How to Apply the 4 Types of Data Analysis in Your Industry

Using data to make better business decisions has been a common practice for decades. But in the age of big data, companies have access not only to huge amounts of data, but also to tools and techniques that make harnessing it faster, easier and more scalable.

Companies using big data effectively are improving their production efforts, lowering costs and gaining competitive advantages across industries such as finance, agriculture, healthcare, sports and many more. These days, it’s difficult to find companies that don’t use data analytics. Virtually every industry takes advantage of big data.

In this article, we’ll cover the importance of big data in business and spotlight the industries and companies that use big data analytics successfully.

4 Types of Data Analysis Methods

What do we mean by data analytics? It’s the science of evaluating and interpreting data to draw insight for use in improving performance. Decision-makers use data analytics to answer the fundamental questions their companies face — it is essential to modern business.

Data analysts employ a variety of techniques to leverage their data into intelligence. These are considered the four most common types of data analytics. You can learn more about these methods at Berkeley Data Analytics Boot Camp.

Descriptive Analytics

Descriptive analytics is the process of applying data to answer the questions, “What happened?” or “What is happening right now?” Data analysts use historical and current data to monitor real-time events and examine changes over time in any industry.

Companies rely on descriptive analytics to give context to their data. For instance, a company might report second-quarter product sales of $100,000. However, a descriptive analytics report might show sales were down from the first quarter but up from the previous year’s second quarter. Decision-makers can use that insight to address the strengths and weaknesses of their business units.

Diagnostic Analytics

Where descriptive analytics examines what happened, diagnostic analytics addresses why it happened. Companies turn to diagnostic analytics to help them understand successes, troubleshoot problems and make more informed decisions.

According to the data analytics company Selerity, diagnostic analytics is an advanced branch of data analytics that uses regression analysis, data mining, data drilling and other techniques to find correlations and causes within data. Diagnostic analytics builds on the intelligence extracted from descriptive analytics, giving decision-makers a more contextual look at their data.

Predictive Analytics

Data analysts perform predictive analytics to answer this question: “What is going to happen?”
The objective of predictive analytics is to calculate the possibility of future outcomes by assessing historical and current data.

Predictive analytics certainly isn’t new — businesses have been using data to generate forecasts and predictive models for decades. But the use of predictive analytics has grown with the rise of AI, machine learning, data mining and other technologies that have made models faster to build and better equipped to handle vast amounts of data. These tools also are important in creating predictive models from the varied structured and unstructured data that companies now generate.

Prescriptive Analytics

The question a prescriptive analytics model seeks to answer is this: “What should we do?” Data analysts investigate data to suggest options to be explored and determine actions that should be taken. This is a growing field of data analytics.

As with predictive analytics, prescriptive analytics exercises some of the latest methods and tools in data science: graph analysis, neural networks and machine learning. When used effectively and with the proper data, prescriptive analytics can help decision-makers chart a business course based on data-driven projections rather than instincts or hunches.

Industries That Use the Types of Data Analytics

Data analytics touches every corner of the business and commercial landscape. Companies and organizations that grow crops, build houses and cars, develop medicines, treat patients and create entertainment all benefit from data analytics.

These 17 data analytics books can help you implement data analytics successfully in your industry.

Healthcare Analytics

Healthcare providers collect an enormous amount of data about patients: reported symptoms, diagnoses, treatment procedures and medications, as well as demographic data regarding their age, race, location and more. Providers are harnessing this data to help improve care delivery, treat disease, use resources more productively and deliver more preventative care, according to HealthITAnalytics.

Healthcare analytics employs multiple types of data analysis.

Descriptive Analytics in Healthcare:

Providers use descriptive analytics to determine how many people live with diabetes or heart disease, or to anticipate the contagion rate of a virus. Dashboards employ descriptive analytics to update COVID-19 cases regionally, statewide, nationally and globally. And, researchers used descriptive analytics to assess “clinical features and epidemiological factors” of confirmed COVID-19 cases in China.

Diagnostic Analytics in Healthcare:

Diagnostic analytics is helping doctors go beyond assessing patients’ symptoms. For instance, Fresenius Medical Care explains that diagnostic analytics can help providers understand why patients went to the hospital or left treatment.

