Merilee Kern, MBA is a brand strategist and analyst who reports on industry change makers, movers, shakers and innovators: field experts and thought leaders, brands, products, services, destinations and events. Merilee is also founder, executive editor and producer of “The Luxe List,” as well as host of both the “Savvy Ventures” business TV show that airs nationally on FOX Business TV and Bloomberg TV, and the “Savvy Living” lifestyle TV show airing in top U.S. markets. Connect with her at TheLuxeList.com, SavvyLiving.tv, at LuxeListReports on Facebook, Twitter and Instagram, and on LinkedIn at MerileeKern.

In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game — particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before.

AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on — particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”

However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done — especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines.

“To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” says Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI — an AI solutions firm automating data supply chains to propel and actualize data monetization.

Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now — the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability.

Manifesting new market opportunities

Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. This can not only significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus.

Machine learning advancements also help companies better monetize their data and establish new revenue streams.

Reconcile emotions with actualities

Data generates value, which leads to the generation of money. Previously, it was difficult to sift through mass amounts of data and pinpoint relationships. Today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages.

Scale statistical models for actionable models

Neural networks connect the “human decision-making process” to factuals — a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But, when these multiple data sets are connected, it becomes quite evident that no two humans — or customers — are exactly alike.

Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet, the treatment protocol will change. This is enabling highly personalized medicine. But, the same processes, principles and benefits hold true in non-health categories as well — encompassing all industries, across the board.

Future-proof, anti-fragile data supply chains

From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights.

It starts with data acquisition — garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched, and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling.

Multimedia monetization

Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless.  In fact, retail and finance industries have been early adopters of this technology — and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization.

Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it.  As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind.

Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson.

Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust. C&IT