Harness your data to make weak AI your strength

Presented by DataStax

For enterprise IT, 2020 was a year defined by coming through in the clutch.

Most organizations successfully stood up with new ways for employees to work remotely or interact with customers far faster than previously thought possible.

But as we transition from focusing on maintaining business continuity toward driving growth, we should not lose sight of the forest for the trees.

The leaps forward companies made in response to the COVID crisis set them up to benefit from virtuous cycles that complement and reinforce each other to turbocharge growth.

This results in growing the scope and scale of digital interactions with customers that increases an organization’s ability to pervasively deploy “weak AI” to improve the top and bottom line. As more organizations do this, the cycle of innovation for relevant open-source tools and technologies accelerates. Most certainly, companies that will win the decade will make the most of this. Some are well on their way.

Delightful real-time experiences are the foundation

Some data-driven, real-time app experiences are so good at saving us time, hassle, or money — or even expanding our horizons — that the word “delightful” may come to mind.

Spotify, Netflix, or Waze might be top of mind for some. For others, it might be Target’s popular Circle app — “your shopping and  saving sidekick” that promises you will “never miss a deal.”

These examples reflect a broader pattern. A Boston Consulting Group survey, for example, found that consumers who experienced the highest level of personalized experiences were more likely to buy something other than what they’d originally planned, spend more  money, and also report a higher net promoter score score for the retailer.

Using data that originates in applications and is streamed, stored, and immediately analyzed to  initiate action means winning some combination of more and more valuable interactions with customers — and winning more customers.

This can create a customer-facing virtuous cycle if you are able to re-deploy more and more types of data to improve existing experiences or offer new ones.

The Home Depot is a great case study for how this works.

Real-time experiences that contributed to resilience during the pandemic included rapid deployment of curbside pickup functionality. It contributed to an 86% increase in app and mobile sales for the company in 2020.

But it also helped enrich the company’s arsenal of data: total quarterly customer transactions (both in-store and online) increased by nearly 73 million year-over-year to 447.2 million.

That’s a lot of additional data to feed the AI that a company might deploy. For example, it could fuel voice-activated product searches that are smart enough to  identify groups of items that would be typically needed for specific projects.

Ubiquitous weak AI is the reward

As you serve customers with more and more types of data-generating interactions, more and more types of data flow through your organization’s operational systems. You can create a data-driven virtuous cycle within your organization, too.

More data for any function or combination thereof (sales, service or support, inventory management, logistics or supply chain management, sensor monitoring, or even staff scheduling and credit risk assessment or fraud detection, for example) positions you to make widespread use of “weak AI.”

Simply put, “weak AI” is delegating to  computer systems tasks traditionally handled by people. It won’t necessarily make headlines but it can materially improve business process efficiency and outcomes, over and over again, provided the scope (type) and scale (volume) of data available is sufficient to recognize patterns.

“Process intimacy” among internal teams such as finance or human resources might be considered a parallel concept to the “customer intimacy” that externally focused business units bring to bear in finding valuable ways to use data.

Functional teams can use weak AI to (for example) to reduce errors, spoilage, re-work, or stockouts.

At The Home Depot, for example, models are watching weather activity and inventory in order to give replenishment teams a jump on events like hurricanes so they can route items like generators to stores where demand is likely to surge.

The open source cycle of innovation is your ally

Open source software (OSS) already plays a vital role in enterprise infrastructure.

As the basis of competition shifts toward AI and machine learning, there is good reason to make leaning into the OSS ecosystem part of your data strategy.

OSS dominates the toolbox of technologies with which to deliver delightful, smart,  real-time experiences. This includes, for example, Apache Cassandra (open sourced by Facebook in 2008), Apache Kafka (open sourced by LinkedIn in 2011), and Apache Pulsar (open sourced by Yahoo in 2016).

Everyone has access to a growing number of best-of-breed technologies, but your organization has something no one else does: the data generated by your unique combination of brand, operating model, and customer experiences. You should seize the opportunity that OSS presents to focus your people on tapping into domain knowledge to innovate.

Both Target and The Home Depot, for example, make OSS the foundation for the custom apps that they build to drive differentiated experiences (for the latter, building on OSS is a mandate).

This almost certainly will lead to contributing to the OSS ecosystem (as both of these retailers do) in ways that grow out of driving your distinctive data strategy.

That’s a virtuous cycle, too: because in doing so, you’ll find other members of the ecosystem who happened to arrive at similar problems to solve.

As more organizations compete on data, the OSS cycle of innovation for enabling technologies will accelerate, expanding the boundaries of what’s technically possible without your organization bearing the full cost and risk of R&D — and increasing the returns of betting on OSS.

Time is of the essence

The good news about complementary virtuous cycles is they deliver compounding returns over time.

The bad news about compounding returns over time is that any delay getting in the game can make catching up a tall order.

Learn how leading organizations improve the custom experience and boost revenue with data in the research report, “The State of the Data Race 2021.”

Bryan Kirschner is Vice President, Strategy at DataStax.

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