What Does Data Refinery Mean for Business in 2018

Isaac Asimov, a famous American science fiction writer, once said: “Any fool can tell a crisis when it arrives. The real service to the state is to detect it in the embryo.”

Many business leaders share the notion of detecting and managing crisis before it happens. As we ring in 2018, business leaders are actively looking for solutions that accurately predict a crisis (or success).

Image Source: IBM Big Data and Analytics

Is it possible to predict a crisis before it comes?

Thankfully, now we have data science at our service to detect patterns and make predictions.

Increasing number of companies in various sectors are adopting data-driven decision methods. Business leaders rely on technology such as artificial intelligence and machine learning technologies (AI & ML) to provide meaningful insights from a heap of data.

This has resulted in increased use of various data-driven solutions such as data-as-a-service, analytics-as-a service, and insights-as-a-service. It is estimated that, in 2018, with the advent of AI & ML, the insights-as-a-service market will double.

AI Technology — a chance for complete transformation

Although most market sectors would benefit from adopting AI and ML technology, here we provide an overview of three sectors that are being completely transformed by AI technology:

1. Banking — AI gives a better understanding of buying patterns

Advanced analytics enables financial analysts and bankers to deliver more value to their customers. For instance, thanks to using AI, banks obtain a better understanding of behavior patterns of their customers. This enables a bank to avoid customer churn (i.e., retain current customers) and proactively offer them products that meet their needs. Many banks adopt chat bots (AI technology) to ensure existing and new customers are supported as they shop for new financial products or services.

Financial institutions have also adopted AI technology to monitor fraudulent activities and also to anticipate risks such as loan defaults. These initiatives help in cost savings and ultimately result in a positive customer experience.

In summary, the operational efficiency of banking sector improves by embracing advanced analytics in their workflow. In 2018, this trend continues as banks or even credit unions adopt data science for decision making.

2. Insurance — Use Data Science to build better customer experience

Insurance companies are using data science to build a better customer experience. For example, in the auto insurance industry, technologies such as computer vision help in image analysis of collisions and thereby effectively assess repair costs. The repair shops provide services at a fair price, which ultimately results in cost savings for the customers.

AI and ML technology enable insurance companies to spot risk factors prior to providing insurance. In 2018, insurance companies are obtaining data about risks such as roof damage from drone images prior to providing home or renters insurance. Furthermore, insurance companies might be able to provide personalized warnings to prevent accidents using real-time analytics such as weather, traffic etc.

3. Manufacturing — ML results in optimizing production

Predictive maintenance is one ML application that enormously benefits companies in the manufacturing sector. Machine failures or overzealous maintenance activities result in downtime, which ultimately reduces profits. Based on real-time data input such as temperature, type of raw materials, and pressure, ML solutions predict the right time and type of machine maintenance needed. This results in optimum utilization of available resources and thereby optimizing production. In 2018, predictive maintenance will be adopted by mid-size and smaller companies too.

AI solutions for an organization depend on company size, the business problem at hand, and available expertise. Large, mid-size or small companies are looking for AI solutions to meet their specific business problems such as customer acquisition. Instead of having their own data science unit, many companies are choosing to collaborate with external data science companies that can provide the necessary expertise to build AI solutions.

AI & ML technology is being accepted not just in developed markets but globally. It is estimated that by year 2020, the global AI market will be valued at $1.2 Trillion. To be a part of this amazing future, the time to include advanced analytics in your company’s workflow is now. Time and tide wait for none.

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