By Brynne Henn - July 8, 2021
More than ever, business resilience is a function of advanced analytics and automation. And yet the vast majority of companies struggle to adopt new technologies, like artificial intelligence, which can take advantage of their analyses.
In fact, a 2020 Harris Poll showed that 86 percent of business leaders struggle with incorporating automation in their businesses—even though 92 percent of them said it was essential to a thriving and flourishing business. Stakeholders noted how automation was particularly important in a post-pandemic landscape.
The number one challenge cited in the survey? The perceived complexity of big data.
Trying to understand how the top companies unlock the full potential of their data—allowing them to generate simulations, anticipate market outcomes, analyze the supply chain, and more—can make many smart people want to sheepishly reach for a glossary.
The good news is, Sisu is here to help you cut through the jargon of data analytics. Let’s dive in.
It’s unlocking our smartphones; it’s auto-filling our online searches; it’s transforming healthcare; it’s even defeating grandmasters in chess. AI is becoming more and more ubiquitous in our daily lives. But what, exactly, is it?
In the world of data science, there is no universally accepted definition of AI. The pioneering mathematician Alan Turing famously conjectured that artificial intelligence is realized when computers can autonomously think like human beings.
One CEO in the space more recently defined AI as the “science of making machines smart.” Anything that involves computers and the automation of human tasks involves AI. Various types of AI can now perform human feats, including vision, speech, and even logical decision-making.
As it relates to most businesses, AI gets used for things like workforce optimization and executive planning. But it’s just one example of the kinds of advanced analytics tools that are driving revenue at the world’s top companies.
Advanced analytics refers to the process of turning data into actionable insights with the help of high-level statistical tools and techniques. To use a metaphor, AI is one shiny galaxy within a broader universe of advanced analytics.
The two biggest galaxies are predictive analytics and prescriptive analytics.
Predictive analytics refers to data-driven processes that forecast future events in the life of a business. These are tools that help to answer questions like, What if these trends continue?, or, What will happen if we do X?
There are many advanced analytics solutions out there, but most of them rely on sophisticated statistical modeling and simulation, tapping into both internal and external data warehouses. These tools show stakeholders what’s around the corner with respect to anything from customer satisfaction to workforce productivity.
Prescriptive analytics use data to generate recommendations for what to do about these future scenarios. These are analytics platforms that make recommendations—often built on logic models like heuristics—for what stakeholders should do to anticipate, mitigate, or prepare for various trends. If computer systems are plotting a business path forward, it’s driven by prescriptive analysis.
Advanced analytics have become especially popular on the demand side of the business. They’ll often be utilized to better understand what consumers want and how to market to them. Advanced analytics can also generate recommendations in areas like workforce optimization and supply chain efficiency.
Elements of AI are increasingly woven into the fabric of these analytics tools. Data mining, semantic analysis, and promotional forecasting are all examples of longstanding analytical tools that can now be enhanced by AI.
It’s not always easy to differentiate AI from traditional business intelligence because there is a large degree of overlap. But there are distinct properties that are dead giveaways of AI.
One is natural language processing (NLP). When a computer system demonstrates the ability to understand or reproduce human language—and not just numeric data and code—it’s likely using AI. One of the most complex areas of AI research, NLP uses advanced probability to help computers decipher words and speech patterns. Some NLPs enable a computer to not only comprehend language but also generate language in response, like the AI technology that powers some chatbots and autocorrect.
Another distinct feature of AI is machine learning, or the ability to self-learn. True AI systems can not only accomplish a task (say, generate a market forecast), they adjust to real-time changes (like macroeconomic trends) in data analyses, and update their own programming in perpetuity—all on their own.
Other forms of AI (such as the navigation technology in self-driving cars) take advantage of something called deep learning, a subtype of machine learning that uses a computer’s neural networks to sort through complicated data related to spatial recognition and other visual tasks.
You might be wondering: aren’t my business intelligence tools like Looker and Tableau using algorithms to make us smarter? Yes, but while those tools provide a snapshot of a company’s data—either historically or at the present moment—they don’t have the future-minded capabilities of advanced analytics.
Prescriptive and predictive analytics are keys to closing the gap between BI tools and decision-making. And when businesses succeed in doing that, they create a synchronized system of decision intelligence. For stakeholders, advanced analytics and AI provide data-driven recommendations on complex issues like resource allocation, allowing them to do more with limited resources.
Simply put, computers are capable of spotting patterns in data that humans could take years to find. And you won’t know what they’ll find without getting the most out of AI.
That doesn’t have to mean all your coworkers are suddenly robots. There are many shades of gray to how businesses adapt to a new landscape of advanced analytics and AI.
Some situations call for augmentation, which is when AI draws insights or makes recommendations for stakeholders but humans remain in the driver’s seat.
Some situations call for automation, which is when AI is empowered to make decisions without human involvement, such as making an adjustment to a work order or a use case.
By using these tools and eliminating the rote work of crunching data sets, it will free up employees to be more creative, collaborative, and human-centered in their day-to-day work.
As the industry’s first decision intelligence engine using augmented analytics, Sisu equips companies with the real-time insight about why their metrics are changing to fuel smart growth. We use machine-learning tools to draw deeper insights on everything from customer retention to revenue analytics to proactive strategic planning.