Tag Archives: data discovery

Part 3 – 4 ways analytics can deceive you

Last week I shared how two organizations have suffered from lack of the right data and incorrect data.  This week I finish off the 3 part series…… (Part 1, Part 2)

The last two examples relate to analytics governance. Analytics adoption began as IT-led, coordinate projects but as desktop and cloud applications became more accessible to individuals, their use spread across the enterprise. Michael Goul is an associate dean for research and professor of information systems at Arizona State University’s W. P. Carey School of Business, has spent the last few decades studying artificial intelligence and business analytics. While he agrees that data science has the potential to revolutionize commerce, he also thinks too many companies are rushing headlong into the field without putting proper governance systems in place, and in some cases this has led to disaster.

In our third example, the lack of Analytics governance provided an opportunity for fraudulent behavior. A media company was going through a rapid growth phase. They expected that their customer satisfaction, measured by their NPS score, would likely dip during this time. They were watching close for an indication that they need to ramp up their investment in customer service. However, as they grew, their NPS remained curiously constant – which seemed rather odd. After 9 months, the CEO was so concerned that they might be missing something that he brought in an outside consulting firm to help them understand how they delivered the same level of customer service while they expanded so rapidly. It turns out that the person who created the dashboard for the Executive team was using an ungovernable desktop BI solution and was being paid a bonus based on the NPS score performance. When the score started to slip, the individual who created the dashboard was faced with losing his bonus so he opened up the spreadsheet that feeds the dashboard and nudged the number up manually so that he still got paid his bonus. What started out as a small adjustment, grew to a 40% variance over the 9 month period. How many customers got frustrated with the declining level of service in that period and switched providers? Despite their attention to this metric, they got blindsided by not knowing. (misrepresented)

Our final example is the opposite side of the same coin – related to analytics governance. Executives have become reliant on dashboards as single-screen “snapshots” of performance. But dashboards are not the magic view some managers treat them as. Although they can convey important measures, dashboards cannot always provide the nuance and context necessary for effective data-driven decisions. The data can be 100% correct but the visualizations can be very misleading. Here is an example from Harvard Business Review. It is for a large package delivery company who wanted to reduce vehicle accidents by offering drivers the option to upgrade their GPS to a system that would help them avoid high-risk traffic areas. After monitoring drivers’ behaviors, a frontline manager checked the dashboard and found, to her surprise, that the accident rate was actually higher with the upgrade.Picture1

At first glance, it appears that drivers who upgraded their GPS were in more accidents, which might lead someone to suggest they downgrade back to what they had. In reality, the upgrade was actually quite effective and the manager would have seen this had they compared accident rates for “safe” drivers versus “accident prone” drivers.

Picture2

For both groups, the upgrade made them safer. So why did the accident rate increase for the entire fleet of drivers while decreasing for each group? Because in this case almost all of the accident-prone drivers chose to use the upgraded device and almost all of the safe drivers kept the old device. Preexisting driver behavior was confused with the effectiveness of the upgrade.

THE REALLY INTERESTING THING in this case? The visualizations were accurate as was the data displayed.  They just did not show the whole picture when looking at all factors leading to accidents. Joel Shapiro, Executive director data analytics program at Northwestern’s Kellogg School of Management, says “Perhaps the greatest danger in using dashboards for decision making is in misattributing causality when comparing elements on the dashboard.”

These scenarios are real and they are happening every day.  Might they be happening in your organization? …..

Speed isn’t the only winning Formula for Analytics!

Speed! Seems like everybody wants to go faster and faster.  But here is the question:  Why is speed only half the story when it comes to analytics?  IMAGE$1508AC4836EB4FE0.gifYeah, the desktop business intelligence and data discovery vendors all tell you that they will get you there quickly but will they get you there safely in first place???

 

I was speaking with Derek Daly, former Formula 1 race car driver from Ireland, about what it takes to win in both Formula 1 racing and in today’s business environment.  It was a fascinating topic as many people think you just have to go fast to get to the finish line.  The reality of Formula 1 racing is that it is about both speed and the right actions.  Drivers must make split second accurate decisions at the speed of thought.  Otherwise, they end up driving their car into the wall and in the worst case scenario they die.

This made me think about my day job which is in the analytics space.  I hear so often from prospects and clients that they need speed…they need insights faster than anyone else.  Well, what happens if the “fast insights” are accurate but don’t give you the nuance behind the data? Or even worse, what if they are just inaccurate?

The fast insights that are offered to you by the data discovery vendors get you the answer of “what happened” fast but often leave out the answer to “why something happened”. This perception about needing fast insights can lead to some very dangerous decisions.  Just because you got there quickly does not mean the answers are right.

