Data Storytelling: An Intersection of Art & Science

Data Storytelling: An Intersection of Art & Science

A few years ago I met Walter Isaacson, former Chairman of CNN, Editor of TIME, and author of Steve Jobs’ biography. If you can’t tell from his pedigree, Isaacson is a great storyteller. He also wrote about other famous innovators including Benjamin Franklin, Albert Einstein and Leonardo Da Vinci. I only had time to ask him one question, so I made it a good one,

“What did Jobs, Franklin, Einstein, and Da Vinci have in common that made them such great visionaries?”

Isaacson smiled and responded, “All great innovators operate at the intersection of Art and Science.” I think Isaacson would agree this balance applies to data storytelling as well. Truly effective storytelling drives business action, and this occurs with the right mix of facts, visual presentation, and contextual narrative. Finding this balance is a challenge, but with the right tools and methodology, you can go from creating flashy dashboards to actually informing decisions.

Data Storytelling

Over the past decade, there has been a massive push for companies to leverage data. We are starting to see the Rise of Chief Data Officers. Humans are visual by nature, so we have also seen increased adoption of user-friendly visualization tools like Tableau, Qlik, Power BI, and ThoughtSpot. As the push for data democratization and access to data continues to increase, we need to ensure data is being effectively communicated and consumed – not just put into a pretty dashboard.

Enter Stage Left: Data Storytelling.

What is Data Storytelling? Data Storytelling is translating data in an easy to understand the way to help people take action on the business. There are three main components to data storytelling:

components of data story telling

The final component, the art of communication, is still on the starting block. However, by establishing a methodology and using new technologies to support us, we can realize the full value of our data, inspire action, and transform Data Storytelling from an industry buzzword into an effective boardroom practice.

Capturing Business Context

All BI and Analytics initiatives should aim to do the following: make money, save money, or protect against risk. However, only 20% of analytics insights are predicted to produce a business outcome through 2022 according to Gartner. To unlock greater value, analytics teams and business leaders must radically change the way they communicate.

Rather than just deliver report requests, analytics teams must establish a dialogue with the business to understand the context. Context includes goals, challenges, and potential decisions that the business will make. In creating this dialogue, gaps in understanding will appear. These gaps will highlight the best questions to ask of the data. Ultimately, the answers to these questions will deliver the value business leaders have been seeking.

Using Technology for Storytelling

Once the context has been established and the right questions are being asked, analytics teams, can use technology to help communicate information with a narrative to increase understanding. We use reports and data visualization tools now. Data visualization helps us see blatant patterns, but it isn’t ideal for communicating context and situational nuances. We also shouldn’t assume interpreting a visualization is easy for everyone. With the global Data Literacy rate struggling around 24%, delivering an isolated report or visualization is risky – the information can easily be misinterpreted and lead to costly decisions.

New technology, like Digital Hive’s Analytics Hub, enables companies to easily balance the art and science of data storytelling so they can communicate and understand the entire business narrative – and ultimately make the best decisions.

By bringing together reports, visualizations, and dashboards from all of your different BI tools into a single storyboard, you can mix best-of-breed technology to deliver all of the facts. Contextually, you can incorporate video, custom messaging, presentations, and data literacy support assets to complete the narrative and inspire action.

The ideal balance of data, visualization, and narrative can now be achieved without the limitations of any one tool or technology because you can use all of your tools together seamlessly.

Theia gameboard showing assets from different BI tools sitting side by side, creating a full datastory
*Storyboard storyboard incorporating visualizations from 2 different BI tools, context from Google Drive, and custom messaging.

Conclusion

To increase the value of analytics for the business, we must find a greater balance between the art and science of data storytelling. When looking to improve the art, we must change the way analytics teams and the business communicate context. Then, we need to ask impactful questions of our data.

Finally, when delivering our findings, we should leverage technology to support us by using data visualization and data storytelling tools to communicate insight within a narrative.

Analytics from different BI systems side by side
*Image shows an example Digital Hive gameboard/storyboard with assets from multiple BI tools sitting side by side in a single view.

Digital Hive and Data Storytelling

Digital Hive dynamically displays content from any information system seamlessly in one unified platform – providing the easiest, most efficient, and customizable experience for the delivery and consumption of data stories on the market today. Behind the scenes, Digital Hive defends users from change-disruption, tracks analytics adoption, and reduces the IT backlog.

