How Companies Use Big Data Analytics

how companies use big data

How Companies Use Big Data Analytics

Today’s business is built on big data and predictive analytics. However, many companies are still stuck using outdated approaches that have limited success. The first step to be able to use big data is to understand its definition. According to Wikipedia: “The term ‘big data’ refers to large, complex, and interconnected databases and services used by organizations”. In the past, companies used a single RFM (Radio Frequency, Quaternum, and CCD) to obtain behavior-based customer segmentation.

With the introduction of big data, companies finally had the chance to further refine their traditional technical models with more detailed, object-oriented data, including individual customer website browsing history, the browsing history of friends and relatives, the activities of social network users, and so on. The benefits of using this new platform were immense; for one, it allowed quick and easy integration of complex technologies into a business’s operational systems and processes. Companies no longer needed IT departments to manage and support big data; they now had access to an entire world of information and could act on it immediately. Another advantage was in making operational, big data more useful for decision-makers. Now, instead of spending days, weeks, months, or even years collecting, managing, and analyzing the same old data; a company could now use all the information gathered and analyzed to make informed decisions and implement solutions more quickly.

Machine Learning Algorithms: Machine learning algorithms are a relatively new concept, yet have proven quite popular in applications such as Google’s smart phone smartphone and Apple’s iPad. Machine learning uses large database systems to analyze large sets of unstructured data; it makes use of artificial intelligence (self-improvement) and includes reinforcement for better results. In other words, it’s a form of deep learning where computer programmers teach machines how to create and perform certain tasks, ultimately providing business owners with better options and better results.

Deep Learning: Deep learning relies on the principle that once you can find a solution by manipulating an educational system, you can then instruct a machine how to solve the same problem. This is similar to how humans learn, wherein they take whatever they learn and apply it to different situations. For example, once you know that a person’s gender will impact the price he will charge for a product, you can design an algorithm whose output is gender-neutral so that all customers will end up being charged the same price. Similarly, once you know which web browsers generate more sales for your company than others, you can design an algorithm that targets those particular browsers and makes sure that they receive the most traffic so that sales will increase. Such solutions allow companies to take advantage of newly available customer big data while implementing new and improved services and processes.

Historical Data Science: Another popular usage of deep neural networks is in the realm of operational, big data. Operational big data encompasses any information which can potentially impact organizational performance, such as customer demographics, product specifications, or environmental concerns. With this data in tow, IT professionals can use artificial intelligence (ANNs) to achieve answers to many questions which can include how sales fluctuate from week to week, which clients prefer to call in with a specific inquiry, or which product is most popular among your demographic. Such answers are critical to the success of your business. Therefore, if you want to use artificial intelligence to address operational big data issues, you must train an agent which mimics your company’s customers and interacts with them on a one-to-one basis.

Crowdsourcing: Many large and established companies have taken to crowdsourcing in order to get a cheaper and more efficient answer to big data questions. This includes problems like understanding a complex piece of code, designing an effective online marketing campaign, or tracking the progress of project development. In essence, crowdsourcing is the process of soliciting ideas, organizing the participants, and providing feedback. Today, even small companies employ crowdsourcing in order to solicit responses to their products or services. Because of its low cost and high effectiveness, it is no wonder that many large companies turn to crowdsource solutions.

Distributed Computing: Another way that companies use big data analytics is through the use of Distributed Computing. The goal of this type of computing is to pool resources via the Internet instead of having every computer on a server. By using Distributed Computing, companies can save money by not having to buy more servers, power, and space than is necessary. In addition, this method is extremely efficient, as it reduces the time wasted on calculations related to large data sets.

As more companies use big data analytics, more organizations will realize the benefits of incorporating these solutions into their own business processes. However, as a business owner, you cannot simply throw up a website and expect your IT department to mine information from Google, Yahoo, or the government for your company’s data. Big data analytics requires manual analysis and interpretation of often large amounts of data. Therefore, it is imperative that you work closely with an experienced and talented team that has the knowledge, tools, and training to handle all of the information that is pertinent to your company.