“If you can't explain
it simply, you don't understand it well enough.” Albert Einstein
Einstein didn’t need a
sales force to go out and “find customers” for his groundbreaking insights
about the structure of matter, time and space. He published scientific papers
at the age of 26 that brought instant acclaim in academia and eventually,
accolades from every strata of society. He had a mind that understood the
structure of the universe and could also explain these weighty concepts not
only to his peers but to the average person as well.
ventures don’t employ a team of “Einsteins” but instead train and employ sales
people (sales engineers and account managers) to go out and peddle their
products against other companies with similar ambitions. In this case, it’s not
the physics of the universe that needs explaining, it’s the increasingly
complex landscape of the intersection of technology and business refinement and
operations. With the advent of machine learning, artificial intelligence, and
other seemingly brainy technology game changers, businesses need to get a much
more in-depth understanding of how these can change their business and increase
profitability, sustainability and portfolio of services.
Don’t get me wrong –
I’m not suggesting sales people aren’t intelligent and don’t understand their
customers. They simply aren’t embedded in the day-to-day complexity of client
problem solving and all that goes into crafting the solution – from algorithms to
analytics and strategy creation.
A relatively new
concept, infonomics, is a promising new area of identifying, monetizing and
acting on data, is changing the classic idea that ‘information is power’. Sure,
everyone knows that the more you know, the better your chance of doing the
right thing at the right time. The sheer amount of data generated by today’s business
interactions and the “internet of things” alone requires a steady increase in
resources, time and effort to find the golden needles lying in the vast
haystacks of data. Meanwhile, the meter is turning on the cost to produce,
store, categorize, back up, restore, process, etc. all this data.
Here’s the challenge: traditional IT
sales teams are out there with products to sell that continue to fulfill their
pipelines while furthering the problem: sell more storage, faster storage,
cloud compute, secondary backup, failover software, on-prem hyper converged
compute and storage. None of these help solve the problem – by no fault of the
sales teams or their prospects, but because many companies don’t have a strong
grasp on where the value of their data exists. As a result, business as usual
is easy and the past of least resistance for a sale.
MIT estimated in a 2017 study that
only 0.5% of data stored is ever processed and analyzed. This isn’t an
infrastructure problem, it’s a value identification and optimization problem. How
can we identify valuable data for the business to drive automation and ensure
the right people can access it decisions? Today, this is best solved by
experienced consultants and engineers that can take plain text problems and
challenges and translate them into algorithms, workflows, process improvements
that help solve the equation, not just sell the next greatest piece of tech and
hit a quota.
Rather than fielding a sales team to
identify opportunities and operating in the traditional hunter roles, we’re
leveraging consulting experience that can identify and solve problems, whether
they be process improvements, growth challenges, hardware and software
optimization, or how we employ machine learning smartly.
Clients and customers are
increasingly savvy in their approach to finding solutions to their problems. We
have doubled down on shortening the connection between the technology that
solves problems, the people that can translate the solution, and continuously
refining the whole ecosystem systematically.
Experience and knowledge sells
itself. Infonomic Data is a consulting firm first and a software company second
today. Over our evolution, we’ll transform to a much more software led solution
when customers are ready for that step in the journey. We understand the
complexities of bringing artificial intelligence, machine learning, deep
learning, and cognitive science into use throughout an organization so well
that we can explain it — simply – without a sales team.