In the face of global, economic and political instability businesses leaders are under pressure to make fast, data-backed and actionable decisions. A Gartner survey revealed that 65% of respondents say that decisions are more complex than those they were making two years ago, and 53% of them feel more pressure to justify their decision making. Gut instinct is not enough. This desire to underpin executive decisions with data likely explains why 80% of executives believe any business decision can be improved by using Artificial Intelligence and automation.
Enterprises are climbing over themselves to incorporate artificial intelligence (AI) and machine learning (ML) into decision making. But AI and ML platforms can only deliver the envisaged value if they are built on a clean, accurate data set. AI is not a magic bullet that is bought off the shelf, plugged in and starts churning out answers. Automation is far more brittle – even if the right algorithm is applied, if the input data is poor quality and in the wrong format, the results will be worthless.
Quality Data is First. AI is Second.
AI-generated mapping is only as accurate as the rules it’s taught. If a state and a city have the same name, then they must be labeled as so. If a machine learns ‘New York’ to mean the state, then it won’t be able to identify ‘New York’ the city as its own entity. ‘Niagara Falls’ could just as easily be labelled as being in ‘New York’, as could the ‘West Village’. Machines need to understand the difference. AI must be able to distinguish between two names being the same and when it is an unnecessary and misleading duplicate.
Most data is much more complex than just its initial label. If ‘New York’ is fed into the machine twice and is inaccurately interpreted, the machine might delete it as a duplicate. If ‘New York’ is in the dataset twice, but the data is confused, the mapping technology might understand the five boroughs as the five boroughs of New York State and ‘Buffalo’ as a city in the state of New York…City.
When applying an AI algorithm to data spread across complex customers and entire organizations, it needs to be sorted and labeled accurately for it to have any meaning. The algorithm on top of the data must be able to recognize and learn from a variety of labels and formats. The scale and complexity of data applied to full entities of similar names makes this increasingly difficult – several similar phrases and datapoints may confuse the algorithm. Thus comes the need for the machine to learn from a full compilation of different domains and regions so that it’s fed from the most difficult and precise map. This is what is referred to as context.
Context is Key When it Comes to Data
According to a recent survey by Quantexa, only 22% of IT decision makers believe their organization trusts the accuracy of their data. This is owed largely to a lack of context – one in nine customer records is a duplicate, which means that organizations are unable to differentiate between the different labels. This inability to identify differences between customers will slow down and confuse decisions, thereby defeating the point of data analysis in the first place.
This is likely why many organizations are moving away from big data. Gartner revealed that by 2025, 70% of organizations will shift from big data – that once seemed like the way of the future – to small and contextualized data. This data identifies connections between customers and companies and adjusts models to reflect a rapidly changing world – which highlights the need for organizations to feed more detailed data to its machines.
The importance of context comes not only within each organization but from external sources, too. For example, financial institutions may use Companies House data and electoral role information to identify if an individual is at the address they say they are at. These individual datapoints make up the full ‘context’ required to make decisions. Understanding these connections can improve customer-related decision making, as well as help identify areas of risk before it becomes overwhelming.
Traditional technology such as Master Data Management (MDM) hasn’t been able to supply organizations with complete views of individual customers. This is where entity resolution is coming into play, by offering a better solution. Entity resolution can give a complete picture of a customer, simplifying the scale and complexity of today’s organizations.
The Solution? Entity Resolution.
Quantexa’s same research reveals that only 27% of organizations are leveraging entity resolution technology to unlock the context needed to effectively draw insights from its data. This technology can de-duplicate data at speed, identifying that one person, organization, bank account, contact information, and more to create one entity. Of all the organizations struggling to adopt new methods and gain the trust in data necessary to make efficient and accurate decisions, 27% is simply not enough.
The intricacies of each customer need to be grouped together into a single, identifiable customer. Entity resolution technology sifts through the vast amounts of data and recognizes that despite characteristics and qualities that may match other customers, each entity is unique and may be constantly evolving in its own way. This includes both internal data, such as customers, transactions, products, and communications, as well as external data. External data can include registry sources and watchlists that, when applied, can add much-needed context to this increasingly complex backdrop of data.
Without entity resolution, places on the map would not be distinguishable from its own unique characteristics and organizations would struggle to derive actionable insights to support critical business decisions. Every city and state needs to be understood holistically, and in context. This tech is the solution that brings it all together and will allow organizations to make trusted and more informed decisions.
Feed Machines Clean Data and Get Clean Results
The current economic landscape requires organizations to wisely invest in AI and ML that will not only speed up decision making but will ensure that each decision is accurate and backed by trusted data. Artificial intelligence is only as smart as the data that is fed into it, so the data must be precise. Data must be enriched with context that spots hidden connections in order to give a full picture to customer datasets. To do so, organizations must harness Decision Intelligence technology, which takes large amounts of data and identifies individual identities, and then creates actionable insights for business leaders. Only clean, trustworthy data will allow organizations to make clear, trusted decisions.
Uma Rajagopal has been managing the posting of content for multiple platforms since 2021, including Global Banking & Finance Review, Asset Digest, Biz Dispatch, Blockchain Tribune, Business Express, Brands Journal, Companies Digest, Economy Standard, Entrepreneur Tribune, Finance Digest, Fintech Herald, Global Islamic Finance Magazine, International Releases, Online World News, Luxury Adviser, Palmbay Herald, Startup Observer, Technology Dispatch, Trading Herald, and Wealth Tribune. Her role ensures that content is published accurately and efficiently across these diverse publications.