When your organization can leverage all, rather than a subset of its data, tremendous things become possible. According to a McKinsey study, If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. Current examples of big data benefits exist in the financial services, retail and media industries. However, the one I find most interesting was recently in the news, and it is about how Target found out a teen girl was pregnant before her father did.
Data-driven feats like this are possible because forward-thinking companies like Oracle and IBM are helping companies design more efficient ways of managing their ever expanding volumes of structured and unstructured data. In this effort, the challenge is not just about the capturing of the data, but its organization, storage and analysis in a manner that is effective enough to provide business value to the users. This is the challenge of big data.
According to Teradata, an industry leader in data warehousing and business intelligence, “Big data exceeds the reach of commonly used hardware environments and software tools to capture, manage and process it within a tolerable elapsed time for its user population.” What this means is that while big data has the potential to deliver significant business value, it will not easily fit into your current IT environment, or be managed by your existing data management tools like the Relational Database Management Systems (RDBMS). The following are four key attributes of big data:
1. Value: There’s no argument about the fact that big data has to have value. While the value may not be visible at first, there must be the potential to gain significant competitive advantage or other business value from the data set in question.
2. Volume: As its name suggests, big data is usually high on volume; it is big. When you consider the data sources in question – social networking sites, corporate websites, and machines – it is easy to understand how the data sets can become very large, very fast.
3. Velocity: Big data is usually not just high on volume. It is usually high in velocity as well. What this means is that the values of the data sets change at a very rapid rate. For example, think about a device that reads vital health statistics of a person – like heart rate and body temperature – on a per second basis.
4. Variety: Data generated by an ERP system which supports the transactional operations at a company is likely to be of one format: text. However, data generated from social media sites are usually a combination of structured and unstructured data including text, audio, video and images. This increased variation in the data formats presents a serious challenge in terms of storage, organization, analysis and presentation.
In the second part of this article, I will be outlining some steps for extracting business value from big data. Stay tuned 🙂
Image by Teradata.