Let’s start with an example. Traditionally, banks targeted older customers for wealth management services, assuming that this age group would be the most interested. Using advanced analytics, banks found that younger clients (aged 20 to 35) are actually more likely to transition into wealth management — a clear example of how analytics is helping businesses in recent years to find patterns and analyze them, bringing value to the table, which would have resulted in bias and incorrect conclusions when done manually.
Needless to say, data and analytics capabilities have made a leap forward this decade and are now considered a necessary tool for organizations when it comes to making business decisions. Morgan Stanley calls the 2010s “the data decade,” data now being a critical corporate asset that comes from the web, billions of phones, sensors, payment systems, cameras, and a huge array of other sources
As the volume of data has grown exponentially, more sophisticated algorithms have been developed, and computational power and storage have steadily improved. The improvements in artificial intelligence and machine learning have given enterprises a way to search and sort through data, pulling the most useful insights that can help businesses change with their customers. The convergence of these trends is fueling rapid technology advances and business disruptions.
Gartner distinguished analyst in Data Analytics & Strategy Douglas Laney said earlier this year that by 2022, 90% of companies will have detailed business plans that “explicitly mention information as a critical enterprise asset and analytics as an essential competency.”
It’s easier for enterprises to make long-term decisions when leaders are using accurate data that has been analyzed and sorted. Some companies are now looking to data for short-term insights as well, opting for platforms that can give real-time information based on an ever-increasing set of data.
In the last 10 years, leading companies such as Google, Apple, Amazon, Facebook, and many others have used their capabilities not only to improve their core operations but to launch entirely new business models. All of these companies have managed to leverage the vast amounts of information they get from their multitude of users – whether it be their search habits, the posts they share, the products they buy, or the music they listen to (using artificial intelligence or, more accurately, deep learning) – into major revenue streams.
Read more: E-tailers Turn to AI, Analytics, Video to Woo Customers
As AI becomes increasingly advanced and more widely adopted, we’ll start to see a lot more companies – big and small – turning to AI in order to come up with better data strategies and win customer adoption, and to better compete against their competition.
Data and analytics have underpinned several disruptive models in recent years. For example, hyperscale digital platforms can match buyers and sellers in real time, transforming inefficient markets. Granular data can be used to personalize products and services—and, most intriguingly, healthcare. New analytical techniques can fuel discovery and innovation. Above all, data and analytics can enable faster and more evidence based decision making. ƒ
Read more: Why Health Data is the Next in Big Data Analytics
The decade was marked by advances in machine learning that can be used to solve a tremendous variety of problems—and deep learning is pushing the boundaries even further. Systems enabled by machine learning were used to provide customer service, manage logistics, analyze medical records, or even write news stories. Recent breakthroughs in natural language processing (NLP) could expand that impact even further. The value potential is everywhere, even in industries that have been slow to digitize. These technologies could generate productivity gains and an improved quality of life—along with job losses and other disruptions.
In fact, the biggest barriers companies face in extracting value from data and analytics as they fail to incorporate data-driven insights into day-to-day business processes. Another challenge is attracting and retaining the right talent—not only data scientists but business translators who combine data savvy with industry and functional expertise. A McKinsey report finds out that most companies are capturing only a fraction of the potential value from data and analytics.
Nonetheless, the potential of data-driven analytics is incredible. Data and analytics are already shaking up multiple industries, and the effects will only become more pronounced as adoption reaches critical mass. An even bigger wave of change is looming on the horizon as deep learning reaches maturity, giving machines unprecedented capabilities to think, problem-solve, and understand language. Organizations that are able to harness these capabilities effectively will be able to create significant value and differentiate themselves, while others will find themselves increasingly at a disadvantage.
