Markel

Marta Magnuszewska

White (2019) describes analytics combined with the modern culture of data as an environment of experimentation, empowerment, curiosity, critical thinking, and collaboration. Achieving such an environment requires a shift in how we work. A culture of data framework combines people, process, and technology to bring real value from data to the organization, improving the speed of decision making and decreasing operational costs. The keywords “provide value from data to the organization.” To achieve such transformation, all the decision-making employees, from individual contributors serving the customers to process and people leaders, need to "speak" data. While conversant in business models' people, process, and technology capabilities, most executives do not "speak data." Therefore, the first logical question is: What exactly is data?

Data is surprising, inspiring, emotive, compelling, and persuasive. Data is power; it finds answers, portrays truth, inspires us to tell stories, motivates us, and opens our eyes. Data highlights patterns, prediction, and outliers, and communicate the big picture, the hidden details. Data will propel us further, it will make us smarter, it can find the cure.

Data comes in different shapes, sizes, and forms. It can be structured and unstructured, primary or secondary, qualitative and quantitative. In the last few years, data has moved from the stack to flow, static to dynamic. The amount of structured and unstructured data being generated and stored has exploded recently into exponential progression due to the digitalization of data and connectivity of devices achieved with IoT (Eberendu, 2016). 

Doctor Olga Kamenchuk Nisbet, Professor at Northwestern University, defines structured data as organized in rows and columns, often serving as the primary source of data collected by the organization. Structured and primary data is generated from customer interactions, product usage, supported internal applications, communication networks, employee interactions, and services produced by the company. Structured data is easy to use and measure. Unstructured data is characterized as text data generated through the tracking of daily transactions, social media posts, sensor-generated digital images, videos, audios, and clickstreams, which encompass contributions from organizations and individuals. There is also secondary data, often collected by others and sold for profit. Organizations purchase these secondary data points to enrich their primary data to learn more about their customers. 

"To achieve such transformation, all the decision-making employees, from individual contributors serving the customers to process and people leaders, need to "speak" data."

Data can be broken down into two different categories: quantitative data and qualitative data. Quantitative data is represented in a numerical format, while qualitative data cannot be measured; these are often data dimensions containing categorical variables that can be used to view quantitative data. It is common to generate quantitative analyses based on quantitative data by counting categorical data instances; these can be associated with society, as well as instances and durations. Oftentimes, companies combine all the different data sources to analyze the enriched data points and to predict and derive data insights about their customers. For example, they might produce a 360-degree customer overview to predict what their customers need at a given time, even before they know they need it - making a proactive data-driven decision. 

There are so many potentials to generate from data-driven insights. For example, marketing can develop purpose-driven leads, leaders can spot employee disengagement, and medical teams can study patients and procedures for future improvements. In the insurance industry, these enriched data points will deliver needed insights to frontline decision-makers. For underwriters, data insights will provide details around risk categorization, categorizing prospects based on viability and complexity, prescribing effective outcomes, and a set of priorities to focus on. This will lead to faster prospect processing and onboarding, resulting in more quotes and more business. In the insurance industry, since policies often generate claims, insurance professionals should also be equipped with data insights and tools that allow them to appropriately address their pending claims. We use predictive models, unstructured notes, and process-related tasks to inform the examiner what to focus on in order to deliver adequate and timely support to the customer while fully understanding their customer's sentiment. Data equips organizational leadership with insights needed to make proactive decisions on actions to ensure smooth operations, with visibility to issues and needs to pivot and adjust accordingly. All of these scenarios are possible with data. 

Yet one of the most significant difficulties with data and analytics is that it can be difficult to explain and understand (Davenport, 2013). In addition, if misused, data can also be misinterpreted. False data behind claims from a medical school in London created a worldwide scare over the measles, mumps, and rubella vaccine and led to mistaken data correlations based on particular case studies that appeared to link vaccines to autism. It was a perfect example of misinterpreting data correlation and implying causation; vaccines cause autism. False! But many parents whose children were diagnosed with autism, like my sister, in search of the why, believed in such data points. To prevent the causation of autism, other parents with young children stopped vaccinating them, producing even more dramatic impacts on these children's health when they get sick with either measles, mumps, or rubella. In order to achieve professional benefits and personal growth, speaking the language of data and translating it correctly into actions should be a priority. 

Professionally, the world of work faces an epochal transition. By 2030, according to a recent McKinsey Global Institutional report, as many as 375 million workers, about 14% of the global workforce, may need to switch occupational categories as digitalization, automation, and advanced artificial intelligence disrupt the world of work (Illanes, 2019). Leaders should use data and stop leading on "gut feeling." Individual contributors' actions should be derived from data and orchestrated for easy consumption during customer interactions. New skills are emerging, skills are evolving, and yesterday's skills are expiring. So how do we teach everyone to "speak" data? The concept of data literacy has become an essential part of all organizations' curriculum, from the executive team to individual contributors. Companies should require and support everyone to learn how to "speak" data.  

I would encourage everyone to understand the difference between the various data forms and the concept of correlation and causation since one does not imply the other. Therefore, make life-related decisions based on data, but only information that is accurate and correctly interpreted. Moreover, don't be afraid of change and technology, as it will make you smarter and more robust in the current age, enabling you to understand and "speak" data.