Click here to download our e-book 7 Steps to Drive Data Literacy‘ or book a quick 30 min 1:1 demo with a Digital Hive expert!

Poor Change Management is Killing Analytics Value

Poor Change Management is Killing Analytics Value

Unless it pertains to politics or parking meters, people dislike change. Why? Change involves work, learning new skills, and the possibility of failure. This makes people uncomfortable and resistant. Maintaining the status quo is simply easier. When we look at why BI & Analytics initiatives fail, the reasons are not usually technical problems, but people problems related to change management and communication. Yet, in contrast to the average stakeholder, individuals who lead change are enthusiastic advocates and willing to put in the extra effort.

Why is this?

Champions of change understand the “Why, What, and How” of the change that is taking place.

Most stakeholders do not understand the “Why, What, and How” This is where D&A strategies are failing. If you map these 3 critical pieces of information to hot trends in Data & Analytics it is very clear. Industry challenges include:

  • Understanding the potential value of data – Rise of CDO (Data Culture)
  • Developing the skills to use data – less than 24%  (Data Literacy)
  • Using BI to make decisions – less than 35% (BI Adoption)
  • Producing a positive business outcome – less than 20% (Value)

Why, What, and How…

The “Why” must be the basis for change, and if it does not bring significant value to stakeholders, the initiative is doomed from the start.

All three of these factors are important, but the first is the most critical. To determine value, there must be strong communication between the analytics team implementing technology and the stakeholders who will use it. This is when we need to determine:

  • What are the business goals or outcomes?
  • What decisions will be made to reach these outcomes?
  • What information is needed to make decisions and act?

This channel of communication between “Analytics” and “The Business” has been historically very weak. One reason that “change management” and “communication” are the most poorly executed components of an Enterprise Data Strategy, is because Data & Analytics initiatives are championed by technologists. While data scientists might be some of the smartest in the room, technology is their passion, not people. So, technology is what they focus on and the people side of analytics gets neglected. This has led to the rise ofAnalytics Translators and other intermediaries.

The titles for this role are wide-ranging and have little consistency, but the need for an individual to lead the change management aspect of D&A initiatives is apparent. Call this person an “Analytics Translator”, a “Data Champion”, a consultant…whatever gets the job done effectively.

The Solution

This is the hard part. Every company has unique needs, strengths, and weaknesses. There are a number of things to consider when improving the change management aspect of your Enterprise Data Strategy or Digital Transformation effort in addition to a focus on the above:

  1. Do you have a clear leader and advocate for D&A on the executive team? Hiring a CDO is now a must.
  2. How does your analytics team work together and communicate with the rest of the business? Does your company have a dedicated analytics team or a (CoE) Center of Excellence? Should your company have a centralized or decentralized analytics team?
  3. Do you have dedicated individuals responsible for developing Data Culture, Data Literacy, and driving adoption? This is a full-time job. Hire for it.
  4. Is there a focus on business outcomes first and technology second?

We believe establishing an Enterprise Analytics Hub helps solve many of the challenges related to Data & Analytics change management. By centralizing all BI content in a single location and user-experience, you establish a foundation from which to build a data culture, communicate with end-users and receive feedback on business needs. You can also launch embedded data literacy campaigns and increase BI adoption by providing a single point of entry, and insulate end-users from the disruption that comes with the introduction of new tools and sunsetting of legacy systems.

BI Consolidation is Almost Impossible

BI Consolidation is Almost Impossible

750 million people use it daily and many use it as their primary business intelligence tool. You might find this surprising if you consider Excel was invented in the 1980s, nearly 40 years ago, but, so were Business Objects (SAP), Microstrategy, Hyperion (Oracle), and Cognos (IBM). And guess what? Those tools are the foundation for most of the Fortune 1000’s data strategy.

Has your company recently implemented Tableau, Qlik, or Power BI? Well, even those tools are now between 10 and 30 years old! Not to mention, they probably co-exist in your company with one of the other BI tools I mentioned.

Let’s fast forward. Arguably, the hottest analytics company on the market right now is ThoughtSpot. With their “Search and AI-driven capabilities, they are at the cutting edge. If you have implemented ThoughtSpot, I am 99% certain it co-exists alongside AT LEAST one of the aforementioned BI tools.

 “What’s the point, Spencer?”

