Maximizing the value of big data analytics

Big data technology has impacted our lives in multiple ways in recent years. By analyzing large amount of data, scientists, governments and businesses have been able to make important discoveries that have improved our standards of living and changed the way we live and work. Companies today use big data analytics as a way to discover trends to improve the way they conduct business and bring value to stakeholders.

Big data sets can go up to several terabytes in size. It can be challenging to analyze massive amount of data especially when organizations are under pressure to deliver. The need for insights are often time sensitive too, which means that data needs to be interpreted quickly and accurately to enable better decision making.

Without the appropriate tools, managing and analyzing big data becomes a frustrating and time consuming process. Here are a few ways how organizations can maximize the value of their big data analytics.  

1. Blend data

Successful utilization of big data analytics allows organizations to tackle new or current business problems by determining potential pitfalls in a business plan or discovering inefficiencies in operational processes. It can even unravel new market segments, allowing companies to bring new products and services to cater to these segments. To do so, it is important to be able to consider data from across multiple sources to get the best grasp of the situation before making important decisions.

In data analytics, time is of the essence. Business leaders and decision makers often require real-time or close to real-time information to make informed decisions. However, it is estimated that 80 percent of an analyst’s time is spent on preparing and cleaning data for analysis. This leaves only 20 percent for actual analysis work, and precious time is wasted on preparing the data for analysis.

It is therefore important to use a tool that can effectively Extract, Transform and Load (ETL) the data for analysis. We predict that in 2015, ETL tools will become more widely adopted as companies look to ways that will make data preparation an easier process with less need for complex IT infrastructure. 

A good ETL tool should be able to blend data from across different sources, including public data. This ensures that leaders have all the relevant information in one location, allowing for more efficient decision making. It also ensures that leaders are getting their information from a single source, reducing the risk of mis-communication.

2. Help leaders help themselves

 Companies should consider allowing managers and approved staff members to utilize self-service analytics. Many organizations require staff to make a request with IT to get any data analytics, especially big data, done. This often leads to IT being inundated with requests to generate reports.

As mentioned, big data analytics allows companies to tackle business problems. These problems are best understood by managers, staff and employees in the field. IT staff may not fully comprehend what is required and can provide reports that mismatches with what is needed in the field. This coupled with the correspondence time can result in managers not receiving the information in time to support their quick decision making.

By enabling staff with self-service analytics of big data, companies are empowering them to ask questions, identify problems and make informed decisions to get things right. Staff are also able to combine the data with other public information to achieve insights into market segments. Companies also free up IT resources to let them focus on more important and strategic responsibilities.