In today’s hyper-connected world, businesses of all sizes have access to vast amounts of data. From website analytics to customer demographics, this form of data can be utilized to extract valuable insights, make informed decisions, and boost overall business performance. However, despite the potential benefits of data-driven decision-making, many data professionals remain unconvinced about the value of data, which often leads to a lack of investment in data management and analytics.
The Value of Internal Data
To change the perception of data as a cost center, Chief Data Officers (CDOs) must demonstrate the ROI for data in a manner that’s compelling for business decision-makers. To do this, we must move away from thinking about data as a passive commodity and instead focus on turning it into “data products” that can increase enterprise value.
The Data Monetization Index provides a way to measure the value of internal data. It calculates the value of a company’s data divided by the value of the company. By using this measure, companies can gain a clearer understanding of the true value of their data and use this information to make informed decisions about investments in data management and analytics.
Case Study: iRobot’s Consumer Data
To take these ideas out of the hypothetical realm, let’s look at a real-life example. iRobot, a company that makes automated devices for home cleaning, generates an enormous amount of valuable consumer data. The data generated from iRobot’s Roomba series of products can be used to improve household cleaning performance, inform brand innovation, and influence product design decisions.
Amazon’s Failed Attempt to Acquire iRobot’s Product Line
For years, iRobot’s consumer data has been sought after by companies wishing to enter the smart home market. However, when Amazon recently made a bid to acquire the product line, the Federal Trade Commission (FTC) halted the deal because Amazon was already too powerful and ubiquitous to acquire this trove of consumer data. The FTC decision was a clear indication of the value of consumer data and how it could be used to give a significant competitive advantage to companies.
Assessing Data Value
To perform a value assessment, one can conduct a comparable analysis using either a bottom-up or top-down model. A bottom-up model evaluates the value of data by examining how it contributes to specific business functions or processes, including the data generated from products, customer interactions, and financial transactions. On the other hand, a top-down model assesses the overall value of data to the company by considering market factors such as competitors, industry trends, and target markets.
Tangible data value can convince stakeholders to invest in data. In conclusion, by evaluating internal data through the Data Monetization Index and assessing its value through top-down and bottom-up models, companies can quantify the benefits of investing in data management and analytics. The tangibility of such a number can ultimately convince business stakeholders to invest in data, make smarter decisions, and improve enterprise value.