What is Data Analytics? does it help businesses today? Data analytics refers to the process of inspecting, cleansing, transforming and modelling data with the goal of identifying trends and drawing conclusions. Analytics can be applied to any area in which data is available, namely social media marketing, healthcare or business intelligence.

This is a relatively new term that has been gaining popularity in the last 5 years with the rise of data mining and business intelligence. As a process, Data Analytics means many different things to many different people depending on the context of its use. In general, it can be considered as a process that provides answers to questions from data being used in various ways.

In a marketing context, analyzing data gained from customer engagement will provide answers to what is working and what may need attention using reports and dashboards. Data analytics combined with predictive analytics goes beyond simply providing answers – this combination makes it possible to drive decisions – decisions that are based on actual facts rather than assumptions or gut feelings.

In all cases, Data Analytics plays an important role in decision making by improving the speed of analysis and increasing accuracy, both of which lead to fewer mistakes, fewer delays and less costly errors.

What technologies are used in data analytics?

The technologies that are typically used in data analytics are data mining tools, data warehouses, reporting tools, dashboards and predictive analytics. The goal of applying these technologies is to create an environment that facilitates the extraction of useful information from large amounts of raw data for the purpose of business decision making.

With the sheer volume of data being generated every day by various channels, having a structured approach toward making sense of all this information is an important step in being able to capitalize on it. Data Analytics offers benefits across almost all levels of engagement with existing customers as well as potential new customers.

From what data analytics can be applied, the benefits are infinite. Data Analytics, in general, is used to look for trends and patterns in large amounts of data. Considering the volume of daily data generation in our world today, this technique has an important role to play in business intelligence.

The important point to note is that it is all about linkage. The use of mobile devices, social media platforms and other means of capturing behaviour data need to be drawn together into one integrated system that delivers all the necessary information required for decision making at a single place.

Data analytics can be used in a wide spectrum of applications from marketing, finance, to risk management. Data analytics has been responsible for improving the efficiency and quality of decision making across different sectors.

In business intelligence, for example, data analytics is used to gain better understanding of customer interactions on social media platforms, as well as on websites. Using various data science automated processes data analytics can be used to analyze the effectiveness of current customer engagement strategies.

The rise of platforms such as Twitter and Facebook giving more visibility to content from these sources, as well as the increase in mobile users, means there is a growing need for better customer engagement and these companies use a lot of data analytics to get.

This allows businesses to gain insights on what works and what doesn’t work through an analysis of relevant data points that are correlated with sales channel activity, sales performance or customer satisfaction.

Is computation part of data analytics? What is data analytics really defined as?

There are two dominant approaches to defining data analytics. The first approach is to draw a distinction between the analysis of data and the use of applied statistics for decision making.

The second approach is to consider analytics as “data + computation”. This view treats data as raw elements and algorithms as the concept that transforms raw data into something that can be analyzed.

However, using these two definitions, it becomes evident that Data Analytics covers a wide range of subjects beyond just statistics. Furthermore, they both miss out on any consideration of “data + computation” in isolation.

Conclusion

Data Analytics is essentially the process of using data to uncover insights, trends and patterns in order to gain a better understanding of an organization. Data Analytics is becoming more important for companies across all sectors as the amount of data being generated from different sources becomes higher.

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