Data Profiling

All business decisions rest on foundations of customer, product, sales and financial data. Companies spend millions on initiatives intended to manage data assets more effectively, such as CRM, MDM, and ERP. For these projects to be "successful", the data within these systems must be of high quality. Data profiling, therefore, is not only a critical first step in evaluating the data that populates these applications, but also a requirement to manage, maintain, and monitor their lifetime value.

Understanding the structure, format, and accuracy of data and its relationship to other data elements helps businesses specify, control, and manage enterprise data assets. Identifying, analyzing, and understanding the state of enterprise data upfront - what is present, absent, corrupted, and misfielded - before you begin any data migration or integration process from legacy systems, can predispose success or failure. Upfront profiling mitigates data challenges that increase risk and impede sound decision-making.

Uncovering underlying Issues

Data profiling presents insights into the condition of the data that resides in data sources throughout the enterprise. Automated data profiling applies pre-crafted, out-of-the-box business rules to multiple data elements across disparate databases and applications to expose what would likely be unforeseen correlations. It reveals relationships between data elements (attributes) within a data source or across multiple data sources. It then applies statistical analysis to attributes to identify data issues including:

  • Incorrect data
  • Missing data
  • Data anomalies
  • Misfielded data
  • Nulls

By revealing complex relationships among scattered data sources within the enterprise and providing insight into the structure of data, data profiling and discovery imbue companies with the confidence to move forward with ambitious data architecture projects.

The process of data discovery can be complex and resource intensive. By leveraging automated data discovery software UML's data experts make the process easy. UML can help you accomplish an in-depth data discovery and profiling to uncover the "unknowns" that might otherwise be overlooked and automatically capture a complete and true profile of your data, its metadata, and related data quality assessment.

Data Quality

Plagued by multiple and incomplete customer views riddled with inaccuracies and inconsistencies, multi-national organizations face common hurdles as they work to consolidate disparate global data into meaningful information.

At UML our team of data quality experts have one goal and that is to help you deliver "accurate" information fit-for-your-business, however, wherever and whenever you need it. We call this "Peak Data Quality." PDQ demands an expertise that knows who to ensure correct and consistent data at every touchpoint.

Achieving peak data quality requires a partner that can help you to define and automate the consistent enforcement of your corporate data standards across business-critical applications, systems, and platforms. Our experts can help you build rules to standardize and cleanse customer, product, and financial data from any source based on your needs.

Single, trusted views of customer, product, financial, and supplier data are critical to global business success. They provide a holistic composite of customers and suppliers across countries, languages, and character sets, and forge the foundation to deliver consolidated and accurate enterprise reporting.

Data Quality in Customer Relationship Management

A CRM system is only as good as the information within it. Customer data should be duplicate-free and ready to help you build better relationships. Turn to UML to help you ensure the accuracy of name, address, telephone number, e-mail address, and other information assets that make your CRM system. With the ever-growing volume of data that all companies must all deal with, it's easy for data to get out of synch and fragmented. Whether we get data from the web, call centers, the sales force, or even outside sources, duplicates seem to find their way into our CRM system.

Whenever this happens, UML is there to help:

  • Profiling - With accurate profiling, understand the data challenges early and create a realistic plan to improve it.
  • Matching - Gain a complete view of your customers by eliminating duplicate and redundant information.
  • Record Consolidation - Create the most accurate data by integrating multiple customer records into a single best-of-breed record.

Data quality is about having the right information in the hands of the right users at the right time. UML can help with:

  • Postal Validation - Reduce postage expense and ensure delivery with name and address data that meets postal standards.
  • Standardization - Ensure that your name and address data follows local and global standards, making it easier to share between systems.
  • Reference Data Integration - Meet any business objective by enriching and enhancing customer data with external reference data.

Ultimately, it is the customer who benefits from your attention to data quality by your ability to serve them.

Data Quality in Data Warehouse

Data quality is critical to data warehouse and business intelligence solutions. Better informed, more reliable decisions come from using the right data quality technology during the process of loading a data warehouse. Data warehouse project delays tend to stem from lax attention to data quality issues.

  • Use UML to profiling and assess the quality of your data and create an accurate plan and timeline to meet your quality goals.
  • Leverage UML ETL resources to help you load your data warehouse and operational data stores.
  • Score data quality and measure the overall health of data that feeds your data warehouse. Immediately see the impact of your standardization and normalization processes on data by tracking key performance data quality metrics.
  • Once you've cleansed your data, it's important to keep it clean. Let UML help you build these ongoing quality processes:

Quantify, improve and monitor data consistently across your enterprise

To protect the long-term value of enterprise applications and keep poor data quality at bay, you need to manage, maintain, and monitor regularly all data in every enterprise application.

Discover - Quantify Conditions

Perform automated data profiling and discovery to reveal the true content, structure, rules, relationships and quality of your data within and across multiple sources.

Develop - Data Quality

Develop and deploy processes to parse, standardize, cleanse, match and enrich enterprise data to create a complete and consistent single view for your enterprise.

Deploy - Data Quality Services

Deploy and integrate reusable data quality services across your enterprise environment delivering consistent rules and results in batch, real-time and through web services.

Manage - Data Governance and Stewardship

Rate and score data quality, trend and monitor data rule compliance and provide data quality visibility to the organization through a web-based dashboard.

Customer, Product, Financial and Supplier Data

Design and deploy data quality rules and processes across all data domains and source applications from a single platform solution.

For additional information on UML's data quality consulting services, contact us today and we will be happy to discuss your requirements.

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