Tag Archives: visualization

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? …..

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:

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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.

 

 

Analytics for everyone? For free? Get out!

Making analytics accessible to all users has been a challenge for years.  Companies like Tableau and Qlik have done a decent job of reaching finicky business users by providing visualizations and data discovery capabilities.  They are largely one trick ponies in each of their areas of strength.  That is just not going to work for me. I am an only child with some strong Veruca Salt tendencies (Think, I want it all and I want it NOW!).  So I am particularly happy to talk about  the Watson Analytics announcement today.

Here is what the market is saying:

As a former marketing operations leader, I am thrilled to see these types of capabilities being offered directly to marketing, sales, human resources, and finance professionals.  For the first time these business users can have all of the analytics capabilities they need in one location without ANY technical knowledge required.  From snazzy visualizations to predictive analytics Watson Analytics offers the analytics you need all in one single location to answer questions like:

  • How can I make sure I target the hottest leads?
  • How can I increase the value of the customers I already have?
  • How can I create more successful campaigns?
  • How can we attract and retain the right employees with the right skills?
  • How can we create and keep top performers?

I have neither the time nor the patience to play around with analytics tools.  I need to get the answers to these from my data and make decisions NOW.  Watson Analytics gives me what I need and it is cloud based….so I don’t need to download and install anything!  Woohoo!

Watson Analytics is starting with a beta program.  I am looking forward to getting my grubby little mitts on it over the next several weeks.  You too can get an early glimpse by registering for the beta here!

3 critical attributes for analytics solutions

I recently had the chance to attend the Gartner BI Summit in Las Vegas.  If you are in the analytics space and have not attended this event, you really need to check it out.  My colleague Marcus Hearne shares his experiences at the event in his blog post. The event is Gartner‘s opportunity to share their point of view but also for analytics vendors of all sizes to share their points of view.

So what did I take away from this event?  Put quite simply, analytics vendors must design solutions and capabilities that are fast, easy and smart.  For too long, analytics have been relegated to the to the wizards, witches, sorcerers and sorceresses in their lair.    It does not need to be that way.  As Gartner analyst Rita Sallam pointed out in the keynote, mere mortals can demystify the magic and understand the “tricks” behind the magic.

As I sat through the various vendor sessions I picked up on the key themes of fast, easy and smart.  Let’s talk a little bit about what that means:

Fast:  Each of the sessions I sat through demonstrated the speed of the analytics offerings.  This should just be an expectation period, no discussion.  You need answers and you need them fast.   We expect to get answers quickly in our personal lives…why should we have a different expectation at work?  It was more than just raw speed.  It is also about getting up and running quickly.  This can be done by adopting cloud based analytic solutions.

Easy:  Analytics solutions must be dead simple to use – I am talking as easy as a telephone, microwave or a garden hose.  If analytics require a PhD or reading a long manual…there will be no broad adoption.    But it is much more that just easy to use. The information must be easy to understand.  This can be accomplished through powerful visualizations.  Remember: A Picture is worth a thousand words.

Smart:  Many of the latest and greatest innovations feature industry specific or use case specific solutions, such as Predictive Maintenance and Quality.  Analytic solutions must be specific to the users’ role and address specific pain points.  More and more offerings will include some form of intelligence to make the analytics all the more powerful.

To hear more about analytics I recommend visiting a Business Analytics Summit in a city near you.