How Companies Use Big Data Analytics to Make Better Business Decisions
In today’s technological world, businesses are finding ways to implement big data technologies into their daily operations. Customers, suppliers, and other decision makers want more information and are willing to pay for it. Businesses are turning to advanced analytics to help them understand their customers, identify competitive concerns, and make informed decisions about their operation.
How companies use big data analytics varies greatly by industry and depends on the perspective of the company. For example, supply chain companies rely heavily on machine learning in order to optimize their process and improve their profit margins. Human executives still have a direct impact on business operations though. Executives still must manage people and keep the organization running smoothly. With the implementation of intelligent software systems and the leveraging of complex technology networks, human interaction has largely been replaced by computer automation.
Machine learning is a subset of big data analytics that focuses on using artificial intelligence to predict and create patterns from large amounts of unstructured data. Machine learning approaches to analyze massive amounts of data without being selective about the factors it counts. In other words, it can take a simple event and predict how many times similar events will occur in the future. In this way, it can make statistical comparisons across different time periods and situations. Machine learning uses a form of natural language processing called reinforcement learning. This relies on the idea that if an employee performs a certain action many times a specific response is given.
One example of how companies use big data analytics is gas companies that want to maximize their profit margins. For example, gas companies often sell gas at certain times of the day. They can get a big discount when they sell during those times. Using complex algorithms, gas companies can predict when they may be able to sell their gas next so that they can maximize profits and cut expenses.
Another example is how a trucking company such as a DHL operates. Trucking companies typically have millions of records of big customer data. A trucking company may want to know more about its customers, what they order, how often they order, and where they live. To get this information, a trucking company uses a combination of manual and machine learning techniques. Trucking software can analyze the data, map the big customer data into easy to read charts and tables, and then determine where to optimally locate trucks and drivers.
Another example of how companies use big data analytics is how utility companies monitor the energy usage in homes and businesses. By using sophisticated equipment and algorithms, utility companies can monitor the energy usage in a home, office, or building. With this information, the utility company can determine where its money is best spent. The utility company can also improve its customer service by providing answers to customers’ specific questions. Using big data analytics, the utility companies are able to provide custom solutions to specific customer needs, rather than having to perform research on each customer individually.
Deep neural networks, also known as deep neural networks, are examples of how companies use big data analytics to improve their operational big data science efforts. Deep neural networks have been described as “neural networks of complex units with multiple levels of inputs, which are formulated to work together via a directed learning algorithm” by the Stanford Research Institute. Deep nets are used in online advertising, search engine optimization, natural language processing, image processing, speech recognition, product catalogs, and more.
Predictive analysis is one of the key pieces of an overall improved predictive management approach. In order for a business to profit from predictive analysis, it must be able to interpret and apply the results of its analytics better than a human could. Machine learning and predictive analysis are key components of this advanced analytics, and this is just the beginning. Using data analytics to make better decisions and improve operations at companies such as the gas industry will only continue to advance as more businesses realize the profit potential of this powerful technology.