It’s said that cash is king, but it isn’t the only asset companies need to weather today’s tumultuous environment. In fact, both small and medium-size businesses and enterprises need to revitalize their data efforts as a way to not only improve operations but to cut costs and increase profits.
While organizations spend countless hours optimizing the assets on their balance sheets, they often overlook the hidden value of the information they collect. “Data is the last untapped asset on the balance sheet,” explains ElectrifAi CEO Edward Scott, who explains why data is so valuable, as well as how businesses can glean more ROI from their data.
Why Meaningful Data is a Must
Most companies operate in a digital-first environment. Scott believes that, while data is important, companies can’t create value from it in its raw state. “Data’s a messy and really difficult business,” Scott says. “Even the large companies are struggling with data and how to turn data into a strategic weapon to drive their business.”
Businesses try to collect data through enterprise resource planning, call centers, and customer relationship management, but that doesn’t solve the information puzzle. “It’s the data that encompasses all of the customer information, or product information, or reputational information, or otherwise, that needs to be harnessed to drive value.
That’s what the companies are struggling greatly to do. We need greater intelligence to drive value out of what’s in the CRM, ERP or call center,” Scott says.
In Scott’s experience, it all starts with clean data. Prebuilt machine learning and natural language processing solutions like ElectrifAi’s make it possible to clean, process, and apply this information – even in overwhelming amounts – to battle tested models and quickly derive consequential business value.
According to a report by global management consultants McKinsey & Company, solving this problem through ready-made solutions like ElectrifAi can result in tremendous gains. “Productizing” – turning data into a product — can deliver use cases 90% faster, data management reduces the total cost of ownership by 30%, and proper data management reduces overall compliance and organizational risk.
This is something that ElectrifAi’s clients have seen firsthand. ElectrifAi’s solutions focus on supply chain network and inventory optimization, call center optimization, cash flow prediction, demand forecasting, dynamic pricing, spend optimization, as well as customer retention and product cross sell/upsell.
These led to tangible results that improved clients’ top and bottom lines. “The solutions that we’re talking about today drive your top-line revenue, optimize your operations, and reduce costs,” Edward Scott adds.
Three Ways Organizations Can Get More Value from Their Data
Accurate data offers a gold mine of actionable information, provided that companies properly manage it. Edward Scott believes that organizations can generate more value from their data by following these three practices.
Let Domain Experts Interpret the Data
C-suite leaders are often behind the drive to invest in information, although they frequently don’t understand the technicalities of data science. They understand all too well, however, how much their enterprises have spent on cloud migration and platform investments and now are demanding a return on investment.
To get the most value possible, companies need access to data engineering and data science expertise. Typically these skills are in short supply as the big tech and cloud providers have gobbled them up and driven up wage inflation.
Companies who want to drive value from data are often faced with a stark choice: (a) invest in a large team of internal experts, platforms and tools which could cost easily over $5 million and take 12 months, or (b) try to leverage a generic, off the shelf machine learning solutions recognizing that the inability to tune or customize the solution for a company’s specific needs will limit the efficacy of the overall solution.
Off-the-shelf machine learning solutions generally won’t solve this problem because success in machine learning requires deep industry and company specific knowledge. It requires domain expertise, which is why data experts need to be leveraged to drive that last mile customization. Many organizations tap external experts like ElectrifAi with deep domain expertise and large libraries of pre-built machine learning solutions to quickly turn their data into a weapon to drive top line revenue growth or to optimize operations.
But even if an organization has an internal team, solutions like ElectrifAi free up a team’s resources to focus on higher-priority projects. “We help them accelerate and prioritize,” Edward Scott explains. This allows internal data science teams to tackle backlogged requests while generating more value from their data.
Personalize AI to Your Organization
In an effort to make meaning from their data, many organizations try generic models without domain expertise. While these solutions might generate high-level information, they certainly aren’t customized to an individual company or its particular business challenge.
It would typically take a lot of time and resources to produce a customized AI model. Instead, ElectrifAi takes a hybrid approach that balances the convenience of prebuilt models with selective customization.
“We minimize the customization with the client’s data. That’s part of our scalability, in addition to the knowledge reuse, and focus on certain industries and use cases,” Edward Scott explains. His team carefully and quickly tunes machine learning solutions to ensure clients see time to value within six to eight weeks. ElectrifAi calls this Last Mile Ai and Consequential Al. This removes the need to start completely from scratch, which would usually take 18 months to scale.
The ultimate goal of data is to turn it into a strategic weapon, but Scott believes that organizations are just beginning to scratch the surface.“ We are among the first to productize machine learning with pre-built machine learning and NLP solutions.
Our key innovation is dropping the domain expertise required to solve a particular problem into machine learning code and packaging that as a docker image that is able to be quickly and flexibly deployed in any cloud or data center – unlocking the business value in 6-8 weeks. That’s the power of data, That’s what the C-suite wants,” Scott explains. “We are the first ones to do that.”
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