Digital transformation is often described as a foundational change in how an organization delivers value to its customers. The ability to discover data elements from data silos, cleanse, identify the value of the data, and then deliver personalized customer experiences from enterprise data is a necessity for analytics and insights. The velocity, volume, and variety of data add a new layer of complexity to the analytics and insights foundation. To address this complexity, you need capabilities for data discovery, data enrichment, and data integration.
Many enterprises gather and store data about customers in a disparate manner without generating much value. When creating a 360 view of a customer, you need to get information from several systems to fetch customer name, address, and geography, contact information, along with any touchpoints that have been created over the years.
At a high level, enterprises have 3 major data-related pain points to drive customer insights.
Customer Data Discovery
The likelihood that anyone knows all the information sitting across all these systems is low, which means you are missing relevant and valuable data when you are developing a 360 view of the customer. We can even say that the accuracy of the 360 view of the customer is dependent on the richness of the available data. The customer data could reside in various files, databases, data warehouses, ERPs, CRMs, and other systems. Having in-depth knowledge of customer data and the ability to access them becomes a challenge without an automated data discovery solution.
Customer Data Enrichment
The diversity of data sources brings abundant data types and complex data structures and compounds the complexity of data integration. Not all data is equally good, data quality issues can occur from duplicate data, unstructured data, incomplete data, incorrect/misaligned data, different data formats or even the records can be out of date. Integrating enterprise-wide data with data quality, securing PII and sensitive data has become a key requirement to operationalize the needs of businesses.
Customer Data Integration and Standardization
Standardizing on a shared data language and common semantic for business applications to simplify sharing across systems has been a challenge for data developers. The data schemas that include entities, attributes, semantic metadata, and relationships vary when the customer data is stored in multiple systems in an enterprise.
What Is Microsoft Dynamics 365 Customer Insights?
Dynamics 365 Customer Service Insights helps both agents and customer service managers make better decisions and improve customer satisfaction. Customer Service Insights connects with your Dynamics 365 Customer Service and Omnichannel for Customer Service data to provide your support organization with out-of-box AI and BI capabilities, such as customer service analytics, similar case suggestions and more.
How Informatica Data Discovery, Data Enrichment, and Data Integration Capabilities Help You Be Successful with Dynamics 365
Discover Customer Data Using Informatica’s Enterprise Data Catalog: Informatica Enterprise Data Catalog’s AI/ML driven scanning and cataloging capabilities can help discover, enrich with metadata and gain insights from the data estate of the enterprise that provides data classification, business glossary association, profiling statistics, data patterns, detailed data lineage and data relationships. When dealing with customer data, there is a huge emphasis on Personal Identifiable Information (PII) and sensitive data, Informatica’s data discovery and cataloging technology enables safeguarding of customer and sensitive data.
Ingest, Enrich, and Cleanse Using Informatica Intelligent Data Management Cloud (IDMC): Informatica’s data quality and data integration capabilities can help turn raw data into information. Based on your needs, you can build data integration pipelines using Informatica’s AI-driven Intelligent Data Management Cloud to improve the value of your data by cleansing it using data integration, data quality, and security transformations.
Use cases include cleansing customer addresses, deduplication, and masking PII/sensitive information. Or it can be as simple as joining and aggregating customer records. With Informatica, you can do it all.
Standardize Using Informatica’s Common Data Model (CDM) Connector: With Informatica Intelligent Data Management Cloud you can integrate enterprise customer data and standardize it into a Common Data Model (CDM) format, which gets delivered into a Microsoft ADLS Gen2 data lake and can be directly consumed using Dynamics 365 Customer Insights and other Azure services.
- CDM provides a shared data language and a common data semantics for business and analytical applications, which simplifies sharing of data across applications.
- The CDM also provides a set of standardized, extensible data schemas that contain entities, attributes, semantic metadata, and relationships.
You can choose to reuse existing data models from the data lake or create a new data model based on the attributes from the source system. The data is written into the lake with a manifest file and model JSON file associated with a data file.
Informatica’s enterprise data accelerator is a solution that uses Informatica’s Enterprise Data Catalog and Cloud Data Integration to discover and integrate the breadth of your customer data throughout the enterprise together into the data lake, cleanses through the data quality lens to establish trust in the data, and helps you make decisions based on the data that is being consumed from your Dynamics 365 Customer Insights.
To learn more about the Informatica enterprise accelerator for Dynamics 365 Customer Insights, please watch the on-demand webinar, “How Better Data Accelerates Microsoft Customer Insights.”