Role of Big Data Analytics in Enterprise Decision Making

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Role of Big Data Analytics in Enterprise Decision Making

Businesses involve a lot of risks before you actually get to witness the part of success. You require a lot of thinking and intuitions before coming to a well-informed decision about where you should head.

Initially, when enterprises were to make decisions, there was a high level of dependence on factors like the experience and intuitions. With times changing, and better solutions and techniques coming to our aid, that decision making is now basing upon more analysis and less intuition!

Big Data Analytics has been a major revelation for enterprises since it has made the roots of businesses more strengthened.

We have now come to acknowledge the time when before developing marketing strategies, enterprises like to conduct an in-depth research on the customers to analyze their behavior.

This is unlike the time where enterprises had to stick to the traditional methods where feedback from the customers was the only source of understanding.

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This analysis has been able to complete because of the information that we can gather using Big Data. The success of making more accurate decisions could happen because the customers could be understood more accurately, thanks to Big Data!

As of 2018, more than 50% companies are using big data analytics in their business, which shows a tremendous growth as compared to 2015, when the number was only 17%.

Further, let us move on to the various stages of Big Data Analytics and its assistance in transforming the way enterprises take decisions.

Stages of Big Data Analytics

It is quite clear that the technology of Big Data is immensely massive. The information it is able to collect and analyze is no joke and is definitely not that easy to process.

Understanding the pattern of Big Data Analytics and how it works can be complex. Hence, we have broken down the concept into its various stages.

  1. Identification of Goals
  2. Improvisation
  3. Collection of Data
  4. Refining Data
  5. Implementation of Tools
  6. Execution

 

Identification of Goals

The initiation of the process of Big Data Analytics happens even before you actually start collecting the data. It starts with the setting of objectives, or the assumed end-results that a business wants to achieve.

The identification of goals is very necessary to proceed. To analyze the performance and to know whether the business is edging towards success, it is important to know the direction in which you need to head.

Which is why the setting of goals is important in the first step of Big Data Analytics itself!

Improvisation

The next step is the improvement of the performance metrics that you use in order to reach your goals.

The benefit that you attain from this is that it avoids the collection or analysis of any data that has no significance whatsoever.

For instance, your goal is to enhance the number of customers retaining in your business. To analyze that, you can choose one of your metrics as the percentage of customers that are restarting their subscriptions or memberships.

This way, only the data you require is collected and better analysis can take place.

Collection of Data

This is the main and the most important stage in the process of Big Data Analytics. Once you have established your goals, move towards actually compiling the data.

Focus on collecting user’s data from more and more diverse sources. Remember, the more data you have about your customers, the more it becomes easier to understand their behavior, and the easier it becomes for you to reach your goals.

However, make sure that the data you are collecting is relevant to your objectives. Otherwise, it is just going to be a tedious task to segregate everything accordingly.

For instance, the data you collect can be from sources like tracking the clicks of the users, or how they move around on a website.

Refining Data

It is not necessary that all the data you collect is put to use. An important step in data analysis is the cleansing of data. Only keep the data that is important, and significant.

This is because using data that has no sense will only lead to generating meaningless results, which would end up confusing you.

In this stage, you also need to categorize your data, set them in their places. How well you have consumed your data is the main factor in defining whether or not you will be able to reach your goals.

Now, the question is how can you refine this data? How can you set your priorities right? Answer the question “How will we use the data?”

Once you get the answer to this question, you can segregate the data on the basis of that!

Implementation of Tools

Once you are done with all the standard stage of identification and prepping up your data, this is the stage where you apply them all!

The statistical and analytical methods are applied here so that you can gather the best insights for your business.

There are a lot of tools and models that you can use for the analysis part. However, which one would be the most accurate for your business is for you to decide.

How you see and understand the business will determine the models that you choose!

Execution

This is the final stage in the process of Big Data Analytics wherein you execute all the strategies that you have developed so far.

Your goals become your reality, based on how well you transform your insights! Focus on being accurate and determine everything at its own speed. Otherwise, things might backfire and you might not end up achieving your goals.

Also, this stage is a continuous process, it keeps on repeating itself. Keep on making improvements to your business according to the results you are generating.

There are always bigger and better goals that you would want to achieve.

Conclusion

The inclusion of Big Data analytics in decision making has and is still proving to be immensely beneficial for enterprise mobility solutions. But even with the various benefits that it comes with, there are some barriers too.

The most primary one is that decision making in enterprises was done with experience, knowledge and key understanding of the business.

On a completely new level, it is still hard for enterprises to adjust to the fact that all the decisions would now happen on the basis of logistics and analytics.

Which is why it is still going to take some time for Big Data Analytics to completely sink in. But so far, the results of this technology in businesses has been great!

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