At Informatica World 2021, our CEO Amit Walia announced the Informatica Intelligent Data Management Cloud, and Chief Product Officer Jitesh Ghai discussed the key attributes and services provided. (Watch the video from the keynote announcement.)
In this blog post, I’m going one layer deeper to show how the Informatica Intelligent Data Management Cloud improves simplicity, productivity and scale of master data management (MDM). Let’s look closer at the three ways the Intelligent Data Management Cloud improves master data management.
1) Simplifying master data management
The practice of master data management requires a core set of data management capabilities. Informatica’s Intelligent Data Management Cloud simplifies the technical architecture for master data management by integrating comprehensive data management capabilities into a microservices-based, API-driven architecture. When you license master data management and 360 solutions, you get core services required for master data management like data integration, data quality, master data consolidation, and application integration in an all-in-one, usage-based offering.
This graphic shows the comprehensive services and capabilities provided by the Intelligent Data Management Cloud.
From a master data management and 360-degree view perspective we seamlessly integrate Informatica’s Intelligent Data Management Cloud services into an All-in-One solution. Let’s quickly walk through how the services are used to deliver a single source of truth, and 360 views and insights:
- Data cataloging – The data landscape is increasing in complexity with master data located in multiple applications, databases, data warehouses and data lakes spread out across multiple clouds and on premises. Data cataloging capabilities such as discovery, domain identification and lineage help you understand where all your master data resides across the enterprise.
- Data integration – Once you know where the master data is located, you’ll need to bring it together and move it between sources. Data integration capabilities that support bulk/batch, advanced streaming, elastic and serverless approaches help you address all master data management use cases.
- Data quality – The primary goal of master data management is ensuring the accuracy, completeness, and consistency of data across sources. Data quality that includes profiling, parsing, standardization, validation, and enrichment capabilities helps you improve the precision of analytics and efficiency of operational processes.
- Master data management – Consistency of master data is much easier to maintain if you deduplicate records and centrally manage a single golden record. Master data consolidation capabilities such as match, merge and survivorship lineage help you ensure the core set of attributes are consistent across the enterprise.
- API and application integration – Master data golden records need to be distributed to applications/sources and shared across business processes that may extend beyond your internal systems. Data orchestration that includes messaging, API and event-based publishing capabilities help you manage the delivery of master data in support of digital business.
- Data governance – Master data is critical to core business processes and consequently should be highly governed. Data governance capabilities for defining master data policies and the rules used to execute and enforce those policies help you ensure compliance with internal standards and external regulations.
- Data privacy – Master data also contains sensitive data about customers, employees and partners, which is subject to a variety of regulations. Data privacy capabilities like access controls, consent management and anonymization help you demonstrate compliant and ethical management of sensitive data.
- Data marketplace – Broad and consistent use of master data throughout the organization improves analytics and decision-making. A data marketplace that provides create and publish, search and shop, and fulfill and track capabilities helps you create a data-driven organization.
One of the challenges with most master data management offerings is limited capabilities – both in terms of breadth and depth of functionality provided. That means you must settle for suboptimal functionality or try to integrate third-party solutions. Our all-in-one approach gives you the simplicity and speed of a business 360 application with the power and flexibility of the Intelligent Data Management Cloud.
Reducing technical debt
A point-product approach is problematic: Connecting and updating these point solutions over time creates a technical debt that reduces an organization’s ability to quickly respond to changing market conditions. It’s a big risk for the business. In fact, according to MIT Sloan Management Review, 70% of IT and business leaders say technical debt severely limits their ability to innovate.
Simplifying the system development lifecycle
The shared services provided by the Intelligent Data Management Cloud also simplifies the system development lifecycle. For example, automating the tracking of project dependencies between governance policies, quality rules and master data management models, as well as the promotion of all the required components from development to quality assurance to production as part of the system development lifecycle. And, development teams can update microservices-based capabilities independently and put them into production, simplifying and accelerating upgrades.
Making data easier to find and use
Simplicity is also delivered in the context of data access and consumption. The combined capabilities within Informatica’s Intelligent Data Management Cloud enables greater understanding and insight using a master data knowledge graph to visualize relationships between master data domains (e.g., customers, products and locations) as well as unstructured data and content (e.g., web chat logs, service contract PDFs or product maintenance manuals). And, data marketplace capabilities simplify finding and compliantly accessing master data for use in analytics, marketing campaigns and other activities.
2) Increasing master data management productivity
The simplified technical architecture described above, as well as prebuilt functionality like data models, rules, workflow, user interfaces and the 150+ out-of-the-box data connectors, accelerates deployment of master data management and increases administrative productivity. The Intelligent Data Management Cloud also provides role- and exception-based task management, allowing diverse groups of people with varying technical skills to work on master data activities in parallel. Orchestration of workflow includes push notifications to responsible parties and automatically assigning new owners or rerouting tasks based on different conditions.