Fast ≠ Right

The real issue is “confident decisions.”  To build real confidence, you have to start by fixing the data problems that can undermine your analytics – ensure you have all the data you need and that the data and analytics are trusted. makedecisions_0You also need the full spectrum of analytics which address all questions bringing context to your analysis.

  • Descriptive (What happened?) ,
  • Diagnostic (Why did it happen?)
  • Predictive (What is likely to happen?) 
  • Prescriptive (What should I be doing?)

That way you’ll cut through the uncertainty of the what, while getting deeper insights into the why to understand what you should actually be doing!

Just like the Formula 1 race car driver – business leaders need speed but they need to take the right actions and have confidence in their decisions.  For business leaders, they may not drive their car into the wall but making the wrong decisions quickly can lead to lost revenue, lost customers and in some cases industry fines.

You can learn more by joining us in person on November 16th at the Manhattan Classic Car Club. Learn more and register here:  ibm.co/2i6NLEA

 

 

Why speeding up your analytics is 1/2 the story!

Speed! Seems like everybody wants to go faster and faster.  But here is the question:  Why is speed only half the story when it comes to analytics?  Yeah, the desktop business intelligence and data discovery vendors all tell you that they will get you there quickly but will they get you there safely in first place???

I was speaking with Derek Daly, former Formula 1 race car driver from Ireland, about what it takes to win in both Formula 1 racing and in today’s business environment.  It was a fascinating topic as many people think you just have to go fast to get to the finish line.  The reality of Formula 1 racing is that it is about both speed and the right actions.  Drivers must make split second accurate decisions at the speed of thought.  Otherwise, they end up driving their car into the wall and in the worst case scenario they die.

This made me think about my day job which is in the analytics space.  I hear so often from prospects and clients that they need speed…they need insights faster than anyone else.  Well, what happens if the “fast insights” are accurate but don’t give you the nuance behind the data? Or even worse, what if they are just inaccurate?

The fast insights that are offered to you by the data discovery vendors get you the answer of “what happened” fast but often leave out the answer to “why something happened”. This perception about needing fast insights can lead to some very dangerous decisions.  Just because you got there quickly does not mean the answers are right.

Fast ≠ Right

The real issue is “confident decisions.”  To build real confidence, you have to start by fixing the data problems that can undermine your analytics – ensure you have all the data you need and that the data and analytics are trusted. makedecisions_0You also need the full spectrum of analytics which address all questions bringing context to your analysis.

  • Descriptive (What happened?) ,
  • Diagnostic (Why did it happen?)
  • Predictive (What is likely to happen?) 
  • Prescriptive (What should I be doing?)

That way you’ll cut through the uncertainty of the what, while getting deeper insights into the why to understand what you should actually be doing!

Just like the Formula 1 race car driver – business leaders need speed but they need to take the right actions and have confidence in their decisions.  For business leaders, they may not drive their car into the wall but making the wrong decisions quickly can lead to lost revenue, lost customers and in some cases industry fines.

You can learn more by joining us in person on November 16th at the Manhattan Classic Car Club. Learn more and register here:  ibm.co/2i6NLEA

 

 

So what the heck do you do with Watson Analytics?

It has been a little over a year since IBM’s Watson Analytics was released.  Watson Analytics, a smart data discovery tool, continues to deliver easy analytics for everyone.  Some competitors are trying to catch and imitate what IBM has delivered but frankly, their latest news is more “snooze worthy” than newsworthy.

Because Watson Analytics is so easy to use and visually appealing, IBM has seen strong adoption (1 million plus users!).  Over the past couple of months there have been some amazing additions to an already fantastic product (see Watson Analytics for Social Media).

Watson Analytics is so powerful and can address so many different uses cases.  I am frequently faced with the following query:  So, I get it!  I have a ton of data and I need a way to make sense of it.  But what exactly do I DO with it??

This really depends on what your role is.  In our work lives we each fit into some type of role:

  • sales guy
  • marketeer
  • finance professional 
  • operations director
  • supply chain manager
  • head of strategy

So, as you read this and think about what YOU can do with Watson Analytics, think about the data you need to better visualize.  Think about what finding patterns within your data could do for you.  Finally, think about how adding social media data to your analyses can make you analyses event more effective.

But most importantly let’s put this in context of some of the major roles and use cases that will matter to you:

Screen Shot 2016-04-14 at 9.52.01 PM.png

Over the next several weeks, I will be sharing the details behind some of these specific use cases and providing a “how to do this yourself” approach.  Check back to hear more about how #watsonanalytics can help address many of these use cases.