Most of us encounter the use of analytics in our everyday lives and give little thought to its use. Have you ever applied for a credit card or loan and were asked to provide a list of your outstanding financial obligations? Or, perhaps you applied for health insurance and were required to provide a summary of your health history. Providers request this information to help determine whether you are credit worthy or insurable based on analysis of others with similar histories. Welcome to the world of analytics.

But what about the use of analytics to manage the workplace? Imagine being able to predict which of several hundred job applicants are most likely to be successful on the job. Or being able to predict which employees are most likely to leave the organization in the future, or worse, file a charge. Analytics can be used to assess employee engagement, and it can even be used to optimize employee development initiatives.

Leveraging workplace analytics in this way may help companies streamline processes, resulting in saved time and money. But are there risks? Several reports from agencies such as the Federal Trade Commission and the White House have warned of the risk of making biased decisions based on analytics. Last Fall, the Equal Employment Opportunity Commission even held a public meeting regarding the use of big data in employment during which it examined the risks and benefits of big data analytics in the workplace.

Despite risks, properly designed analytics platforms can yield a host of benefits and may significantly lessen the likelihood of liability. Of course, algorithms used by employers to make decisions could be tainted by bias – for example, race and gender could be incorporated into an algorithm used by company officials to determine who should be hired or promoted. Even if race and gender is not explicitly included, an algorithm could result in the unintentional disproportionate exclusion of a particular race or gender group, that is, disparate impact. But these concerns also exist absent the use of algorithms. Humans, but their very nature, bring unintentional biases reflecting their life’s experiences and intuition to everyday decisions. Humans also may bring inconsistency to the decision-making process. Properly designed analytics platforms based on neutral data science are highly consistent and efficient.

Indeed, algorithms should not be designed to explicitly incorporate protected characteristics such as race or gender. And employers must monitor their analytics use for evidence of disparate impact. The most effective of these platforms provide guidance and should never be solely relied upon by employers when making decisions.

(And while we’re at it…what are Big Data, Business Intelligence, Artificial Intelligence, Data Science and IoT?)

To the newly initiated, introducing one’s self to the field data analytics can be intimidating. Navigating through a dizzying array of terms can be a difficult and tedious task. In this post, we bring to you a brief laymen’s glossary to many of the new words and phrases that are sure to become a part of your everyday vocabulary.

Data Analytics – In its most basic form, Data Analytics refers to the practice of using data to draw conclusions that may help inform a decision or a future business practice. One type of data analytics,  Predictive Analytics, refers to the practice of using data collected about past events to predict the likelihood of various possible future events. For example, employers may use predictive analytics to predict who is most likely to leave their organization in the future based on an analysis of the characteristics of those who have left their organization in the past. Still confused? Watch Moneyball® –  it’s a fantastic movie.

Big Data – Perhaps the term that is thrown around with most abandon, Big Data refers to massive collections of data that, due almost entirely to their volume, require special methods and technologies to manage and analyze them.  This term is often used generically to describe large or complex data sets.

Business Intelligence – Generally refers to the tools and methods used by an organization to analyze data from various sources for the purposes of optimizing business decisions. For example, a company may analyze the nature and source of its revenue stream to better inform sales strategies.

Artificial Intelligence  – Phrase often used to describe complex processes or systems that are capable of performing tasks that are typically thought of as requiring human intervention or intelligence. An example includes speech recognition. Don’t believe us? Ask Siri® or Alexa®.

Data Science –A broad and highly interdisciplinary field of scientific inquiry that relies heavily on quantitative tools and methodologies to better understand the natural world. Data scientists are practitioners of data science, and are typically employed by organizations and companies, like Jackson Lewis, wishing to leverage available data to help manage process more efficiently, assist in decision-making, develop new products powered by complex statistical algorithms, or to develop entirely new algorithms and ideas.

IoT, or the Internet of Things – A shorthand way of referring to the interconnectivity of numerous devices over the internet. It may include computers, cell phones, or any other device that today may be connected to the internet, such as refrigerators, air conditioners, and other household appliances. Have you ever remotely set your house alarm from your smart phone?  Congratulations, you have experience with IoT!

Data Intelligencer Reporter – An insightful new blog about workplace data analytics brought to you by Jackson Lewis’ Data Analytics Group.

Since its founding more than 50 years ago, Jackson Lewis has prided itself on delivering first class legal services, cutting edge preventive strategies and positive solutions to some of the most challenging workplace law challenges. As the workplace has evolved, so too has Jackson Lewis’ award-winning services; it is in this tradition that Jackson Lewis has established a Data Analytics Group. The Group is comprised of a multidisciplinary team of lawyers, data scientists, and statisticians that help clients effectively manage the workplace with data-driven solutions.  In all matters, we combine our legal knowledge with powerful analytics insights to provide clients with critical information, while still maximizing the protections afforded by the attorney-client privilege. We provide industry-leading data analytics services to help optimize recruitment practices, assess employee engagement, predict attrition and future headcount needs, evaluate potential liability, and provide novel advice and counsel services about the the proper use and design of analytics platforms. Our clients benefit from the powerful combination of our attorneys’ collective decades-long experience and our data team’s modern analytics skills.

From minimizing legal risk to improving planning and decision-making, it is crucial that employers recognize the value in leveraging data as a management tool. With the launch of the Data Intelligence Reporter, our Data Analytics Group will provide timely, insightful, and practical insights into managing the workplace using data-driven solutions.  It will change the way you think about managing the workplace.  The future of your workplace has arrived.  We hope you will join us.