Example of master data management productivity gains
A multinational consumer packaged goods company significantly increased productivity through simplified workflow and self-service capabilities. Here are a few examples:
- They cut the number of workflow steps in half
- They reduced required documentation by 75%
- They shortened approval times by 80% for supplier onboarding processes
Additionally, supplier self-service and automated data quality and verification checks helped increase compliance with numerous local regulations. They also automated publication of supplier master data to applications such as inventory management, purchasing, financial and marketing systems, which drove further productivity improvements in activities like demand forecasting, production planning and financial reporting and analysis.
AI-powered automation supercharges productivity
The Intelligent Data Management Cloud also drives productivity gains through artificial intelligence (AI)-powered automation. Using the CLAIRE AI engine, you can automate master data discovery and domain identification. Or automate the mapping between source and target schemas when importing new master data records.
The following graphic shows some examples of how AI-powered automation can increase productivity of data management activities associated with master data management.
AI-driven data management use cases
In terms of boosting productivity, here are a few more AI-driven data management use cases to consider:
- Stewardship of master data: With the Intelligent Data Management Cloud, business users can write data quality rules based on their business knowledge using plain text. CLAIRE uses natural language processing (NLP) to automatically convert text into rule code, then automatically maps the rule to master data in different sources and executes the rule on those sources. AI also automates deduplication and consolidation of master data records based on match, merge and survivorship algorithms.
- Consumption of master data for analytics: CLAIRE can recommend alternative data sets with higher quality and additional data sets that broaden the context of analytics. Once the desired data is selected, the provisioning of the master data to a data warehouse or lake can also be automated if the consumer has the appropriate authorization.
- Governance and privacy of master data: AI capabilities within the Intelligent Data Management Cloud allow you to automate classification of master data such as personal data and the mapping of privacy policies to those records. Build on that efficiency by automating enforcement of the policies like dynamic data masking at query and prevention of cross-border transfers of master data.
3) Scaling master data management
As the sources, volume, use cases and users of data continue to grow, scalability is critical to the practice of master data management. With the Intelligent Data Management Cloud, you can automatically increase or decrease infrastructure resources as needed to adapt to variable workloads.
Easily scale services up or down based on demand
As requests for services increase, new instances of the service can be launched – and when requests decline, service instances can be shut down. This elastic scalability is enabled using:
- Microservices architecture
- Kubernetes for container orchestration
- Spark for runtime execution
Ingestion of new master data records, and match, merge and deduplication are some examples of master data processes that require elastic scalability.
Scale master data discovery and domain identification via intelligent automation
The Intelligent Data Management Cloud increases scalability of master data discovery and domain identification. The broad array of prebuilt connectors and use of AI techniques like clustering, data similarity and semantic tagging help automate inspecting tens or hundreds of millions of columns across thousands of sources to find master data. With organizations using 100 applications on average  , data volumes forecasted to grow at 20% or more a year, and data lakes growing at 30%, scaling master data discovery and domain identification requires intelligent automation.
Do more with master data – including driving customer experience initiatives
With the Intelligent Data Management Cloud, you can increase the scalability of master data use cases. For example, customer experience requires the ability to orchestrate the flow of data across processes and applications spanning multiple areas of the business. Our capabilities support multiple architectural patterns including messaging, API calls and event-based data flow, which helps you scale your customer experience efforts across every business function, channel and interaction touchpoint of the customer journey.
In this video, see how the Intelligent Data Management Cloud orchestrates master data flow in an order management process.
Scaling master data use to all employees across business functions
The Intelligent Data Management Cloud increases scalability of master data users. Search capabilities across structured and unstructured data and graph-based network navigation makes master data access and use easier for people with a wide range of technical skills. Scaling broad and consistent use of data at all levels of the organization helps improve business outcomes.
In this video, Jenn Yim, Data Supply Chain & AI Program Manager at TELUS, a leading communications and information technology company explains how they use Informatica to scale master data use across marketing, billing, call center and other functions to provide a superior customer experience.
Realize the benefits of master data management
I’ve detailed how Informatica’s Intelligent Data Management Cloud improves simplicity, productivity, and scale of master data management. It’s important to remember these three benefits are interrelated and act as self-reinforcing feedback loops: Simplicity increases productivity, which, in turn, increases scale that can further increase simplicity.
Want to learn more? Join us at the MDM 360 + Data Governance Summit.
During the two-hour event, Gartner’s Malcolm T. Hawker will explain how to optimize master data management program value. And, Informatica product leaders will host two breakout sessions on Business 360 and Data Governance and Privacy, where you can hear from your peers and get guidance on data management challenges. Save your spot now – sign up using the links below: