|By Jeffrey Abbott||
|February 18, 2015 11:30 AM EST|
Circa 1993, I was tired of looking through the couch for spare change and realized that my summer job had not produced enough income to cover my day to day expenses for the upcoming school year. Given that I had classes in the day, a night job serving food and drinks in a bar or restaurant was among the most appealing, and lucrative options for a college student. Unfortunately, waitstaff and bartender jobs were highly coveted and no business owner had any reason to risk hiring someone with no experience. You can’t get a job with no experience, and you can’t get experience with no job. There was seemingly no way to get in. Job applications required a list of my previous employers. My landscaping, lifeguarding, and camp counselor background was not persuading potential employers that I could do the job. Finally, a friend of mine said they just started in a new restaurant and it needed waitstaff. The combination of a known job opening, a personal referral, and me lying through my teeth that I had experience finally secured me a coveted position. Finally, I had broken the Catch 22 and made it into the club. Floyd, I am sorry I lied to you about having experience. I was young and I needed the money.
Fast Forward 22 years, and I’m seeing a similar Catch 22 with big data. Big data pundits are promising big things such as improved decision making, enhancing customer experiences, improving security and IT operations, and infrastructure modernization. All you have to do is empty your wallet on a big data “solution” and everything gets better. Well like the restaurant owners, why would any business take such a risk when the promises are so lofty, so vague, so subjective, and unproven? Any smart business, or restaurant, would say “I need proof before I invest in this.” They’d say “What does improved decision making mean for us? What decisions will be improved? How much will that help? How hard will it be? How will we know if it’s paying off?” “What else could we do instead?” One could ask similar questions about any of these top-level big data use cases. The conundrum is that big data is uncharted water for most businesses and they don’t know where or how to start. Big data can do so many things, and the biggest obstacle facing most businesses with big data is identifying the right use cases. To make a business case for investing in big data, someone needs to first explain (or show) how the data will be monetized. This is the Big Data Catch 22: You can’t buy a big data solution if you don’t know what insights you’ll get, and you don’t know what insights you’ll get until you buy a big data solution.
One of the best ways to break the big data catch 22 is a Big Data Vision Workshop:
The idea is to get IT and the business to come together on the top strategic initiatives, and use a sampling of the available data to investigate what questions can be asked of that data to generate a list of potential big data use cases that support an initiative. Next, data science experts create models and visualizations to determine the types of insight that could be gleaned from the data. And through a combination of feasibility assessment and benefit analysis, the use cases can be prioritized against KPIs and success metrics to identify the optimal set of go-forward big data use cases and resulting benefits to the strategic initiatives. This process creates the building blocks for a business case, and recommends a plan charter for the big data solution that will do the best job of accelerating the strategic initiatives.
- What’s In and What’s Out for IT in 2014
- The Data Lake Has Landed | @ThingsExpo #BigData #DevOps #IoT #M2M #API
- How to Save Money While Doing Green IT
- Big Data Technology - the Rebel without a Cause | @BigDataExpo #BigData #DataLake
- Creative Use Cases for 3D Printing
- Don’t Call #BigData Dead | @CloudExpo #IoT #AI #ML #MachineLearning
- IT and the Mobile User Experience
- Data Science says: “You don’t want a Ferrari.”
- Live From Strata + Hadoop World: Dry Lakes, Salt Lakes, Data Lakes
- The Big Data Catch 22