This week, InformationWeek executive editor Doug Henschen published an article about the 2013 Gartner Magic Quadrant for Business Intelligence and Analytics Platforms, which came out in early February. (You can read the report in its entirety here.) We’re proud to be positioned in Gartner’s Leaders quadrant for the third consecutive year!

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InformationWeek quoted Gartner analyst and report co-author Kurt Schlegel as saying, "Almost every user organization I talk to now is looking at making data discovery a more significant part of their BI and analytic platform architecture.” He commented that the benefit is improved agility because business users are freed to explore data and find new insights without having to put in requests to IT for new cubes or reports. QlikTech CEO Lars Björk was quoted in the article as saying, “Others are now confirming that [data discovery] is where the puck is moving, and it's a great testament that we're in the right place.”

The most exciting part of this year’s Magic Quadrant is that in our leadership of the data discovery category, QlikTech has helped transform the entire BI industry.  But we’re not resting on our laurels. There’s more to come. Check out the series of blog posts about “QlikView.next,” the next generation of the QlikView Business Discovery platform.

We all like stories. Why? We can lose ourselves in them for a time. Stories can make us feel as if we are experiencing something new. This also explains why movies and, to an even greater degree immersive video games and virtual worlds, are so compelling – but that’s a topic for another day.

I appreciate the insight about the human brain and storytelling in the December 5, 2012 Lifehacker article, “The Science of Storytelling: Why Telling a Story Is the Most Powerful Way to Activate Our Brains,” by startup co-founder Leo Widrich. With data storytelling one of the product scenarios of “QlikView.next” (see the related post, “Data Storytelling with ‘QlikView.next’”), the article grabbed my attention.

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Widrich pointed out that when we read words on a PowerPoint slide, for example, our brain goes into language processing mode; the brain is trying to decode words into meaning. In contrast, when we are engaged in storytelling (either on the telling or listening side), not only are the language processing parts of the brain activated—but also any other part of the brain that we would use if we were experiencing the events in the story.

Wait, it gets even cooler. Because this brain activity happens in both the storyteller and the person listening to the story, storytellers can synchronize their brains with the recipient of the story. Whatever the storyteller is experiencing, they can induce the listener to experience too.

What does this have to do with Business Discovery? A whole lot. The same principle applies to numbers on a page or screen as to words. If we just see the numbers in black and white our brain goes into processing mode. We try to figure out what the numbers mean.

With numbers, how do you get your (data) point across? How do you convey the emotion behind your discovery or proposed decision? How do you get others on board with you? If someone is telling or listening to a story about the numbers – how the numbers came to be, why they matter, what their implications are, and what should be done about it – more peoples’ brains (and more of their brains) are engaged. Telling stories with data requires a connection to the data being analyzed.

And, to take this idea all the way to its conclusion, isn’t brain synchronization the nirvana of business intelligence? The nirvana of BI is alignment – getting everyone on the same page so the organization can move as one in the right direction, based on facts.

See these related blog posts:

Thanks to Tom Mackay, principal solution architect at QlikTech, I recently came up on the article, “Seven Dirty Secrets of Data Visualisation” by programmer Nate Agrin and data visualization developer Nick Rabinowitz. In addition to shining light on some best practices in data visualization, this article helps illuminate the difference between standalone data visualization tools and a Business Discovery platform.

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The article covered seven dirty secrets of web-based data visualizations; I have thoughts about a few of them:

  • Secret no. 1: Real data is ugly. The article points out that before data can be accessible to, and useful with, data visualization software, a data expert has to find, acquire, load, clean, and transform it. This problem is compounded when the data comes from multiple sources. A lesson? Look for Business Discovery software that enables you to begin working with and analyzing even imperfect data without requiring a whole boat load of external (ETL extract, transform, load) capabilities. Discovery begins with the data. Being able to immediately see the mis-typed, out-of-range, or missing lets you begin tackling the problem from the start.
  • Secret no. 3: There’s no substitute for real data. When getting your hands on software to help you make sense of your data, try it out with your own real data – not dummy sample data. It’s in quickly and easily being able to see the associations in your own real data that the “a-ha” moments occur. This experience may be especially powerful with data sourced from disparate systems. “There is nothing like the moment when a user sees associations between data they have never been able to bring together before,” says Tom Mackay, principal solution architect at QlikTech. He’s speaking from experience; he’s seen it happen time and again at customer sites.
  • Secret no. 6: Visualization is not analysis. Agrin and Rabinowiz argued that visualization is a tool to aid analysis, not a substitute for analytical or statistics skill. True. But it’s not either/or; an analytic app can contain both detailed data, with complex statistical expressions applied, and visualizations that help simplify the picture. Users can analyze the data in the way that is most comfortable for them. And: clear, well-labeled, interactive charts and graphs that are part of an analytic app make it easy for even non-data analysts to explore information and derive insights and meaning – critical in a world where information is strategic and we all have to know how to work with it.
  • Secret no. 2: A bar chart is usually better. “The coolest looking visualisations are often the least useful,” the authors wrote. They pointed out that the cost of the novelty and visual appeal of some data visualizations is clarity of meaning. This potentially leads to comparisons that distort the data and takes viewers to false conclusions. The authors made the same point about animations (“secret no. 5: animate only when appropriate”) and recommended making animations simple, predictable, and replayable. I’d add that another complicating factor is the variety of devices people are using to interact with analytic apps, including tablets and smartphones. We offer some design suggestions of our own in this Technical Brief: “Mobile User Interface Design Best Practices.”
  • Secret no. 7: Data visualization takes more than code. “. . . Creating visualisations that offer real insight or tell a compelling story still requires a particularly wide range of real skills in addition to coding, including graphic design, data analysis, and an understanding of interaction design and human perception,” the authors wrote. A complicating factor is that data experts tend not to be graphic designers. To get past this dichotomy, check out the tips our experts offer on the QlikView Design Blog and take a look at how we are focused on closing the gap between data and design with the next generation of QlikView (see the post, “A Gorgeous and Genius ‘QlikView.next’ – The Best of Scandinavian Design”).

