What is big data? For many companies, it’s the integration of massive amounts of diverse information-gathering resources into one centralized platform. As time goes on, more of this information will be used by management to improve company operations and decision making. Today, there are four main uses for big data analytics:
Analysis – Companies engaged in business need to know what is happening in their industry, but what is not commonly known is how that information can impact their bottom line. Data analytics allows management to make informed decisions on where to focus their resources to increase company revenue and productivity. Today, companies employ analytics not just for operational improvement, but also for finding new opportunities in areas such as customer service, information technology, healthcare and so forth.
Activity Detection – In almost all big data analytics applications, the data is automatically analyzed based on activity patterns. The goal is to discover what is working and what is not. For example, a company may acquire a list of mobile users accessing an application. Through analysis, they determine which users are most likely to make purchases or take other actions that improve company performance. This information helps managers prioritize tasks and make other important decisions that have a direct bearing on company profitability.
Operational Management – Perhaps the most important aspect of data analytics is discovering what is working and what is not. Many companies rely heavily on analytics to track and analyze employee attendance and performance levels. By closely monitoring how people are performing, they are able to make changes in company culture and structure that lead to increased productivity and profitability. Today, big data tools are often used for training purposes or to evaluate newly hired employees. They can even be used on specialized tasks such as those managing procurement and inventory.
Machine Learning – While traditional approaches to statistical analysis have been very successful, machine learning techniques are a recent development. Machine learning deals with analyzing large and complex data sets via supervised or unsupervised methods. Typically, business owners use it to find out what products are selling well, how their competitors are matching up against them, and what consumers are searching for. Data mining is one way companies use big data analytics to aid product development. It allows companies to mine through massive databases and identify profitable trends. For example, a new machine can recognize sales trends so that marketers can fine-tune their campaigns accordingly.
Social Media – In today’s social media world, big data analytics has taken center stage. Companies like Facebook and Twitter have long since been successful at helping companies obtain new customers and at keeping their followers engaged. However, companies must also exercise caution when utilizing these online platforms to gather this information. They need to make sure that their information collection and analysis are tightly controlled. Facebook and Twitter have recently taken measures to make collecting information more tightly controlled, so it is best to tread lightly. This doesn’t mean though that businesses cannot have an impact on the social media landscape.
Customer Loyalty – One of the main reasons why big data analytics has become so popular is because it enables companies to better understand customer loyalty. Loyalty is a complex quality of any customer. In general, a customer will choose only with some degree of emotion when it comes to purchasing a product or service. While a customer may have no desire to purchase something just because it is “old news”, they might still possess a degree of loyalty if the item was perceived as a reward for being a loyal customer. Businesses can tap into this quality of a customer through data analytics.
Data mining can be used to provide financial results. Examples of tools that derive financial results from big data analytics are: binomial tree models and multiple linear regression. Another tool that is often used in analytics is fuzzy logic. Fuzzy logic is a type of machine learning that makes statistical analysis much easier. The data mined from analytics can be used to train computers to recognize patterns and make insightful decisions about the world around them.