Big Data is a very big deal for many industries. The onslaught of connected devices and IoT has created a large spike in the sheer volume of data that businesses collect, process and analyze. In addition to big data comes the opportunity to unlock enormous insights for each industry, small to big. This article uncovers the definition of Big Data and shares some of its advantages.
The term ‘big data’ refers to the volume of data that must be processed, analyzed and made available to end users. This definition is extremely ambiguous and depends on what the end users actually need the information for. Large businesses may need to process billions of pieces of data per day to remain competitive and efficient. On the other hand, small businesses and start ups may only need a small amount per day. Thus, the term can be used as a starting point for large-scale data collections, while the smaller business may use a different definition to define the volume and nature of data they require.
Data volume is directly proportional to the level of processing power. Big Data requires distinct and consistent high velocity data processing to derive value from it. Value extraction requires using tools that can quickly detect trends, make sense of large volumes of unprocessed data and extract useful insights from it. Data science is a set of algorithms and programming techniques that are designed to make sense of and implement data from large pools of unprocessed data.
The second major advantage is terabytes and petabytes of data storage capability. Big Data is not about how much data you can collect; rather, it is about the volume of data that is required to effectively analyze the data to provide insights and solutions. The definition of big data thus refers to the volume of information that must be processed in order to provide value. This requires a lot of hardware resources and expertise. Hadoop is one of these platforms that helps data intensive applications to run on low resource machines, therefore lowering the overall cost of running the applications.
Big Data also refers to time series. Time series are sets of related data. For example, a sales report may contain customer demographic information, such as age and gender, and product attributes. Data quality is also an important factor in defining big data.
Big Data also enables better decision making. It provides a great deal of analytical power and allows the sharing of accurate data in real time. Decision makers can use this information to make decisions faster. It also gives a competitive edge to organizations because it lets users compare historical data to current data and vice versa. Decision makers can exploit Big Data to their advantage by applying it to their own business requirements. For instance, retailing organizations can use big data to understand consumer buying habits, or travel agencies can use it to provide personalized travel packages based on customer preferences.
Big Data may also affect the way we do business. Companies will need to learn how to extract value from big data. Experts in IT are predicting a ” Cloud revolution” where providers of big data will increasingly own servers and network infrastructure and data centers. They will then lease or sell their data to third parties. Some may argue that this increases efficiency but others believe it lowers data quality because companies may have to share too much data, which may be of poor quality. Whatever the case, managing big data has a lot of promise for today and into the future.
The definition of big data may be fluid as it continues to evolve. At the very least, it continues to define new possibilities and challenges for tomorrow. IT professionals must continue to work on developing tools and systems that make big data easy to extract and analyze effectively. They also need to develop methods for managing and protecting the data. It also needs to be made easy to process, meaning that even people who are not computer experts can use it.