Well, let’s tally up the number of BI tools you have. You certainly use Excel, most likely have a legacy BI tool like SAP, Oracle, or IBM and there is a good chance you’ve introduced a 2nd generation data viz tool like Tableau or Power BI. If you are cutting edge, you might also have an augmented analytics tool like ThoughtSpot.

So you probably have 3 BI tools, if not more. Our thought exercise is supported by research from Gartner and Forrester, as well as an informal survey I conducted on LinkedIn, and I haven’t even touched on tools with analytics capabilities like Salesforce.

The point I am trying to make is that it’s very hard to keep up with innovation, resulting in the co-existence of many multi-generational analytics tools. Enterprise companies are simply too big and move too slow to keep pace while simultaneously consolidating technology to a single platform.

“Who cares? What’s the problem?”

Having multiple BI tools makes it hard to use analytics and make decisions. All of your end-users are concerned with analytics tools, instead of DECISION MAKING. This creates silos of BI assets, making it difficult to find information, hard to drive BI adoption, impossible to establish data governance, consistency, or ease of use. This is a huge obstacle to establishing a strong data culture or effectively executing a change management strategy. To put it plainly, it makes things difficult. People don’t like difficult. People like easy. People like fast.

“What is the solution?”

I’m glad you asked! ? The solution is Digital Hive. Digital Hive is “Your Intelligent Enterprise Portal” that surfaces and recommends analytics in a personalized experience.

How are Triathlons and Analytics Similar?

How are Triathlons and Analytics Similar?

Recently, I finished my second Ironman 70.3 triathlon (for the record, that is NOT me in the photo). The triathlon is made up of three segments: swim, bike, and run. While the finishing time is reflected as a single value, there are 5 distinct times that comprise the total. Not surprisingly, the times for the swim, bike, and run portions are included, but also in the mix, are the two transition times. A transition is the time spent between the different events. Once an athlete crosses the finish line, these five times are summarized for the final result.

During this last race, I had some extra time (way more time than I expected) to ponder how the results are conveyed, and how there is so much more to the triathlon story than just the final time and the sum of the parts. There is the impact of the weather, how much training time was spent in each discipline, how effective that training was, conditions of the course, etc. Yet, when the dust settles and the results are in, it’s only these handful of metrics that are displayed.

The same scenario exists in analytics and business intelligence deployments. So much emphasis is placed on displaying the final results in a dashboard or report, that a lot of supporting context is lost. Context that could help to justify the results being seen, highlight and validate that previous business decisions have paid off, or worse, had a negative impact. This is why the current trend, one that isn’t being adopted quick enough in my opinion, is to start telling data stories. 

A data story is where in addition to analytic assets, contextual information is included to help guide and inform the user. This way, users with varying levels of data literacy can all arrive at the same interpretation of the data. Now, special care must be taken to not lead the witness by framing the story with bias from the author (Just the facts, ma’am. Just the facts), but by and large, data stories are much more effective at delivering the desired message to the masses.

Being a data geek, a triathlon, and the whole training process, is FULL of metrics and various data points, so it’s a little disappointing that the results are displayed the way that they are. Hmm, maybe a data story should be built around all of the data gathered while training for an event like a triathlon …

Predicting the 2019 Masters

Predicting the 2019 Masters

April showers brings May flowers … but more importantly, April brings the Masters. My name is Scott, and I’m an analytics nerd, with a golfing habit.

Although I enjoy the PGA tour, the Masters has always been special to me. Trevor and I (having both been fortunate enough to walk the fairways at Augusta on different occasions) were recently talking about the upcoming tournament. More specifically, who stood the best chance of winning and who didn’t. Still basking in the glow following the Gartner Data and Analytics conference, I thought surely there must be a way to use analytics to provide statistical evidence that our golf knowledge was above average.

Before I knew it, Trevor had built some Tableau workbooks that pulled in statistics from various sources, that then fed into a prediction model. Now because the prediction wasn’t accurate (didn’t align to my predicted outcome), I decided to enhance the prediction model … for accuracy purposes, of course. NOT so that the outcomes better aligned to my predictions. Having been impressed by a recent demo that I saw, my tool of choice was PowerBI.

Ultimately, we ended up with a LOT of different visualizations (that mostly proved me wrong, or should I say, didn’t prove me right?!). Word quickly spread through the office, and to our friends, of the *cough* work *cough* that we were doing, and people naturally wanted to see the outcomes. Now, I don’t know if you’ve ever tried sharing many different visualizations, coming from different systems, to people who don’t really understand analytics, but let me tell you, it ain’t easy.

Fortunately, we both work on Theia, which is designed for telling data stories. Although our Masters debate argument research isn’t business related, we decided to create a data story to share with those interested parties. At the end of the day, a data story can be about anything, even a golf tournament. Plus, using Theia to tell our Masters story is a good way for us to justify our time and effort as ‘work related’.

I’m not going to share the winner of the 2019 Masters with you just yet, but keep your eyes open as we will be sharing the results through social media next week. Monday and Tuesday will showcase the analytics that went into the prediction, with the winner being revealed on Wednesday. Watch, or follow, @HeyTheia next week for all of our Masters fun, I mean work.

Semi-legal disclaimer: I did say that our golf knowledge was only above average, so don’t go making any large bets using our visualizations or predictions. 

How Uber Will Change the BI Space

How Uber Will Change the BI Space

“Edwin, your Uber has arrived.”

This common notification appeared on my phone as I quickly headed outside of my hotel in Sydney, Australia. Hopping into the car with Lynn, the CEO of Motio, we were visiting customers and prospects to present our new software solution product, Theia, and we were in a deep discussion when suddenly, we stopped. We were both surprised that we reached our destination within 5 minutes. We apologized to our Uber driver for driving such a short distance and he replied, “No worries mates, happy to bring you safe.” This pleasant Uber experience contrasted a terrible experience I had with a taxi in Amsterdam. My taxi driver refused to drive me a short distance, which forced me to walk through the pouring rain.

During our meeting,  the VP of Business Intelligence explained that she has a great team of BI professionals, architects, and administrators. Her team enjoys all the fancy dashboards and reports with the use of two different tools: IBM Cognos BA and Microsoft PowerBI. But her business users have voiced their displeasure with these software products. She understands that people will always have complaints, such as, “BI is too expensive” and, “The delivery does not always meet the expectations.” Even arguing about how, “DOMO has been introduced at some places, while other groups use a bit of Qlik,” she continued to explain “Well, they all want to try every new shiny tool that is being introduced.” She stressed, “We need to do a better job.” We would all like to be better organized, but where to start?

After a full day of meetings, we decided to grab a beer at The Lord Nelson Brewery Hotel and talk about the conversations we had and the issues we heard. We discussed how nice it was for our Uber driver to take us on a trip that was less than a Kilometre. I began to tell Lynn about my taxi experience in Amsterdam. We both laughed, but we began to ask ourselves, why did the Uber driver take us whereas the taxi driver refused to take me? The answer was simple. For the taxi driver, I was a one-off deal. Even if I were to file a complaint with the taxi company, would he ever hear about it? But for Uber drivers, it’s different. Every ride counts and high ratings are crucial for their success.

This feedback system that we see in Uber is sorely missing in BI. When BI professionals develop reports and dashboards, they may receive feedback when it’s presented to a few key end users. However, when the dashboard is live and used by hundreds of end users, BI professionals might not even be aware of any issues all these users have. System thinking, feedback loops, and applying the concepts of cybernetics are essential parts of every organization. But we also need to remember that the post-customer experience plays an important part of the delivery process that is widely applied in the consumer world. Whether it’s Hotels.com, TripAdvisor, or Airbnb, customers tend to provide feedback. It’s this feedback that drives excellence.

The next day, we had another meeting with the VP of Business Intelligence. We discussed our idea of implementing a rating system for BI. “Why don’t we let your end users rank the reports that your team builds? With a simple click, end users can rank the reports they use and provide feedback. This will help your team to become better. They will be like Uber drivers who strive to deliver great service, unlike ordinary taxi drivers. It’s also more fun!”

The VP responded, “I don’t know. It sounds nice, but most of my BI tools don’t offer this.” We replied, “But that is the beauty of creating one entry point for all of your BI tools. It’s presented in one experience and one navigation structure. It doesn’t matter what the overall tool offers, this one entry point contains it all. This one entry point is called Theia.”

Later that night, we ended up at The Lord Nelson Brewery Hotel again, this time celebrating a successful sale. We were determined to introduce Theia as one entry point for all our customer’s BI tools, including the newest trend of BI customer experience.

When we finished our beer, we decided to head back to the hotel to get some more rest in preparation for tomorrow’s customer meetings. As we walked outside, we quickly realized that it was raining heavily outside. “Should we call for an Uber? Well, no, let’s walk, the Airbnb is only two blocks away”.