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Learn Fractal Analytics and AI Best Practices


Groups within companies trying to improve their analytics and AI capabilities can learn from each other, or they can learn from other organizations that provide similar services to their company. Fractal Analytics, a global analytics and artificial intelligence services company jointly led by the United States and India, is part of the latter group. Fractal was founded in 2000 and today has over 3,500 employees and 16 locations. It recently received a significant investment ($360 million) from private equity firm TPG and is valued at over $1 billion.

The success and growth of Fractal is an interesting story in itself and indicates how important analytics and AI have become for large organizations. But after speaking with co-CEO Pranay Agarwal, I concluded that the company offers many lessons for internal analytics and AI groups. Below are five Fractal attributes that other companies could adopt.

  1. Focus on decisions and how you can improve them—Fractal’s mission is to help fuel every decision in their client companies. They believe that better decisions mean better results for their clients. This emphasis on decision-making means that the company’s approach to problem solving is reverse decision-making: it starts with the decision to be made, then thinks about how to improve it. Empowering customers to make better decisions, they not only include the resources of structured and unstructured data, analytics and AI, but also design thinking and behavioral sciences. They deal with different types of decisions, but most are fast, repetitive and with a high level of data feedback. Each such individual decision has a relatively small risk of substantial loss. They start by looking at the industry they work in, they map the value chain or value drivers in the industry, and go even further to determine what decisions are needed to improve the drivers. Each of these approaches could also be adopted by analytics and AI groups within enterprises.
  2. Clarity on product offerings and capabilities—Analytics and AI are very broad fields. Each service provider must specialize in a relatively small set of offerings that they can repeat and reuse often. Fractal is now quite a large company, so it can have more specialties. As I mentioned above, Fractal focuses on quick, repetitive decisions, including the “next best action,” or the next conversation to have with a client; what price to charge (dynamic pricing); on which channel to best serve customers; forecast demand and supply; managing revenue growth and similar issues. The company has developed a set of platforms that support each of these common decision types.
  3. Combine organic growth and acquisitions for talent and capabilities“Organic growth is often the best way to preserve a culture, but given the scarcity of analytical talent and specialized capabilities, it can also be worth making targeted acquisitions, even if your company is not in the business. analytical services sector. Fractal has made several acquisitions, including Neal Analytics for cloud offerings, Samya.ai for revenue growth management, and Final Mile, a behavioral science consultancy. He has also made majority investments in other analytics-related businesses, including Analytics Vidhya, a data science community and training company. The combination of organic growth and acquisitions has helped Fractal create a wide range of capabilities.
  4. Build a strong culture with specified values“An analytics or AI team within a company will obviously adopt some of the values ​​of their larger organization, but it is also possible to establish cultural principles within a smaller group. Fractal leaders believe that their values ​​have been very influential in the successful growth of the company for over 20 years. The four values ​​expressed include the priority given to the customer (measured by the Net Promoter Score, which remains above 70); learn and grow (with Fractal Analytics Academy and Analytics Vidhya, and they also design programs for clients); think big and act fast (which they accomplish by reinvesting 10% of their income, among other means); and be extremely trusting and responsible. This last value means that they assume a positive intention on the part of their customers and colleagues. They have been on the Great Place to Work list for five years and this year they achieved this recognition in all five locations.
  5. Incorporate ethical guidance and capacity“Today, everyone seems to have understood that analytics and AI have an ethical dimension. It’s still too early to do much in most companies, but I would say that every analytics/AI organization needs some sort of ethical framework or guidelines, and should also have a governance structure soon. Fractal, unsurprisingly, already has these components in place. Agarwal told me very simply, “People who drive the adoption of AI in our society should ensure that it is used ethically. We are driving AI adoption, so we need to invest in AI ethics. They have an internal team that oversees ethical issues and a framework to manage them. The framework, much like true machine learning, allows them to evaluate each analytics or AI solution and score it on criteria such as transparency and bias/fairness. Not only do they use the framework internally, but they also “produce” it for customers. Every analytics organization should move in these directions.

Just as internal supply chain groups can learn valuable lessons from companies that make supply chain work their core business—UPS, DHL, and FedEx, for example—analytics and AI groups within companies should consider companies like Fractal Analytics as a model for internal construction. successful analytics and AI practices.