Once data is under management in its best-fit leveragable platform in an organization, it is as prepared as it can be to serve its many callings. It is in position to be used for purposes operationally and analytically and across the spectrum of need. Ideas emerge from business areas no longer encumbered with the burden of managing data, which can be 60% – 70% of the effort to bring the idea to reality. Walls of distrust in data come down and the organization can truly excel with an important barrier to success removed.
An important goal of the information management function in an organization is to get all data under management by this definition, and to keep it under management as systems come and go over time.
Master Data Management (MDM) is one of these key leveragable platforms. It is the elegant place for data with widespread use in the organization. It becomes the system of record for customer, product, store, material, reference and all other non-transactional data. MDM data can be accessed directly from the hub or, more commonly, mapped and distributed widely throughout the organization. This use of MDM data does not even account for the significant MDM benefit of efficiently creating and curating master data to begin with.
MDM benefits are many, including hierarchy management, data quality, data governance/workflow, data curation, and data distribution. One overlooked benefit is just having a database where trusted data can be accessed. Like any data for access, the visualization aspect of this is important. With MDM data having a strong associative quality to it, the graph representation works quite well.
Graph traversals are a natural way for analyzing network patterns. Graphs can handle high degrees of separation with ease and facilitate visualization and exploration of networks and hierarchies. Graph databases themselves are no substitute for MDM as they provide only one of the many necessary functions that an MDM tool does. However, when graph technology is embedded within MDM, such as what IBM is doing in InfoSphere MDMhttps://www.ibm.com/us-en/marketplace/infosphere-big-match-for-hadoop – similar to AI (link) and blockchain (link) – it is very powerful.
Graph technology is one of the many ways to facilitate self-service to MDM. Long a goal of business intelligence, self-service has significant applicability to MDM as well. Self-service is opportunity oriented. Users may want to validate a hypothesis, experiment, innovate, etc. Long development cycles or laborious process between a user and the data can be frustrating.
Historically, the burden for all MDM functions has fallen squarely on a centralized, development function. It’s overloaded and, as with the self-service business intelligence movement, needs disintermediation. IBM is fundamentally changing this dynamic with the next release of Infosphere MDM. Its self-service data import, matching, and lightweight analytics allows the business user to find, share and get insight from both MDM and other data.
Then there’s Big Match. Big Match can analyze structured and unstructured customer data together to gain deeper customer insights. It can enable fast, efficient linking of data from multiple sources to grow and curate customer information. The majority of the information in your organization that is not under management is unstructured data. Unstructured data has always been a valuable asset to organizations, but it can be difficult to manage. Emails, documents, medical records, contracts, design specifications, legal agreements, advertisements, delivery instructions, and other text-based sources of information do not fit neatly into tabular relational databases. Most BI tools on MDM data offer the ability to drill down and roll up data in reports and dashboards, which is good. But what about the ability to “walk sideways” across data sources to discover how different parts of the business interrelate?
Using unstructured data for customer profiling allows organizations to unify diverse data from inside and outside the enterprise—even the “ugly” stuff; that is, dirty data that is incompatible with highly structured, fact-dimension data that would have been too costly to combine using traditional integration and ETL methods.
Finally, unstructured data management enables text analytics, so that organizations can gain insight into customer sentiment, competitive trends, current news trends, and other critical business information. In text analytics, everything is fair game for consideration, including customer complaints, product reviews from the web, call center transcripts, medical records, and comment/note fields in an operational system. Combining unstructured data with artificial intelligence and natural language processing can extract new attributes and facts for entities such as people, location, and sentiment from text, which can then be used to enrich the analytic experience.
All of these uses and capabilities are enhanced if they can be provided using a self-service interface that users can easily leverage to enrich data from within their apps and sources. This opens up a whole new world for discovery.
With graph technology, distribution of the publishing function and the integration of al data including unstructured data, MDM can truly have important data under management, empower the business user, be the cornerstone to digital transformation and truly be self-service.