Forbes Business Council, Newsweek Expert Forum and Rolling Stone Culture Council member Merilee Kern, MBA, is founder, executive editor and producer of “The Luxe List,” as well as host of both the “Savvy Living” lifestyle and travel and “Savvy Ventures” business TV shows. 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.

To compete with maximum, sustained impact and mitigated opportunity loss, rapidly monetizing data is now the game’s name — 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 as with the COVID-19 pandemic the globe is currently immersed in.

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.”

Novel coronavirus matters aside, 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.”

“To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” says Rita Sallam, distinguished VP analyst for Gartner.

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.

 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. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets.

This generates and maximizes value in several ways. Relative to the bottom and top lines, not only can it 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. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages — critical insights for pharmaceutical developers and physicians.

Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof.

 Reconcile emotions with actualities

Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There were very rudimentary tools like regression and correlation, but today’s analytics call for understanding what extracted data actually means. Leaders use data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking while prompting them to ask new questions.

“Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.”

Scale statistical models for actionable models

We often separate our data as facts, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those facts — 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.

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

“Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.”

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. 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.

“All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable.” As one of the world’s leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. “New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products — single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.”

For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization.

“Overall, AI is ushering in a new and more sophisticated era of data literacy,” he concludes. 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