The arrival of enterprise-ready generative AI tools in late 2022 put the need to leverage this data in sharp focus. Given recent months’ enormous hype and heightened expectations around generative AI, having a robust data strategy has become the key imperative for organizations keen to leverage its potential.
Fortunately, data analytics can help organizations identify and extract actionable insights from this underutilized data to support smarter decision-making, streamlined back-office processes, and enhanced business performance. To accomplish this feat, though, business and analytics leaders must ensure data quality while securing the right leadership, employee buy-in, and a data-driven culture.
The benefits of operationalizing data
By 2025, the amount of data in the world will grow to more than 180 zettabytes, according to Statista. This includes the massive streams of data generated by everyday business applications: customer interaction logs, supplier contacts, conversion tracking results, employee and workforce management information, customer feedback data, research results, invoice processing receipts, vendor management. From payroll processing solutions to employee onboarding tools, these technologies produce data whose potential is often underleveraged. That’s changing, however, as organizations turn to data analytics to examine this data, identify patterns, and create models that surface relevant information and recommendations that can lead to more informed decisions.
“Data analytics technology has made huge strides in the last couple of years,” says Sharang Sharma, vice president of business process services at Everest Group. “It’s really phenomenal to see the amount of data that some of these tools can analyze and generate insights from.” In fact, the analytics and business intelligence software market is expected to double in size by 2025, reaching a value of $13 billion, according to Gartner research.
Organizations are already discovering new and innovative ways of operationalizing business data through data analytics. These use cases span industries and demonstrate the power of data analytics to identify inefficient internal processes, particularly back-office workflows, and enhance them for improved business performance.
A grocery store chain, for example, might examine its supply chain data to pinpoint the causes of bottlenecks and delays. Not only do these insights allow the retailer to address delays and act ahead of the curve, but they enable warehouse and procurement managers to optimize inventory in ways that can prevent product waste, customer frustration, and unnecessary costs.
An insurance business might analyze the data generated by human resource management systems to develop new operational insights. Consider, for example, a health insurance company that takes the time to examine data associated with its employee onboarding process. It might identify factors that cause some new hires to take longer than others to become fully productive—and as a result, the business can implement training modules that are designed to boost productivity and minimize turnover. These types of applications are a particular advantage, of course, in highly competitive sectors and in today’s tight labor market.
In a customer support environment, operational efficiencies can be achieved when data analytics tools are used to monitor interaction activity. Certain data patterns may point, for example, to a sudden surge in call volume. Recognizing these patterns can help organizations prepare their staff for upticks and more strategically allocate resources based on fluctuating demand. The result: cost savings, improved customer experience, and new operational efficiencies.
A new training model, dubbed “KnowNo,” aims to address this problem by teaching robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Andy Zeng, a research scientist at Google DeepMind who helped develop the new technique, says that while robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense.
For example, when asked to bring you a Coke, the robot needs to first understand that it needs to go into the kitchen, look for the refrigerator, and open the fridge door. Conventionally, these smaller substeps had to be manually programmed, because otherwise the robot would not know that people usually keep their drinks in the kitchen.
That’s something large language models (LLMs) could help to fix, because they have a lot of common-sense knowledge baked in, says Zeng.
Now when the robot is asked to bring a Coke, an LLM, which has a generalized understanding of the world, can generate a step-by-step guide for the robot to follow.
The problem with LLMs, though, is that there’s no way to guarantee that their instructions are possible for the robot to execute. Maybe the person doesn’t have a refrigerator in the kitchen, or the fridge door handle is broken. In these situations, robots need to ask humans for help.
KnowNo makes that possible by combining large language models with statistical tools that quantify confidence levels.
When given an ambiguous instruction like “Put the bowl in the microwave,” KnowNo first generates multiple possible next actions using the language model. Then it creates a confidence score predicting the likelihood that each potential choice is the best one.
The news: A new robot training model, dubbed “KnowNo,” aims to teach robots to ask for our help when orders are unclear. At the same time, it ensures they seek clarification only when necessary, minimizing needless back-and-forth. The result is a smart assistant that tries to make sure it understands what you want without bothering you too much.
Why it matters: While robots can be powerful in many specific scenarios, they are often bad at generalized tasks that require common sense. That’s something large language models could help to fix, because they have a lot of common-sense knowledge baked in. Read the full story.
—June Kim
Medical microrobots that travel inside the body are (still) on their way
The human body is a labyrinth of vessels and tubing, full of barriers that are difficult to break through. That poses a serious hurdle for doctors. Illness is often caused by problems that are hard to visualize and difficult to access. But imagine if we could deploy armies of tiny robots into the body to do the job for us. They could break up hard-to-reach clots, deliver drugs to even the most inaccessible tumors, and even help guide embryos toward implantation.
We’ve been hearing about the use of tiny robots in medicine for years, maybe even decades. And they’re still not here. But experts are adamant that medical microbots are finally coming, and that they could be a game changer for a number of serious diseases. Read the full story.
We haven’t always been right (RIP, Baxter), but we’ve often been early to spot important areas of progress (we put natural-language processing on our very first list in 2001; today this technology underpins large language models and generative AI tools like ChatGPT).
Every year, our reporters and editors nominate technologies that they think deserve a spot, and we spend weeks debating which ones should make the cut. Here are some of the technologies we didn’t pick this time—and why we’ve left them off, for now.
New drugs for Alzheimer’s disease
Alzmeiher’s patients have long lacked treatment options. Several new drugs have now been proved to slow cognitive decline, albeit modestly, by clearing out harmful plaques in the brain. In July, the FDA approved Leqembi by Eisai and Biogen, and Eli Lilly’s donanemab could soon be next. But the drugs come with serious side effects, including brain swelling and bleeding, which can be fatal in some cases. Plus, they’re hard to administer—patients receive doses via an IV and must receive regular MRIs to check for brain swelling. These drawbacks gave us pause.
Sustainable aviation fuel
Alternative jet fuels made from cooking oil, leftover animal fats, or agricultural waste could reduce emissions from flying. They have been in development for years, and scientists are making steady progress, with several recent demonstration flights. But production and use will need to ramp up significantly for these fuels to make a meaningful climate impact. While they do look promising, there wasn’t a key moment or “breakthrough” that merited a spot for sustainable aviation fuels on this year’s list.
Solar geoengineering
One way to counteract global warming could be to release particles into the stratosphere that reflect the sun’s energy and cool the planet. That idea is highly controversial within the scientific community, but a few researchers and companies have begun exploring whether it’s possible by launching a series of small-scale high-flying tests. One such launch prompted Mexico to ban solar geoengineering experiments earlier this year. It’s not really clear where geoengineering will go from here or whether these early efforts will stall out. Amid that uncertainty, we decided to hold off for now.
