11 Ways to ‘Game Plan’ for Big Data

11 Ways to ‘Game Plan’ for Big Data

Collaborate with all business users—such as analysts, data support teams and marketing execs—to get a sense of existing pain points that new approaches and solutions can address.

Evaluate available technologies and determine what systems, storage and network assets must be upgraded—then present a business case for these deployments based upon proven ROI.

It’s one thing to come up with new strategies to take advantage of big data. But if leaders and stakeholders aren’t willing to make the necessary changes to respond, then it’s a waste. The business case helps greatly here.

Your “coach,” for example, could be a consultant or firm with a distinguished track record in analytics. Your “quarterback” is a business-side exec who can keep everything focused on strategic outcomes. Your “linemen” are the folks in the trenches who can get the job done.

Make sure from the start that teams are focused on the needed outcomes. Goals must determine technology, not the other way around.

Make sure you’re introducing a new capability at least every three to four months.

It’s not about gathering all the data you can. It’s about gathering all the data that will lend strategic, meaningful insights.

You should have clear requirements for collecting and cleansing the data, as well as classifying it and defining its relevance.

Ranked by Gartner as a top 10 strategic tech trend, in-memory computing improves real-time, data-driving decision-making by eliminating high latency and the need to access data from disk-based storage.

NoSQL enables high-performing, high-availability storage at Web scale, so IT can manage massive data streams with rapid response times.

Historic data is very useful. But even more so when it supports predictive analytics to forecast what business trends to expect in the future.