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Calibrate for crisis: 3 trends every data-driven business should prioritize in 2023

By Dan Sommer, Qlik's Global Market Intelligence Lead

With the year coming to a close, it's important to look back on where we've been before we can consider where we're headed. Most notably, inflation only worsened, and as we entered late fall/early winter, a crack in the tech hiring spree began to emerge. While it's true that many firms are still desperately seeking talent, the golden days may be over for employees for the time being as businesses struggle to adapt to a world of economic challenges.

As we look forward to 2023, we must consider the role technology will play in shaping what's ahead. How businesses take advantage of that technology, and how quickly it's deployed, could shape their ability to withstand additional hurdles and future black swan events.

Data and decision velocity is paramount to the future of supply chains

The good old days of buying products precisely when they're needed may be over, particularly for anyone in the market for a new automobile. Consumers can wait anywhere from a few months to two years for their vehicle to arrive at the dealership. While the problems associated with things like toilet paper shortages have been mostly resolved, consumers continued to run into new product disruptions, including those involving baby formula and peanut butter.

Prices have increased amid the many supply chain constraints. If organizations wish to keep up, they need to react much faster. This is turning out to be the compelling event to implement a pipeline with real-time data. A report by IDC shows that as businesses adjust their data expenditures on data capture and movement technology, more than half (60%) will go toward streaming data pipelines. This will enable a new generation of real-time simulation, optimization, and recommendation capabilities, which will be paramount to the future of supply chains. And once you have data velocity in place, it needs to be paired with decision velocity as well, through techniques like application automation, process mining, and robotic process automation (RPA). Ideally, they should also focus on pre-acting to forecast issues before they begin, driving a need for scenario-modeling.

NLP could unearth a new era in powerful data

In the summer of 2022, a Google engineer claimed that one of the company's chatbots (named LaMBDA) had achieved consciousness, or a human level of self-awareness. Google stated that his claims were unfounded and the engineer was fired for violating company security policies - but this incident shows how far machines have come in a short time. ChatGPT is the perfect example, allowing anyone to generate text with a simple prompt. The results, driven by memory and an impressive ability to understand syntax, has made at least one high school English teacher predict the end of English class. Its capabilities are so advanced, bloggers are using ChatGPT to write silly song parodies that could inspire an AI version of "Weird Al" Yankovic. The potential is immense, and with a half-dozen bigger language models now in development, the societal consequences could be significant.

Data and analytics will also be impacted by the rise of natural language capabilities in the form of natural language generation (NLG) and query (NLQ). Both technologies can be combined to create a conversational experience with data, allowing anyone to receive answers to their questions - including questions they didn't know they had. But there are risks, both in terms of accuracy and bias, that could diminish the value of the answers that users seek. "Information pollution" may dilute the value from applications synthesizing large data sets. This requires the need for proper governance. Without it, the negative side effects of AI could overtake the value that businesses sought to obtain when they adopted the technology.

AI deeper in data pipelines empowers talent to focus on value-adding tasks

As the world shifts from the Great Resignation to the Not-So-Great Period of Layoffs, businesses are inevitably pulling back on both investments and hiring. The irony here is that a talent pullback won't change the need for great workers, nor will it do much to resolve any shortages that plague key industries. Data Engineer is one of the most sought-after job roles there is.

Organizations can make a difference, and relieve some of their talent challenges, by utilizing artificial intelligence (AI) and machine learning (ML) techniques also for data management. It could, for example, utilize automations for anomaly detection, use just-in-time deployment, and auto-classify content.

Removing menial tasks should open up time for more value-adding activities. According to a report by IDC, only 18% of the time is spent analyzing data; the remaining 82% is spent collectively among other, more time-intensive tasks such as searching for, preparing, and governing the appropriate data. With AI and ML in place to help lessen the load, the hard-to-come-by data talent would be free to focus on value-adding tasks that propel the business forward.

The future of data is quickly evolving

The last 12 months have not been kind to the business world, which requires a different mindset. The C-suite needs to calibrate for crisis in a world with eroding margins and distributed data. It's time to invest in streaming data pipelines to better optimize their data strategy and improving the performance of their supply chains. Enterprises can also take advantage of advances in natural language to democratize data. Last but not least, organizations may find new benefits from AI and ML, which have the power to reduce the immense workload piling up for data talent. These are some of the most critical trends shaping 2023 and the years to follow, and they may ultimately change the way businesses use and interact with their data.

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ABOUT THE AUTHOR

Dan Sommer is Senior Director, Global Market Intelligence Lead, at Qlik. He is responsible for the supply, demand, macro, and micro picture. He is a former Gartner analyst specializing in markets, trends, competitive landscape evaluations, and go-to market strategies.