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Why You Should Embrace Machine Learning – You Can’t Afford Not to

Author: Jorge Anicama | | March 8, 2022


Recent research indicates that most companies (80%!) were unsuccessful in fulfilling their aspirations of implementing Machine Learning (ML) systems in 2021.

That reality presents a barrier to ML implementation in 2022, as emerging innovations in these autonomous computing systems are built on their earlier foundations. However, the economic promise of the post-COVID pandemic era suggests that there is no time to waste. Pursuing the full range of economic opportunities will require an equal embrace of the technologies capable of maximizing those options. Companies wanting to thrive in the future should launch or continue their ML implementation as soon as possible. Datavail is here to help.

Why Embrace ML?

A ML data model provides users with one of three distinct ML strategies, each of which provides a specific type of business intelligence: descriptive, predictive, and prescriptive. Each particular type provides equally precise answers to business questions, so choosing which type to use depends on the question asked and the solution needed. For example, a descriptive ML model would answer questions about where, when, and why production machinery failed. A predictive ML model informs decisions regarding setting production and sales goals for the next quarter.

Just as there’s no end to the number of potential business challenges every organization faces, neither is there an end to the analytic possibilities offered by unique ML models.

There’s No Reason Not To

Many C-Suite occupants cite ‘insufficient funding’ as the primary reason why they haven’t yet fully invested in the ML opportunity. In some cases, depending on the industry or type of business, that reason may be appropriate.

However, in most cases, there are simply too many positive ML use cases and outcomes to justify the delay:

Automation and augmentation – Using technology to automate business practices and tasks can result in significant financial and time savings. Jobs that are routine or repetitive lend themselves particularly well to an automated system. The ML element allows these systems to ‘self-update’ their effort based on incoming data, relieving their human counterparts from having to perform those functions as well.

Efficiency improvements – Doing tasks faster and more efficiently reduces costs while also enhancing productivity. ML programs facilitate improved efficiencies across the enterprise:

  • As noted above, automation of some tasks opens the opportunity for the human worker to turn their attention to a more productive activities. One survey revealed ML adopters’ internal process efficiencies rose by up to 30%.
  • Production efficiencies also rise when ML-revealed system bottlenecks are alleviated.
  • Regulatory compliances are also accomplished better with ML programming, which can evaluate the myriad of data contained in compliance documents faster and with more comprehension than a human worker can do.

Predictions and forecasts – ML models are not crystal balls. Instead, they analyze the patterns within existing data pools to predict or forecast likely future events, such as rises in product demand or reductions in the need for certain services. Even more impressive: the ML model itself evolves as it incorporates emerging data and feedback, so its prognostications can become more accurate over time.

Strategic decision-making is enhanced through insights gleaned from predictive ML models, which helps the enterprise develop processes to both increase revenues and cut costs. For example, in a production situation, the ML program may reveal where production costs might be cut without reducing product quality. Making the change increases revenues without also causing additional expenditures. With more finely tuned production data at hand that incorporates the variabilities of both markets and consumers, leaders can set more optimal price and production values.

Enhancing customer satisfaction – It is truly a global marketplace out there, and competition is both everywhere and as close as your hand-held device. Businesses are tasked now more than ever with providing their customers with individualized, finely tuned commercial experiences every time their consumer logs onto their website. They are also burdened with consumer-related data lakes that are too immense for human contemplation. The ML program provides the digital tools needed to address both these challenges:

  • It retains individual consumer data and retrieves it when that shopper signs into the website. The data can contain every detail about every previous interaction, including products purchased, prices paid, shipping details, browsing histories, and more.
  • Based on their previous activities, the program can then ‘suggest’ similar options to the customer. Using data generated by the individual consumer, the program can provide them with options specifically tuned to their personal preferences.

ROI – Not insignificantly, research indicates that the return on the ML investment (ROI) pays back quickly and impressively. A Deloitte study demonstrated that the first-year ROI on ML investments ranges from two to five times its implementation cost. Leaders concerned by initial expenditures can look forward just a few months to see those investments flowing back into company coffers.

The value of ML investments is already well-proven, and emerging data indicate that the use and benefits of ML will only grow. The ML and data analytics masters at Datavail are available to help your enterprise both envision and experience your ML investment’s benefits. Reach out to us today.

For more information regarding ML and selecting the right data models to get the most from it for your organization, download our white paper, “Tips for Machine Learning Model Selection.”

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