How big companies are using analytics data for their success

Data analytics, also known as conducting an analysis of data, is a type of process that inspects, cleans, transforms and remodels data to fetch useful information and suggest conclusions. It deals with multiple facets and approach and uses variety of techniques in different business models and other domains.

Here are four most common big data analytics strategies that have propelled companies to the front lines.

• As a business grows, it becomes more data-rich. Hence, when at the initial stage of analytical process, it is important to ask the right questions. The huge magnitude of data makes it difficult to keep its track, thus making it vulnerable to be trapped in an endless round of data analytics. Asking the right questions ensures helps in identifying situations regarding big data analytics will drive a positive impact on the business. These questions act as clear markers and guide a business triangulate between the sources of data.
• Let us consider an instance where a telecom company is looking to find a solution for millions of low-income individuals struggling with revolving credits. The executives of this business would use the data pertaining to payment history of their network users, and this will eventually help assess their potential to repay the loans.
• Business is all about price versus volume and inventory costs versus out-of-stock scenarios. These are usually referred to as tradeoffs. Before the advent of big data analytics and big data analytics software, these tradeoffs were made on sheer gut instinct rather than using data related to it. Big data analytics are more of a real-time data which help a business help eliminate the guess work and taking decisions based on gut instincts. The importance of data analytics can be measured from the fact that a transactional company could not measure the same sales impact through traditional media when compared with its investments on the social media.
• It is important to keep big data analytics report simple as too much of information can be overwhelming. Let us take an example of a midsized B2B company that analyzed a large percentage of the company’s business was provided by a very small proportion their customer base and vice versa. Hence, to garner more sales from both the sizes of the customer base, the company used big data analytics and used predictive models to identify local markets. They used big data analytics software that helped them filter in higher customer sale potentials. The software was simple and had an interactive visual interface that found out potential customers with the help of the area zip code. This was, in turn, used by district managers who prioritized and deployed a sales team to those zip codes that could fetch them maximum business. The business analytics software then helped the B2B company to double their sales rates and cut down on sales cost.

At the same time, it is important to realize that crunching data does not entail success in the business. It is as important as putting your business on the web domain and making your business an e-commerce juggernaut. The adoption of transformative technology needs creative management that is well versed with facts.