Leveraging Data with Predictive Analytics

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In this special guest feature, Rex Ahlstrom, Chief Strategy & Technology Officer, BackOffice Associates, highlights ways to leverage data with predictive analytics. Rex has over 28 years of technology industry leadership experience. He specializes in enterprise software within the data integration and information management space. Responsible for BackOffice Associates’ current product strategy, marketing and technology in addition to analyst engagement and partnership development, he previously served as CEO of two software start-ups and held major leadership roles at multiple Fortune 500 organizations, including SAP. Ahlstrom earned a Master of Science in Electrical Engineering from Johns Hopkins University and a Bachelor of Science in Electrical Engineering degree from Drexel University.

Predictive analytics is one of many transformative technologies set to make an enormous impact for organizations in the Big Data landscape. If predictions in a Markets and Markets report are any indication of organizations’ willingness to jump on board with the technology, it looks like the bandwagon’s going to have to get a whole lot bigger. The report estimates the global predictive analytics market will grow from $2.7 billion in 2015 to $9.2 billion by 2020.

It’s no wonder. Organizations using the technology have an eye on increasing their bottom line and competitive advantage by mining the gold in the bounty of data they’re collecting. Predictive analytics uses collected data and a combination of artificial intelligence, machine learning, and algorithms to predict future outcomes. Companies can use those predictions to make better decisions.

The intersection of data and technology has taken predictive analytics one step further by making it more accessible. New, easy-to-use applications with interactive dashboards have opened the gate to the analytics playground. Business users looking for real-time information are now equipped to play alongside IT with self-service tools that allow them to access and use data on demand. However, organizations must ensure that their data is business-ready and relevant in order to glean the major benefits of predictive analytics.

Building a Strong Team through Information Governance

New players and new technology mean it’s more important than ever for companies to have a comprehensive governance policy that covers data management and analytics use. Governance initiatives need to outline the rules of the game for everyone, as well as ensure that everyone is playing by the same rules. Establishing and protecting the integrity of data and processes is the foundation for making good decisions.

Building an information governance framework and implementing policies as part of the company culture needs to include:

  • A team captain — a chief data officer who can break down internal silos and initiate a company-wide effort
  • Goals and needs — A vision that specifies the mission of the information governance initiative and outlines the problems to be solved
  • Strategy and technology — A plan and solution that meets the company’s goals and needs

Leveraging Information Governance with Predictive Analytics

As a company implements governance initiatives across the organization, predictive analytics can help. Predictive models identify, analyze and classify data, providing a real-time view of what’s happening in an enterprise. For instance, management could view record-keeping policies in departments to look for inconsistencies or issues with how they’re being implemented against the established governance policy. They can pinpoint gaps and areas to improve and determine how to tackle the issues based on the value a solution brings to the business.

For example, a company may want all vendors coded for payment in net 30, but when they look at data term codes, they discover that they have up to 20 different variations to use when establishing vendors in the system. Using data quality and governance policies can bring light to these types of areas that are inconsistent and bridge the gap between policy and the reality of what’s actually happening. A predictive analytic could use this information along with other data elements within and across financial applications to predict future cash flow trends.

Assigning dollar values to inconsistencies also uncovers gaps. For instance, what if customers are marked for deletion in the system but still have open accounts receivable? When the customer is deleted, so is revenue if the account isn’t paid in full. Flagging data errors with a specific dollar loss value shows the material impact on a company’s bottom line.

Information governance strategies that include predictive analytics can help companies manage data to make improved decisions and optimize overall business processes. Using the right predictive analytics solution in today’s self-service environment makes better data, controls, and strategy more accessible and easier to put to work for a stronger bottom line.


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