You need to discover fire before you can launch a rocket!

I was recently in Germany attending the Gartner Data and Analytics Summit in Frankfurt. I had a chance to meet many clients and prospects who are struggling to get their analytics projects off the ground. Every client wants a moon shot – to leapfrog immediately to space flight without having even discovered the wheel.

I also had the opportunity to meet with an amazing thought leader in analytics, Alexander Thamm and we discussed this topic in depth.

Alex Thamm, the CEO of Alexander Thamm GmbH has helped some of the leading companies in Germany achieve amazing results with analytics. In our discussion, Alex shared what he is seeing: organizations want the nirvana promised by AI and Cognitive technologies. The challenge with many of these organizations is that they do not even have the basics down. A recent HBR article backs this view up. Many organizations either do not understand, don’t appreciate or even worse, are in denial of what is really needed to be successful to move up the analytics curve.

So, as I sat there at Alex’s headquarters in Munich, Germany I thought about an analogy I have shared multiple times because I have too frequently experienced vendors pitching the magic of artificial intelligence and analytics only to see a deer the headlights type of gaze.

So I shared the following analogy with Alex to see if this is what he is also seeing.

Explaining artificial intelligence and advanced analytics is like explaining the space shuttle launch to cave men. First you start a fire. Once the fire is going and strong enough you need to get yourself a tin can. You take that tin can and fill it with some type of combustible fuel and set it on top of the fire. Enough pressure will build up and send the tin can into the sky. The tin can will go around the earth. Voila! You have space travel. The cave men tend to sit there completely astonished…..amazed really. After overcoming their initial amazement….they eventually say: “Wow, that is absolutely amazing!!!!! Incredible! So, how did you get the fire??

Alex’s reaction was clear. This is what he is seeing too. He like the analogy so much he recommended I share it as a blog post.

I am not saying that most organizations are filled with cave men. I AM saying that we as vendors owe it to our clients to help them progress along the analytics curve. If we are truly dedicated to our clients’ success, sometimes we need to have the tough conversation with the client – Yes, I understand you want to have artificial intelligence across your organization (moon shot). I recommend you start with getting your data collection and management processes (fire) down first and then we can build up to your moon shot.

A step by step approach to planning the perfect office party!!

So, it is that time of year again….the holiday season from Thanksgiving (in the US), to Chanukah, Christmas, Festivus and New Years.  It is during this season when many companies decide to throw an office party.  If you are tasked with finding the “perfect” location to host your party, you know you have your hands full in pleasing everyone…

There is a way you can hedge your bets and find the “perfect” location which will please most people. How about using social media and your social network to understand peoples opinions and sentiment about certain locations.

Check out the steps below to get started!!!

Click here to register for a free trial of Watson Analytics for Social Media.

Angry Tweeter? Social media is so much more….

Let’s talk social – internet data is abundant and growing every single day.  As marketers, we have got to find a way to take advantage of it.  Social NoiseBut the reality is there is a LOT of information just waiting for us to take advantage of.  With that said there is a heck of a lot of noise in that information and we need to find an effective and efficient way to find the signal through the noise.

As a marketer I always looked at social data as the typical things we think about social information – twitter, facebook, pinterest, Instagram etc… traditional data sourcesMany marketers like myself view social media as about engaging the angry tweeter…..getting to that person who loves/hates my products to address their needs.   This approach is most often used for demand generation or customer service. These traditional data sources are best used for that type of approach – engagement.

Most of the marketers I speak to want to use the traditional data sources for:

  • Metrics – how many followers or likes to I have
  • Engagement – how do I market to the angry tweeter using social


While these two items are important and those data sources helpful, they are not the end all be all!  There is a third way we can look to social data and it will expand your ability to TRULY understand your market and audience:

  • Listening – how do I understand broad target segments to develop a communication strategy, develop the right messaging?

It is this THIRD area we are starting to see take off.  Listening is about finding the right data at scale and automatically classifying it to find patterns.

The ability to listen and to hear what people are saying means that marketers can make customer driven decisions leading to increase revenue, decrease cost and mitigate risk.  But if you are going to do TRUE listening to customer sentiment, you are going to have to expand our horizons beyond the traditional data sources and in some cases ignore the traditional data sources as they are often full of junk.

Social Data has moved beyond the realm of  just the traditional channels.traditional plus non traditional  We need to consider information on the internet as a source – this will include the traditional channels – facebook, twitter, pinterest etc….But it will also include things like news stories  – Market Watch, Thomson Reuters, AP etc.  It delves in to the areas of BLOGS – WordPress, WIX, MEDIUM etc…It will also include reviews from forums and review sites…Think about the wealth of information that can be found on the Amazon reviews alone…..And seriously consider REDDIT.  (I was blown away by the richness of the data on REDDIT!! – Follow up blog post on this to come!)

If we have the foresight to expand our thinking about social data to be “All internet information” ….social media can guide your overall marketing strategy, feed your messaging, enable you to identify broad target segments for your campaigns.   To do this we need to think beyond just the typical sources of information to help expand our use case scenarios…..

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.


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? …..

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

4 ways your analytics can deceive you

This is post 1 of a 3 part series to help dispel the myth that fast and agile analytics are always better. 


How do youNugget  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.cost of not knowing.png There is a “cost of not knowing” and 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 also open the opportunity to make better data-driven decisions and find opportunities for competitive advantage? We went to work studying market trends and interviewing hundreds of customers and reviewing thousands of projects and the common theme we have been hearing is all around “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 and pattern-based planning to drive sounder decision and identify trends before they could even know which questions to ask…….

To be continued……. 4 questions to ask yourself!

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Trump: Sentimental Tweetist!

I recently saw this blog post which was shared on linked in by Ahmed Sharif from Convergence Consulting Group.  It is really a great tutorial about how to use Python for sentiment analysis.

I found the topic fascinating but as a “mere mortal” (aka Business User) there was no way I was going to be able to open up Python, Tweepy, or any of the other tools in that tutorial.  It is just not in my DNA.  For a data scientist or a technical person, Python is an extremely powerful tool.  What about for the average business person? Say, in marketing, customer service or even product development… could they access twitter data or other internet data to understand sentiment?

I used this social media analytics solution which aggregates, structures and classifies ALL of the data for me in one location.  The solution then applies a variety of analytical techniques against this now structured information to provide me recommended topics I should explore, share of voice and most importantly SENTIMENT.  Sentiment is how people feel about a given topic.  Whether you are PRO or ANTI Trump, there was a LOT of sentiment out there and each side is fighting for their share of voice.

So what did I find?   What is being said and how do people feel about it? I found a mix of sentiment.  Screen Shot 2017-10-25 at 3.54.35 PMSome people were extremely positive in their topics about the president while other were extremely negative.  There was even some ambivalence as indicated by the grey dot.  If I were doing some damage control or political campaigns, I could use this information to identify those topics people are most interested in without needing to manually group aggregate and classify the information myself.  This saves a ton of time and highlights topics I had not necessarily even thought about.

Who was talking about the President and more specifically the handle @realdonaldtrump?  I see a mix of genders, split right down the middle.  Screen Shot 2017-10-25 at 4.20.24 PM

Now, this gender data is self reported in Twitter and other internet sources..  I can only see what people are willing to share on their profile which about 60% or so chose to do.  If I were running a marketing campaign this would help me identify who to target.  I can further refine this information to determine the topics each gender is interested in.

This is just a bit of a taste of what you can do with social media analytics.  And here is the key:  I was able to do this analysis in 5 minutes without writing a single line of code!