50 min
CDO Matters Ep. 16 | The Death of the Single Version of the Truth with Jeff Jonas CDO Matters Podcast
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- Business
The truth isn’t always black and white. Sometimes, it requires more context and background when attributed to different scenarios and situations.
The same can be said about your data and whether a “single version of the truth” can be properly applied to multiple use cases for your business.
In this episode, Malcolm interviews Jeff Jonas, the Founder, and CEO of Senzing — a software company on the leading edge of developing “entity resolution” solutions — which solve the growing challenge of uniquely identifying people or objects across multiple systems of duplicate, low-quality data. Malcolm and Jeff discuss how advances in technology are fueling more modern forms of entity resolution, where companies are now able to implement more context-centric approaches to complex matching, particularly within their master data management (MDM) programs.
As technical as “entity resolution” may sound, the two uncover the global effect this technology has on people each day — including the job of the Chief Data Officer (CDO). Also known as “disambiguation” or “fuzzy matching,” effective entity resolution allows software systems or data stewards to decipher whether records for Richard Smith and Dick Smith may represent the same person, even when it is not overtly suggested by the data.
Jeff describes how entity resolution sits as a foundational component of data within MDM, customer relationship management (CRM), know your customer (KYC), supply chain and every other major business process that relies on accurate, trustworthy data.
Jeff correctly notes that aside from horrible customer experiences that may arise from a lack of effective entity resolution, “it creates a lot of waste for companies to think you are two or three people instead of one.” Citing a person’s example of having their name represented three distinct times in a hotel loyalty club database, he emphasizes the toxicity that comes with a lack of focus on entity resolution for companies who are trying to be both customer and data-centric.
While many companies — particularly those already investing in AI/ML — may be attracted to implementing DIY solutions for entity resolution, Jeff notes that it’s “super expensive to build”, especially given the complexity and diversity of data, and even language itself. The ability to understand meaning across objects, cultures, languages and even alphabets is at the core of reliable entity resolution and building bespoke solutions for tackling these complex problems — at scale — is beyond the capabilities or budgets of an overwhelming number of companies.
When considering a “single version of the truth”, Malcolm unpacks the 30-year history of large, monolithic enterprise resource planning (ERP) suites that created the mindset of master data only living in a singular place within the organization. Thanks to the democratization of IT, the “single version” mindset is shifting both as a practicality and as a business need. Today, master data can be sourced from a single location while supporting multiple versions of truth based on the use case of that data.
In talking about the evolution of large-scale entity resolution, its use in MDM to enable multiple versions of the truth and the legacy requirement to have data stewards manually review records, Jeff notes, “There are definitely times…when you want a human to take a look and make an adjudication. But, I will tell you, in large-scale systems, you don’t have enough humans.”
Rather than adding more people into data stewardship roles to support higher confidence matching, Jeff advocates the approach of widening the pool of data used by entity resolution processes — beyond just name and address — to make match decisions, including the possible use of third-party data sources.
The last few minutes of the conversation go deep into AI/ML, and how these new technologies are used to augment human data stewa
The truth isn’t always black and white. Sometimes, it requires more context and background when attributed to different scenarios and situations.
The same can be said about your data and whether a “single version of the truth” can be properly applied to multiple use cases for your business.
In this episode, Malcolm interviews Jeff Jonas, the Founder, and CEO of Senzing — a software company on the leading edge of developing “entity resolution” solutions — which solve the growing challenge of uniquely identifying people or objects across multiple systems of duplicate, low-quality data. Malcolm and Jeff discuss how advances in technology are fueling more modern forms of entity resolution, where companies are now able to implement more context-centric approaches to complex matching, particularly within their master data management (MDM) programs.
As technical as “entity resolution” may sound, the two uncover the global effect this technology has on people each day — including the job of the Chief Data Officer (CDO). Also known as “disambiguation” or “fuzzy matching,” effective entity resolution allows software systems or data stewards to decipher whether records for Richard Smith and Dick Smith may represent the same person, even when it is not overtly suggested by the data.
Jeff describes how entity resolution sits as a foundational component of data within MDM, customer relationship management (CRM), know your customer (KYC), supply chain and every other major business process that relies on accurate, trustworthy data.
Jeff correctly notes that aside from horrible customer experiences that may arise from a lack of effective entity resolution, “it creates a lot of waste for companies to think you are two or three people instead of one.” Citing a person’s example of having their name represented three distinct times in a hotel loyalty club database, he emphasizes the toxicity that comes with a lack of focus on entity resolution for companies who are trying to be both customer and data-centric.
While many companies — particularly those already investing in AI/ML — may be attracted to implementing DIY solutions for entity resolution, Jeff notes that it’s “super expensive to build”, especially given the complexity and diversity of data, and even language itself. The ability to understand meaning across objects, cultures, languages and even alphabets is at the core of reliable entity resolution and building bespoke solutions for tackling these complex problems — at scale — is beyond the capabilities or budgets of an overwhelming number of companies.
When considering a “single version of the truth”, Malcolm unpacks the 30-year history of large, monolithic enterprise resource planning (ERP) suites that created the mindset of master data only living in a singular place within the organization. Thanks to the democratization of IT, the “single version” mindset is shifting both as a practicality and as a business need. Today, master data can be sourced from a single location while supporting multiple versions of truth based on the use case of that data.
In talking about the evolution of large-scale entity resolution, its use in MDM to enable multiple versions of the truth and the legacy requirement to have data stewards manually review records, Jeff notes, “There are definitely times…when you want a human to take a look and make an adjudication. But, I will tell you, in large-scale systems, you don’t have enough humans.”
Rather than adding more people into data stewardship roles to support higher confidence matching, Jeff advocates the approach of widening the pool of data used by entity resolution processes — beyond just name and address — to make match decisions, including the possible use of third-party data sources.
The last few minutes of the conversation go deep into AI/ML, and how these new technologies are used to augment human data stewa
50 min