Improving strategic outcomes with advanced analytics

Nicholas Northcote: Strategy development is always going to require creative and thoughtful executives to set aspirations and make bold choices, but we believe advanced analytics is an opportunity to bring more science to the art.

We see four ways that companies can use analytics in their strategy-setting practices.

The first is to identify early-stage trends. Some companies are using AI engines to track, in near real time, the evolution of trends that matter to their businesses, based on news alerts, investment data, patent filings, and other inputs. They then use the results to decide when to trigger strategic moves related to those trends.

The second application is around identifying new growth opportunities. Here, again, AI can complement traditional brainstorming methods to help companies reveal what we call nonobvious growth opportunities, such as granular areas where competitors are present but your company is not, potential acquisition targets, or even new applications for products and services already in your portfolio.

The third application is reducing bias in decision making. By using historical data about the strategic moves and performance of thousands of companies, business leaders can calibrate the likelihood of a strategy succeeding before allocating resources to it. For example, if you are planning a transformative merger, knowing that 70 percent of large acquisitions in the past decade destroyed value could be helpful. It would give you a fact base to challenge and stress-test the plan by asking questions like, what makes us different? Are we overestimating returns or synergies? What would it take to get execution right?

The last one is using analytics to anticipate complex market dynamics. Tools such as agent-based modeling can help you understand how the actions of customers, competitors, regulators, and other market players could combine to affect demand, supply, and prices, allowing you to extract proprietary insights.

Sagie Davidovich: Less than a decade ago, it would be borderline impossible to build a machine that mines the web, which contains hundreds of billions of pages that include all sorts of documents, from patents and clinical trials to news publications and Wikipedia.

But the web is imperfect. It is full of biases, contradictions, outdated or partial information, as well as inconsistencies in formats.

We wanted to build a machine that could read the web, connect the dots, and synthesize insights, providing answers to complex research questions and creating coherent, holistic, and data-driven decision support.

‍Sagie Davidovich: Anything from discovering novel applications for existing chemicals or products to finding root causes of outcomes by identifying cause-and-effect relationships that exist on the web. In our work with pharma companies, there is a fascinating application around discovering pathways from a certain gene to a particular disease.

While the results of these analyses may not appear in any single scientific article, by connecting the dots, we can uncover the path and make serendipitous discoveries. Another arena of applications relates to sustainable development goals, such as addressing the climate crisis, discovering how to drive sustainable agriculture, or fighting child detention through our pro bono work with NGOs and governments.

When you have the web in a box, the universe of applications is quite unlimited.

What was the problem the company was trying to solve?

Sasha Vesuvala: It was a private-equity-owned materials company that specializes in superhard materials, and the challenge for it was growth. When we broke the problem down, we realized there were three issues within it.

First, superhard materials is a constantly evolving space and it was important for this company to keep tabs on the next set of trending superhard materials so it could anticipate the industry’s and its customers’ changing requirements.

The second question was, “We know X, Y, Z applications and, therefore, A, B, C customer segments for the materials we focus on. But are there niche applications out there that we do not know about or completely different customers segments for our materials?” This could present an opportunity for margin-accretive growth because it might not require significant R&D investment but simply mean capitalizing on the existing materials and know-how.

What's more, they were open to inorganic growth. This is a fragmented space, and there are many specialists in individual materials, so the third question for the company was, “How do we identify the universe of potential acquisition targets or partners?”

How did the company envision using SparkBeyond to get those answers?

Sasha Vesuvala: I want to underscore that we do not believe these are solely technology-driven answers.

When it is an expert-plus machine, when the human continues to apply judgment, that’s when you get ideas that make sense.

On question number one—help me identify the next set of trending superhard materials—we could tell the platform to research such materials. Many of them show tremendous momentum in patents, and that can be a leading indicator before the materials become widely applied commercially. That information could come from publications, the news, or, if you are in pharma or healthcare, from clinical trials or grants. In this case, SparkBeyond searched materials such as boron tribromide and new processes being used to treat or create these superhard materials.‍

This helped us identify materials such as nanodiamonds that this company found of great interest. It was also interested in exploring new technologies and processes that could give it a head start.

Think of it as being able to do thousands of expert calls in minutes. As long as the information is out there, the machine will find the answers because it can read incredibly fast at the level of a first-year university student.

SparkBeyond differs from your traditional web search in that it does not return individual links that you need to click through but actually returns answers. We could also define the sources by, for example, telling the platform to focus only on patents and publications if we were interested in scientific information.

What share of the applications that the company focused on were new?

‍Sasha Vesuvala: Conservatively, 60 to 70 percent of applications that the company prioritized were new, and it had not considered them in the past. Now, the sources of all these insights are on the web, in patents, or journals, but not necessarily in journals that a traditional hard-materials R&D scientist or business-development manager looks at.

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