The volume of data that is being collated globally by companies, organizations and agencies is virtually inconceivable, and that amount continues to grow at a brisk pace. Potentially, businesses have the opportunity to glean important insights that could give them the edge over the competition.
In reality, however, little of the data that is being collected is subjected to analysis.
Big data is not important simply because it exists - your data is a true asset only if you can do something with it.
Companies that apply proper analytics to their data, however, typically find ways to reduce costs, increase operating efficiency and make smarter decisions. Marketers discover ways to optimize their offerings, understand their customers' behavior and build customer relationships — which is why all marketing professionals need a working knowledge of data science.
The problem that many businesses have with big data is that they are overwhelmed by the sheer volume of the data, so much so that they cannot find a way to analyze it properly. Data is stored away for a future analysis that never seems to happen. They try to apply the same rules to big data that have always been used for data management, only to find that traditional methods no longer work. At some point, it becomes obvious that they can no longer view data integration as a separate discipline. Instead, they must treat data integration as part of an overall strategy that includes data governance, data quality and data management.
Taking a holistic approach to big data allows companies to apply the concepts of big analytics. Big data is most valuable when it can be harvested as quickly as it is needed and analyzed as thoroughly as is needed. However, in many organizations, only subsets of data are available for analysis due to constraints such as IT platforms that cannot handle the volume of the collected data.
Fortunately, new advances in technology have made it possible for any company to harness both big data and big analytics:
- Storage costs have declined significantly. In 2000, storing one gigabyte of data cost approximately $16; today the cost is approximately $0.07
- Technologies have been developed specifically to store and process large volumes of data.
- Methodologies, including virtualization, parallel processing, grid environments and clustering have leveraged the power of cloudcomputing and high-speed connectivity to extend possibilities.
Three technologies are proving especially useful in managing big data and extracting meaningful intelligence from it.
- Big data information management: Big data present all of the issues common to data management but on a much larger scale. Issues such as data quality, determining the data that would give the most value for analyses and how to manage workflow are magnified. With the right technologies, however, these issues can be made manageable.
- High-performance analytics: Successful businesses need access to information within minutes or hours rather than days, weeks or even months. High-performance analytics can use big data to offer timely insights. It allows options, such as deciding whether to analyze all data or a subset, to give companies control over how deeply they want to mine data for specific purposes.
- Flexible deployment options: It is not practical for some companies to support an IT infrastructure to manage big data. Thanks to cloud computing, it is no longer mandatory to keep all data and run all analytics on local servers. Flexible deployment allows you to store all of your data in the cloud, locally on dedicated servers or in any combination that best suits your unique requirements.
New technologies have put big data analytics within the reach of every business, not just large corporations. More than 33 percent of all companies are currently practicing some type of advanced analytics, according to a survey by TDWI. Those that understand all of the possibilities offered by big data and corresponding analytics will reap the greatest benefits, including an edge over their competition.