July 13, 2017
Do you quiver when you the topic of Big Data comes up? Do you slink away when your manager comes to talk to you about the latest presentation from a vendor about the transformative power of Analytics, Business Intelligence (BI), Artificial Intelligence (AI), Machine Learning (ML), and Predictive Analytics (PA) etc.? If so, you’re not alone.
“Without data, you’re just another person with an opinion.”
W. Edwards Deming
Understanding the Terminology
Today’s leading EHSQ vendors have incorporated BI and analytics tools into their platforms to allow users to mine their data for reporting and analysis. The difference between BI and analytics is rather fundamental to this post. BI most commonly refers to analyzing historical data whereas analytics incorporates a future-looking element to it. So, when you are prepping your OSHA, EPA, or other submissions, you are gathering the information from a BI query. When you choose attribute A over attribute B to evaluate the performance of a safety program because that attribute shows a stronger relationship to reducing risk – that is analytics.
Now that we’ve got that settled, let’s turn our attention to the field of analytics – starting with Artificial Intelligence.
According to SAS, AI has been around since the 1950’s when it was developed to explore topics like problem solving and symbolic methods. In the 1960s, the US Department of Defense took interest in this type of work and increased focus on training computers to mimic human reasoning. An example of how you might use AI today is Google Translate. Google uses words, expressions, or sentences that are written into the information field and contrasts it – and the consolidated consequences of all past translation asks – in the selected language using propriety technology. They use voice recognition and statistical machine translation to make it happen.
For EHSQ professionals, AI is being deployed as safety chatbots. Nordsafety provides a solution that can proactively alert users to potential safety hazards using an automated chat solution.
AI contains many sub-fields, which include:
According to Whatis.com, machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data. Machine learning often uses similar mathematical techniques and models as in predictive analytics, but we’ll get to that later.
I think one of the most popular (and valuable from a stock perspective) is Netflix. Netflix has about 100 employees dedicated to writing its recommendation engine. Why is this so important? According to Netflix, they know that if a user doesn’t find something to watch within the first 60 to 90 seconds, the risk of abandoning the service increases substantially.
My colleague, David Vuong, wrote a blog post on Machine Learning in EHSQ. According to him, current examples of techniques used by EHSQ professionals, such as Bayesian Decision Theory, is also used in some machine learning practices.
Deep learning is the next frontier in AI. Deep Learning takes machine learning to the next level. It’s been described as the “cutting-edge of the cutting-edge.” It is where computers attempt to perform tasks which would mimic human learning by introducing abstract layers between the input and output variables. Some examples of deep learning include the navigation of self-driving car recoloring black and white images, and precision medicine which looks at developing medicines tailored to an individual’s genome.
Predictive analytics is also a growing field. It is an example of applied intelligence where it “finds patterns in data and predicts value in future by using that pattern.” It won’t tell you what will happen but forecasts what may happen with an acceptable margin of error. Predictive analytics uses classical statistical techniques and is supervised by a Data Scientist.
Predictive Analytics is being readily adopted in EHSQ. At Cority, we believe there are opportunities to remove risk from operations using data. Our experts are examining the different types of data and how to create results that will be meaningful to safety performance.
Cority’s (Medgate’s) Analytics Program was launched in late 2016 as a multi-phased strategic initiative aimed at harnessing the power of over 6.5 million employee records and millions more data points, securely managed by Cority on behalf of its industry leading customers.
So, What is Big Data and How Does it Fit into the Equation?
Big Data is a term that describes the large volume of data – both structured (organized in a database) and unstructured (can include text messages, pictures, etc.) – that inundates a business on a day-to-day basis.
According to industry expert, Bernard Marr, Big Data is the fuel that powers AI.
It’s the explosion of data—the raw material of AI—that has allowed it to advance at incredible speeds... In the past, AI’s growth was stunted due to limited data sets, representative samples of data rather than real-time, real-life data and the inability to analyze massive amounts of data in seconds. Today, there’s real-time, always-available access to the data and tools that enable rapid analysis. This has propelled AI and machine learning and allowed the transition to a data-first approach. Our technology is now agile enough to access these colossal datasets to rapidly evolve AI and machine-learning applications.
Additional industry sources state that predictive analytics is, “an enabler of Big Data… Predictive Analytics enable organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective.”
What Does This Mean?
Big Data and its fields are here to stay. For organizations, it’s time to take note and build teams to understand how to leverage their data in combination with public data in order to transform into insight-driven organizations.
As Arthur C. Clark, Scientist and Author stated, “Information is not knowledge, knowledge is not wisdom, and wisdom is not foresight. Each grows out of the other, and we need them all.”
About the AuthorMore Content by Jessica Shields