#EMBA27- Data Analytics

As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field.

However, it includes many techniques with many different goals.

The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.

The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics. This information provides new insight from data. Advanced analytics addresses “what if?” questions.

The availability of machine learning techniques, massive data sets, and cheap computing power has enabled the use of these techniques in many industries. The collection of big data sets is instrumental in enabling these techniques. Big data analytics enables businesses to draw meaningful conclusions from complex and varied data sources, which has been made possible by advances in parallel processing and cheap computational power

Uses of data analytics

By now, we are pretty familiar with the theory behind data analytics, but it’s all still feeling rather abstract. What does data analytics actually look like in action? How is it used in the real world?

The truth is, data is absolutely everywhere—which means that data analytics has almost infinite uses across all kinds of businesses and organizations. Broadly speaking, data analytics is used to make faster and more informed decisions, reduce overall business costs, develop more effective products and services, and optimize processes and operations. In more specific terms, data analytics might be used for the following:

To predict future sales and purchasing behaviors
For security purposes—for example, to detect, predict, and prevent fraud within the insurance and banking industries
To evaluate the effectiveness of marketing and advertising campaigns
To optimize marketing efforts through more accurate targeting and personalization
To identify and eliminate bottlenecks within a certain process
To boost customer acquisition and retention
To boost customer engagement on social media
To develop risk management solutions
To identify opportunities for innovation and create new revenue streams
To increase supply chain efficiency

The above list is by no means exhaustive; anywhere data is being collected, data analytics can be used to glean useful insights and inform future practices.