Using AI and adverse media to get the competitive edge

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What if your competitors could predict your next move even before you could? It may sound impossible, but with faster machine learning (ML) and smarter artificial intelligence (AI) analysing big data, anything is possible. There are currently approximately 4.57 billion internet users worldwide. Anything that anyone says about your brand, positive or negative is recorded and can be analysed, interpreted and scored, putting your business reputation and success at risk.

Adverse media, or negative news, has for a long time been used in the financial sector as a risk management tool to identify potential criminal behaviour during on boarding and periodic reviews of customers. But big data scientists like, Mark Germishuys, CEO of NGA, (Ngidi Germishuys and Associates) is revolutionising its use to offer strategic business intelligence to customers who are looking for a competitive edge. He offers the following advice.

Incorporate AI and ML into your business strategy

AI and ML is essential to extracting and analysing big data. It’s not only easier, it’s also a lot faster. A business utilising this technology can access anything from – competitor announcements encompassing revenues, fraud, operational issues, opportunities, new product launches, legal, own-brand health, regulatory, and positioning changes. This can be done in a matter of minutes and downloaded with one click.

AI creates new predictive capabilities   

With AI data can be scrutinized to find out anything from – who is talking about your business and where associated content is originating from, through link analysis and geo-mapping analytics. This can help to find new markets to enter or increase presence, predict and analyse competitors’ actions, improve product development, reduce reputational risk and beat competitors to market. It can also improve and personalize consumer interaction, give consumers what they want and increase market share. It makes finding new products and tech that will disrupt the competitive landscape easier, identify legislative or political issues or stand out from the crowd with a distinct corporate identity and tone of voice.

Competitive intelligence vs industrial espionage?

It’s market research of your competition. The data sources are freely available to the public. It includes anything from financial reports, press releases, blog posts, social media channels, forums, government databases and public records. Anyone can access the same sources, however without AI the results are limited and lack accuracy.

The power lies in Unstructured data

Almost every business today, collects and stores data. Unstructured data is an untapped resource, a gold mine of information one can use to gain an edge on the competition. As much as 80% of all data stored is unstructured and without business intelligence it can’t be efficiently mined, analysed, interpreted and scored. This is because unstructured information is typically text-heavy, and contain data such as dates, numbers, and facts as well, and can cause irregularities and ambiguities that make it difficult to understand using traditional programs.

Ethically trained AI supersedes human intelligence 

By utilizing implication detection algorithms and sentiment analysis trained on real human behaviour one is able to leverage opportunities faster. Ethical AI and ML makes the process more efficient and due to very little human interaction, it’s far more accurate and unbiased. It can radically reduce reputation risk and improve the transparency of organizations by responding or identifying risks earlier.

AI is getting smarter by the minute

The more advanced Ethical AI becomes the more precise the results will be. AI can be trained to block out any text that is false and can skew the results, like paid-for media or repetitive content.

Mark Germishuys
Mark Germishuys

Mark Germishuys, is a big data pioneer and CEO of NGA, which launched in 1999 after securing a patent to build South Africa’s first data consolidation and online real-time reporting method.

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