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. 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: story boarding, data visualizations and data narrative.
The art of communicating using data and analytics, 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.
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 Enterprise Portal 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.
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.
*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.
Gartner currently covers over 250 analytics vendors in their research. By the time you are done reading this article, I wouldn’t be surprised if there were two new vendors or tools. With the recent explosion of business intelligence and analytics tools on the market, you might find yourself drowning in a (data lake) of information and possibilities. What visualization tools should we use? Am I ready to pursue predictive analytics? Who should be using these tools? Whether your company is at the beginning of its analytics journey or operating at the bleeding edge of technology and strategy, one common theme will always be important – culture and mindset. A lack of organizational buy-in can hinder even the most well designed, thoroughly vetted analytics strategy. When it comes to data culture here are 6 essential topics to consider:
1. Data Culture is Decision Culture
Data culture may be experimental – but the objective is always to make better business decisions. Collecting data for data’s sake is useless. A great place to begin leveraging analytics is where people are already making decisions. Communicate with the leaders of specific business units and determine what critical information they use to make daily decisions. To go a step further, consolidate this information in a curated analytics experience for each department, group, or role. Once these groups begin leveraging the unique analytics relevant to their most common decisions, they will become curious what other insights they can discover. To continually improve the value of analytics, it is important to implement an effective feedback loop between business end-users and report developers. Report-rating and commentary mechanisms are critical capabilities necessary for feedback and communication between users and developers to improve the quality, scope, and impact of informational assets.
2. Data Culture and the C-Suite
There isn’t an executive you will meet today who would admit that data is not a priority in their decision-making process, but many don’t actually have a comprehensive understanding of how analytics can benefit the larger organization. Often times it is difficult for executives to define valuable problems for the analytics team to solve. Find ways for your analytics team to engage with and educate the C-Suite so that leadership understands the value behind the entire organization using analytics. A great way to demonstrate this value is by delivering a complete view of the business to your executives via a personalized business intelligence command center. Consolidate the most important KPI’s, reports, and visualizations from various tools and systems in a single pane of glass for quick, effective, executive decision making.
3. The Democratization of Data
The first step when trying to generate organizational excitement about using data for decision making is to simply get analytics in front of different groups within your organization. The informational assets available now, your reports and visualizations, might not be perfect yet – but the sooner you make them available, the sooner you can improve them. By presenting analytics to your end-users regardless of your analytics maturity, you expose them to the power of data-driven decision making, and before you know it, they will be asking for more. This is crucial in securing the organizational buy-in required for the additional investments in business intelligence that you need. One of the most effective ways to increase analytics adoption is to remove the barriers to access, and put analytics in front of your end-users through an easy to use, single point of entry for all the analytics assets you provide.
Check back after the holidays for 3 additional areas of focus when building a strong Data Culture within your organization.
If your efforts to create a rich data culture are going to be successful – identifying, recruiting, and partnering with enthusiastic data champions within the organization is an absolute must. C-Suite mission statements and company-wide initiatives that are as disruptive as digital transformation require catalysts at each level of the business to bridge long-term vision with front-line execution and adoption. This means educating and empowering middle management and the leaders of specific business units to lead the charge. The best culture catalysts will be business leaders and their ability to sell the value of analytics to their respective teams. Knowledge workers on the front-line live and breathe their daily work. Business leaders can articulate the impact data and analytics will have on daily decision making in the language of their unit and drive adoption – a process which is essential to securing top-down dedication to change. When looking at the current state of data literacy and analytics adoption within the organization you might feel that some groups are not yet advanced enough for increased access to business intelligence. You may be correct that data literacy levels are not at ideal levels, however, you can’t learn to read if you don’t have a book! Creating curated analytics experiences with varying amounts of business intelligence for different groups and roles is a great way to slowly increase access to data and drive data literacy over time. Data literacy will be crucial in every role within the next few years, so there is no better time to start than now.
5. Uniting Talent and Culture
The competition for data talent is fierce and growing, and as a result new roles and titles are emerging within the business. In the Higher Education space during 2018, CDO was more likely to mean Chief Diversity Officer than it was Chief Data Officer. However, things are changing, and they are changing fast across all industry verticals – take for example the evolution of the Data Scientist function. How does this effect culture? Given the growing need for data talent across all industries, it is now less important to hire from within your industry as you traditionally might for management, marketing, and sales roles. When it comes to emerging data talent, it is more important to find great talent that fits within the company culture of change and innovation – regardless of industry. Additionally, a diverse range of perspectives on how to extract value from data and analytics will add value to business outcomes and will help push the momentum of change within the organization.
6. Data Culture, Risk, and Ethics
The last topic we will discuss is the necessity to address risk and ethics in your data culture. Data management is increasingly important, including the ability to understand who, how, and what data people are using to make decisions. Misuse of data can institutionalize unfair biases like racism and sexism. Audit capabilities are increasingly important and valuable in understanding what data and reports are being used to make decisions.
Data culture is essential to driving the initial and continued success of BI and Analytics initiatives. No matter what stage of analytics maturity your organization is at, remember that it is important to identify daily decisions that can be influenced first, continue to educate the c-suite on the value of business intelligence, provide easy access to BI for everyone, recruit the leaders of business units to drive front-line adoption, hire great talent and include diverse perspectives, and maintain and unbiased and ethical approach to data use.
