Database Marketing FAQs
That depends on what is best for you, our client. Our marketing database architecture allows our clients to take advantage of a fully managed service by Black Oak, in which case we have secure channels to handle client data. Alternatively, we can set up the marketing database at the client’s site.
Yes, and we also work with a variety of vendors and experts in the field who can manage database installations of any size.
3. Does the marketing database use standard processes and tools, or is it a custom install for each customer?
Both, we have a core solution that we configure to each client using industry standard and cutting edge proprietary Black Oak products.
Entity Resolution and Master Data Management FAQs
Dr. John R Talburt defines entity resolution as, “A body of knowledge and practice related to the activities supporting a process to decide whether two entity references to real world objects in an information system are referring to the same object or different objects.” This means we can take millions (and now billions), of references to real world objects such as consumers or products and decide which ones are references to the same object (equivalent) and which ones are not. This process is relatively easy for humans, but extremely difficult for machines.
Big Data has two major implications for an organization. The first is that data governance becomes an imperative. As data volumes increase it will become more and more difficult to effectively manage your information assets without compliance to clear and well-designed data management policies. The second is a change in the IT processing paradigm to take advantage of distributed processing. Organizations are rapidly shifting from traditional relational database systems to IT systems that use new tools (i.e. the Hadoop Distributed File System, Hadoop map/reduce, HBase, Hive, and Spark) to exploit the processing power of many interconnected computers.
Our solution is to keep all data context. Because most systems have limited processing power, these systems routinely discard data in an attempt to reduce the volume of data to store and process. For example, most master data management (MDM) systems try to keep only one “golden” survivor record. We believe information in isolation has less value than when it has full context. Our approach is to take advantage of the new Big Data technologies to keep all of the identity information in its original form along with all of the processing metadata. Retaining this full context not only gives deeper insights into the data, it also increases the understandability, traceability, and auditability of the system.
Our solutions support both deterministic (true/false) and probabilistic (i.e. Scoring Rule) matching. The choice depends on many factors including precision, cost, and scale. Deterministic rules are easier to develop and maintain, but in some applications may give less precise results than could be obtained with probabilistic rules. However, in those cases where the higher precision probabilistic matching is possible, these cases also require more time and effort for the initial analytics and setup, as well as higher levels of ongoing system monitoring and maintenance.
Yes, not only do we have the ability to manually override match rule decisions in our system (we call these assertions), we also have indicators that tell us which matches are more likely to be in error, and visualization tools that assist in investigating these cases. Once an assertion is made, it embeds metadata into the system that inform all life cycle management. For example, if we assert that John Smith and John Smith Jr are different people, the embedded metadata will prevent these identities from being merged in future processing.