These days, data is easy to collect, but harder to interpret. With the Internet of Things and digitization of information, there are billions of data points that can lead to effective solutions for a wide variety of industries. But what use is all this unstructured data? Such massive quantities can be overwhelming, and the value gets diluted. Computer science techniques, such as machine learning and blockchain, can help make sense of the data by finding patterns that can predict future behaviors. Predictive analytics allow people to make advanced predictions about unknown future events based on data from the past.
Not Just for Marketers
Businesses can use predictive analytics to understand their customers and forecast sales, and now a wide range of organizations are using data to forecast trends and behaviors, according to CIO. It’s not only about predicting future revenue, but also getting specific enough to have the right inventory in the right places. For example, a car company can analyze data to predict which models and colors will sell at each dealership. Another more traditional way to use data for marketing is for targeting advertising and offering discounts to customers who are likely to buy something. Retail companies track customer browsing and purchasing patterns, and algorithms generate customer segments so companies can offer discounts or adjust prices in a precise, targeted way.
But similar techniques can provide insight into different fields, such as manufacturing, healthcare and energy. Aerospace companies, medical researchers, financial firms and logistics companies have all found effective solutions derived from predictive analytics.
While marketers may use big data to predict potential customers’ behavior, healthcare providers use data to understand their patients and diseases. For example, predictive analytics can inform scientists on which strains to include in a vaccine like the flu shot. According to the Centers for Disease Control, the viruses in the flu vaccine are selected each year based on surveillance data that indicates which viruses are currently circulating and forecasts which viruses are most likely to circulate during the coming season.
Recent studies have shown that algorithms can predict and diagnose diseases such as cardiovascular illnesses and cancer with incredible accuracy. In a study published in Nature, deep learning models used 46,864,534,945 data points from electronic health records to predict patient outcomes. The algorithms were highly accurate at predicting whether patients would die in the hospital; if patients would need to be hospitalized again in the next month; “prolonged length of stay … and all of a patient’s final discharge diagnoses.”
Mechanical failures are bad for any business, but predicting problems before they occur can save time and money. That’s why, according to CIO, manufacturing companies are starting to analyze data to optimize raw material delivery and predict when different machines will fail so operators can be proactive and keep manufacturing supply chains moving.
Commercial airlines are especially interested in predictive analytics. In aerospace, predictive maintenance can help avoid flight delays, and algorithms can help pilots with fuel efficiency. In an industry where timeliness is essential, and fuel is one of the biggest business expenses, sifting through data can quickly pay off, said Aviation Today.
Aircraft are already equipped with avionics that collect data. The next step is to analyze all that data to find patterns that reveal inefficiencies. Aviation Today reported that Qantas Airways uses a data analytics platform based on 10 billion data points to provide automated updates that “identify fuel efficiency gains and offer operational insights” to help pilots reduce fuel burn and emissions. Pilots can see a visual representation of their performance (and that of other pilots) at different times throughout a flight to see where and when they could be saving fuel.
Similarly, China Airlines used a fleet-planning platform that “led to a 10% increase in line management process efficiencies,” which resulted in an annual cost savings of $560,000. The airline also “achieved an average reduction of 30 days layover in scheduled aircraft maintenance, resulting in a savings of $1.3 million,” Aviation Today reported.
UPS is using 1 billion data points per day to cross-reference with historical delivery trends to predict future demand, according to the Wall Street Journal. By analyzing details like package volumes and routes, logistics companies can direct resources so that the right number of trucks are available at the locations where demand will be the highest at any given time.
Energy companies use algorithms to forecast “long-term price and demand ratios,” as well as to “determine the impact of weather events, equipment failure, regulations and other variables on service costs,” according to CIO. Retail and manufacturing industries also use similar techniques to make sure they don’t tie up their money in unnecessary inventory. Ultimately, an entire smart city can use data to improve quality of life for its citizens.