I recently caught up with Steve Yurko, CEO of APEX Analytix to discuss his views on the big data technology landscape in 2020. Steve has served as CEO of APEX Analytix since 2009. Since then, he has helped a dedicated team of associates add a record number of new Global 2000 audit and technology clients, expand global capabilities and markets served, launch revolutionary new software solutions and produce more audit recoveries for clients than in any period in company history. Steve is a 1988 graduate of UNC-Chapel Hill and has earned his CPA.
insideAI News: How will companies change their approach to big data in 2020?
Steve Yurko: Over the past few years, we’ve seen the proliferation of new companies and solutions with a goal of increasing data quality and understanding what can be done with data to deliver actionable insights. This year we’ll see organizations add value in the decision-making process with insights mined from big data analysis. In 2020, APEX is doubling down on using big data to help our customers operate more effectively, from risk management to managing vendor relationships.
insideAI News: What role does AI/ML currently play in analyzing big data? How do you see this role evolving in 2020 and beyond? What challenges stand in the way of this AI evolution?
Steve Yurko: Machine learning analyzes big data and creates predictive models based on its analysis. AI comes in and builds on top of machine learning to create new models, enabling the machine to learn something it was not instructed to learn. The role of AI in this analysis will move increasingly toward finding nuggets of gold by extracting those pieces of information that companies can use to answer questions like, what can I do with my idle cash? Or, how can I optimize payment terms across my suppliers? Predictive models built by AI and machine learning are changing the way companies approach answering questions like these.
As companies look to run data environments on AI/ML, the challenge will be making this a smooth transition from traditional architecture. Traditionally, an AI/ML system consumes data environments to fulfill the data needs. The transition to running data environments on AI/ML must be done smoothly in order to prevent failures in the existing systems and to implement robust systems. Even a slight failure in data management will result in significant costs to the business in terms of operational time, other resources and user trust.
insideAI News: What methods will help companies operationalize and deliver a greater ROI on their data management solutions?
Steve Yurko: For large organizations, consolidating data across multiple ERP systems, each with its own vendor, customer and item master records, will be the first step in operationalizing their data management solutions. Consolidating these records to create the most complete and accurate version, in other words the ‘golden record,’ is something we’re seeing many organizations looking to do in 2020.
With a consolidated view of vendors, a global company would want to perform a thorough parent-child relationship analysis to see where there are opportunities to turn local contracts into national or global contracts and negotiate better terms. Additionally, they could identify areas for better payment terms and increase working capital. Depending on the size of the company, a change in a week of payment terms across the supplier base could increase working capital by hundreds of millions of dollars.
insideAI News:How can insights gleaned from big data impact a company’s customers? What are examples of the challenges advanced analysis can help solve?
Steve Yurko: Data is the driving force of present-day business decisions. We view this as a huge opportunity to not only give customers access to high quality data, but to take it a step further by making sense of this data and using it to add value in the decision-making process. Going back to a previous example, insights gleaned from AI and machine learning analysis help solve customers’ most pressing business questions, or even questions they’re not yet asking themselves. We’ve seen many organizations looking to find a use for their idle cash, which can be millions of dollars. Some traditional options include putting this money in a bank or investing in stock, but insights from big data allow customers to think outside the box. For example, businesses can reduce costs and earn money back in their supply chain by identifying which suppliers will likely give discounts if invoices are paid early, which can be determined using AI/ML solutions.
insideAI News: Do you think 2020 could be the year companies deliver on the promise of big data?
Steve Yurko: Yes, with a focus on obtaining the most accurate data and gaining valuable insight from that. Doing so will depend heavily on the use of AI/ML solutions for big data analysis. Over the past decade, AI/ML technologies showed great success in decision making applications solving the pain points like classification, natural language understanding, complex pattern recognition and predictive analytics. These same techniques can be transformed into data management solutions to achieve autonomous, self-managing databases.
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