Data is being exploded every day and the IT teams are wrestling with the data management, security, accessibility, end users to get the analytics in however manner they can. Recently, there were some customers who challenged whether it is possible, or even it is relevant for defining an enterprise data analytics strategy in today's environment of self-service and continuous disruption. As we all know that data is growing at a faster rate than ever before. By the year 2020, every person who is online will create a rough sketch of about 1.7 megabytes of new data every sec, every day and top of that about 44 zettabytes of data will exist in the digital universe by that time. There are many other factors which are also affecting the data-driven organizations, which lead to the huge demand for data analytics solutions from business leaders.

What does Enterprise Data Analytics Means?

Enterprise analytics actually refers to the process of having a data, business and the process analytical capabilities all across an enterprise. This helps to provide the organizations with the ability to collect analyze and process analytical data either in all or most of the business functions. This is primarily a form of big data analytics where the business can perform the processes of analytics on the data which stored across the organization. On Google alone, the users altogether perform more than 40,000 search queries every sec, therefore each organization requires a dedicated platform to capture and analyze the data at these increasing rapid speeds.

This analytics is used by data analysts, big data analysts or web analyst for extracting the meaningful and knowledge data or relations from the raw data repositories which it already has. The enterprise analytics solutions can be a standalone information system which can be offered by using the data mining, business intelligence, web analytics, big data analytics solutions, etc. Enterprises have been the mining data for years, even for centuries that starts from handwritten books keeping ledgers but by the late 1970s, the advent of database management system took place. The growth of operational system rapidly took place along with the data gathering.

Need of Data Analytics

Below are given some of the key reason why organizations need to consider the enterprise data analytics as discussed below:

Because the time lag can hurt the organization, therefore, the central business teams will no longer own the software and will not be responsible for disseminating insights to the other departments. Everyone within the business requires accessing that platform, just not only to analyze the data, but they also need it to gain the insights for their individual roles.

• Leverage multiple data sources

Across the world, about 90% of all the existing data is developed within a period of just 2 years. All the sources of that data are predicated on increasing the coming year's record, whether it is a transactional data from POS terminals or the sensor data from home appliances. The setups which are utilizing the big data require a comprehensive coverage of all these data sources.

Characteristics of Data Analytics

• Use data proactively

The big data is not just a guide for the inexperienced; hence it is the tool to solve the problems and to test the hypothesis. To take a recurring business problem such as low sales during the third quarter and the use of a massive pool of data resources, for determining the root cause of the concern. You must understand the underlying data sets behind the data analysis which is the key to technology services.

4 Pillars of Analytics Success

Here are 4 key pillars which help to develop a solid analytics strategy for any software development services provider or industry which can be adapted across the cultures and company sizes as given below:

• Drive Innovation

1. Identifying the external trends to inform strategic decision
2. Create and test new ways to transform current practices
3. To solve business problems, educate the creative use of data and analytics
4. Encourage a new culture of data-driven innovation by bringing external speakers

• Develop Talent

1. Recruit, develop and retain the diverse world class team
2. Encourage the culture of trust which rewards the right behavior
3. Develop continuous learning tools which support internal & external clients
4. Identify the data point’s opportunities across the customer journey