Groups within companies that are attempting to improve their analytics and AI capabilities can learn from each other, or they can learn from other organizations that provide similar services as their business. One of the latter group is Fractal Analytics, a global analytics and AI services firm that’s jointly led from the US and India. Fractal was founded in 2,000 and now has over 3,500 employees and 16 locations. It recently received a large ($360 million) investment from private equity firm TPG, and is valued at over a billion dollars.
Fractal’s success and growth is an interesting story in itself, and indicative of how important analytics and AI have become to large organizations. But after speaking with Co-CEO Pranay Agarwal, I concluded that the company offers many lessons to in-company analytics and AI groups. Below are five Fractal attributes that other companies could adopt.
- Focus on decisions and how you can improve them—Fractal’s mission is to help power every decision in their client enterprises. They believe that better decisions mean better outcomes for their clients. This focus on powering decisions means that the company’s approach to problem-solving is decisions-backward—they start with the decision to be made, and then think about how to make it better. In enabling clients to make better decisions, they include not only the resources of structured and unstructured data, analytics and AI, but also design thinking and behavioral sciences. They address different kinds of decisions, but most are high velocity, repetitive, and with a high level of feedback from data. Each individual decision of this type has a relatively low risk of substantial loss. They begin by looking at the industry they are working in, they map out the value chain or the drivers of value in the industry, and take that one step further to what decisions are needed to improve the drivers. Each of these approaches could also be adopted by analytics and AI groups within companies.
- Clarity on product offerings and capabilities—Analytics and AI are very broad fields. Every service provider should specialize in a relatively small set of offerings that it can repeat and reuse often. Fractal is now quite a large company, so it can have a larger number of specialties. As I mentioned above, Fractal focuses on high velocity and repetitive decisions, including “next best action”, or the next conversation to have with a customer; what price to charge (dynamic pricing); what channel on which to best serve customers; forecasting 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.
- Combining 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 may also be helpful to make targeted acquisitions—even if your company is not in the analytical services business. Fractal has made several acquisitions, including Neal Analytics for cloud offerings, Samya.ai for revenue growth management, and Final Mile, a behavioral science consultancy. It has also made majority investments in other firms related to analytics, including Analytics Vidhya, a data science community and training company. The combination of organic growth and acquisitions has helped Fractal create a broad set of capabilities.
- Build a strong culture with specified values—An analytics or AI team within a company will obviously take on some of its broader organization’s values, but there is room for establishing cultural principles within the smaller group as well. Fractal’s leaders feel that their values have been highly influential in the company’s successful growth over more than 20 years. The four expressed values include putting the client first (measured by Net Promoter Score, which stays over 70); learn and grow (with the Fractal Analytics Academy and Analytics Vidhya, and they also design programs for clients); think big and act fast (which they accomplish through reinvestment of 10% of their revenues, among other means); and extend extreme trust and be accountable. The latter value means that they assume positive intent from their clients and colleagues. They have been on the Great Place to Work list for the last five years, and this year achieved that recognition in all five locations.
- Incorporate an ethics orientation and capability—These days everyone seems to have realized that analytics and AI have an ethical dimension. It is still early days for doing much about it in most companies, but I would say that every analytics/AI organization needs some sort of ethical framework or guidelines, and should also soon have a governance structure. Fractal, not surprisingly, already has these components in place. Agarwal told me very straightforwardly, “The people driving adoption of AI in our society should ensure it is used ethically. We are driving adoption of AI so we need to invest in AI ethics.” They have an internal team overseeing ethical issues, and a framework for managing them. The framework—in true machine learning fashion—allows them to evaluate each analytics or AI solution and score it on such criteria as transparency and bias/equity. Not only are they using the framework internally, they are also “productizing” it for clients. Every analytics organization should be moving in these directions.
Just as internal supply chain groups can learn valuable lessons from companies that do supply chain work as their primary business—UPS, DHL, and FedEx, for example—analytics and AI groups within companies should view firms like Fractal Analytics as a model for building internal successful analytics and AI practices.