InfoTrellis

Informatica MDM – SDP approach

A master data management (MDM) system is installed so that the core data of an organization is secure,  is accessible by multiple systems as and when required and does not have multiple copies floating in the system, in order to have a single source of truth. A solid Suspect Duplicate Process is required in order to achieve the 360 degree view of an entity.

The concept of Suspect Duplicate Processing represents the broad category of activities related to identifying entities that are likely duplicates of each other. Suspect duplicate processing is the process of searching for, matching, creating associations between and, when appropriate, merging data for existing duplicate party records in the system.

To achieve this functionality, Informatica MDM has come up with its own Suspect Duplicate Processing (SDP) approach. An organization based on its use case can opt any of the following two approaches:

Deterministic Matching Approach

Fuzzy Matching Approach

Deterministic Matching Approach

Deterministic Matching uses a series of rules, like nested if statements, to run a series of logical tests on the data sets. This is how we determine relationships, hierarchies, and households within a dataset. Deterministic matching seeks a clear “Yes” or “No” result on each and every attribute, based on which we define whether:

Two records are duplicates

should be resolved by a data steward or

Two unique entities.

It doesn’t leave any room for error and provides the result in an ideal scenario. But most of the data in organizations is far from an ideal scenario. These are the cases when the Fuzzy Matching Approach of Informatica comes handy.

Mastech InfoTrellis’ diverse expertise in the Big Data space, has helped to assist global enterprises in their Big Data initiatives

Big Data Analytics Hub

Mastech InfoTrellis offers managed Big Data Analytics Hub Solution Centered on Hadoop, which enables customers to consolidate multi-channel data of various formats into a single source. Big Data Analytics Hub enables self service analytics by different business functions.

AllSight – Customer Intelligence Management

AllSight Customer Intelligence Management System which delivers Enterprise Customer 360 by ingesting structured and unstructured data from disparate data sources across the organization.

IBM Big Data Solutions

IBM Big Data Solutions combine open source Hadoop and Spark for the open enterprise to cost effectively analyze and manage big data. With BigInsights, you spend less time creating an enterprise-ready Hadoop infrastructure, and more time gaining valuable insights. IBM provides a complete solution, including Spark, SQL, Text Analytics and more to scale analytics quickly and easily.

Web Services Overview: Web Services are services available over the web that enables communication and provide a standard protocol for communication. To enable the communication, we need a medium (HTTP) and a format (XML/JSON).

There are two parties to the web services, namely Service Provider and Service Consumer. A web service provider develops/implements the application (web service) and makes it available over the internet (web).  Service Provider publishes an interface for the web services that describes all the attributes of the web service. Service Consumer consumes the web service. For the Consumer to consume the web service, the consumer has to know the services available, request and response parameters, how to call the services and so on.

Hence we can define Web Service as a standardized way of integrating web desk applications using XML, SOAP, WSDL and UDDI open standards over an internet protocol backbone. XML is used to tag the data. SOAP is used to transfer the data. WSDL is used for describing the services available and UDDI is used for listing what services are available.

“Data analytics” refers to the practice of taking masses of aggregated data and analyzing them to draw important insights and information contained in it. This process is increasingly aided by new software and technology that helps examine large volumes of data for hidden information that can help us in many areas and healthcare is one of those areas.

80% of all healthcare information is unstructured data which is so vast and complex that it needs specialized methods and tools to make meaningful use of the data. The new and emerging technologies like artificial intelligence (AI), machine learning, and predictive analytics are bringing in powerful tools for healthcare technologists and thought leaders to capture these data and process it effectively and efficiently for the complete transformation of the healthcareindustry.Physician decisions are winding up increasingly prove based, implying that they depend on expansive swathes of research and clinical information rather than exclusively their tutoring and expert sentiment. This new treatment state of mind implies there is a more prominent interest for big data analytics in medicinal services offices than at any other time. There is almost certainly that big data has developed as a defining moment changer for the healthcare industry to enable it to advance to another level.

“Data analytics” refers to the practice of taking masses of aggregated data and analyzing them to draw important insights and information contained in it. This process is increasingly aided by new software and technology that helps examine large volumes of data for hidden information that can help us in many areas and healthcare is one of those areas.

80% of all healthcare information is unstructured data which is so vast and complex that it needs specialized methods and tools to make meaningful use of the data. The new and emerging technologies like artificial intelligence (AI), machine learning, and predictive analytics are bringing in powerful tools for healthcare technologists and thought leaders to capture these data and process it effectively and efficiently for the complete transformation of the healthcareindustry.Physician decisions are winding up increasingly prove based, implying that they depend on expansive swathes of research and clinical information rather than exclusively their tutoring and expert sentiment. This new treatment state of mind implies there is a more prominent interest for big data analytics in medicinal services offices than at any other time. There is almost certainly that big data has developed as a defining moment changer for the healthcare industry to enable it to advance to another level.

