“Any reinvigoration of US manufacturing will also require reinvention,” notes McKinsey & Company in its “State of AI in 2022” report. The report goes on to explain that five years ago, manufacturers were seeing value from AI use in operations and risk management. Today, value creation is also being seen in marketing and sales, product and service development, strategy and corporate finance and supply chain management. In fact, AI adoption has grown so much in manufacturing that 18% of big data jobs are within the manufacturing industry.
As manufacturers achieve AI successes with internal data, such as operational efficiency using Internet of Things (IoT) data, internally generated data leaves gaps. As a result, many have begun incorporating external data into their analyses. How can third-party data help and what datasets can prove useful?
Stay Alert to Reputational Signals Involving Your Business
These days, news travels fast. And the pace of news distribution represents only part of the challenge. The volume of news and social commentary expands by the second. Given both the volume and velocity of news data creation, manual monitoring isn’t the answer.
By ingesting news data into AI-powered analytics platforms, you can extract meaningful insights from the data much faster. You can track mentions of your company, brand(s), and competitors in the news so you know what’s being said and can respond quickly to emerging threats or opportunities. You can also analyze sentiment in news articles and social commentary to better understand shifts in customer behaviors and inform public relations strategies.
And with Environmental, Social and Governance (ESG) commitments top-of-mind, analyzing ESG news and ratings can help you see trends, understand the ESG priorities of consumers and investors, identify ESG related innovation—all of which can help you craft more effective communications about your ESG commitments.
Capture a More Comprehensive View of Risk
These days, every organization needs a solid risk management process to mitigate reputational, regulatory, financial, and strategic risk. For manufacturers, that process must be even more robust because of the complexities of global supply chains. Not only do you need to navigate due diligence for supplier on-boarding, but you need to maintain a constant watch on a wide range of risk factors related to your business.
Third-party data integrated into your risk management tools can help in a variety of ways.
Use an adverse media feed to quickly surface red flags related to your company, products, and the third parties that you rely on, be they business partners or suppliers. In fact, a growing number of regulators and industry groups recommend making adverse news screening an integral part of your risk management workflow.
Likewise, ESG news and ratings data helps you stay informed about potential ESG risks related to suppliers along your entire supply chain. (That’s on top of the reputational value mentioned earlier.) Plus, as more countries make ESG part of their own agendas, including ESG in your risk management process is likely to go from ‘nice to have’ to ‘need to have’ in response to new sustainability laws.
Embedding regulatory data feeds, such as Politically Exposed Persons (PEPs), sanctions and watchlists enhances risk visibility too—particularly during times of geopolitical and economic volatility. It’s a must when bringing on new suppliers and for meeting regulatory expectations around anti-bribery and corruption laws worldwide.
Find Valuable Insights for Product Development, Marketing, Sales, and More
Analyzing external data can also benefit other departments across your organization. Legal and patents data can be analyzed to anticipate innovation in the industry, including competitor moves that you need to prepare for. Product development can also benefit from using news and other third-party data sources to inspire innovation.
News and social commentary can provide context that helps you make sense of internal customer data. Marketing can gain a line of sight into consumer sentiment to help refine messaging for better engagement.
Analysis of external data can also help you understand the impact that events have on demand for your products. It’s about context, again. Internal sales data may show spikes or drops. When combined with third-party data, predictive models can assign reasons for sales numbers that can be used to optimize sales efforts going forward.
A report on the state of data science in 2022 reveals that data professionals still spend more than one-third of their time wrangling data. Choosing a data as a service provider that can deliver semi-structured, enriched data enables you to by-pass some of the data prep so you can start using and uncovering insights from data, sooner.
See how manufacturers can power up AI initiatives with Nexis® Data as a Service.