Predictive Analytics in Healthcare:

Providers can use predictive analytics to assess which patients face a high risk of rehospitalization based on historical data or where a seasonal flu hotspot might develop. The Pan American Health Organization called predictive analytics “critical” to the fight against COVID-19, saying it “allows us to estimate the pandemic’s behavior within an acceptable degree of uncertainty by establishing when and under which conditions countries can expect increases, peaks and reductions in new cases (incidence) and deaths (mortality).”

Prescriptive Analytics in Healthcare:

According to IBM, prescriptive analytics is helping providers determine proper courses of action regarding care delivery and organizational operations. Using prescriptive analytics, providers can model treatment methods and costs using “what if” scenarios to recommend the best treatment plans for patients and providers.

Sports Analytics

The 2004 book Moneyball, which detailed how the Oakland A’s built a baseball roster based on data, changed the ways in which teams and the public view sports analytics. Today, professional, collegiate and high school sports teams use data analytics to construct rosters, develop strategies, implement fitness and recovery programs and enhance the fan experience. Data analysts study nearly every aspect of every game, from spin rates in golf to when NFL coaches should punt on fourth down.

Descriptive Analytics in Sports:

Professional golfers chart their results based on various factors (hole length, weather and course conditions, club selection, tournament round in which they’re playing) to gain an overall picture of their game.

Diagnostic Analytics in Sports:

Baseball players can study their situational hitting results (facing certain ball-strike counts against a pitcher, how they fare against a fastball or curveball, etc.) to help decide on strategy and swing improvements.

Predictive Analytics in Sports:

Predictive sports analytics is a cottage industry, as organizations develop models to project won-loss records, career player-performance metrics, ticket-holder renewal rates and much more. Predictive analytics in sports also has expanded with the rise of legal sports betting nationwide.

Prescriptive Analytics in Sports:

EdjSports uses predictive and prescriptive analytics to run billions of simulations based on play-by-play data involving NFL and college football teams to model what will happen in upcoming games. Prescriptive analytics are also used in major league baseball to help determine the optimal time to pull a pitcher from the mound during a game.

HR Analytics

Companies that once relied on resumes, performance reviews and instinct to recruit, hire and manage employees are migrating to HR analytics. According to LinkedIn Business, 50 percent of CEOs say they use data analytics (or “talent intelligence”) to find and retain employees. And that number is growing.

Descriptive Analytics in HR:

Turnover rate, which measures how many people leave a company in a defined time period, is an example of a descriptive analytics metric that impacts any company with even a small employee base.

Diagnostic Analytics in HR:

Using turnover rate, and associated data, companies can determine whether employee departures are normal or a reason for concern. According to the Society for Human Resource Management (SHRM), companies derive tangible financial benefit from understanding their turnover rates.

Predictive Analytics in HR:

Companies apply predictive analytics in recruitment to many HR initiatives, according to SHRM, including employee surveys, hiring assessments and competency models. Predictive analytics can turn these metrics into models that project employee performance, satisfaction and retention rates.

Prescriptive Analytics in HR:

Once hired, employees need proper onboarding, training and resources to do their jobs effectively. Companies are using prescriptive analytics, SHRM writes, to determine which onboarding methods will be most effective for this employee, what training materials will best address this employee’s learning curve, and which training formats will be most effective for them.

Agriculture Analytics

Growing food has become a data-driven industry at both the macro and micro levels. The United Nations’ Food and Agriculture Organization maintains a dashboard of 21 sustainable development goal indicators that uses data to track everything from agricultural genetic resources, to the productivity of small-scale producers, to water stress and sustainable fish stocks. Meanwhile, precision agriculture relies on data analytics and data collection tools such as sensors and drones to model better methods for land use and food production.

Descriptive Analytics in Agriculture:

Growers and food producers take advantage of data dashboards that monitor crop production, fertilizer and water use, soil health and so much more, giving them a real-time view of their products.

Diagnostic Analytics in Agriculture:

USAID, a humanitarian agency, conducted a research project that identified the needs of different types of small dairy farmers by applying diagnostic analytics to variables such as the household’s income and education levels, access to emergency funds and whether they owned a mobile phone. This helped the organization tailor the types of aid offered to specific groups in a more effective manner for the greatest positive impact.