Promote automation, reuse and agility with the industry's only full integrated end-to-end enterprise data integration platform.

Informatica's modern data integration infrastructure combines advanced hybrid data integration capabilities and centralized governance with flexible self-service business access for analytics. By providing a robust integrated codeless environment, teams can collaboratively connect systems and transform and integrate data at any scale and any speed.

Mastech InfoTrellis’ diverse expertise in the Big Data space, has helped to assist global enterprises in their Big Data initiatives

Big Data Analytics Hub Mastech InfoTrellis offers managed Big Data Analytics Hub Solution Centered on Hadoop, which enables customers to consolidate multi-channel data of various formats into a single source. Big Data Analytics Hub enables self service analytics by different business functions.

AllSight — Customer Intelligence Management AllSight Customer Intelligence Management System which delivers Enterprise Customer 360 by ingesting structured and unstructured data from disparate data sources across the organization.

IBM Big Data Solutions IBM Big Data Solutions combine open source Hadoop and Spark for the open enterprise to cost effectively analyze and manage big data. With BigInsights, you spend less time creating an enterprise-ready Hadoop infrastructure, and more time gaining valuable insights. IBM provides a complete solution, including Spark, SQL, Text Analytics and more to scale analytics quickly and easily.

Data Quality – Overview Data Quality is the process of understanding the quality of data attributes such as data types, data pattern, existing values, and so on. Data quality is also about capturing the score of an attribute based on some specific constraints. For example, get the count of records for which the attribute value is NULL, or find the count of records for which a date attribute does not fit into the specified Date Pattern.

Managing your Data Quality This means that we can weigh the quality of data to any extent irrespective of the available data being good or bad. This Data Quality report can be captured with the complete data details, at record level or even at the attribute level. Using this report, business can identify the quality of data and make out how it can be used to help / benefit the customer. A plan can also be worked out to enhance the quality of data by applying business rules and correcting the required information based on the business needs.

This blog post aims at bringing out the significance of data quality, data quality report generation, steps involved in automation of the data quality report using the scheduler feature of Informatica IDQ.

Deriving Quality Data We have tools in the market to generate these Data Quality reports based on the input data we provide with configuration of some business specifications. An important solution provider in the market for Data Quality report generation is Informatica IDQ which is formulated to generate profiling reports and Data Quality reports.

Using niche technologies, Mastech InfoTrellis enables customers to extract, transform and load data from disparate source systems to centralized data repositories like Master Data Management Hub, Big Data and Analytics Hub.

Etl performance tuning Metadata management Data quality monitoring Cross platform integration Data modelling Data profiling

Our Solutions Informatica Intelligent Data Integration Informatica Intelligent Cloud Services Collibra Data Governance

A master data management (MDM) system is installed so that the core data of an organization is secure,  is accessible by multiple systems as and when required and does not have multiple copies floating in the system, in order to have a single source of truth. A solid Suspect Duplicate Process is required in order to achieve the 360 degree view of an entity.

The concept of Suspect Duplicate Processing represents the broad category of activities related to identifying entities that are likely duplicates of each other. Suspect duplicate processing is the process of searching for, matching, creating associations between and, when appropriate, merging data for existing duplicate party records in the system.

To achieve this functionality, Informatica MDM has come up with its own Suspect Duplicate Processing (SDP) approach. An organization based on its use case can opt any of the following two approaches:

Deterministic Matching Approach

Fuzzy Matching Approach

Deterministic Matching Approach

Deterministic Matching uses a series of rules, like nested if statements, to run a series of logical tests on the data sets. This is how we determine relationships, hierarchies, and households within a dataset. Deterministic matching seeks a clear “Yes” or “No” result on each and every attribute, based on which we define whether:

Two records are duplicates

should be resolved by a data steward or

Two unique entities.

It doesn’t leave any room for error and provides the result in an ideal scenario. But most of the data in organizations is far from an ideal scenario. These are the cases when the Fuzzy Matching Approach of Informatica comes handy.

Mastech InfoTrellis offers best of breed Master Data Management Services enabling Customers to harness the power of their Master Data. Mastech InfoTrellis has successfully delivered Master Data Management Projects time and again over the past decade.

Performance tuning Production support Health check Solution architecture Needs assessment Program strategy & roadmap Solution upgrade Design and development

Our Solutions

IBM InfoSphere Master Data Management Cloud Customer 360 For Sales Force Informatica Intelligent Master Data Management IBM PIM For Manufacturing

Mastech InfoTrellis offers best of breed Master Data Management Services enabling Customers to harness the power of their Master Data. Mastech InfoTrellis has successfully delivered Master Data Management Projects time and again over the past decade.