Solving Business Problems with Advanced Data Analytics

Executive Insights4 minutes readJan 7th, 2016

Decision makers at all levels face a balancing act between being decisive and taking the time to properly analyze decisions that will affect their business. Advanced data analytics helps with this challenge by integrating techniques such as machine learning, statistics, cloud computing and cognitive programming and coupling them with the appropriate underlying data to improve decision making. The worldwide explosion of disparate data has gone beyond the imaginable. The ability to capture, analyze and use critical data often needs to occur at machine speed, well beyond human comprehension and reasoning. This will get more exacerbated with today’s Internet of Things where billions of devices will be connected to the internet creating data that will need to be to pulled and correlated to provide insightful results.

But this wealth of data does pose challenges in respect to data analytics. “In many cases there can be too much data to process, a lack of understanding of the importance of the types of data collected coupled by the inability to access and process the data that does exist,” states Rod Fontecilla, Ph.D., vice president application services and chief data scientist at Unisys. There is also a lack of trained industry specific individuals that know how to analyze the data and correlate the different data sets such as social media, asset management, events, etc. Organizational cultures also play a vital role. Those organizations that have budget limitations, are not data driven or open to sharing data can inhibit them from progressing with data analytics.

Data science is very complicated so many organizations typically focus on one or more capability areas first within Enterprise Data Management and then build their expertise as they grow. Components of a holistic Enterprise Data Management model include data governance, data integration, data visualization, data intelligence, advanced data analytics and business intelligence.

In addition to people skills, there is an entire data analytics methodology and framework within a reference architecture platform behind the scenes that is both scalable and elastic to ingest the large and diverse amounts of data. This environment includes the internal data infrastructure and/or the external infrastructure through cloud providers that are leveraged to host the massive amounts of data. Analytic platforms which include tools such as Platfora, RStudio, Hortonworks and Tableau typically provide the advanced analytic environment to mine and report on the data.

But it is not all about the technology. “You need to start with the business problem to determine what data needs to be analyzed to solve the problem. Properly identifying, weighing and vetting data sources is a key factor in getting predictive analysis right. For a quick proof-of-concept, three to four different sources of data are needed to be collected over at least a three year span to give you good correlation results,” adds Fontecilla. Organizations cross industry are looking to improve their business through advanced data analytics. For example, they are looking to increase customer satisfaction, increase efficiency through volume forecasting, decrease loan delinquency, predict potential revenue loss, and identify security issues. Resolving these business problems and others like them can help the organization’s bottom line that can translate to substantial savings.

Organizational business units and the IT organization need to work together to solve these problems and begin to leverage the power of advanced data analytics. Many service providers, like Unisys, can provide the expertise and technology to help organizations get started. Beginning with a proof of concept is a great approach allowing you to crawl, walk and then run in your data analytics journey from descriptive to predictive to prescriptive insights.

Rod Fontecilla recently presented at Harrisburg University Data Analytics Summit II discussing how big data analytics can be used to solve mission critical business problems. Watch the livestream recording.

Learn more about the Unisys offerings around Advanced Data Analytics.

Tags-   Advanced Data Analytics Big data Data Management Data Science Internet of Things