Using Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors (BRENNOR JACOBS)

From Digital Culture & Society

Droll, A., Khan, S., Ekhlas, E., & Tanev, S. (2017). Using Artificial Intelligence and Web Media Data to Evaluate the Growth Potential of Companies in Emerging Industry Sectors. Technology Innovation Management Review, 7(6), 25-37. Retrieved from Brock University.

Artificial Intelligence is being adopted by many companies as they grow, but what if companies had the means to a intelligent tool that could be used to evaluate the growth and competitive potential of not just existing firms, but new technology startups as well. The authors of this article focus on the emerging precision medicine sector of the healthcare industry. This emerging industry, promotes healthcare customization and incorporates healthcare with medical decisions, practices, and products designed for patients with the same treatment needs. Although many business firms are reluctant to share information on business interactions and competitive moves, the prospective of such a tool could provide businesses with valuable insight for potential stakeholders.

The emergence of the precision medicine industry has aided the development of important tools employed in precision medicine (molecular diagnostics, imaging, and analytics). The development of these tools could be innovative for the industry going forward, and for technology startups there is potential to win big. The goal of these startups is to develop platforms that can analyze important data, which can contribute to more accurate diagnosis. Precision medicine offers customizable treatment programmes that provide individualized care, as opposed to prior treatments that targeted the ‘average patient’ regardless of the individual’s characteristics, genetics, and lifestyle. The overarching goal is that advancements in precision medicine will promote the understanding of personalized diagnostics based on the analysis of patient data.

A problem that arises for researchers is that many companies are in their early stages and do not like to share information on competitive advantages. Investors must look for information available online in order to examine potential. The authors try to decrease the insufficiency of information withheld by startups in relation to their competitive differentiation. The authors suggest that keyword-based web searches along with public information, made available by third party companies, has potential for innovation. Fine tuning a competitive intelligence platform, with the task of evaluating media perceptions of competitive advantage and growth potential, may potentially create new innovative ways to adapt to changes in the precision medicine industry.

A need then emerges for the development of an intelligence tool that could use available public information online to evaluate growth potential and competitiveness. The authors of this article used a historical and textual analysis by analyzing publicly available data online, and utilize a keyword-based web search and real-time media monitoring. They use the Gnowit Cognitive Insight Engine, a AI platform which uses machine learning to analyze the importance of documents, and by combining this data with a machine learning approach, the authors aim to learn if this correlation of information could produce useful insight into the growth potential of startups in the industry.

The authors began by examining the relationship between online media discussions that focused on business growth of companies in the pharmaceutical and biotech industries, and their innovativeness, ranked by independent third party organizations, which tend to be the most unbiased. The Gnowit engine was then implemented to textually analyze data from publications available online. Gnowit, crawls information data from the largest newspapers and extends its search toward local regionals.

Researchers then optimize the Gnowit engine for mining information related to industry growth in the precision medicine sector. After careful examination of books related to business, keywords were selected to produce a final search query focusing on: market growth, business growth, competitive differentiation strategy and other abbreviations. The keywords were then connected to terms like personalized medicine and precision medicine.

The Gnowit engine compares companies on their relationship to specific single signals, and can search several signals at once. The final search query, named Precision Medicine Growth (PMG), is then performed by the Gnowit engine in the following steps:

1. Retrieve the collection of the latest published articles that match the query heuristic. 2. Categorizes the content of these articles in a way that leads to the construction of a set of textual terms and numeric weightings for each term. 3. Adds the individual document term-weighting vectors together to create an aggregate term-weighting vector for the entire PMG heuristic. 4. Stores the aggregated heuristic vector as a retrievable context vector that can be used to provide numeric strength-of-similarity analysis with other similar heuristically-developed aggregate vectors.

The authors then used a case study of 23 companies ranked by their innovativeness in precision medicine. The engine was able to produce another search query for articles that relate to a company’s name and categorizes the information accordingly. The engine then produces a numerical value for the strength of correspondence and can be used to evaluate the potential growth in the precision medicine sector.

One limitation to the Gnowit engine however is that it does not search across all the articles available on the internet, only those that were published during two weeks prior and ending at the present moment. Discovering the explanatory and analytical potential of the Gnowit search engine, could someday be used as a competitive intelligence tool for investors. Another limitation to the study is also related to the Gnowit engine. The engine only monitors online discussion for a two week period and could skew results if an unusually high number of articles are written in that short time period. Further advancements in technology will allow the Gnowit engine to expand its two week limit. Searching longer time periods will expand the effectiveness of the engine and potentially over time develop, in order to monitor real time changes.

Further research must also include qualitative research to understand social parameters of human behaviour that are involved with the competitive advantage of startup companies. Taking social factors like human preferences into effect will also further legitimize AI decision making, giving it the ability to make more well informed choices based on human needs.