Digital Themes


What is analytics?

Analytics is all about interpreting raw data and transforming it into actionable insight. Data analysts use computational tools such as mathematics, statistics, and predictive modeling to find meaningful patterns in data in order to get a clearer picture of how to solve a business challenge. Given these future predicters, prescriptive analytics is used to identify the best course of action. Essentially, they are scientists who use data to make hypothesis and analytical tools to validate them.

Data analysis is organized into three phases—descriptive, predictive, and prescriptive. Descriptive analytics takes historical data and analyzes it to understand changes that occur in a business over a defined period of time. Predictive analytics builds on this analysis and uses it along with algorithms and machine learning to predict future business outcomes based on past experience.

To stay competitive and accelerate innovation, businesses are turning to data analytics and business intelligence to obtain actionable insights from big data in key areas including sales, marketing, production, quality and training. Thought leaders in analytics and insights are also looking to applications such as Amazon Elastic Map (EMR) to scale data analytics capabilities to respond to changes in data traffic, realizing greater operational efficiencies and differentiating themselves from key competitors.

From the office to the shop floor, analytics are helping businesses:

  • Find the best way forward. Data analysis gives businesses the power to translate raw data into something meaningful, actionable and transformative. Through business intelligence and predictive analytics, companies are harnessing big data to forecast enterprise trends, anticipate success and remain competitive.

  • Boost the bottom line. Predictive analytics use a combination of artificial intelligence (AI) and machine learning to connect businesses with consumers. Using web analytics, it’s easy to target customers with the right product at the right price at the right time, maximizing sales and profits with minimal human interaction.

  • Transform shopping experiences. Through rapid response technology, data and analytics are increasing the value proposition of seamless digital transactions. Creative payment options including point of sale financing, mobile wallets and real-time credit approvals are emerging as a direct response to consumer preference, allowing them to make purchases or obtain credit in seconds.

  • Monetize data. Analytics has made it possible for companies to identify value in their own data and sell direct access to that data for a profit. Marketing and data firms create, buy, analyze and sell large amounts of data to third party sites every day for profit, who repurpose the data to the benefit of the companies who provided it.

  • Mitigate risk. Security and fraud analytics are a valuable asset in protecting physical and financial assets, as well as intellectual property from internal and external threats. Through the use of statistical modeling and big data methodologies it is possible to detect potentially fraudulent activity and anticipate any future activity, improving fraud risk management.
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