Keeping Up With Massive Data Sets Through Big Data Analytics

In a study conducted by Accenture and General Electric (summarized in this Forbes feature), it has been found that businesses are upping their competitiveness scale through Big Data Analytics and Internet of Things. Companies, according to this analysis, are investing their technology budget on Big Data analytics at varying amounts.

The aviation industry for example recently named Big Data analytics as its top priority. This is also true for manufacturing companies.

Now, we must pose the folloiwng questions. Why are companies and industries clamoring for Big Data analytics? What’s with the hype? Is it more than a passing hype? To answer these questions, we will try to dissect Big Data analytics from its subtle and obvious benefits within various companies.

What is Big Data Analytics?


Big Data is a collection of data that is too big for the conventional database systems. Since it’s too big, it requires companies added effort to process. However, through Big Data analytics, Big Data can help companies make intelligent decisions and improve their business operations.

An example of Big Data includes petabytes and exabytes of data. These huge amounts can consist of information from millions of website visitors. It may range from sources such as mobile data, sales and social media. For business entities, this data can mean a whole world of profit and better customer understanding.

When big data analytics is used, these large sets of data derived from the company’s vault will then be turned into actionable insights. The right platform or software can do this. It can then be applied in business to increase retention of customers, help in product development and gain advantages compared to competitors. If done right, the analytics can answer industry questions, boost sales and can improve customer profiles.

Challenges in Big Data Analytics


However high end and modern big data analytics technology is, it still faces many challenges. Issues include data integration, data volume, skills availability, solution cost, availability of platform that can pull unstructured data.

Data integration is the first challenge that we must meet before we can take any meaning from our analytics. This includes the idea of pulling structured and unstructured data and combining them. Data volume on the other hand poses a challenge of speed of information availability depending on the volume. Skills availability on the other hand may pose a challenge when people who are knowledgeable of the whole process and technology are scarce especially since there are many new concepts being introduced.

Finally, one of the biggest challenges facing Big Data analytics is the solution cost and the platform availability. These issues go hand in hand since a good platform that can pull unstructured data as smoothly as it can pull structured can be costly in terms of upfront cost as well as maintenance.

Although these challenges can be overcome, careful planning and skilled personnel can help everything go smoothly.

Specific Example of How Big Data Analytics is Used


Big Data analytics can be very useful in many ways. It can analyse data and do the following:

  • Decode DNA in a couple of minutes
  • Identify gene/s that possibly cause disease
  • Predict plans by terrorists
  • Create a profile of your visitors or customers
  • Check which ads are likely to be responded to by consumers
  • Check the factors that make your website visitors leave
  • Analyze which product sells the most across platforms

How Can You use Big Data Analytics Based on their Types

graph-163549_1280Big Data analytics can be meaningful in various ways. It can help in aiding business through the following types of analytics:

  1. Prescriptive
  2. Predictive
  3. Diagnostic
  4. Descriptive

Prescriptive Analytics


Prescriptive analytics is the first type of analytics you can employ for your business. Based on its name, it denotes the analysis of data to reveal future trends and actions that should be taken by a company. It involves the recommendation of possible steps and actions that an organization can possibly take.

Although it is very useful, this type of analytics is seldom used. According to Gartner, only 3% of organizations use predictive analytics. Among industries, predictive analysis could have helped measure certain factors and determine the field to focus on (i.e. problems and possible solutions).

Predictive Analytics


We said that prescriptive analytics is only being used by 3% of organizations. Predictive analytics on the other hand gets a tad higher of the percentage at 13%.

Predictive analytics focuses on scenarios that are likely to happen. It makes use of predictive forecast. The analytics may come to certain conclusions based on statistical analysis, data mining and modeling techniques among others. Recent as well as past data is also needed to make the prediction as good as possible.

Most experts warn companies about the common misconception that Predictive analytics can tell what will happen in the near future; it doesn’t do that. What it does is provides a forecast and may use the word “probability” to suggest what might happen.

Predictive analysis is usually very helpful in terms of marketing, sales and analysis of leads.

Diagnostic Analytics


Diagnostic Analytics checks the reason behind why a certain event happened. To be able to do this, the analytics will pull data from past performance to come up with the diagnosis.

Diagnostic Analytics works well when determining statistical data to assess certain factors. For example, the analytics will pull out data from old sales to check which marketing techniques generated the most sales. Another example is the analysis of past social media campaigns. Checking the data can diagnose or pinpoint which social media platform has brought in the most website visitors. After this happens, the company can increase visibility on the said platforms.

Descriptive Analytics


Descriptive analytics is a real time report of current happenings that is usually monitored on real-time dashboards. This type of analytics is usually not so valued by experts, putting it at the bottom of the analytics chain. However, this type of analytics can still offer valuable insights.

Based on the incoming data, assessment can predict a likely performance in a certain field of an industry. One visual example of descriptive analytics includes a sales cycle which can help businesses categorize customers by their preferences as well as the sales cycle overall.


So, is this just hype? A passing fad? We guess not, and we have various statistics to prove that. The big question is, will you harness the benefits of Big Data and analytics in your business?

Big Data analytics is already evolving and its landscape is fast changing. You must keep up with the way the technology is moving to stay in the competition. No one knows what gold you can mine from your data, but one thing is for sure: it will be relevant, timely and actionable.


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