Data Analytics: The Future Of Dealing With Data

Data Analytics
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Data Analytics: The Future Of Dealing With Data

Since the time when the Internet has taken over the world, the importance of data and the rate at which data is increasing is at higher levels. Data can be organized as structured, semi-structured and unstructured. Handling and analyzing different sorts of data is the major challenge these days. Day-by-day, we can see that the amount of data emerging is drastically increasing, and the need to manage such vast amounts is very important. Choosing the right type of storage becomes the first priority in the field of Data Analytics
When you have to analyze data, it involves: • Studying the data.
• Storage medium.
• Characteristics.
• Security measures, and many more items

Big data consists of large data sets which are not possible for the traditional database systems to handle. Data storage techniques now used for data include clustered networks and object-based storage. The ability to store large amounts of data is what is necessary for business executives to use big data. Unique techniques are needed to analyze big data.

We need to have knowledge about methodologies, adopt technologies and ensure to improve skills with technology. In the earlier years, executives relied entirely on structured data. Later on, unstructured data were also provided with methods to handle and analyze this kind of data easily. It poses opportunities and challenges for the business.

Let’s just consider a simple example of an Excel sheet that holds different values, such as customer details, order details, inventories, etc. As it contains different kinds of information in one sheet, it has to be structured in such a way that it is easy to access whatever information is required from it.

Analyzing data is like handling business and technology together, which is a great challenge. Proper analysis of data reduces risk. Analytics architecture refers to infrastructure, tools, and practices that lead to access and analysis of information to enable easier decision making for businesses.

Let’s take a look at the big data analytic steps

Data extract and feed: Collect the data that you want to store and feed it to the storage device.
Discovery and visualization: This involves knowing about what sort of data it is, sorting it if it is unsorted, and saving it. There can be two types of data – public data and confidential data. Public data is the data that can be shared across the wider public. Confidential data is only accessed by a few authorized users. To develop the security of information, authentication, authorization, and validation processing software must be stored. To manage so much of data requires visualization of how you can store your data and develop the respective framework for it.
Decision support: After you store and validate data, unnecessary data has to be removed and a decision has to be taken as to which operations have to be performed on which data and so on.
Data and applications: Once decisions are made you are ready to apply these data into the areas where you want it to be used.


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