What Is Big Data Analytics and How Can it Benefit Your Business?
What are big data and why should you care? Big data has been described as a paradigm shift in how the IT industry looks at information and the way it’s processed through different layers of the enterprise. The shift from traditional computing architectures – desktops, laptops, tablets, smart phones, etc – to “big data” clouds and big data centers has many benefits for businesses, but also many drawbacks that need to be addressed if your organization wants to continue moving forward in this new data paradigm.
First, understand what is big data analytics and its central focus – understanding customer insight – before diving into its definition and implementation. Big data is a discipline which takes various approaches to analyze, collect, manipulate and then exploit data sets that are too large or complex for the traditional data processing applications to deal with effectively. Such tools as Hadoop, Spark, and the recent Map-reduce framework and libraries enable quick and reliable aggregation, extraction and processing of large amount of data in a consistent and performant manner. But big data analytics also poses some challenges for data analysts and managers because it introduces new technologies into the business environment. As these tools become more powerful and as they are able to provide insights which were not possible in the past, managers and executives will need to take an inventory of the pros and cons of using such tools and decide whether or not to use them for specific purposes.
One of the key benefits of using such tools is the increased efficiency which can be brought about by leveraging a much broader variety of data sources. Traditional IT infrastructures were unable to handle the volume of data that was coming in daily from a wide variety of sources like social media, mobile devices, and online retail sales, which required new and different solutions. There was a corresponding increase in the cost of IT infrastructure and software usage. This increased cost was passed on to consumers and businesses. By using advanced analytics and tools, however, organizations have been able to reduce their overall IT costs, improve customer satisfaction, and get closer to their business goals.
Another benefit of using big data analytics tools is the empowerment it gives to executives and managers. Traditional IT infrastructures were not equipped to deal with the massive amounts of data that were coming in from a broad range of sources. Organizations were literally working day and night to manage the day-to-day activities and data flow, but were unable to address the bigger picture. With the introduction of advanced analytics technologies and applications, however, organizations have been able to address the key issues which have always been a problem, such as training, operational efficiency, and data management.
The biggest advancement in the field of big data analytics has come through the use of machine learning. Machine learning allows an organization to rapidly scale up its data mining capabilities in order to find patterns and trends. Such capabilities have allowed companies to make more efficient use of their massive data sets, allowing them to make better use of what they already have and turn that raw material into a large amount of potential profit.
One of the major benefits to organizations is that they are now able to take on additional workloads without having to build new infrastructure or hire new employees. With the help of analytics technology, organizations are able to extract valuable insights from their massive data sets. These insights allow organizations to make better decisions for their business models, products, and services. It’s no secret that at the end of the day, a company needs to be profitable. By providing insight into trends and potential problems, analytics can give organizations a competitive edge over their competitors and ultimately, lead them to increased profitability. Big data analytics provides these organizations with the kind of analytical edge needed to compete in today’s marketplace.
Another advantage to organizations using advanced analytics techniques like machine learning and in-depth processing is that it creates a win/win situation for both. On one hand, by helping businesses improve their performance, they are also helping them reduce their expenses. By cutting unnecessary expenses, companies are better positioned to realize the profit potential of emerging trends and technologies. However, by identifying, monitoring, and then quickly acting upon these emerging trends and technologies, companies will be able to capitalize on them and ultimately improve their bottom line.
The primary goal of predictive analytics is to make predictions. Machine learning uses data mining and other techniques to make predictions about what types of patterns and activities will likely occur next. While predicting what will happen in the future may not always be correct, using predictive analytics gives businesses an edge over their competition because they know ahead of time what is likely to occur. With this information, they can make better decisions and invest in areas that will increase their chances of success. These methods are just two of the many ways that predictive analytics uses data mining and machine learning to create superior decisions for organizations.