Data Analytics: A Key Component Of Digital Transformation
Market Drivers, Benefits, Risks, And Challenges Of Data Analytics
The utilization of data analytics remains a common aspect of digital transformation. An increasing number of enterprises view data as a commodity, which explains how this became a major area of technology investment across several industries. Organizations are willing to invest vast resources into gathering, creating, and analyzing data.
Jeff Orr, freelance writer and emerging technology consultant, told Enterprise Mobility Exchange that data analytics is regarded as the study of company datasets to identify important insights. It should also be noted that there are different types of analytics for enterprises. Some of the most common types of analytics include:
- Descriptive analytics: This includes the elementary reporting and business intelligence conducted by most enterprises.
- Prescriptive analytics: This involves technology that provides suggestions for human action.
- Predictive analytics: This includes using data insights to anticipate human action in the future. The predictions are commonly from a recommendation engine.
Many enterprises aim to become a data-driven organization, which typically involves using strong fundamentals to make data-driven decisions.
“Strong fundamentals include both financial value and strategic value being generated for customers and investors,” said Ryan Martin, principal analyst at ABI Research.
The collection and organization of data analytics is a common aspect that is often tied to other digital transformation components, such cloud computing and the Internet of Things (IoT). Data analytics is often the driving force for those organizations embarking on a digital transformation. Numerous departments within an enterprise could have a technological need for data analytics, such as sales departments that have customer relationship management (CRM) software. One of the sectors that have embraced data analytics is the financial services industry, which uses the technology to help with the detection of fraud.
Across the board, enterprises in all industries —and of different sizes —can greatly benefit from data analytics. For example, on supply chains, data analytics can manage the flow of ingredients on the assembly line. Data analytics can also aid enterprise automation processes for numerous applications, such as providing insight about when a machine or a system will fail. Overall, enterprises that embrace data analytics will see improved productivity, which will enhance important business decisions.
Risks And Challenges
Although there are many benefits associated with harnessing data analytics, there are also some risks to keep in mind. On the surface, data might seem useful, but data by itself really does not provide immediate assistance. In order for that data to be used correctly, it has to be organized by employees. In some enterprises, that additional data is only beneficial if it is acquired, organized, and shared in real-time. A real-time enterprise (RTE) refers to a company that has current data readily available in all systems. Many assembly lines are relying on real-time data to improve quality control and to detect fraud. A lot of work goes into achieving this level of real-time data, such as with the use of edge computing, and it is very challenging.
Want To Learn More About Digital Transformation?
Before embarking on a digital transformation, an enterprise should identify the needs and objectives. Read about cloud computing and all of the other key technology components in Enterprise Mobility Exchange’s exclusive report on digital transformation.
This article was originally published on Enterprise Mobility Exchange.