Big Data = Big Money: The ROI of Business Intelligence

The ‘Big Data’ revolution is here; companies are collecting data on transactions, interactions, and every metric imaginable. However, such undertakings aren’t cheap. Massive data collection can have massive costs. Which begs the question – how can companies guarantee ROI for their business intelligence efforts?

The ‘Big Data’ revolution is here; companies are collecting data on transactions, interactions, and every metric imaginable. However, such undertakings aren’t cheap. Massive data collection can have massive costs. Which begs the question – how can companies guarantee ROI for their business intelligence efforts?

In today’s business environment, moving slowly to leverage new technology can be a death sentence. Almost as damaging is the adoption of poorly understood technology in order to not “fall behind” competitors, which often results in huge spending and little to no financial return.

It seems like a simple statement, but “‘Big Data’ equals ‘Big Money’” can have opposite meanings for similar people. To one executive, the money spent on data collection efforts is a ‘big’ reason to not get involved. To another executive, ‘big’ refers to the money that can be made through efficiently leveraging data resources to reveal hidden business intelligence. They’re both right.

According to a 2013 Wikibon study, companies receive 55 cents in return for each dollar invested in data analysis and business intelligence efforts. In other words, companies are losing 45 percent of their investment in data analysis – hardly a recipe for success. A contrasting study from McKinsey on Marketing and Sales determined that consumer-facing businesses can increase the return on investment (ROI) of their marketing efforts by 10-20 percent through increased investment in data collection and analysis. Globally, this increased the sales of the consumer-facing businesses profiled in the study by $200 billion. The McKinsey study also revealed that companies labeled “Big Data leaders” are five percent more productive and six percent more profitable than their competitors. The contrasting results of these studies reveals the existence of ‘right’ and ‘wrong’ ways of leveraging data, and that a great many companies are doing it wrong.

According to business intelligence advisors such as SAS Analytics, “ultimately data governance is not about the data. It’s about how better control and management of data enables business strategy, improves business outcomes, and reduces risk.” In a data-driven reimagining of a famous JFK quote: “ask not what your data can do for you, ask what you can do with your data.”

Simply aggregating data isn’t enough. Companies must collect the right information. Data that isn’t actionable can only impact the bottom line negatively. Collection of non-actionable data can kill an analysis effort before it begins. To prevent such a failure, companies suffering from data collection sticker-shock can reduce their costs by incorporating data analysis into every aspect of their operations. Data analysis and its impact on an organization must be an organic part of the business decision-making process in order to be effective.

Numerous case studies from business intelligence vendors show how companies leverage collected data to add value. From the call center that reduced their average handling times and repurposed $1.1 million dollars in annual salaries for a multi-million dollar savings, to the hospitals using data analysis to capture revenue leakage from under-billed services, data analysis can have a huge impact on companies’ bottom lines.

In short, ‘Big Data’ is ‘Big Money’ in many ways. To ensure that they see said ‘Big Money’ in their ledgers, companies must approach data analysis in the same way that a data strategist does: in an intentional, organized manner.