Big Data Analytics Examples and Best Practices

Analytics combines science and art. Analytics can be used to improve customer service, product and brand development, and organizational performance. The term was first used in a paper published in 1969 by Dr. Bernard aboard the NASA Spaceflight MSG-MSN. “The Analysis of Behavior”.

Analytics is the systematic mathematical analysis of statistical data points or data sets. It is employed for the discovery, identification, and communication of useful trends in data. It also involves applying statistical techniques towards efficient decision making in business performance. Business analysts apply analytics to enhance business performance by improving data points and methods for managing and planning future outcomes. For example, the analytics can help managers forecast business performance over the next three years.

Analytics help managers design and fine tune policies and systems for organizations to achieve their business goals. It thus enables businesses to make better use of information technology resources, achieve financial competitiveness, improve product and brand development, become more profitable, and reduce operational cost. By enabling businesses to collect and analyze data and information from various sources, they can gather insights that they can then apply towards their future actions. The best part is that the analytics provide management with direct, real-time insights. They can thus plan their strategy more efficiently towards reaching their business goals.

Today’s business needs a multitude of complex tools to effectively analyze, collect, and present data. Analytics helps in building a digital data management (EDM) platform by providing fast, real-time access to business data. In fact, the future of the enterprise lies in its ability to analyze and collect large quantities of information in a fast and efficient manner. The key to this is having an agile analytics ecosystem in place. This ecosystem must support models, whether it be traditional reporting or model management, as well as various other complex applications such as complex event processing, social networks, web services, and mobile devices.

Businesses today need to know more about how to make better decisions. The sooner they adopt this mindset, the sooner they will realize the potential of using data analysis and business analytics to boost their performance. Many companies are now beginning to realize that they cannot simply rely on internal resources to do a good job in managing and handling their data. They need to make better use of technologies and programs that are offered by the analytic technology community to make their business more competitive, efficient, and result-oriented. Traditional business analytics programs are still not as robust and flexible enough to meet these challenges.

Analytics has the potential to dramatically change the way companies perform and manage business. With the right analytics platform in place, companies can draw direct, actionable insights from massive amounts of information quickly and easily. By turning data into useful insights, managers can make informed decisions faster and make better decisions for future business goals. The key is to have the right analytics platform and application in place, coupled with the appropriate data cleansing processes and reporting capabilities. In order for a business to fully reap the benefits of analytics, it must have a dedicated analytics team to execute the necessary analytical functions. The following 4 ways to use data analytics to your advantage are:

Data is only as good as the data it contains. While the analytics technology may be powerful, it does not give insight into the most important aspects of a business. Therefore, in order for analytics to work, it must be capable of modeling the relationships between the different aspects of an organization such as the people, the products, the business models, the infrastructure, the marketing inputs, the sales outcomes, the operational outcomes, and the customers themselves. This allows big data to be assimilated in a way that makes sense. This way, managers can extract the most relevant insights from the data in order to build relevant business models and align them with the best possible outcomes.

By using analytic technologies and techniques, you will also learn how to deal with large volumes of unprocessed or sometimes incorrectly labeled data. Large amounts of unlabeled data have the potential of being useless or even corrupting your analysis results. For this reason, the biggest step towards realizing a successful analytics strategy is the proper labeling of the relevant datasets. Examples of working datasets include the following: consumer churn, panel studies, surveys, and target markets.