What Is Data Analytics?
Data Analytics (DA) is a term that refers to extracting meaningful data from raw data by using specialized computing methods. Data Analytics is a qualitative and quantitative technique which is used to embellish the productivity of the business. It is basically an analysis of the high volume of data which cause computational and data handling challenges. The systems utilized in Data Analytics help in transforming, organizing and modeling the data to draw conclusions and identify patterns.
Data Analytics is also known as Data Analysis. Data Analytics is primarily and majorly used in Business-to-Consumer (B2C) applications such as Healthcare, Gaming, Travel, Energy Management, etc. The technologies and techniques of Data Analytics are widely used in commercial industries that help companies in taking more-informed business decisions. It is involved in n number of industries as it helps the organizations in data-related decision making and verifying the existing business models.
As DA is majorly used in B2C applications, it helps businesses in generating revenues, optimizing customer service and marketing campaigns, gain a competitive edge over rivals, improve operational efficiency and respond quickly to emerging market trends. Data Analytics can be considered as an ultimate solution in achieving desired business goals and to enhance business’ performance.
To be a Data Analyst, it requires several skills like programming skills, statistical skills, machine learning skills, communication and data visualization skills, etc.
Data Analytics process faces several challenges. Let’s talk about the key challenges and how to overcome those challenges:
1. Handling Enormous Data In Less Time:
Handling the data of any business or industry is itself a significant challenge, but when it comes to handling enormous data, the task gets much more difficult. Critical business decisions should be taken effectively, but we need to have strong IT infrastructure which is capable of reading the data faster and delivering real-time insights. In order to overcome this challenge, you can use Apache Hadoop’s MapReduce that helps in splitting the data of the application in small fragments. This process makes the data measurable.
2. Visual Representation Of Data:
Another important task is the visual representation of data. You need to represent the data in an easy format that makes it readable and understandable to the audience. Handling an unstructured data and then representing in a visually attractive manner could be a difficult task. To recover this issue, the data analyst can utilize different types of graphs or tables to represent the data.
3. Application Should Be Scalable:
The major factor to consider is the scalability factor of the of the applications. Several organizations are facing the same issue where the volume of data has been increasing each passing day. Due to the multiple layers between the database and front-end, the data traversal takes time. To overcome this issue, the organizations should take care of the application’s architecture and technology to reduce performance issues and enhance scalability.
While learning about Data Analytics, let’s have a brief look towards the guiding steps to make effective use of it:
1. Define The Questions:
Your questions will define your work process. So, define your questions and ask measurable and clear questions. Define your problem clearly and design the question in such a way that it either qualify or disqualify potential solutions.
2. Set Appropriate Measurement Priorities:
This point covers two different scenarios, i.e. decide what to measure and how to measure. You need to think about these situations. Deciding on how to measure the data is really important before the data collection phase as it also has its own set of questions.
3. Collect Data:
After defining the questions and setting up the measurement priorities, now you need to collect the data. There are a few things to consider while organizing your data:
- Determine the information you can collect from existing database or sources
- Create a file name to store the data. It saves time and prevents team members to store same information twice.
- Try to keep your collected data in an organized way.
4. Analyze And Make Data Useful:
Now is the time to analyze the data. You can manipulate the data in multiple ways by plotting and searching correlations or by building a pivot table. The pivot table will help in sorting and filtering data and calculate the maximum, minimum, mean and standard deviation of your data.
5. Interpret Results:
Data Analytics is incomplete without compelling visualization. This is the time to interpret your data. Interpreting the data will answer all the data-related questions.
At times the challenges can be easily predictable, but what really matters is to overcome the challenges using available resources and solutions. These above-mentioned steps will you guide to make the effective use of Data Analytics in your business.
Contributed by: Ritesh Patil, Co-founder of Mobisoft Infotech that helps startups and enterprises in mobile technology and gives exclusive startup IT services. He loves technology, especially mobile technology. He’s an avid blogger and writes on mobile application. He works in a leading Android development company with skilled Android app developers that has developed innovative mobile applications across various fields such as Finance, Insurance, Health, Entertainment, Productivity, Social Causes, Education and many more and has bagged numerous awards for the same.
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