The importance of human intelligence grows in an AI-driven world

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In my 30 years of working in information management, I have always encouraged businesses to use the insights provided by data.

In the early days of my career, data was scarce and expensive to store. Today, data is in abundance with free cloud storage available everywhere. Many organizations do not realize the knowledge this data provides to help them make decisions.

The irony: businesses have access to their own crystal ball that can show them the future. The key for business is to have employees with skills to turn that data into information.

Some industries focus on analytics as their core product. Genetic research uses analytics to recognize genetic patterns, isolate disease origins, and hopefully find cures. Network security employees use analytics to detect abnormalities in network traffic and spot hackers. Tax professionals, fraud specialists, and the IRS officials use analytics to spot abnormalities in financial statements and tax returns.

Other industries need analysts in specific areas of the business, such as accounting, marketing, forecasting, sales, quality, or production. These employees use analytics to create business critical information for their department. The department manager uses this information to make decisions that provide the competitive edge their business requires. Studying the past trends and patterns can reveal important information about the future.

With the need to convert data to information in every industry and every business, the demand for employees with data analytic knowledge and experience is not surprising. Some businesses cannot justify another employee dedicated to analytics, so they re-train current employees. Large businesses may decide they need a dedicated analyst. Both types of employees will need a solid educational experience which combines knowledge, theory, and analytical tool application.

The ideal educational experience for these students provides the knowledge needed of statistics, database design and management, programming, data mining, visualization and predictive modeling. Statistics is at the core of these studies. Data analytics is considered by many as applied statistics. Database design and management is important as the student will often find data stored in database structures.

Modern learners need to understand how to create data warehouses which function to create specialized datasets for analysis. They must understand how to structure these warehouses and create the scripts which run and populate the warehouses.

Programming languages such as R, Python, and SAS are well-known and required in data analytics. Data mining is the field of searching through large datasets and looking for patterns. Visualization is turning the data into charts, graphs, tables, and pictures that allow the user to better see summary data and trends. Predictive modeling takes the past data and builds models to predict variables into the future.

The foundational knowledge and theories must be accompanied by experience using the tools in the business. Employers want someone who has actually done analysis with industry tools.

Modern learners must practice using programming languages to examine large data sets. They must use visualization and begin to experiment with conveying information through pictures and charts versus numbers. Since employers look at the student’s resume often in the form of a project or work that has been created, profiles on Github are critical to obtaining a job in the field. These profiles of work provide a portfolio for the employer to examine knowledge and application.

Certifications from major analytical tool vendors also can assist the student with demonstration of knowledge to an employer. Numerous large analytical tools vendors offer certifications for class completion or skill demonstration. SAS, one of the largest statistical and analytical tool vendors, offers a joint educational partnership certificate with institutions who have programs and courses that require application of tools in the analysis of large data sets. Other industry specific certifications like Google Analytics offers verification of knowledge in the area of web analytics.

Once trained, these employees provide the information businesses need to make decisions. Businesses that have often relied on human instinct and intuition can now use data to look into the future. Trusting that data-derived information, often, comes hard for some business executives who worry about the accuracy of analytics.

Computer programs actually reduce the bias that human intuition can contain. Through tools like predictive modeling, analysts can look into the future allowing businesses to adapt, grow, and gain a competitive advantage.

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