Data Analytics is a broad term and encompasses tools and techniques required to fiddle with large volume of data. A general misnomer is Data Analytics and Data Analysis are same. But in reality Analysis is a subset of Analytics. With this understanding we can formalize a definition of Data Analytics is as below:
It is a process of obtaining valuable information from raw data using tools and techniques that gives business a clear and definitive understanding of what is going on. ..
Data Analytics is a continuous process: As mentioned above, in Analytics we use lot of tools to instigate the automation process for collecting raw data. These tools are capable of cleaning and transforming the raw data before it is presented as report. Once these sets of tools are installed and deployed, they periodically fetch raw data and perform all the necessary steps and then generate reports. No manual intervention is thus required.
Data analytics can be used on any size data collection, but as time goes on, companies can begin to collect big data or a large amount of data. Since methods can be used to forecast the future using historical data, analytics becomes more accurate. Data tools may gather information from a variety of sources and places.
Analytical Data Understanding
Data analytics can help businesses in a variety of industries. Data analytics can be used by manufacturing firms to measure workloads and ensure that machinery is running at full capacity. Data analytics can be used by financial firms to assess market risk. Data analytics can be used by retailers to assess consumer loyalty and forecast retention rates.
Whatever you want to test with data analytics, you'll use a method that looks something like this:
- Know what information to be gathered.
- Gather the information (computers, online sources, personnel, environmental sources, etc.).
- Arrange the information so that it can be analyzed (this is where software can replace the manual and disparate storage across spreadsheets and store all data in a centralized and secure location).
Make sure the results don't have any gaps or repetitions. After it has been washed, it can be used for inspection.
Why Data Analytics Matters?
In today's fast-paced business world, decision making process should be fast and accurate. Besides, a stiff competition amongst business coerce organizations to run ahead of the curve. It has been found that an effective Data Analytics platform can actually act as catalyst in achieving collective goals. It aids in the optimization of total results and can have a huge impact on the bottom line. This is due to the fact that data analytics aids in the detection of inefficiencies that result in waste. You can make the required changes to minimise costs and increase the bottom line once you've established them.
The following are some of the applications of data analytics:
- Assess risk (banks and finance companies use data analytics to assess a customer's credit risk and industry dynamics)
- Identify trends
- Predict results
- Speed up the time it takes to make informed decisions
- Find out how happy your customers are.
Besides, an effective analytical report helps the key stakeholders to ask the question “Why”. It also sparks the further exploration on short term future of the business.