7 Tips to Succeed with Big Data
Big data just keeps getting bigger and more useful. Some companies housed data before they could harness it, and now they are reaping the rewards.
The reality is that big data will continue to grow. Whether your data is in a spreadsheet, a database, a data warehouse, open source file systems like Hadoop, or in all of those, you need the flexibility to quickly connect to data and consolidate it. Never mind the actual size—it's the principles of collecting, and especially leveraging your data that are important.
This paper will teach you where to start. These are 7 best practices for approaching your big data, and how to utilize it to its fullest potential.
We've also pulled out the first several pages of the whitepaper for you to read. Download the PDF on the right to read the rest.
Just when you thought big data couldn’t get any bigger, it got bigger still. Regardless of its actual size, big data is showing its value. Organizations everywhere have big data of all shapes and sizes. They recognize the importance, the opportunity, and even the imperative to pay attention. It has become clear that big data will outlive those who ignore it.
Organizations that have already tamed big data — the multi-structured mass they stored before they knew its worth — are improving their operational efficiency, growing their revenues, and empowering new business models.
How do they do it? Their techniques for success can be summarized in seven tips.
1. Think long term by thinking short term
If you worry about staying current with big data technology, you’re not alone. Everything is evolving so fast that it’s impossible to know which tools, platforms, and methodologies will be best this year or next.
Relax. This rapid evolution can work for you.
Every year, vendors will get better and better at using big data. Relational and online transactions systems (OLTP) will become more efficient and smarter, whether running on-premise or on the cloud. Techniques will develop to ease relations between Hadoop and data warehouses. And all the time, products will come to market to meet your particular needs ever more exactly.
So stay loose. Stay open to the possibilities of new products, as long as they deliver enough value to justify bringing them into your existing environment. Maintain a business intelligence platform that directly connects to a wide variety of formats. You’re now ready for anything the market can provide.
2. See through the false choice
Which will your organization need, Hadoop or a data warehouse? Ah, but this is a trick question. Not only can Hadoop and data warehouses work well alongside each other, organizations actually benefit from their collegiality.
The data warehouse is best to crunch your important, structured data and to store it where BI tools and dashboards can find it easily. But it’s weaker and slower for analytic processing and some types of transformation.
Let Hadoop do that. Also, though Hadoop is weak in interactive queries and data management, it’s good at gulping down your raw, unstructured, and complex data.
Together, they form a symbiotic relationship. Imagine, for example, the data that executives use to project their inventory needs for next year. The data set is probably massive, and there’s too little time to model it, restructure it, or otherwise prepare it for the data warehouse. When executives are done with it, perhaps in only a week, they’ll dispose of it. That’s when Hadoop steps up to store and refine the data and send a sample to the data warehouse.
“Big data isn’t a replacement for data warehousing,” writes Third Nature CEO Mark Madsen in his article “What big data is Really About.” “Nor is it an island to be maintained separately. It’s part of the new IT environment.”
Don’t fall for the Hadoop-or-data warehouse trick. You can and should use both.
3. Bring big data down to eye level
Big data comes down to eye level when you visualize it. A 2013 report by Aberdeen Group found that “at organizations that use visual discovery tools, 48 percent of BI users are able to find the information they need without the help of IT staff.” Without visual discovery, the rate drops to a mere 23 percent.
Also, managers using visual data discovery were 28 percent more likely than peers without visualized data to find timely information, according to the study.
Perhaps most important when it comes to big data, the report found that visualization also encourages interaction with the data. Managers using visualized data are more than twice as likely as their peers to interact extensively with it (33 percent vs. 15 percent). They’re also much more likely to ask questions on a whim, questions that are often inspired by insights that arose a moment before.
Exploring data visually lets the data’s story unfold vividly in a way the brain can grasp in a flash. ”A light bulb goes off,” says Wells Fargo Vice President of Strategic Planning Dana Zuber says, “You just don’t get that with a spreadsheet.”
Visual analysis allows you to do two things at any moment:
- Change the data you’re looking at — because different questions often require different data.
- Change the way you look at it — because each view may answer different questions.
With these simple steps, you enter what’s called the Cycle of Visual Analysis: you get data, view the data, ask and an-swer questions, and repeat. Each time, your inquiry deepens along with your insights. You may drill down, drill up, or drill across. You may bring in new data. You may create view after view as your visualization speeds and extends your thinking.
When you’re ready, you share. Colleagues ask and answer their own questions — accelerating the whole team’s in-sight, action, and business results.
