We probably would’ve heard the idiom “more than just a pretty face” many times. It could’ve been directed at oneself, a friend, or any random stranger. However, did you know that it holds true for a business intelligence (BI) tool as well? That’s the inspiration behind the title. Let us focus on the one word that has caught your attention.
What is insights?
“Insights” is probably one of the most overused terms in the entire data analyst ecosystem. Go to any business leader, data analyst, or startup founder, and ask them the reason for investing in a BI platform. The answer will always be “It helps me with actionable insights”.
But is it enough for a BI tool? We try to address that answer through how data analytics has evolved, what is the future, and how humans can attain that future.
The Evolution – How Modern Data Analytics has Progressed
Evolution is a process defined by nature and it’s applicable to all things – living and otherwise. Data analytics and information systems are also not a pariah to the concept. The evolution of data analytics can be classified into four different levels.
- Descriptive Analytics: Often called the most basic form of data analytics, descriptive analytics deals with breaking down big numbers into consumable smaller formats. It helps in understanding basic trends but doesn’t help with deeper analysis and stops at answering the “what”. Small everyday operations rely on descriptive analytics for their day-to-day planning
- Diagnostic Analytics: The next step in the analytics journey, diagnostic analytics is aimed at understanding the “why” behind an occurrence. Studying the cause will help organizations mitigate or plan better for their future. Diagnostic analytics uses data drilling, data mining, and correlation analysis to uncover underlying causes
- Predictive Analytics: Predictive analytics is the science of predicting future scenarios in business based on historical data. It relies on advanced statistical analysis, data mining, and machine learning for the system to come out with a comprehensive prediction. It helps business leaders in data-driven decision-making and proactively mitigating risk
- Prescriptive Analytics: Being the final stage of the data analytics maturity curve, prescriptive analytics feeds on the results of descriptive, diagnostic, and predictive analytics results to suggest a probable cause of action and help businesses make informed decisions.
However, looking at data analytics evolution through the lens of prediction is a unidirectional approach. We’ve to consider another dimension also to understand how data analytics have evolved, with respect to three factors as represented.
- Time taken by the tool to analyze and deliver information
- Convenience in which information is accessed
- Channels through which information is accessed
But for a BI tool to go from good to great, it is not enough to deliver insights at the point of your choosing.
The Future – From Deriving Insights to Augmenting Decisions
I would like to demonstrate the future of data analytics with a small story. A story of how New Zealand won the America’s cup of 2017. For those who do not understand America’s Cup, they are the world’s oldest sailing competition. It’s also the top prize sailors vie to get at least once in their lifetime. Many countries take part in the program to prove their maritime superiority, especially modern thalassocracies who definitely have a stake in the program.
The modern version of America’s cup is an equal competition of both technological advancements and sailing capabilities. Simulators mimicking the physics of wind flow, wave formation, and the vessel’s behavior are used to come out with the most efficient design for the ship. The better the effectiveness of the ship’s design, the higher the chances of success. So it must be a no-brainer that millions of dollars are pumped into bringing out the best possible design for the ships.
The Emirates New Zealand team of 2017 figured out the importance of having the best design at the onset of their planning phase. They understood that the biggest letdown factor in ship design was human sailboat crew members. Humans, how much ever they’re trained were not adapting fast enough to the different preset conditions loaded onto the simulator. With the criticism of sounding like a bad pun I would say, they were after all “human”.
Now I request the readers to let me digress a bit from the story. Remember Saruman from the LOTR lore? He was known for creating an army of Orcs – The ones he “manufactured” from falling down a lot many trees. Before the onset of the battle at Helm’s Deep, the central characters would rue how the Orcs were able to run faster, without eating, sleeping, or taking a rest, and would cover large amounts of ground in a single night.
An eerie coincidence, I would say (because LOTR’s shire and some other great locations were reconstructed in New Zealand for its eye-catching natural beauty) the Emirates New Zealand team came out with their own version of the “Orc”.
An AI bot that was taught to sail by the engineers.
- The bot needed neither sleep nor rest, not even food, which meant it was free of human fatigue
- The bot was equipped to run 1000s of simulations simultaneously, 24/7, that the human crews could not even fathom
Of course, within 8 weeks the AI started to beat the soldiers in the simulator and a few weeks down the line, the student became the master. The AI bot started teaching the soldiers some new tips on effective sailing methodologies. It would come out with new methods that would question the “gut feeling” of these sailors but would end up working.
The AI bot was continuously fed with data from the various data stores, so analysis and decisions happened simultaneously.
This accelerated the learning process of the sailors on different maneuvers, different design systems, and more. Needless to say, the Emirates New Zealand team won the cup that year.
The Bridge – A Great “Conversational” Tool for Business Insights
As business leaders, this is an exciting time to be alive. So many advances are happening around the world across all frontiers with the aid of technology and few are witnesses to that. I’ve explained the evolution of data analytics and what the future holds in terms of adaptive AI-driven decision-making.
But the fact remains that, nobody can wake up one fine morning and say “I’ll ask my AI system for my next budgeting or hiring strategy”. It takes an effective change management policy because frankly, people have been used to working in a specific way and humans are creatures who crave comfort.
Another factor is that the “unknown” has always terrified us, humans. So introducing an AI-augmented decision-making process as part of the organization’s business intelligence will be a challenge. Expect pushback from most of the workforce for the simple fear of them losing their jobs despite the fact that AI also needs humans to function. America’s cup-winning strategy for New Zealand was formulated with AI but actual humans won the race.
It will always be AI+humans and never AI vs humans. But how can we convince the human workforce? Especially in the data analytics and BI space?
By introducing a conversational insights-powered platform, one which uses conversational AI to derive insights. There is a multitude of reasons for investing in conversational insights which can range from democratizing data access to the personalization of insights.
However, the simplest and most effective reason for using a conversational insights platform – It will be like asking your colleague or a friend to help you get answers to the data question.
Humanize the AI systems in the data analytics and decision intelligence space, adoption will skyrocket across business functions.
Interested to know more about how conversational insights make the data analytics process more human? Check out our webinar where we speak about how conversational insights ease information access.