Mu Sigma, one of the world’s largest pure-play decision sciences and analytics firms, helps companies institutionalise data-driven decision-making and harness Big Data. Mu Sigma solves high-impact business problems in the areas of marketing, risk and supply chain across 10 industry verticals.

With over 2,500 decision sciences professionals and more than 75 Fortune 500 clients, such as Dell and Microsoft, Mu Sigma has driven disruptive innovation in the analytics industry with its interdisciplinary approach combining business, math and technology, and its integrated decision support ecosystem comprised of technology platforms, processes, methodologies and people. We spoke to Dhiraj Rajaram, the company’s CEO and Founder.

What Does Mu Sigma Do Differently To Other Data Analytics Companies?

Our focus at Mu Sigma is to help our clients make better decisions based on data. The objective is that they learn to institutionalise the use of data and create a culture based on data-driven decision-making.

By combining maths, business and technology with behavioural sciences and design thinking, we can help them harness Big Data and support them in making crucial business decisions. Mu Sigma views data-driven decision-making as a journey, beginning with data engineering and proceeding to data science, and then to decision sciences.

Big Data & Analytics Are Big Buzzwords, But How Are They Evolving?

Businesses are transforming quickly and so is the world’s data, which is doubling every year. Increasingly, analytics and data-driven decision-making is being sought as a necessary skill by organisations. Being able to understand and use their data to make business decisions can give organisations a competitive advantage.

In the last 100 years, we’ve shifted from constructing large machines, to developing data bases, software operating systems, Internet, search, social connectivity and so on. The next generation is not solely based on moving data, but also about moving information, ideas and insights.

Like the traditional supply chain in the manufacturing world, we need to think about a Decision Supply Chain. As a result, the ability to bring multiple disciplines together (Business, Math, Technology, Behavioural Sciences) will become critical. Big data, analytics and decision sciences are more important today than ever before.

How Do Decision Scientists Differ From Data Engineers & Data Scientists?

Data engineering is the application of technology to help collect, store, process, transform and structure data, which prepares it for analysis. Data science integrates and builds on data engineering by applying maths, which involves analysing and evaluating the data, then creating business models to help answer questions and solve business problems.

They might create an analytical model for a business team, such as supply chain management or a marketing department. But having worked in the analytics and data-driven decision support area for many years, I believe that leveraging data effectively to enable better decisions requires more than just data scientists.

Instead of having different people to do the programming and business analysis jobs, we tried an integrated approach – create a decision scientist – a business analyst, applied mathematician and programmer all rolled into one.

Decision scientists build on the skills of data scientists by adding the elements of business context, design thinking and appreciation of behavioural aspects of decision making. They are not solely focused on data, but on helping companies make better decisions by applying the models created from data into any specific business context.

Is There A Growing Demand For These Data Experts?

The speed of business transformation means that organisations have to deal with more and more complex business problems, which require solutions in real time. Over the past few years, this situation has established the growing demand for people who can analyse data and help companies make decisions based on the insights generated.

But in order to derive maximum potential out of the trove of data and make it more consumable for decision makers, it is necessary that individuals on the job have the right grasp of the interdisciplinary fields of maths, business and technology. Decision scientists are equipped with the right skillset, toolset and mind-set to help organisations make analytics more pervasive in organisation, thereby institutionalising data-driven decision-making.

Furthermore, an ecosystem of people, processes and platforms is required to leverage data. The people component alone is not sufficient. One needs a man-machine eco-system. The endeavour to establish such an integrated eco-system should be regarded as a journey and not a destination.

Since 2004, Mu Sigma Has Scaled To Become A Multi-Million Dollar Company. What Did You Learn During The Process Of Scaling Up?

Like every start up, we’ve been faced with challenges as we’ve grown. Our success is a result of how we’ve handled these challenges, and more important, learned from them in the process. There are three aspects I have learned from Mu Sigma’s growth from the beginning.

Stay true to the vision. Every entrepreneur has a vision. Many people might doubt their vision. Sometimes, it could feel like the more important their vision is, the more people will oppose it. It’s quintessential therefore, that you stand your ground and prove that your business can deliver in the way that you intended. This is a critical process in building a company. My vision was tested when I started the company and still gets tested today.

Fix mistakes, fast. Building a business from the ground up is difficult, and you have to make tough decisions. But if you fix mistakes quickly, they won’t cause you damage in the long run.

Character and mind-set is just as important as talent. We wouldn’t be the company we are today without the first set of people we hired with the character and mind-set that they had. Most people look for knowledge and experts, but I looked for a learning (learn, un-learn and re-learn) mind-set and the ability to operate in fast moving agile environments. The learning mind-set is still in place today when hiring talent.

What Were Your Intentions Behind Starting This Company?

I am what I call an entrepreneur by accident. During my stint as a management consultant with Booz Allen Hamilton I noticed that organisations were facing three core challenges triggered by one macro trend – “The speed of business transformation”.

Organisations needed to solve new business problems on a constant basis. This meant that experiential knowledge was becoming obsolete faster than ever and learning was more important than knowing. The traditional consulting paradigm would not suffice and new a paradigm was needed that focused on extracting learning from data by separating the signals from the noise

Learning and separating signals from the noise. Addressing this required a holistic perspective that brought together business, maths, technology and behavioural sciences, rather than just depending on one or two dimensions. An interdisciplinary approach alone could let you take ‘Prior Knowledge’ and ‘New Learning’ to generate insights.

Innovation was increasingly becoming a function of chance due to the speed of business. This was like throwing darts at a moving dartboard and hoping to hit the bull’s eye. The only way to innovate better and faster was to either throw darts better or throw more darts by reducing the cost of experimentation. Mu Sigma’s business and operating model subliminally encompass these perspectives.

How Is Analytics Changing Business?

Strong analytics have the capacity to drive innovation and help businesses make better decisions. Because of the explosion of data and increasing business complexity, companies are struggling to separate the signal from the noise and make the right decisions.

Decision sciences and analytics help here. They allow organisations to infer and learn from the data rather than rely only on gut. This is leading to a change in culture within organisations where even every day decisions are supported by data-based insights and a culture of test and learn.

What Does A Company Need To Have The Right Management System For Analytics?

We believe that you need an integrated man-machine eco-system that brings together technology platforms, best practice processes and people to create a successful analytics initiative. It’s not just about the creation of analytics but also the translation and consumption of analytics.

To be effective, business problems need to be articulated and translated into analytical problems. Analytical problems need to be solved. Analytical solutions then need to be translated back into business solutions. These business solutions then need to be communicated, socialised, implemented and consumed by the organisation to realise the benefit from data-driven decisions. Organisations need to create governance and operating models that covers all three of these interlocking aspects of creation, translation and consumption to enable better decisions using analytics.