Informatica MDM MDE Batch Process in a nutshell

Data is siloed across wide variety of platforms in an enterprise environment and the data needs to be processed, cleansed, and mastered to ensure it is same across source systems for effective reporting and analysis. To cater to this need, Informatica provides Master Data Management (MDM) product called Multi-Domain Edition (MDE). To master the data in this tool, the data needs to be loaded into the Informatica MDM Hub. In Informatica MDM data can be loaded in two different modes (i) Batch and (ii) Real-time.

In this blog we will delve into Batch Processing for loading the data into the Informatica MDM Hub Store, and  the various features and options that are available. Before diving into the batch process, let’s understand how data is organized in Informatica MDM Hub Store.

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Top Service Provider for Big Data Analytics Service

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

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.

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.

Informatica Intelligent Big Data

Informatica provides the industry's only end-to-end big data management solution to deliver successful data lakes. Informatica enables you to integrate, govern and secure big data. Informatica’s self-service data preparation and information catalog, role-based tools, and comprehensive metadata management capabilities ensure that big data can be quickly turned into trusted data assets.

Learn more at http://www.infotrellis.com/master-data-management/

Intelligent Master Data Management by Informatica

Informatica Intelligent Data platform spans on-premise, Cloud and Big Data ― ensuring data is clean and accurate, well governed and compliant, secured and easily accessible for analytics.

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.

What is Intelligent Data Integration

Data integration is the process of combining data from many different sources into an application. You need to deliver the right data in the right format at the right timeframe to fuel great analytics and business processes. A data integration project usually involves accessing data, integrating data from one source map to records in another and delivering integrated data to the business, exactly when the business needs it.

Top Service Provider for Master Data Management

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.

IBM InfoSphere Master Data Management

IBM InfoSphere Master Data Management (MDM) manages all aspects of your critical enterprise data, no matter what system or model, and delivers it to your application users in a single, trusted view. Provides actionable insight, instant business value alignment and compliance with data governance, rules and policies across the enterprise.

Informatica Intelligent Master Data Management

A complete master data management solution addresses the critical business objectives digital organizations face. Informatica MDM offers the only true end-to-end solution, with a modular approach to ensure better customer experience, decision making and compliance.

Learn more at http://www.infotrellis.com/master-data-management/

Best Practices in Data Validation

Data Quality is the buzz word in the digital age.

What is data quality and why is it so important?

“Data quality” is the term that is probably hidden but plays an important role in many streams. Data plays a vital role in acquiring a market place, especially in enterprise data management stream.

Data Quality Examples

Following are some examples which emphasize the need for data quality.

• A customer shouldn’t be allowed to enter his age where he has to mention his marital status.
• When a customer enters a store, there is a high possibility that he might miss out his original details to be filled up with the forms, some of it can be in a hurry not mentioning a correct phone number.
• There is also a possibility of the billing staff to wrongly enter the store address as default in place of the customer address which contributes to a bad quality data that gets persisted in the system.

This data may be crucial as the customer might not just be a Guest customer and the customers’ viable interest towards the store becomes obscure.

This blog post speaks on Data Quality, the significance of Data Quality, business impacts, best practices to be followed, and Mastech InfoTrellis’ specialization in Data validation

Business Impacts on Data Quality

Recent researches from Gartner indicate that poor data quality is a primary reason for about 40% of failing business initiatives.

A low-quality data costs around $600 billion dollars for American businesses alone which in turn causes the failure of any advanced data and technology initiatives.

Significance of Data Quality

The successors of the big business clearly understand the importance of quality data.

The quality of data is directly proportional to the:

• The marketing campaigns cost and the determination of the right audience
• Knowing the customers interest
• Converting the prospects into sales
• The turnaround time for converting a prospect into sales
• The precise business decisions that are made
• How accurately you can make business decisions

The integral part is played by the Quality assurance consultants in revving up the data and ensuring that the data that is consumed by the upstream and downstream are credible.

Data Quality Techniques

For any data to be consumed by the system, the data need to be cleansed to understand the data model of the customer and post cleanse, the data needs to be profiled for a deeper understanding of the data model/ the pattern the data is accumulated.

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Blueprint for a successful Data Quality Program

Data Quality – Overview
Corporates have started to realize that Data accumulated over the years is proving to be an invaluable asset for the business. The data is analyzed and strategies are devised for the business based on the outcome of Analytics. The accuracy of the prediction and hence the success of the business depends on the quality of the data upon which analytics is performed. So it becomes all the more important for the business to manage data as a strategic asset and its benefits can be fully exploited.

This blog aims to provide a blueprint for a highly successful Data Quality project, practices to be followed for improving the Data Quality and how companies can make the right data-driven decisions by following these best practices.

Source Systems and Data Quality Measurement
To measure the quality of the data, third party “Data Quality tools “should hook on to the source system and measure the Data Quality. Having a detailed discussion with the owners of the systems identified for Data Quality measurement needs to be undertaken at a very early stage of the project. Many system owners may not have an issue with allowing a third party Data Quality tool to access their data directly.

But some systems will have regulatory compliance because of which the systems’ owners will not permit other users or applications to access their systems directly. In such a scenario the systems owner and the Data Quality architect will have to agree upon the format in which the data will be extracted from the source system and shared with the Data Quality measurement team for assessing the Data Quality.

Some of the Data Quality tools that are leaders in the market are Informatica, IBM, SAP, SAS, Oracle, Syncsort, Talend.

The Data Quality Architecture should be flexible enough to absorb the data from such systems in any standard format such as CSV, API, and Messages. Care should be taken such that the data that is being made available for Data Quality measurement is extracted and shared with them in an automated way.

Environment Setup
If the Data Quality tool is directly going to connect to the source system, evaluation of the source systems’ metadata, across various environments is another important activity which should be carried out at the initial days of the Data Quality Measurement program. The tables or objects, which hold the source data, should be identical across different environments. If they are not identical, then decisions should be taken to sync them up across environments and should be completed before the developers are on-boarded in the project.

If the Data Quality Team is going to receive data in the form of files, then the location in which the files or data will be shared should be identified and the shared location is created with the help of the infrastructure team. Also, the Data Quality tool should be configured so that it can READ the files available in the SHARED Folder.

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