Predictive Analytics in Agriculture:

The U.N.’s Food and Agriculture Organization has concluded that predictive analytics will be necessary for agribusinesses to make accurate growth and market projections to increase their crop yields and profits, while providing necessary food to populations around the globe. That will become especially important as the U.N. projects a worldwide population of nearly 9.7 billion by 2050, putting more strain on food production.

Prescriptive Analytics in Agriculture:

Prescriptive analytics can transform data into recommendations on which crops to plant, when to plant them and the proper soil nutrients required to optimize yields. A 2020 paper published in the International Journal of Data Science and Analysis detailed how prescriptive analytics could be used for these purposes in Nigeria.

Entertainment Analytics

Entertainment is now personal, on-demand and increasingly digital; giving content providers access to unprecedented levels of data about consumer preferences. Analytics in media and entertainment sharpens this data, allowing providers to tailor content to individual consumers while programming to large subscriber bases. Even individual content creators can leverage analytics to determine what content to produce and when to release it.

Descriptive Analytics in Entertainment:

If you’ve scrolled through a “Recommend For You” list on a streaming service, you’ve participated in a descriptive analytics program. Streaming services and content providers log the viewing habits of their consumers and subscribers, then make recommendations.

Diagnostic Analytics in Entertainment:

Beyond tracking what consumers view, content providers also track how long they watch, when they watch, which devices they use to watch and what they watch next. All these factors help providers understand consumer reactions to their programming and how to better engage valuable viewer groups through specific channels and devices.

Predictive Analytics in Entertainment:

The entertainment industry constantly searches for what’s next and predictive analytics helps forecast demand for specific genres of music, shows, movies and video games. Predictive analytics also addresses specific audience segments, providing more opportunities for targeted marketing and advertising opportunities.

Prescriptive Analytics in Entertainment:

Could content providers change their programming schedules based on the weather? Sure. By using prescriptive analytics, they could monitor viewing habits based on seasonal and local weather patterns and adjust their programming accordingly — for instance, by recommending a movie marathon to regional viewers where significant rain or snow is forecast to occur.

Marketing Analytics

Companies are exploring new ways to glean insight about their customers from the immense amount of data these customers provide. Marketing analytics helps companies target specific audiences with the right marketing campaigns, improve their interactions with customers, reduce customer churn and spend their marketing dollars wisely.

Descriptive Analytics in Marketing:

Social media marketing tools offer one example of descriptive analytics in marketing. What are people saying about a company online? Social media tools can track mentions, keywords and hashtags to capture a real-time picture of a brand’s online reputation.

Diagnostic Analytics in Marketing:

Companies with e-commerce websites use diagnostic analytics to understand how well those sites are operating. For instance, are consumers adding products to their shopping carts but not making the final click to purchase? If so, why? Did the site not load properly? Did the buyer lose interest because the checkout process was difficult? Diagnostic analytics can help answer these questions.

Predictive Analytics in Marketing:

Companies apply predictive analytics to forecast how consumers will react to various marketing campaigns and offers For example, what percent of consumers will redeem this discount? Will they only buy the discounted item, or will they also buy additional items? With this information, companies can target their marketing dollars to valued or profitable customers, reaching audiences that provide the best ROI.

Prescriptive Analytics in Marketing:

According to McKinsey, a management consulting firm, retailers use prescriptive analytics to identify popular products at individual stores and recommend pricing, display and promotional changes. In fact, McKinsey concludes that prescriptive analytics can raise a retailer’s same-store sales by 2-5 percent. This is why your grocery store may sell Greek yogurt at a premium price consistently, but if you drive 10 minutes to a neighboring store (from the same chain) Greek yogurt may be significantly discounted when you buy 2 of them (BOGO). The retailer knows, through prescriptive analytics, that customers in your store will buy the product regardless of price, but customers in the next store (who also enjoy Greek yogurt) are more price conscious and will not.

Graphic showing same-store sales growth using prescriptive analysis.

Companies in every industry obtain value from data analytics. Make your company more analytical. Enroll in Berkeley Data Analytics Boot Camp.

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