4 ways your analytics can deceive you
How do you zero in on the right information to make the best decisions with all the technology, data, and new analytics techniques available to you today? How do you find opportunities or identify problems before anyone else?
We have entered the “Age of Required Knowledge.” With all the data available to us—internally and externally—employees and executives are expected to know. There is a “cost of not knowing.” Getting caught not knowing could lead to sensational news headlines accompanied by loss of shareholder value, loss of customers, and even industry fines.
So, how do you find opportunities or identify problems before anyone else? How do you avoid getting caught not knowing something you should? And, how do you also open the opportunity to make better data-driven decisions and find opportunities for competitive advantage? We studied market trends, interviewed hundreds of customers, and reviewed thousands of projects and the common theme we have been hearing all around is: “smarts.”
When you look at business results, it will naturally lead to questions about why certain things are happening. It’s how you answer those questions that determine your level of competitive advantage. Companies that apply “smarts” to those questions are relying on cognitive services, machine learning, optimization,andpattern-based planningto drive sounder decisions and identify trends before they could even know which questions to ask.
But, it’s easy to be misled. Here are some concrete, public examples of how organizations have “missed the boat” because their analytics deceived them, based on four trends we uncovered in our research that were leading to disastrous results for prospects and clients.
Your analytics can easily deceive you if you’re missing data. To make informed decisions, you need to synthesize all the relevant information into your decisions. Companies have made vast improvements in terms of incorporating internal data, but there is a risk of getting blindsided by information that can only be found in external data. Think weather, Dun and Bradstreet, economic indicators, or social media.
In April of this year, a major US airline had an unfortunate incident where a passenger was physically removed from a plane. The company could see the metrics related to social media activity, but by the way they were reacting, it became clear they were missing social sentiment information. The initial incident was rough enough, but the CEO made it worse with a cold, victim-blaming speech. The airline lost a billion dollars in market cap in under 8 hours; not because of the incident itself, but as a reaction to the CEO’s statement blaming the passenger. If only they had been more attuned to the sentiment of their long time customers, they could have reacted faster and headed off the stock slide. The cost of not knowing. (See more examples of social media fails here.)
Incorrect data can create deceptive analytics. Excel remains the BI tool of choice for many business users and analysts. So, there is no shortage of stories where a transposed number, missing decimal, or issue with a minus sign wreaked havoc. In fact, a Forbes article suggests that “Excel might be the most dangerous software on the planet.”
Today, we see manual checks built into processes that involve manual entry. However, the bigger problem lies in places where companies have outgrown legacy systems and use complex Excel models for calculations and to transform numbers as part of a workflow. The very nature of Excel is that the calculation lives in each cell and no mechanism can ensure accuracy. A complex workbook can have thousands of calculations. A MarketWatch article titled, “88% of Spreadsheets have errors,” cautions that “Spreadsheets, even after careful development, contain errors in 1% or more of all formula cells.
