The stakes are high with big data engagements, although the profits have certainly been elusive. Big data opportunities are out there for those who know how to find them. And some vendors like IBM are starting to make it easier to jump on the big data bandwagon with new use cases and reference architectures. IBM reports a 620 percent increase in bid data projects last year, including 1,250 partners at 1,500 customer sites. IDC estimates that big data spending could hit $8 million per organization this year. That certainly spells big data opportunities for VARs, and the possibility of big mistakes.
When selling those big data opportunities to customers, you have to be prepared to walk the prospect through the process, remove the barriers and concerns, and get them to embrace the possibilities offered by big data. Sounds like selling, right? Selling big data is not as easy as selling some other solutions; it certainly has a longer lead time, a lot more moving parts, a bigger financial commitment, and, of course, a bigger payout for the VAR. When you start making your case for big data, be sure you address the potential objections up front, in language that the stakeholders understand.
Mistake 1: Making a weak business case
It’s important that your prospect understand big data in terms that directly affect them and their business. A research study by Echelon One revealed that only 27 percent of industry leaders really understood what “big data” means. It is a misused and misunderstood term. Make sure that you explain big data as part of your business case in terms (and returns) that the prospect can clearly grasp. Be sure to close the gaps between theory and practice.
Like cloud computing, the whole notion of big data is too vague for most executives. You need to make the big data opportunity for the company real, with real returns. Make it relevant to the company’s business. Use examples that will resonate with management, such as an analysis of how using big data for pricing analysis will help with stocking and inventory planning, or how using big data analytics on their social media campaign will reveal new solutions to company problems. Your challenge is to link emerging big data technology to a well-defined business problem. Show how you can solve that problem in a way that either saves money or adds to the company’s bottom line, or both. Remember to choose an example with sufficient potential ROI to offset the costs of the big data initiative.
Mistake 2: Not using business language
We all know that IT professionals tend to talk in bits and bytes, talking about storage requirements and data types. Avoid the geek speak. Decision-makers don’t really care about the growth of corporate data or coping with data storage. They want to know what big data offers in terms of business opportunities. In other words, “don’t tell them how to build the watch; give them the time.” Make your presentation simple, using easy-to-understand visuals, and talk about the business returns. The worst thing you can do is leave management confused, or even intimidated and irritated, by drowning them in terabytes and analytics.
Mistake 3: The plan is too vague
Too often the strategy composed is missing key components. About half of all big data engagements fail to deliver sufficient returns because key components are overlooked or have to be factored in late in the game. Be sure to account for issues such as adequate data storage or data migration. Data migration particularly can be a challenge that affects delivery times, since you have to make sure the data is clean, convert data formats, and deal with other unknowns; migrating legacy data is never easy.
Mistake 4: Not addressing security concerns
There has been a lot of buzz about big data security, especially in the health care market where there are HIPAA compliance concerns. When dealing with sensitive data such as financial or medical records, you need to make sure that security and compliance issues are addressed in advance. Be sure to account for security measures and application-specific tools to prevent data loss or unauthorized hacking.
When working to tap big data opportunities, be sure to address the business needs of the prospect in terms they understand. As with all projects, you want to underpromise and overdeliver, and with big data the key is to make sure that the big data project you present is comprehensive and offers real value. Don’t leave yourself without enough time or budget to accommodate unforeseen challenges, and think through unique problems that the prospect might face, such as security. If you do your homework and develop a comprehensive business case, you will avoid the most common mistakes when selling big data opportunities.