Three Analytics Breakthroughs That Will Define Business In 2021
By Ramya Srinivasan
Analytics are increasingly driving business decisions—and in the wake of the pandemic, companies that embed data-driven digital transformation into their organizational cultures will be leaders, pulling ahead despite a challenging business environment.
“Data is the most valued commodity, and it continues to grow exponentially year over year,” says Sharmila Mulligan, chief strategy and marketing officer for Alteryx, a leader in analytics and data science automation. “The only way for organizations to deliver on the highly sought-after value in data is through analytics automation coupled with upskilling workforces so they can answer more sophisticated questions.”
Nearly half of enterprises are either starting new analytics projects or forging ahead on already planned ones, according to research that highlights the importance of making data-driven decisions in both good and bad macroeconomic climates.
How can organizations scale their data-driven decision making this year? These three recent breakthroughs in analytics and data science are driving true organizational transformation.
1. Analytics Automation: Taming Complex Data While Upskilling Teams To Be Analytics-Proficient
The sheer volume and complexity of data has pushed businesses to the point where they can no longer do analytics manually. If they once lacked sufficient data to make decisions, they’re now grappling with the inverse problem: the need to sift through a deluge of both structured and unstructured data coming from multiple new sources such as sensors, social platforms and mobile devices.
A second challenge comes from a lack of an outcome-driven analytics strategy, ad hoc processes and fragmented technology across the organization. Some teams are stuck in spreadsheets, while others are cobbling together a mishmash of tools to clean and analyze data. The results are lost time, lackluster answers and an inability to get to the most important questions: “what happened?” “why?” and “what next?” This leaves business stakeholders running blind.
“Ideally, an organization operates not with a tool-centric strategy, but rather with an outcome-focused strategy that sets a high bar for the speed at which critical business questions need to be answered,” Mulligan says. “On the technology side, fragmented tools and tapping into data silos are a recipe for disaster. Unifying analytics, data science and process automation—from data discovery to analytics to machine learning and the automation of processes and hand-offs—gives the entire organization the agility needed to drive great business outcomes. This is really why thousands of organizations around the globe rely on Alteryx automation daily.”
2. Intuitive And Engaging: Making Analytics Accessible To Everyone
Analytics has historically been perceived as a discipline in which only people with special skill sets can participate—and then only with the right access to the right data at the right time.
In recent years—in a way that’s similar to what happened with website building, which has become democratized to the point where anyone can put up a site in minutes—analytics has undergone a shift in accessibility. With easy-to-use building blocks and no-code tools, modern analytics automation platforms are accessible to all. Even if your tech knowledge is restricted to “how to open up Google Sheets or a spreadsheet,” Mulligan says, “you can now be an analyst and derive important insights for the business.”
More important, organizations can educate their entire workforces with critical data skills that drive business decisions. Not every worker will build analytics workflows, but every worker should be able to ask questions within their functional area. These questions might include: “What percentage of our customers churn each hour or day?” or “Do we have the right inventory in the right warehouses to optimize delivery times?” And every worker should get the answers to those questions at the press of a button.
In addition to technology democratization, accessibility to answers is a rising trend. Every executive and employee should have access to automated insights via dashboards, reports and views in enterprise applications.
In some cases, Mulligan says, they should also have access to the data and to the steps that defined each analytical process.
“That level of transparency is really important when it comes to analytics and data science, because people do not want to be black-boxed,” she says. “In addition to answering ‘what,’ ‘why’ and ‘what next,’ it’s just as important for business stakeholders to know how they arrived at a certain answer.”
3. Hyperspeed: Answering Every Type Of Question Faster
The X-factor in a data-driven digital transformation that creates exponential business value is speed.
Organizations that spend days and weeks manually repeating analytics processes not only are slower to gain insights than their best-in-class peers, but they also fall further and further behind.
Automating data-gathering and analytics processes speeds time-to-insight. This gives the entire workforce access to data, analytics and insights, all of which accelerate decision making. Soon businesses can move from descriptive “what happened?” analytics to “what’s next?” and “what should we do next?” predictive and prescriptive analytics.
Ultimately, a virtuous cycle emerges that drives quick wins and exponential business value in the form of revenue growth, profit margin, risk reduction and customer outcomes.
“When you automate analytics, make it engaging and effortless and give people the power to answer every type of question, it becomes addictive,” Mulligan says. “That’s when transformation truly occurs and organizations leap forward.”
In Good Times Or Bad, Organizations Need Answers
“Digital transformation” is a broad term encompassing everything from customer experience on the web to supply chain optimization, but at the center of its high-stakes reality is data. To cope with the new normal, organizations need to be able to answer all questions at a speed once thought unimaginable. They also need to give more people the ability to answer questions, which means making analytics accessible, intuitive and engaging.
And given the unforgiving competitive landscape, organizations have to transform now, and correctly. As Mulligan puts it, survival is table stakes. Winning requires an outcomes-focused analytics strategy. An organization’s people, its biggest asset, must be empowered to answer questions swiftly. Upskilling the workforce and digitizing for the future enables organizations to channel the power of data and answer questions big and small.
A former technologist, Ramya Srinivasan writes on data analytics, cloud computing and artificial intelligence.