The phrase “Big Data” is, well, huge. It seems everyone is talking about it, but there’s still a lot of confusion over what exactly Big Data is and how it can be leveraged to achieve business advantage.
Big Data is often described using the three V’s: Volume (the amount of data), Velocity (the rate of change), and Variety (in terms of format, structure, content, and language). Forrester Research goes one step further to define Big Data as “the frontier of a firm’s ability to store, process, and access (SPA) all the data it needs to operate effectively, make decisions, reduce risks, and serve customers.”
But, how can you leverage Big Data to your advantage? The key is Big Data Analytics. Just as Big Data has been a pervasive topic for the last few years, I predict that “Big Data Analytics” will be a major catchphrase in the coming year. In order to take advantage of the many business benefits analytics can provide, it’s important to understand the four key stages:
- Descriptive Analytics – What happened?
- Diagnostic Analytics – Why did it happen?
- Predictive Analytics – What will happen?
- Prescriptive Analytics – How can we make it happen?
When an enterprise makes it to step four in this process, it can begin to experience many significant benefits. For example, you can enable process improvement by streamlining inter-related business activities, make better decisions by using data-based evidence, and support continuous improvement. You can also gain valuable insight into the why? of your data. In a real-world scenario, a bank could analyze Big Data to detect fraud, doctors could determine best protocols, or auto manufacturers could determine the core cause of production delays. All of these scenarios require not just access to Big Data, but effective analysis of that data.
The benefits are clear, and yet, Gartner has predicted that through 2015, 85 percent of Fortune 500 organizations will failto exploit big data for competitive advantage. There are several steps you can follow to avoid Big Data Analytics pitfalls.
- Understand before passing judgment – Analytics has the power to create valuable operational changes, but these may threaten management. Therefore, it’s important to ensure that technical and business leaders trust the analytical outcomes. You should also try to depersonalize the data and create a shared sense of the purposeful impact it can have on business objectives.
- Determine who drives the analytics process – Don’t assume you need to put someone with a math degree in charge of analytics. The Big Data Analytics insights you garner should be easy-to-read and applicable to your unique business needs. You should leverage a data-savvy information worker rather than a mathematician who might add unnecessary complexity.
- Be careful when scoping a project – If the planned project is too large, you may not be able to gain value from your data. Instead, consider an agile approach where you can adjust the project as you encounter feedback.
- Create a strategy to improve business processes and outcomes – Determine measurable performance targets that align with your business objectives. Make sure the entire team is on board to instill a common sense of purpose in business and IT leaders.
- Consider security and data governance – Overlooking these factors can quickly derail a Big Data Analytics project.
By following these steps, you can ensure your Big Data Analytics project is not in the majority that fail or never get off the ground. You’ll be able to use analytics to create a competitive advantage—creating actionable insights that align to your business objectives and that can drive positive change in your organization.
As we look forward to 2015 and beyond, I see the Big Data Analytics ecosystem growing. We’ll see the emergence of more mobile and embedded BI, and an increase in social enterprises that collaborate, adjust, and optimize from insights generated along the business process journey. Stay tuned for more predictions on our blog in the coming month, and check out our webinar to learn how you can benefit from Big Data Analytics today: Big Data Analytics 101: How to Use It to Your Advantage.