Competing on Analytics

With the emergence of analytical CEOs like Brian Cornell, I am increasing hearing CIOs tell me about the importance of data to their enterprises. These CIOs tell me that their business leaders want to “compete on analytics” or to make faster business decisions. At the same time, they will tell me that they “need to provide the intelligence to make better business decisions”. One CIO said it was in fact their personal goal to get the business to a new place faster, to enable them to derive new business insights and to get to the gold at the end of the rainbow”.

For this reason, it should come as no surprise that CIOs said that Big Data and Analytics were their highest priorities. One CIO put it this way, “we have so much knowledge locked up in the data, it is just huge. We need the data cleaning and analytics to pull this knowledge out of data”. The challenge that Mike Yeomans recent described in Harvard Business Review is “big data is not just long, but wide as well. For example, consider an online retailer’s database of customers in a spreadsheet. Each customer gets a row, and if there are lots of customers then the dataset will be long. However, every variable in the data gets its own column, too, and we can now collect so much data on every customer – purchase history, browser history, mouse clicks, text from reviews – that the data are usually wide as well, to the point where there are even more columns than rows”. Making matters even more complex is the fact that CIOs believe their organizations as “entering an era of ubiquitous computing where users want all data on any device when they need it.”

Why Does Faster, Better Data Really Matter?

So why do analytics matter? Thomas H. Davenport says, “at a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation.” A CIO that we have talked to concurred in saying, “today, we need to move from “monthly management by exception to daily management by observation”. Derek Abell amplified upon this idea when he said in his book Managing with Dual Strategies, “for control to be effective, data must be timely and provided at intervals that allow effective intervention”.

Davenport explains why timely data matters this way “analytics competitors wring every last drop of value from those processes”. Given this, “they know what products their customers want, but they also know what prices those customers will pay, how many items each will buy in a lifetime, and what triggers will make people buy more. Like other companies, they know compensation costs and turnover rates, but they can also calculate how much personnel contribute to or detract from the bottom line and how salary levels relate to individuals’ performance. Like other companies, they know when inventories are running low, but they can also predict problems with demand and supply chains, to achieve low rates of inventory and high rates of perfect orders”.

What Then Prevents Businesses From Competing on Analytics?

Moving to what Davenport imagines requires more than a visualizing tool. It involves fixing what is allying IT’s systems. One CIO suggested this process can be thought of like an athlete building the muscles they needed to compete. He said that his businesses really need the same thing. In his eyes, data cleaning, data security, data governance, and data mastering represent the muscles to compete effectively on analytics. Unless you do these things, you cannot truly compete on analytics.

Big Data Changes How We Approach Data

However in the world of big and wide data, we need to flip how we approach data to a certain degree on its heads. Before we make the data great, we need to allow business users to connect their current and new data sources. And this should not take as long as it did in the past. The delivery of data needs to happen fast enough that business problems can be recognized as they occur and be solved before they become systemic. For this reason, users need to get access to data when and where it is needed.

This means that data intelligence is needed to allow business users to make sense out of data even before data quality issues are dealt with. Or even the value of data is proven. Business users need to be able to search and find data relationships. They need self-service so they can combine new unstructured data sources with existing data to test data interrelationship hypothesis. This means the ability to assemble data from different sources at different times.

By enabling the business user to pull in internal and external sources directly and have data relationships presented, they can directly intuit how data relates. In the world of big data not having self-service discovery capabilities are essential to derive real value from the investment. And when the business user is happy with the data, then IT can to directly harvest and correct directly from what the business user has created. This dramatically reduces the time for IT in producing the data for decision makers. This fundamental changes the productivity of all involved within the business intelligence value chain. In sum, big data and self-service data allow knowledge discovery without having any preconceived process.

Parting Thoughts

The next question may be whether competing upon data in particular big data will actually pay business dividends. Alvin Toffler says “Tiny insights can yield huge outputs”. In other words, the payoff can be huge. And those that do so will increasingly have the “right to win” against their competitors as you use information to wring every last drop of value from your business processes.