10 ways to prevent loss of big data enthusiasm
What are ten things you can do to prevent a loss of momentum in your big data projects?
1. Keep pilot projects short—and make them a success!
This is a fundamental for salesmanship and project management. You should always keep pilot projects for new technology concepts short and ensure that the proofs of concept that you apply to them have a high probability for success! This rule of thumb is no different for big data than it is for any other project.
2. Make your pilot project show a business path.
This is an element many sites don’t even think about in pilot project work—especially with big data. They should. Your goal is to show how big data can deliver tangible results to the end business over time—hopefully, a long time. You can economize your pilot project efforts if you also ensure that there is leverage built into the pilot project’s results. In other words, if your goal is to show analytics gathered from how your customers are using your ecommerce website so you can increase your revenue capture, it might also make sense to “stub in” enough common logic (and data) so you can go out and capture analytics from social media sites, in subsequent projects phases also. If your goal is to track Internet of Things (IoT) data that tells you if your trains are running on schedule and if tracks are in good order, you might want to schedule a phase two project that additionally automates maintenance scheduling and expediting with your track crews so they can fix a track before it becomes a hazard.
3. Use metrics—and demand business results from them.
It’s great to measure system throughput and mean time to result for big data from an IT standpoint—but if you don’t have high impact metrics and results from big data for the business itself, the support of end business users is going to wane.
4. Have champions in the business.
Big data initiatives are only going to thrive long-term if they deliver tangible value to the end business. Consequently, big data is not an initiative that IT should pursue in isolation. If you don’t have your business people on board and sufficiently informed so that they know what they should expect from big data and what it is going to take from both the business and the technology sides of the house to get there—you’re better off waiting until you have the groundswell of support you need from the business.
5. Have C-level visibility.
Big data should deliver results that can impact the corporate bottom line through savings or through new revenue opportunities. In other cases, it might deliver significant strategic advantages. But if it’s not doing any of these, it’s not going to capture the attention of C-level executives for long. Stay focused on the end business results of what you’re doing—and communicate progress regularly to your C-level executives and their managers. Communications from IT should always be in plain English (i.e., no technical jargon).
6. Attain organizational support for the build-out of the entire big data pipeline.
The old axiom “garbage in garbage out” (GIGO) doesn’t change with big data. One of the real “drag points” for big data projects is to clean data up so that it is of high quality before you begin to plumb and analyze it. This is where the CIO should call a meeting of high-level executives to explain the end to end big data “pipeline”—and also the necessity of mundane operations like isolating and cleaning up data before it is plugged into analytics processes. People are more tolerant of what they initially might perceive as “non-value-added timewasters” (like cleaning data!) if they understand how the upfront grunt work is going to contribute to better results down the line. By Mary Shacklett read more