Big data is most useful if you can do something with it, but how do you analyze it? Companies like Amazon and Google are masters at analyzing big data. And they use the resulting knowledge to gain a competitive advantage.
Just think about Amazon's recommendation engine. The company takes all your buying history together with what it knows about you, your buying patterns, and the buying patterns of people like you to come up with some pretty good suggestions. It's a marketing machine, and its big data analytics capabilities have made it extremely successful.
The ability to analyze big data provides unique opportunities for your organization as well. You'll be able to expand the kind of analysis you can do. Instead of being limited to sampling large data sets, you can now use much more detailed and complete data to do your analysis. However, analyzing big data can also be challenging. Changing algorithms and technology, even for basic data analysis, often has to be addressed with big data.
The first question that you need to ask yourself before you dive into big data analysis is what problem are you trying to solve? You may not even be sure of what you are looking for. You know you have lots of data that you think you can get valuable insight from. And certainly, patterns can emerge from that data before you understand why they are there.
If you think about it though, you're sure to have an idea of what you're interested in. For instance, are you interested in predicting customer behavior to prevent churn? Do you want to analyze the driving patterns of your customers for insurance premium purposes? Are you interested in looking at your system log data to ultimately predict when problems might occur? The kind of high-level problem is going to drive the analytics you decide to use.
Alternately, if you're not exactly sure of the business problem you're trying to solve, maybe you need to look at areas in your business that need improvement. Even an analytics-driven strategy — targeted at the right area — can provide useful results with big data. When it comes to analytics, you might consider a range of possible kinds, which are briefly outlined in the table.
| Analysis Type | Description |
|---|---|
| Basic analytics for insight | Slicing and dicing of data, reporting, simple visualizations, basic monitoring. |
| Advanced analytics for insight | More complex analysis such as predictive modeling and other pattern-matching techniques. |
| Operationalized analytics | Analytics become part of the business process. |
| Monetized analytics | Analytics are utilized to directly drive revenue. |
