The Applications of Big Data in Business

Big Data

Big data seems to be everywhere in business these days. No matter what your area of study or at-work specialization, you will almost certainly have encountered it. Whether you have encountered skeptics or feverish proponents of big data use in business, you will undoubtedly have been exposed to some strong opinions about analytics’ latest golden ticket field. What, then, is big data?

Big data is, obviously, big. To be considered ‘big data’, information sets must be vast, diverse, and constantly expanding. The volume, velocity, and variety of a dataset can all help it be identified as ‘big’. This kind of dataset is so vast and varied that it cannot be manually collected, coded, and analyzed. Instead, intelligent software capable of machine learning is used. Machine learning and big data are inseparable tools in the world of business and analytics. 

Roughly speaking, big datasets can be grouped into one of two categories:

Structured big data is already possessed by the analyzing organization. It is frequently numerical in raw form. This kind of data is usually entered into a database in an organized fashion and is easy to code into workable, meaningful information. 

Unstructured big data does not fall into simple predetermined categories set by the analyzing organization. It needs to be coded to be understood. For example, data harvested from consumer social media activity would fall into this category. 

Both structured and unstructured datasets are immensely useful in business provided that they are collected, analyzed, and visualized well. Here are some specific fields in business in which big data is being used. 

Market Research

Perhaps the most obvious application of big data collection and analysis in business comes from market research. In our increasingly networked world, more and more data are being produced by consumers every second of the day. By collecting and analyzing large datasets using software, market researchers and analysts can, in theory, get a good grasp of the market’s state, and what it is that people actually want from products. 

More data, however, does not necessarily equal better results. Although huge datasets are naturally more representative of markets than small ones, this does not mean that they are truly representative of a market. For data to scientifically represent a market, an analyst would have to have data available on every potential consumer or collaborator. This is impossible – just as it always has been. Instead, analysts should make use of big data while retaining a critical and scientific approach to any results obtained. Unfortunately, some business leaders have rushed into the analysis of big data as if it was a magical solution – abandoning their statistical analysis acumen in the process. Combining big data research with traditional surveyance and focus group research is necessary. 

Competition Analysis

Competitive business intelligence is regularly carried out but companies looking to understand their rivals. However, researchers have suggested that big data analysis is very likely to influence competitive intelligence in the near future. Whereas traditional competition analysis involves centralized information gathering efforts such as surveys, machine learning aided big data analysis could allow intelligence professionals to gather extremely broad and complete pictures of a competitor’s performance. 

Competitive intelligence isn’t exactly business espionage. Instead, it is more like an inward-looking form of market research. Reliable, competitive intelligence can help a business safeguard a place in the market by filling gaps that competitors fail to identify. 

Human Resources

The workforce of a company is the beating heart that allows it to function profitably and efficiently. In recent years, much has been made about the possibility of a big data revolution in HR. The appeal of big data in this field is obvious: using huge datasets, analysts would be able to identify the ideal candidate for a job by comparing historical worker profiles and their successes and failures. Ultimately, this is a highly impersonal approach to hiring but may lead to a more optimized workforce. Companies offering analytics in the world of HR also claim that using large datasets and analyzing them intelligently can help them identify and fill skill gaps within organizations. 

Human resources leaders are, however, struggling to fully incorporate big data into their practice. There is a degree of skepticism from some experts as to whether big data will actually help create a better hiring and working environment. Closer collaboration between analysts and human resources directors needs to take place to successfully integrate big data and HR fields. 

Trend Forecasting

A rather large element of business strategy development is the forecasting of trends. These trends can be in consumer culture, economic prosperity, material availability, and just about any other field, you can imagine. Naturally, the more historical data that can be analyzed, the more accurate a trend forecast is likely to be. The top business management courses include tuition on big data analytics when discussing strategic planning in part because of the sheer importance of trend forecasting in the field. The datasets that could be considered to be ‘big’ are, by definition, so large and varied that they are unable to be efficiently analyzed by human beings. Instead, intelligent software has made previously unintelligible data easy to visualize. This has completely changed the way people build a business strategy. 

Risk Management 

All profitable business contains an element of risk – often a rather large one. The larger the dataset and the better it is analyzed, the more able a company will be to mitigate risk. Thus, risk management is an essential part of strategic business planning. Building predictive models using large, intelligently harvested data helps to offset the inherent risk involved in all trade. 

Of course, some problems cannot be foreseen, no matter how much data is trawled through. An over-reliance on data for risk management should be avoided. Companies should not be encouraged to avoid contingency planning just because their data rules out the likelihood of an emergency. Confidence in risk mitigation within a market usually ends up contributing to a crash – as it did in the late 1920s. 

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