These 3 Misconceptions About Data May Be Killing Your Business

These 3 Misconceptions About Data May Be Killing Your Business

These 3 Misconceptions About Data May Be Killing Your Business

In 1977, at the Xerox World Conference held in Boca Raton, FL that year, the company's senior executives got a glimpse of the future. On display was a new kind of computer, the Alto, that was designed for a single person to use with nothing more than a keyboard and a small device called a "mouse" that you operated with one hand.

They were not impressed. The tasks that the machine performed were mainly for writing and handling documents--secretarial work in other words--which did not excite them. And for executives who measured their performance by how many copies they generated, they didn't see how the thing could make money.

Times have changed, of course, and today it's hard to imagine any executive functioning without a computer. We're now going through a transformation similar to that of the 1970's. Today, every manager needs to work with data effectively. The problem is that, for the most part, most are as ill equipped as those Xerox executives in the 1970s. Here's what they most need to know:

Numbers that show up on a computer screen take on a special air of authority. Data is pulled in through massive databases and analyzed through complex analytics software. Eventually, they numbers make their way to Excel workbooks, where they are massaged further into clear metrics for decision making.

Read Also:
How Analytics is Making Basketball a More Beautiful Game

Yet where does all of that Information come from? In many cases, from lowly paid, poorly trained front-line employees recording data on clipboards as part of their daily drudgery. Data, as it's been said, is the plural of anecdote and is subject to error. We can -- and should -- try to minimize these errors whenever we can, but we will likely never eliminate them entirely.

As MIT's Zeynep Ton explains in her book The Good Jobs Strategy, which focuses on the retail industry, even the most powerful systems require human input and judgment. Cashiers need to ring up products with the right codes, personnel in the back room need to place items where they can be found and shelves need to be stocked with the right products.

Errors in any of these places can result in data errors and cause tangible problems, like phantom stockouts, which can lead to poor decisions in activities higher up in the organization, like purchasing and marketing. These seemingly small mistakes can be incredibly pervasive. In fact, in one study it was found that 65 percent of a retailer's inventory data was inaccurate.

Read Also:
When programmatic meets artificial intelligence ... the future begins

Lapses like these aren't confined to low-level employees either. Consider the case of Carmen Reinhart and Kenneth Rogoff, two Harvard economists who published a working paper that warned that US debt was approaching a critical level. Their work greatly influenced political debate but, as it turned out, they had made a simple Excel error that caused them to overstate the effect that debt had on GDP. Scientific studies have become so rife with data errors that they have led to a full blowncrisis in which an alarming number of studies cannot be replicated.

Our access to data is always limited. We may look at a day of sales, or a week or even a year, but that is just a small slice of reality. If we look at a typical marketing survey, then what we see is almost always a small sample. Studies are supposed to be controlled to make the sample representative, but the methods are less than perfect.

The upshot is that our numbers are always wrong. Sometimes they are off by just a little and sometimes by a lot, but they never perfectly reflect reality at the moment. That may be the result of controls being overlooked or data mishandled or just plain bad luck, but whatever the reason, we should never take data at face value.

Read Also:
The Role Of Business Intelligence In Social Media Marketing

 



Data Science Congress 2017

5
Jun
2017
Data Science Congress 2017

20% off with code 7wdata_DSC2017

Read Also:
Big Data for Financial Leaders: The Case for Embracing Predictive Analytics

AI Paris

6
Jun
2017
AI Paris

20% off with code AIP17-7WDATA-20

Read Also:
4 Practical Steps to Align Email Marketing with the IoT

Chief Data Officer Summit San Francisco

7
Jun
2017
Chief Data Officer Summit San Francisco

$200 off with code DATA200

Read Also:
Google is using AI to help humans and computers communicate through art

Customer Analytics Innovation Summit Chicago

7
Jun
2017
Customer Analytics Innovation Summit Chicago

$200 off with code DATA200

Read Also:
The broken promise of open-source Big Data software – and what might fix it

Big Data and Analytics Marketing Summit London

12
Jun
2017
Big Data and Analytics Marketing Summit London

$200 off with code DATA200

Read Also:
How Analytics is Making Basketball a More Beautiful Game

Leave a Reply

Your email address will not be published. Required fields are marked *