For more information on driving data decision making, read this article on BI-Modal Analytics
Those of us preaching the power of data – on LinkedIn, on stage at keynotes, and at yearly budgeting meetings – prescribe data as the solution to all problems. However, we all know the execution of a powerful analytics strategy is often more challenging than anticipated. These challenges are especially severe in higher education, even before operating in the midst of a global pandemic and looming economic recession. Now, the hurdles seem larger than ever. Budgets are being significantly reduced or frozen, analytics teams are being defunded and paralyzed in the face of uncertainty. Yet, the greatest action universities can take today, one thing they can control, is to double down on data. Those who choose to do will evolve and emerge stronger. Those who don’t, may not survive.
Adoption is essential
Higher education has always been slow to adopt new technology, which I think is understandable. Universities are large and complex. They have many layers of leadership and often rely on external funding. It’s this complexity that makes data such a valuable asset for university decision making, and in the past decade, major strides have been made to introduce data & analytics tools across departments. The results of these changes, however, have been slow to surface.
One of the reasons that data investments are seeing less than stellar returns, is a product of the university’s organization and structure. Many groups operate independently from one another – especially when it comes to technology purchasing decisions – and this has resulted in a variety of siloed tools and technologies.
However, efforts have been made to create university-wide analytics councils and information management teams, but working backwards to resolve the existing incompatibility of different technologies is still difficult.
Now as we start to see valuable information being generated by many different departments, having an awareness or sight of key information that could be a saving grace to universities in crisis-mode is critical. However accessing, sharing, and acting on this information quickly is still next to impossible.
Everything in one place
Pomona College Branded as ‘ConnectTo’, Pomona’s advancement department used Digital Hive and created a single, unified information portal bringing together IBM Cognos, Microsoft Power BI, Tableau, and SSRS. Last year, Pomona College won a National Silver CASE Award with ‘ConnectTo’. Now, users have one place to go to easily find reports, dashboards, documents, training materials and more.
This has resulted in greater efficiency and productivity across departments, an increase in adoption (more than quadrupled), a reduction in technology management costs, and has directly impacted decision making in regards to fundraising campaigns.
Future-Proofing
While actively investing in new technology projects during a period of uncertainty may seem risky, the greatest risk is in retreating to the status quo. In all areas of our society, both in business and our personal lives, we have experienced a steady increase in digital transformation.
The COVID-19 pandemic is not creating a new normal, it has simply accelerated the inevitable evolution of how we behave and interact. For many businesses who made digital investments early on, this period will mark an opportunity to accelerate past the competition. For others, it’s a wake-up call that the train is leaving the station, and immediate action is required. Unfortunately for the rest, a lack of action when times are good and when times are bad, will result in devastating consequences.
Click here to read more about howDigital Hive ransformed Pomona College’s fundraising efforts or book a demo to see Digital Hive in action.
As companies race to find value in their data and improve the customer experience, many have overlooked an obvious value-add: providing analytics for the customer. Client-facing analytics differentiates companies from the competition, helping increase market share, retention, and even direct revenue if packaged and productized.
Why aren’t all companies doing this already? The data is there, and the reporting, dashboards, and visualizations are plenty.
The Challenge
One of the most difficult aspects of trying to provide analytics to customers is delivering a complete, appealing product. With analytics coming from a variety of internal business intelligence tools, packaging all of this information is far from easy. Both internal and external, users desire a seamless analytics experience. If bringing all of this content together into a single user experience wasn’t challenging enough, each experience needs to be tailored for various audiences.
On top of wrangling content, curating different experiences, and creating a pretty product – facilitating understanding is also a challenge. Chief Data Officers are tasked with fostering data culture, increasing BI adoption, and improving data literacy. However, these initiatives shouldn’t be limited to the internal organization. Companies need to extend this concentration to clients and partners as well.
The Ideal Customer Analytics Portal
Given the needs and challenges discussed above, let’s describe the ideal external analytics portal for clients and partners:
In summary, companies should aim to deliver analytics both internally and externally to clients and partners to maximize the value of data and grow together with strong data-informed relationships using an analytics portal.
The ideal analytics portal integrates reports and visualizations from all of your different BI tools, allows for the curation of content for different groups including data literacy support, and provides an attractive and easy to use experience.
Why Act Now?
If you provide your clients with informative analytics to help them grow their businesses, your business will become an irreplaceable source of value. If you DON’T provide your clients and partners with analytics, someone else will.
The demand for analytics is continually increasing as companies use data to drive decision making. If you are not providing clients with informative analytics to help them grow their business, how are you ensuring that your service will not become an irreplaceable source of value?
Offer your clients a service that goes above and beyond the competition. Replace any frustrations by giving them full autonomy over their analytics environment.
How to create a Customer Portal with Digital Hive
Read more about our enterprise portal solutions, or get in touch, if you want to chat about customer analytics portals – we can show you how simple it is to get set up!
Book a demo with a member of the team. See the full Digital Hive experience as well as some of the branded customer portals we’ve created.
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 …
