What is data Analytics?

First, what exactly is data analytics? In a very short and simple explanation, it is the science of analyzing raw data to make conclusions about that information. There are different types of approaches on data analytics. This includes looking at what happened (descriptive analytics), why something happened (diagnostic analytics), what is going to happen (predictive analytics), or what should be done next (prescriptive analytics). Data analytics relies on a variety of software tools ranging from spreadsheets, data visualization, and reporting tools, data mining programs, or open-source languages for the greatest data manipulation.

Now that we have a brief understanding of data analytics, what exactly is its importance to businesses? Companies that use business analytics experience improved efficiency and productivity, a faster and more efficient decision making, a better financial performance, and they are able to easily identify and create a new product and service revenue.

Here are 3 most important questions you can ask to begin creating big data strategy:

What do we want to know? It is important that you examine what information is truly valuable to your company. Examples will be: improve customer experience, customer retention, make a process more efficient, etc.
How will you collect the data? There are three common methods: directly asking customers, indirectly tracking customers, and/or attaching other sources of customer data to their own. Other companies are now using AI and other technologies. Deciding on how and where you will collect data may mean purchasing software or technology infrastructure.
How will you analyze the data? There’s far too much data available for business analytics professionals alone to sift through. Machine learning algorithms and other forms of AI can sift through large amounts of data 24/7/365 and don’t even need a lunch break. These technologies are making it easier for businesses to make sense of the data and put it to good use. Analysis methods are commonly broken into several categories: descriptive analytics, which looks at historical data to identify trends and patterns; diagnostic analytics, which looks at historical data to determine why something has happened; predictive analytics, which uses statistics to forecast future outcomes; and prescriptive analytics, which uses tests to determine outcomes of a specific scenario.

This is a guide to the creation and implementation of your data strategy. You can also refer to these questions at any time and use them when necessary to update or redesign your strategy. There are other factors that businesses need to focus on when creating a strategy like technology requirements, hiring needs, and data governance.

It is also good to remember that data analytics help organizations to reduce risks. It will help your company make data-driven decisions.

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