Why Consultancies and Software Often Fail to Address Data Challenges

by   |   April 25, 2016 5:30 am   |   0 Comments

Mukund Raghunath, Client Partner and SVP, Mu Sigma

Mukund Raghunath, Client Partner and SVP, Mu Sigma

More data has been created in the past two years than in the previous history of the human race. Every business, no matter the size or sector, must grapple with this deluge of data (or die trying). The stakes are high and the accelerating proliferation of data means that dealing with it grows more complex every day.

In today’s fast-paced and dynamic business environment, organizations have to solve multiple problems on a daily basis. Business problems fall on a spectrum – with “infrequent and big impact” on one side and “frequent and modest impact” on the other. While the former requires heuristic, judgment-based approaches that are the domain of management consultants, the latter requires algorithmic approaches, or better IT.

The challenge is that most business problems fall directly in the middle of this spectrum. They are muddy, complex, and extend across departments, which means that neither software solution providers nor data consultancies are equipped to meet them on their own. Far too often, organizations rely on one side of the spectrum to address a “middle” problem, and fail as a result.

Taken individually, consultants and software are deficient. However, each has a critical role to play in solving business problems, which is why I believe that the new approach favors what I like to call a “man-machine ecosystem.”

Machine

As Marc Andreessen famously said, “Software is eating the world.” Businesses today use SaaS products for HR, marketing, and everything in between. Software solutions enable businesses to automate and simplify processes, like payroll, and answer specific questions, like how many people open marketing emails. They are best suited to improve operational efficiencies and processes.

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However, not all business issues fall into this category. As new, more complicated questions that are not well-defined arise and evolve, software products cannot morph easily. Algorithms don’t deal well with ambiguity. They are not strategic, creative, high-level thinkers, and they are not flexible. The result is that a business either has to use multiple products for each area or type of question, or under-utilize products with a short shelf life. Neither option is ideal, leading to losses in wasted time and money.

The fact is that IT infrastructure and organizations are not agile. They have established processes over the last few decades to ensure data systems are consistent and reliable, and have built infrastructure to support business operations. Unfortunately, these processes do not lend themselves to the speed at which businesses need to operate in order to make decisions from their data. The lack of urgency is frustrating and can prevent a business from staying ahead of the curve from their IT partners.

Man

Conversely, management consultancies have gained importance as businesses realize that they need human help to make sense of their data and tackle big, impactful problems. Consulting companies deploy people with extensive experience and expertise, built by having solved the same problem over and over again.

These “experts” are exorbitantly expensive but, in today’s rapidly changing business environment, their value is questionable. In such an environment, using past solutions as a basis to solve current or future problems is not necessarily a valid approach any more, and consultants may not be equipped or willing to challenge their entrenched ways of thinking. Learning, which is nothing but the rate of change of knowledge, is becoming more important than past knowledge in a dynamic business landscape. In addition, how can organizations expect to sustainably scale their problem solving while paying upwards of $500 per hour?

The Middle Road

The best way to address the vast ocean of “middle problems” is through a blended approach that draws on the strengths of both man and machine while compensating for their weaknesses. In the man-machine ecosystem, decision scientists, analytical processes and methods, behavioral sciences and design thinking, software, and interdisciplinary organizational structures are woven together on a single problem-solving platform that fosters better decision making across the company.

This holistic platform allows companies to learn continuously through a first-principles approach, which Elon Musk (a fan of first principles) defines as “a physics way of looking at the world. You boil things down to the most fundamental truths and say, ‘What are we sure is true?’ … and then reason up from there.”

With this mindset, organizations and individuals can apply ideas and practices from outside their industry and/or job function. They are far more agile and able to fuel a cycle of continuous learning, not bogged down by “expertise” or the strictures of robots. The most powerful problem-solving mechanisms of all are the right people, which we call decision scientists, wrapped up in the bionics of analytical processes and platforms, who look beyond the narrow confines of their industry and embrace ideas from seemingly unrelated businesses.  Like Iron Man, there is nothing they can’t overcome.

Has your business experienced limitations with consultancies? If so, how have you addressed their constraints? I’d love to hear your thoughts in the comments section below.

As one of the early members of the team, Mukund Raghunath has been involved in many aspects of company building at Mu Sigma, where he has played a variety of roles including client management, business development and team building. As a Relationship Head at Mu Sigma, he brings to the table best practices in the art of problem solving and innovative ideas in analytics application across different industries to benefit clients. He works closely with Fortune 500 companies across Retail, CPG, Financial Services and Life Sciences domains to address a broad range of business issues. Mukund earned his Master’s Degree in Computer Science from the University of Illinois and an MBA with honors from the University of Chicago.

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