Using Machine Learning as a Data Storytelling Engine

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Rob_PattersonIn this special guest feature, Rob Patterson of ColdLight examines the benefits of leveraging machine learning to enhance the story that businesses are able to tell with their data. He expands on the importance of taking traditional BI tools a step further with machine learning to exponentially increase the analytical capacity of any organization. Rob Patterson is VP of Corporate Strategy at ColdLight where he is responsible for all go to market strategy, technology partnerships and strategic corporate initiatives. Rob holds a degree in Food & Retail Marketing from Saint Joseph’s University.

Businesses today are dealing with more data than ever before, and analyzing it to create real value for decision-makers continues to be a growing challenge. Although the term big data has become extremely convoluted, the concept of using large amounts of data for analysis is finally starting to have a positive impact. Data story-telling seems to be the next wave of analytics success, and for any business looking to construct a linear story for decision-makers, it’s an exciting concept. However, as it stands today, the process still relies on humans to identify interesting points on which to build and tell the story.

The International Institute for Analytics (IIA) predicts that in the coming year, organizations will finally recognize how critical it is to communicate analytics and develop stories behind data. IIA also foresees 2015 as being the year that automated decision-making will come of age. In order to make the most use of abundant business data, businesses need to have an effective system in place to help organizations solve problems and be as successful as possible. Story-telling is critically important to a business’ decision-making process, and through data, machine learning is capable of helping decision-makers discover not so obvious patterns in data and drive predictive intelligence. This advanced approach to analytics will ultimately create a more holistic and proactive story for decision-makers to provide value and understanding as the market shifts.

Machine learning delivers an assisted way for users to gain advanced insight into why events occurred, what is expected to happen in the future and how businesses should act in accordance with those insights. Traditional BI analytical methods such as OLAP, standardized reporting and in-memory solutions are not set-up to handle advanced analytics causing many businesses to leave out vital pieces to the story that they are able to tell with their data. Analytics teams are being tasked with transforming data into specific business directives, and current BI infrastructures potentially leave value on the table in the form of unforeseen insights.

Although some organizations have begun to embark on advanced analytics initiatives, Gartner predicts that by 2020, information will be used to reinvent, digitalize or eliminate 80% of business processes and products from a decade earlier. The role of machine learning will be to automate the data discovery and advanced analytical processes. Implementing machine learning to assist with mining big data also means developing richer story-telling capabilities. This approach to story-telling will change the way that organizations execute their analytical initiatives by leaning on the computer’s ability to continuously adapt overtime, discover hidden connections in massive datasets and deliver reliable predictive intelligence to decision-makers.

As data grows, human-driven investigation of that data becomes less and less effective causing errors to become more prevalent. In the case of data story-telling, reliance on human interpretation to identify the focus of the story may translate to telling the wrong story or one that doesn’t take into account the entire story. Using machine learning to help create the data narrative enables a more enticing and engaging proposition. No longer are users limited to what they themselves discover. Rather, machine learning opens up a wider margin in terms of what can potentially be found in data sets and used in the narrative.

Through the use of automated advanced and predictive analytics via machine learning, decision-makers gain the ability to look at data like never before. Combining human expertise with unbiased machine intelligence delivers a powerful combination of human and machine interaction of which almost every business can benefit from.  Companies with a clear strategy in place that also adopt machine learning will be able to extract deeper insights from their data. Machines are able to emulate the work of model and algorithms creation, which leads to faster data mining, more predictive intelligence for key business decision-makers and greater scale for the data science community. Manual methods and traditional BI tools are leaving out value, and in 2015, business and IT leaders need to consider a machine learning component to drive their story-telling and decision-making processes.


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  1. Great article. Agree that traditional BI tools were designed for a specific purpose to provide easy access to basic metrics. Now big data and data science can take the value of this information much beyond this initial playing field. That said, the data science tools need to catch up, we are hoping we can help out in this regard over at