5 Big Data Analytics Tools That Can Grow Your Organization More and More

For a number of years, a growing number of organizations of various sizes and segments have been using the Big Data Analytics philosophy as a strategic support tool.

The goal is simple: to improve your work processes and gain valuable insight into market trends , consumer behavior and expectations.

All these codes represent for corporations the possibility of making more precise and, above all, anticipated decisions to the competition . Decisions that, in a scenario of extreme competitiveness, can be the dividing line between success and stagnation of your business.

If you do not want to be marginalized in the dispute over the leadership of your segment, it pays to invest a few minutes of your time in reading this article and discover a little more about the power of this phenomenal big data and cloud tool for your business. Check it!

Understand Big Data Analytics Here

Big Data Analytics Tools

Big Data Analytics is the analytical and intelligent work of large, structured or unstructured data volumes that are collected, stored and interpreted by high-performance software. It is the crossing of an infinity of data from the internal and external environment, generating a kind of “managerial compass” for decision makers . All of this, of course, in an extremely short processing time.

Some of the sources used by Big Data Analytics Tools:

  • Data extracted from Business Intelligence (BI ) tools ;
  • Web server log files;
  • Social media content;
  • Business reports;
  • Texts of consumer emails to the company;
  • Macroeconomic indicators;
  • Satisfaction surveys;
  • Statistics of cellular connections captured by sensors connected to the ” internet of things “;
  • Databases of credit card companies;
  • Loyalty programs ;
  • Product reviews on company websites.

The business advantages of Big Data Analytics

By extracting and combining results from all sources listed in the previous topic, Big Data Analytics solutions can deliver extremely important information for the performance improvement of any type of company in the market. Curious to understand how this happens?

The starting point is the viability of deep analysis of one public by region. Knowing which products are the most wanted and desired in a particular location, you can direct sales to that place and even improve the logistics system to make service more efficient (remembering that all of this can happen in real time).

By better understanding the consumer profile of a particular area, it is also possible to hire a type of skilled labor in dealing with that public. For example, a customer who enters a particular store in the city center may be different from the customer who enters another store of the same brand in another region.

In this context, some steps need to be well understood so that the company can make use of tools such as Big Data and Business Intelligence.

Check out some of them(Big Data Analytics Tools)

Definition

This stage is fundamental, since it represents the moment in which the company needs to ask the questions necessary to solve the problems. Not all data are of interest, and they do not come alone either. So it is at this stage that you define what information is really important to your business and how it will help.

There is no point in asking too many questions if, in the end, there is not enough time to do the analysis. Focus on what is really urgent – the moment you have time to devote to a thorough evaluation.

Your questions may include:

“How will data analysis help me understand why the company lost 20% of its contracts in the last year?”

That way, you will get a signal of what data is relevant: area of ​​contracts, sales, SAC or customer relationships and others – that can give you clues as to how your problem will be solved through the analysis of information.

It is necessary to think if these data are available in the company itself, if there is information that will have to be sought in external banks and also, how reliable these data in the application of strategic marketing.

Collect

Before starting to effectively collect data in strategic marketing, you need to think about the tools that will generate them. It is at this stage that the algorithms and technologies necessary for effective collection are defined.

Storage

Once the collection parameters are set, it’s time to think about how that data will be stored. This will depend very much on how fast this collection will be done, the range of data that will be analyzed, the variety and the capture tools used by the company.

Some will conclude that the management tool used in the institution accounts for this storage. Others will prefer to store this information on a single server, or even opt for a tool that has technology in the cloud.

In the storage phase, it is interesting to think of two moments: before and after the analysis. Does your business need to save this data for how long after the review? Is there confidential information at this bank? How should they be discarded or stored permanently?

Analysis

As the questions that are needed to answer your problems have already been asked early in the process, the data analysis phase will not be like looking for a “needle in the haystack . Managers will know exactly in what mass of data each analysis will be done – and what mechanisms will be used at that stage.

The concept of Data Mining must be understood for this phase to be successful. In this sense, Microsoft defines Data Mining as a process of discovering actionable information in large sets of files. Generally, this process uses mathematical analysis to derive patterns and trends that exist in the data. It often happens that these patterns can not be discovered through traditional exploitation.

Data Mining is essential in strategic marketing, in addition to being divided into a few steps:

  • Forecasting – where sales are predicted and expected server loads or downtime;
  • Risk and probability – where the probable break-even point is determined for risk scenarios, assigning probabilities to diagnoses or other outcomes;
  • Recommendations – when determining which products are most likely to be sold together, generating recommendations;
  • Sequence location – analysis and selection of customers in a shopping cart, which makes it possible to predict future events;
  • Grouping – At this time, customers or events are separated from related items, which makes it possible to do analyzes and predict affinities.

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