Visualizations are just the tip of the iceberg – the iceberg being a person’s understanding of the data. To be able to derive meaning and insight from data, especially complex data sourced from multiple systems, the user requires not only well-designed, clear, concise data visualizations, but the ability to explore the full dataset on their own. They need to be able to ask and answer their own streams of questions without having to go back to an expert for a new visualization every time they have a follow-up question. This, in a nutshell, is the difference between a standalone data visualization tool and a Business Discovery platform.

During this time when student test scores convey doom and gloom and education budgets are pinched, I jump on positive news when I get it. I came upon some good news recently during a conversation with FirstLine Schools in New Orleans, Louisiana. I spoke with Sia Karamalegos, director of data management, and Rebekah Cain, director of development and communication. FirstLine Schools is a charter management organization managing five public schools. The organization has approximately 2,500 students and 300-400 employees and is a grant recipient in QlikTech’s Change Their World program.

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In the words of Gillian Farquhar, QlikTech’s customer ambassador, “Like in most urban environments across the United States, lots of kids in New Orleans come from broken or economically challenged homes and the public education system has had a hard time producing the success it hopes for. Charter management organizations like FirstLine Schools intervened to make a difference early in children’s lives.”

Sia Karamalegos of FirstLine Schools said, “A lot of schools are trying to reform across the United States. A big aspect of reform in public education is using data to drive instruction. A challenge with this, though, is that school leaders, teachers, and special education and intervention staff are generally not data experts. They need to be able to easily access and make use of the data. But the diverse nature of the source data systems we use presented a challenge. Some systems permit users to export data while others don't. The data is in many different formats and the systems each have their own login. For teachers and administrators and even network administrators, this situation created a barrier to being able to make good use of data. People needed a better way to get a complete view of school and student performance.” 

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Rebekah Cain added, “School leaders have a big job. They are already working really hard. So any way we can make it easier for them to access the data they need to do their jobs, the better. Then they can spend their time on things that will improve instruction instead of looking for data.”

Using QlikView, FirstLine Schools is focusing on:

  • Student and school performance and success. FirstLine Schools is collecting data about students such as grades, classes taken, attendance, demographics, behavior (e.g., merits and demerits, suspensions, etc.), and whether the student is in intervention (performing below grade level). The data team assembles data from myriad back-end systems into a cohesive QlikView app that gives constituents a well-rounded perspective on a scholar’s “at-riskness,” trends in a student’s progress over time, and whether the approaches the school is taking with the scholar are the right ones. With QlikView, stakeholders can explore the data, toggling among students or schools or selecting a particular quarter—all the while seeing only the data they are supposed to see. “If a student is not progressing the way we expected,” said Rebekah Cain, “we know what we are doing isn’t working – so we need to add additional resources.”  In addition, a lot of the data the schools track is not about student grades. Schools conduct progress monitoring and interim tests to see if they are improving across large student populations.
  • Administrator dashboard. New Orleans is an open enrollment city; students don’t have to live in a particular neighborhood to attend a school there. What this means for FirstLine Schools is that it has to recruit students. To aid in recruitment and support people who are writing grants and performing other compliance tasks, FirstLine Schools uses QlikView to track and present statistics like enrollment, year-to-date attendance, tardiness, and truancy. Stakeholders can get the info they need when they need it. They can drill down into school or grade level and can select factors like special education status, homelessness, gender, or others to get just the relevant stats they need. QlikView logs into the various source systems automatically using scripts so the data in the apps is always up to date.

