Part Deux – 4 ways your analytics can deceive you

Last week I kicked off a series of posts about how analytics can deceive you.  Today I continue this thread with some concrete, public examples of how organizations have “missed the boat”

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We have seen four trends in our prospects and clients which have led to (in some cases) disastrous results.

The first is related to data missing from an analysis. 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 at risk of getting blindsided by information that can only be found in external data – like 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 information around social sentiment. The initial incident was rough enough but made worse as the CEO made 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 with 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)

The second is related to incorrect data. Excel remains the BI tool of choice for many business users and analysts and there are 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 to perform calculations and 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 entitled 88% of Spreadsheet Have Errors, cautions that “Spreadsheets, even careful development, contain errors in 1% or more of all formula cells.”

This is what happened to a large US based financial services company. They had outgrown their accounting system and inserted a complex Excel model into a process that exported all open positions into Excel so they could be priced at current market rates and the values were returned back into a work stream. Unbeknownst to them at the time, there was an error in one of the calculations that led to Fannie Mae overstating revenue by more than $1B. When they announced the correction, their stock dropped $2.25 per share. The cost of not knowing.

These are two examples of common problems many companies face – MISSING DATA, or not including all relevant data points in an analysis and INCORRECT DATA, just blatantly having the wrong information at hand.  Both of these have have led to significant meltdowns for their respective organizations.  The internet is littered with stories similar to these.

Check them out Google:

  • Social Media fails
  • why excel is bad for data analysis

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