If one thing has become clear in the past few years of big data hype, it’s that getting started with big data is more complicated than implementing Hadoop, and the concept of big data requires more company buy-in than an investment in a piece of technology. It requires having the right talent, an efficient pipeline, integration with BI tools and a culture that encourages using data to find new insights.
If one thing has become clear in the past few years of big data hype, it’s that getting started with big data is more complicated than implementing Hadoop, and the concept of big data requires more company buy-in than an investment in a piece of technology. It requires having the right talent, an efficient pipeline, integration with BI tools and a culture that encourages using data to find new insights.
So what are some things to remember?
1. Visualization
Visualzation is a key step to moving a big data initiative from data collection to data analysis and business insight. A
study by the Aberdeen Group found that among organizations that use visualization tools, 48% of users can find the information they need without going to IT for help. That rate drops to 23% when no visualization tools are used. The same study found that managers with visualized data are twice as likely to interact with data extensively and are more likley to ask questions on a whim. The specific tool used will vary by organization, but failing to couple big data with the appropriate analytics tools will only get you half way to your business objective.
2. Have a Business Objective
Unfortunately, many businesses have seen their big data initiativies fail simply because they didn’t take the first crucial step of setting a buisness objective for the project. A set objective should dictate what technology you invest in, how that technology interacts, who has access to the data and how you act on that data. Without an objective, you may end up with a setup that doesn’t meet your needs or wandering aimlessly through data without knowing what questions to ask or what insights to look for.
3. Provide Ongoing Training and Support
Employees can learn a lot from each other as they experiment with new tools and data sets. Set up a central hub to provide training and where employees can share best practices and tips that they learn. Giving feedback and being flexible is especially important when a project first starts, so you can eliminate ineffective processes or ideas quickly and get to the meat of the project.
There are of course many other big data tips, including those shared in the SlideShare above. What tips would you add?
