The single source of truth in corporate data is like the Holy Grail; great to pursue yet destined not to be found. Many different sources, uses, and perspectives of data typically exist both inside and outside the organization. Why not fully embrace that diversity and create a federated business take on data? Advanced tools – more and more enabled by AI – help to keep a grip on a variety of data sources, data stores, definitions and consumption patterns, wherever they are and whoever owns them. It empowers local units to mind their own business with data yet, be an integral part of the organizational robustness and direction. The best of both worlds, really.
- The increasing need to leverage data both internally and with external partners, means that data needs to be connected and collaborated on in a highly federative way, even if it involves different, potentially unaligned perspectives and views on that data.
- A centrally managed, “single source of truth” datastore (even when it’s not called a “data lake”), does not typically cater for a complex (cross-)enterprise situation with diverse stakeholders, unaligned definitions and viewpoints, and different ways of storing and accessing data.
- A realistic approach to this situation no longer assumes an undisputed “golden record”, just the minimum to enable people and systems to connect the dots and stay synchronized. Quality can sometimes wait, but collaboration cannot.
- Master Data Management is a well-established way to ensure alignment. But meta-data management, process management and automation, self-service exploration and integration, data virtualization and AI all enable “thriving on federation”. Graph databases and other ‘NoSQL’ systems bring yet more powerful ways to access and search distributed, fragmented and multi-format data.
- On the one hand, next-generation data platforms help to bridge the worlds of too centralized, monolithic data architectures and too isolated, fragmented data initiatives on the other.
- A leader in healthcare and life science wanted to open-up distributed data for self-service analytics, creating a data catalog that automatically inventoried every field of data from several data lakes and data stores to maximize the business analysts’ time.
- A global beauty products company spent far too much time finding and aligning its data, with product information residing in multiple systems, with different definitions of standards across regions. Through the implementation of federated MDM, it reestablished its handle on mastering complexity, while freeing up time to work on insights-driven product management and marketing.
- A global consumer goods company, which is disjointed by nature due to its many brands, uses smart integration technologies to keep the accessibility and usage of data orchestrated, despite being held at different places and varied formats throughout the organization.
- Agile access and ownership of data as close as possible to the business, without giving up on enterprise-scale qualities.
- Better inventory of what data assets are available within the organization means increased leverage of data for value creation.
- Enabling owners and users of internal and external data stores to collaborate more effectively to and provide better business outcomes for all parties.
- Quick results and time to market without lengthy, often unrealistic and overly complex unification and standardization efforts.
- Master data management: IBM Master Data Management, Informatica Intelligent Master Data Management, Talend Master Data Management, SAP Master Data Governance
- Data exploration: Informatica Enterprise Data Catalog, Cloudera Navigator, Apache Atlas, Waterline Data Catalog, Microsoft Data Catalog, Collibra, Alation, Ataccama metadata management and data catalog
- Data virtualization: Datometry Hyper-Q, Tibco Data Virtualization, Informatica PowerCenter, Denodo Data Virtualization, Red Hat JBoss Data Virtualization, Microsoft PolyBase, SAP HANA data access and virtualization
- Data integration and platform: Microsoft Azure Synapse Analytics, Snowflake Cloud Data Platform, SnapLogic Intelligent Integration Platform, Trifacta Data Wrangling
- Graph and search: Neo4j graph db, MarkLogic multi-model DB, Amazon Neptune graph db, ThoughtSpot Search & AI-driven Platform