In this special guest feature, Richard Stocks, US Head of Financial Crime at Quantexa, explores why context of data is more important than ever and how customers need to be able to view everything with the context of business agility – discovering risks, threats, and opportunities and be able to quickly capitalize on or protect against them. Richard leverages his 20+ years in the software industry leading BI for blue chip companies like SAP, Iron Mountain and Pitney Bowes to help organizations make better sense of their data by exposing and understanding the context surrounding it. He is trained in every facet from solution architecture, design and development through to marketing, selling and delivery.
In today’s world, context is king. Context drives every decision you make and every action you take. As data is being generated at an unprecedented rate and is becoming increasingly critical to every business, the context of that data also becomes crucial. After all, this is what ultimately unlocks data’s real value.
For data management professionals, context has never been more important than it is now. You are working hard to build data lakes and data warehouses, assemble ETL and ELT processes, centralize and distribute architectures, provide access to multiple data sources that are both operational and analytical in nature, and deliver structured, unstructured and semi-structured content in sub-second response times. Yet your line of business customers are still bringing forward new vendors they believe can deliver what they need, ultimately adding more and more data movement and headaches to your plate. Why? Because even though the letters may look the same, the languages you’re speaking are totally different.
Your internal customers need to view everything with the context of business agility; they need to be able to look at all of the technical data you have presented, and instantly understand what it means. They need to discover risks, threats, and opportunities and be able to quickly capitalize on or protect against them. However, today’s approaches, even with the growth of interoperability, web services and API’s, proliferate databases that host, by design, different versions of a profile, plus different subsets of activity that cannot offer that necessary agility. As environments change, you should be looking for architecturally open technologies that can turn on and turn off as technology trends grow and evolve. When you adopt technologies that have this capability, you are able to tailor the data output to fit a particular audience’s needs.
To make sense of this, let’s first look at the considerable difference between traditional entity resolution and dynamic entity resolution processes. Traditionally, entity resolution is a batch process executed in an order of operations, starting with data parsing, normalization, standardization, matching, potentially merging and survivorship, and enrichment. After taking into account the different data access rights of users, it produces data silos satisfactory for that particular audience’s roles and rights. The challenge here is that when siloed data limits the view of the user to just the unique data which the system has deemed necessary for their use case, it can prevent the larger context from being seen.
In contrast, dynamic entity resolution uses data quantity over data quality, aggregating billions of data points to assemble an investigative, predictive, preventative, and analytical single view. It does this in real-time, making it appropriate and applicable for all audiences and any use case. Therefore, a single view of the customer is built with all the relevant and important data parts that lead to full contextual understanding. This offers the agility and ability to see opportunities, threats, and risks that each party presents to your organization in a quick and easy way, without ambiguity. In order to do this, you should be architecting your business at a micro services level to allow you to move around data in the best way without continually disrupting your business as a whole.
The continuum of context, when related to data can lead to new information, insight, decisions and ultimately action. By implementing data and analytics capabilities that are designed to provide context, you are able to learn more about your customers and design the appropriate processes that will attract similar potential clients or reduce the losses associated with risks and threats.
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