In this special guest feature, Deepinder Dhingra, Apprentice Leader at Mu Sigma, Inc., identifies a new paradigm in decision science: Service as Software. This concept is in response to the popular notion that one day, machines will take over the jobs of humans. Deepinder Dhingra is an apprentice leader for Mu Sigma, a leading global provider of decision science and big data analytics solutions. In this role, he is responsible for integrating math, business, technology and design thinking to ensure that the analytics and insights generated for clients is consumable and actionable. Deepinder has more than 15 years of experience in consulting and sales. He has worked for Mu Sigma for more than 10 years, holding a variety of titles including head of strategy and planning and director of innovation. Prior to Mu Sigma, he was a pre-sales application specialist for Spotfire, Inc. (acquired by TIBCO), servicing major investment banks, asset management companies and hedge funds. He was also a senior consultant at Demantra (acquired by Oracle) and i2 Technologies.
In just a few decades, the world of data-driven decisions has gone through a significant transformation. The underlying driver for this is accelerating change in the business environment. Business models are changing, new competitors disrupt existing businesses and the Fortune 500 list changes every year as former leaders bite the dust.
We see an evolution of four distinct stages of how large businesses have approached problem solving using data.
Before technology, there were people – lawyers, accountants, designers and other specialists who could help businesses understand their businesses better and make decisions to help them create new products and services, restructure through lean times and to power and sustain growth.
The Software Era
During the 90s, computing went through a step change from centralized mainframes to distributed computing. We saw new solutions for decision support – specific software to address specific problems – pricing, CRM, etc., to Swiss Army knife-like software-like spreadsheets and statistical packages. Enterprise software became a growing business to bring packaged software for a growing range of functions, and also custom software to support the business model and processes of large corporations.
The Software as a Service (SaaS) Era
As early as the 1960s, the idea of centralized hosting of business applications saw the idea of utility computing, or time-sharing, pioneered by large companies such as IBM. In the 2000s we gave it another name; Cloud Computing. A marketplace of various vertical and horizontal software solutions was born. Google, Amazon, IBM and even Tesla are beginning to offer AI and Machine Learning functionality for on-demand use of sophisticated software to tackle problems of big data. Everything it appears is amenable to being rented, including machine intelligence.
Service as a Software
Business problems are getting muddier and fuzzier. Problems are more granular and more interconnected. And the problem space is shifting. Software can help decision support, but not in splendid isolation. The challenge is that while software is scalable, software development does not scale well. Software development for products need fairly firm “business requirements,” followed by incremental updates to functionality in release cycles. The software and product paradigms needs to change. Lego blocks of software that do specific things very well need to be stitched together to attack complex business problems. Functionality needs to be iterated quickly with a backdrop of constant and accelerating change.
In this case, the software needs services to orchestrate it.
We would argue that this ecosystem of man and machine is not going to be replaced anytime soon by machines. The right AI in this case is not Artificial Intelligence, but Augmented Intelligence. Artificial Intelligence is exploitative, where the machine is deployed to match, or exceed, human capabilities. Augmented Intelligence is exploitative and explorative, enabling the human to experiment with the problem space and relieving him or her from the tedium of tasks better done by a machine.
We call this new paradigm the “Service as a Software” model.
Businesses will need this “Iron Man” model – decision scientists, donning exoskeletons of software and tools to attack big data (and small!) – to find new ways of solving business problems. As business problems evolve, the model will iterate from services to software to services to software to services to software….. in an iterative model of continuous engagement and improvement of how business decisions are made.
Sign up for the free insideBIGDATA newsletter.