(MENAFN- Digital solutions)
Data is everywhere. It’s part of IT, sales, marketing, customer service, and countless other facets of any business. But as commonplace as data is, it’s incredibly complex. Leaders often struggle to wrap their heads around the technicalities, which leads to misunderstandings about data’s potential as a value driver.
This is why it’s so important for businesses to simplify how they talk about data, artificial intelligence, and machine learning. ElectrifAi CEO Edward Scott explains why simplification is so critical, as well as practical steps organizations can take to untangle their internal data discussions.
Why Businesses Need To Simplify Conversations Around Data
It’s no secret that data, AI, and machine learning are complicated. Even so, organizations need to find a way to simplify these conversations or risk never seeing positive return on investment from their investment in data. “You’ve got to take the tech mumbo jumbo out of the equation, otherwise you're going to lose the C-suite,” Edward Scott shared.
This isn’t an issue unique to small and midsize businesses, either. Massive enterprises struggle just as much, if not more, to simplify data conversations because of their immense size. “Even the large companies are struggling with data and how to turn data into a strategic weapon to drive their business,” Scott said. He argued that businesses need to simplify data conversations for three key reasons.
Increase Machine Learning Adoption
“The data is there. You have to embrace it and then unleash the stored potential of the data you already have. That will be the key driver to enterprise value,” ElectrifAi’s CEO said. “But you need to be able to walk in and connect those dots very, very quickly.”
C-suite approval is a must. Without leadership’s support, it’s impossible to shore up the resources, human resources, and budget necessary to embrace digital transformation with machine learning and AI.
To that end, data teams need to lean in to the value data delivers instead of its technicalities. “What the C-suite cares about is: How do we drive revenue? How do we reduce costs and how do we optimize operations?” Edward Scott said. This is why organizations need to educate nontechnical C-suite leaders on data’s value to increase machine learning adoption.
ElectrifAi’s prebuilt AI models make it possible for organizations to see time to value in six to eight weeks. By optimizing the supply chain, dynamic pricing, spend classification, and more, companies see more revenue growth. “We believe that data is the key to enterprise valuation growth,” Edward Scott explained.
Leaders can’t embrace the benefits of data unless they understand how it can optimize operations. By simplifying data conversations, businesses can identify problems more efficiently and mobilize their data to solve those issues with fewer boundaries.
ElectrifAi’s Tips for Simplifying Data
ElectrifAi provides domain-specific business solutions powered by prebuilt machine learning. Edward Scott’s team has seen how difficult it is for businesses to mobilize their data when they’re mired in complexity.
His team takes the perplexing topics of data, machine learning, and AI and makes them digestible for corporate teams. “We demystify all of that and say, ‘Look, there's power in your data. Your data is the last untapped asset on your balance sheet,’” Scott said. “I think data is tough. And I think that the tech world has made the entire discussion around data more complicated and more difficult. And we need to make this easy.”
Edward Scott recommends teams simplify their scope, opt for prebuilt solutions, and demand transparency to simplify conversations around data.
“So many companies start big with a grandiose vision. They expect data and machine learning and computer vision and [natural language processing] to be the key drivers of that,” Scott stated. However, he discourages teams from biting off more than they can chew. “By focusing on the grandiose schemes, clients and companies lose sight of the particulars that are required to actually execute and show the value. And when they don't see that value, they begin to lose faith in it.”
Instead, organizations can simplify data by starting small. “Define very precisely a business problem. And when you define that business problem very precisely as the problem you want to solve, we will tell the client exactly the data sources that are needed to solve that specific problem,” Edward Scott explained. By gaining quick wins, data teams can explain how these models work and gain C-suite approval for more data projects.
Instead of building models internally, it’s faster, cheaper, and simpler for organizations to opt for prebuilt solutions like ElectrifAi’s.
“What we've done is we figured out how to productize machine learning, natural language processing, and computer vision so that companies can quickly use those things as tools to solve the business problem,” Edward Scott said. “All of our solutions have a common data model. We tell them exactly what we need and how to map that data to the common data model.”
Data’s intricacies often come from a lack of transparency. That’s why Edward Scott insists on radical transparency with ElectrifAi’s clients — which is very unusual for AI and machine learning solutions.
“We work with our clients to innovate, intimately, to co-create and collaborate, to drive the business’s value. I don't believe they're going to steal the code,” he said. “Why wouldn't we be radically transparent with our clients to help them solve their business problems? It's about solving the business’s problem. Reduce the friction.”
Remove Jargon To Find Data’s True Value
“Data's a fun and messy and really difficult business,” Edward Scott said. It’s so rewarding to see ROI from data, but businesses have to understand data first to realize true value from that data. When organizations simplify conversations about data, they can increase machine learning adoption, drive revenue growth, and optimize their operations.
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