution coined this term carries weight.
Now that it’s formally recognized as a legitimate profit strategy, more firms are likely to adopt this model, specifically when developing software. It also signals the end of the “products for people” era of open-source software and ushers in the “Information Age” once and for all. Unlike much of software development, which focuses on the end-user and what they want in products, data wrapping focuses exclusively on improving internal business processes. Some companies have had ethical questions regarding data wrapping and even legal questions surrounding its influence, but the MIT publication attempts to answer some of these questions.
This isn’t to say that businesses haven’t utilized data wrapping to help their customers harness its power, though. For example, a prominent bank in 2016 took advantage of data wrapping to allow consumers to see all of their spending inside their bank portal. All of their credit card, loan, and bank account transactions could be seen in one spot. This simplification of finances makes it far easier for the average person to find success in the personal finance domain. As one of the first customer-facing uses of data wrapping in 2016, many other corporations followed suit, and this is now almost standard in the banking world.
How Do Organizations Leverage Data Wrapping Today?
Since around 2016, organizations have been trying to figure out how to maximize profits through leveraging data wrapping. These companies can make a cross-sectional team of people from their IT departments, acquisitions groups, and analytics groups within their companies.
These groups should then consider the needs of their business as well as their customers. We see this portrayed through the example of the all-inclusive banking portal. The bank foresaw customer utility in creating a compiled analytics dashboard for consumers.
The next step is internal implementation. This involves engineers and creative teams making pilot versions of these ideas. They should then be tested by the intended target audience. User experience feedback should then be harvested by the company to determine which data wrapping ideas hold the most promise.
Data wrapping has immense potential in the corporate world and has remained a game-changer when it comes to increasing the bottom line. Data science and software engineering intersect at just the right point to create yet more value in the world of technology and information.
