People with any experience in lead generation can understand why the need for middle-men has been questioned and will always be questioned. Because more than half the time, people who are in the business of providing leads also find themselves on the end of such doubts. On the other hand, this also means they are not strangers to seeing other middle-man industries getting cut out by disruptors and game-changers.
This begs the question: Do you employ them only for or against the middle man?
Example: Software Leads For Big Data
A recent article from Gigaom demonstrates this same dilemma occurring between data scientists and big data technologies. You may have already read several others on how the massive hype over big data has overlooked the need for scientists to actually make sense of it all. This particular article however speaks of companies who are developing an alternative approach to that problem:
“But in a world where big data can perform instantaneously or ‘at the speed of thought,’ the results are dramatically different. When a user can maintain an unbroken train-of-thought, a fluid interplay starts to occur between asking an initial question, getting a response, refining and asking additional questions, and ultimately getting to a new, unanticipated ‘Eureka!’ moment. Think Google Instant for the enterprise. There are a number of startups that are attacking this problem, including Qubole, Boundary, DataDog and several other stealth companies.”
The purpose of the approach? To cut out the middle-man that is the data scientist:
“New companies are focusing on a combination of AI (artificial intelligence), visualization, faceted search and social collaboration tools to empower hundreds or even thousands of ordinary business users to collectively mine, share and evaluate big data sets and gain insight without the need for a data scientist in the middle.”
This should not only concern the data scientists themselves but also software vendors who use big data to attract BI leads. What kind of message would you send out if this is the kind of technology you will be delivering? Is it a good or bad idea to explicitly state that you are out to cut out the middle-man?
To find the answer, you have to keep the following in mind:
- Be objective – Middle-men do not always arise because a few people decided to be a little opportunistic. They can simply be a natural consequence when new technology gives birth to unprecedented flaws. Always be objective when the flaws of your big data features start to rise even if they somehow minimize the role of a data scientist. Better yet, consult a third party with little to no vested interest but can still measure the results.
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- Identify pros and cons – This automatically results from being objective. You know that your big data features can deliver more artificial insight to assist business users. However, nothing is perfect so try using your next BI lead generation campaign on your current customers to see if the cons have begun drawing their attention.
- Explore more positive possibilities – Another result from objectivity is a focus towards more positive possibilities that give both you as well as middle-men new opportunities to do more. For example, maybe your cons have opened up a new gap that these scientists can fill. It might even challenge them to dig deeper. Who knows?
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Cutting out the middle man is not so bad if your goal is efficiency but make sure your pursuit of software sales leads does not blind you from objectivity.