I asked Sia about FirstLine Schools’ “a-ha moments” with QlikView. In one example, they noticed that for different schools in the system, the rate of in-school suspensions vs. out-of-school suspensions had changed. This realization sparked further investigation. Moving forward, FirstLine Schools is planning to tie in additional data to identify which intervention programs are having the greatest success so they can spread best practices.

Are you inspired? I am.

When I’m asked what customers use QlikView for, I usually respond with the top of mind answers about dynamic reporting, actionable dashboards, and works-the-way-your-mind-works type of analytics. There’s nothing wrong with those answers – that’s why most customers buy business intelligence software, and QlikView in particular. Last week I attended the first-ever QlikView Technology Summit in the San Francisco bay area where I got to mingle with several hundred QlikView customers and prospects. One customer’s use for QlikView blew me away.

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This customer works for a healthcare company. He told me their nurses use QlikView. Not too surprising, I thought to myself, as many hospitals use QlikView to analyze patient data. He then told me how QlikView helps nurses diagnose patients. As patients describe their symptoms, the nurses make selections in the QlikView app. QlikView displays a list of possible diagnoses, narrowing down the options as the nurses make more selections.

Wow! This is the power of QlikView’s associative experience at work. If you’ve ever been to a specialist at a hospital, you usually fill out a very long questionnaire about your symptoms. This annoys me because most of the questions have nothing to do with why I’m there. By using QlikView, the nurse is able to quickly zero in on the problem because all the unrelated options are greyed out as soon as the nurse makes a selection. You might notice that this creative use of QlikView, an ostensibly BI tool, has nothing to do with charts, visualizations, and dashboards. And unlike so-called “expert systems,” where nurses follow a predefined path of questions much like a traditional call center, QlikView allows nurses to follow the patient’s own train of thought and their own experience of their condition.

I can immediately imagine a list of tangible benefits:

  • Faster time to diagnosis
  • More accurate diagnosis (because a newly-trained nurse has access to the same expertise as a seasoned pro)
  • Better patient experience (because they aren’t being asked lots of unrelated questions over and over)
  • More efficient and lower-cost healthcare

At a time when the quality and cost of healthcare seems to be at odds with each other, QlikView shows how hospitals can deliver better care at a lower cost.  Now that’s what I call a win-win situation!

A nighttime murder took place in 1991 in Linwood, California. Half a dozen teenaged eyewitnesses picked a man out of a lineup and he was eventually convicted. No gun was ever found, no vehicle was identified, and no person was ever charged with driving the vehicle.

For two decades, the convicted man – Francisco Carrillo – maintained his innocence. Eventually, forensic psychologist Scott Fraser got involved. He reconstructed the crime scene with a focus on the lighting. He convinced the judge that the eyewitnesses could not possibly have seen the shooter in the dark well enough to identify him; the witnesses’ color perception would have been limited and depth of field would have been no more than 18 inches.

As a result, Carrillo’s murder conviction was overturned and he was released from prison after nearly 20 years.

Scott Fraser told this story in a TEDx talk posted in September 2012, “Why Eyewitnesses Get It Wrong.” Fraser described how even close-up eyewitnesses can create “memories” they could not possibly have experienced. He explained an important characteristic of human memory: the brain only records bits and pieces of an event or experience.

The different bits are stored in different parts of the brain. When we recall those bits and pieces, we have partial recall at best. From inference and speculation and observations that took place after the event, the brain fills in information that was not originally stored there. Our memories – all our memories – are reconstructed.

Reconstructed memories are a combination of an event and everything that has occurred after the event. They are reconstructed every time we think of them. As a result they can change over time. Therefore, the accuracy of a memory can’t be measured by how vivid it is nor how certain we are that it is correct.

In this TEDx talk, Fraser made a couple of recommendations for the criminal justice system. As I was listening to him I thought that these apply to business decisions just as much as to the law. Fraser identified two things are really important for decision making:

  • Hard data. Fraser urged us all to be cautious about the accuracy of the memories we know deep in our hearts to be true. Memories are dynamic, malleable, and volatile. When it comes to workplace decisions, these decisions are very often made based on eyewitness accounts, in essence – on the experience and persuasiveness of people. If all our memories – all our experiences – are reconstructed, our business decisions are just screaming for a more objective rationale. And that rationale is the evidence that comes from hard data.
  • Analytical skills. Fraser advocates for bringing more science into the courtroom by emphasizing science, technology, engineering, and mathematics in law schools. After all, it is law school graduates who become judges. The same is true for business decision making. The volume of data available for use in decision making is ballooning and analytical skills are required to make sense of it. By this I don’t mean we all have to become data scientists. But we need to be able to apply logical thinking to the gathering and assessment of information. We need to be able to ask the right questions.

Which initiatives should we invest in? Where should we open a new retail store? What is the best way to retain our most profitable customers? How can we reduce inventory costs? By using analytical skills and basing decisions upon a combination of hard data and experience we are best positioned to avoid big mistakes – some of which could be as significant as sending the wrong guy to jail.

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