What is big data analytics? How can the process support decision-making? How does it work? This article addresses these questions.
The Meaning of Big Data Analytics
Statistics is a powerful tool that large businesses use to further their agenda. The age of information presents opportunities to dabble with voluminous data generated from the internet or other electronic data capture systems to output information useful for decision-making. The process of analyzing these large volumes of data is referred to as big data analytics.
What Can be Gained from Big Data Analytics?
How will data gathered from the internet or electronic data capture systems be that useful to decision makers? Of what use are those data?
From a statistician’s or data analyst’s point of view, the great amounts of data available for analysis means a lot of things. However, analysis can be made meaningful when guided by specific questions at the beginning of the analysis. Data remain as data unless their collection was designed to meet a stated goal or purpose.
However, when large amounts of data are collected using a wide range of variables or parameters, it is still possible to analyze those data to see relationships, trends, differences, among others. Large databases serve this purpose. They are ‘mined’ to produce information. Hence, the term ‘data mining’ arose from this practice.
In this discussion, emphasis is given on the information provided by data for effective executive decision-making.
Example of the Uses of Big Data Analytics
An executive of a large, multinational company may, for example, ask three questions:
- What is the sales trend of the company’s products?
- Do sales approach a predetermined target?
- What is the company’s share of the total product sales in the market?
What kind of information does the executive need and why is he asking such questions? Executives expect aggregated information or a bird’s eye view of the situation.
Sales trend can easily be made by preparing a simple line graph to show products sales since the launching of that product. Just by simple inspection of the graph, an executive can easily see the ups and downs of product sales. If there are three products presented at the same time, it would be easy to spot which one performs better than the others. If the sales trend dipped somewhere, the executive may ask what caused such dip in sales.
Hence, action may be applied to correct the situation. A sudden surge in sales may be attributed to an effective information campaign.
How about that question on meeting a predetermined target? A simple comparison of unit sales using a bar graph showing targeted and actual accomplishments achieves this end.
The third question may be addressed by showing a pie-chart to show the percentage of product sales relative to those of the other companies. Thus, information on the company’s competitiveness is produced.
These graph outputs, if based on large amounts of data, is more reliable than just simply getting randomly sampled data because there is an inherent error associated with sampling. Samples may not correctly reflect a population. Greater confidence in decision-making, therefore, is given to such analysis backed by large volumes of data.
Data Sources for Big Data Analytics
How are a large amount of data amassed for analytics?
Whenever you subscribe, log-in, join, or make use of any internet service like a social network or an email service for free, you become a part of the statistics. Simply opening your email and clicking products displayed in a web page will provide information on your preference. The data analyst can relate your preference to the profile you gave when you decided to subscribe to a service. But your preference is only a point in the correlation analysis. More data is required for analysis to take place. Hence, aggregating all the behavior of internet users will provide better generalizations.
This discussion highlights the importance of big data analytics. When it becomes a part of an organization’s decision support system, better decision-making by executives is achieved.
TimeAtlas.com (August 23, 2011). Web server logs and internet privacy. Retrieved August 28, 2013, from http://www.timeatlas.com/web_sites/general/web_server_logs_and_internet_privacy#.Uh1Dbb8W3Zh
© 2013 August 28 P. A. Regoniel