Data science is a critical component of business operations today. According to Forrester, organizations that are driven by data science research and insights are twice as likely to be market leaders within their industries. The proof is in the pudding: investment in data leads to big-time payoffs for companies.
While this data has become vital to organizations, data science strategies and practices differ dramatically between industrial and commercial organizations. From the amount and frequency of data inputs to the costs of experiments and models, data sets take on very different forms across these two sectors.
Commercial data science
Commercial data science permeates every aspect of our lives today – from online banking and shopping to business operations and connected devices. In the commercial sector, data scientists are essentially consumer advocates. They identify desirable behaviors and patterns to improve engagement with online users. Data is critical for optimizing these interactions and informing micro-targeting strategies.
Statisticians conduct a wide variety of experiments with commercial data. This process has even evolved to real-time testing rather than just retrospective experiments or testing on a scheduled basis. This helps organizations better understand end-user decision-making and quickly shift tactics to achieve the desired outcomes.
One of these outcomes is to improve ad click-through rates to increase leads and sales. Website views are opportunities for data scientists to optimize real-time experimentation. Commercial data scientists have enough data to effectively run automatic testing, which is very cost effective without impeding standard traffic and engagement processes.
With such an abundance of data, the commercial sector is rapidly transforming online engagement strategies in advertising, purchasing, shipping and community feedback. In fact, digital advertising in the U.S. has been growing at a rate above 15 percent since 2012 and is expected to continue at that pace. Furthermore, according to Gartner, more than 40 percent of data science tasks will be automated by 2020, which will increase data scientists’ productivity in their usage of data analytics.
The industrial sector is also becoming more automated, but data science practices differ significantly from those in the commercial sector today.
Industrial data science
Data scientists in the industrial world are true data wranglers. The rise of connected sensors and connected equipment across industrial plants and facilities has created an abundance of messy data, which must be effectively harnessed for analysis. Messy data refers to missing fields from manual entry and data inputs with inconsistent classification, yielding an inaccurate picture of operations and equipment health.
While data has become the most important tool for measuring risk, improving asset performance and reducing costs – particularly for cash strapped energy organizations – it requires much more hands on management and analysis than data science processes in the commercial sector.
One important differentiator for industrial data science is that it relies on a time series signal. Because data primarily comes from machine sensors, engineers are accustomed to working with successive measurements made over a specific time interval, which restricts data scientists’ abilities to conduct experiments. With significantly less data than that produced in the commercial sector, industrial data scientists and reliability engineers must conduct close control experimentation – often after failures occur. As a result, data scientists and engineers must create physical models using data sets that help train machines to predict future failures.
In order to fully embrace data in asset-intensive industries, organizations require unique experience and machine knowledge. Industrial data scientists must resolve data entry issues, maintain high-quality analytics and subsequently avoid any traumatic asset failure. They not only should be talented with numbers, but also excel at problem solving through leveraging various types of data. Everything can be modeled into a mathematical story, and having the ability to look at data sets and develop strategic insights from a business mindset is what makes data scientists so valuable.
Data science: beneficial across industry sectors
Fortunately, both the commercial and industrial sectors now have advanced software and computing capabilities at their fingertips. In the commercial sector, automation drives real-time experimentation and engagement strategies; for industry, machine learning technology known as cognitive analytics accurately calculates failure rates. Automation and progressive data science techniques are transforming both the commercial and industrial sectors from reactive to proactive strategies, helping both industries save both time and money in the longterm.