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Better Marketing Starts with Better Data

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Improve Data Quality for More Accurate Analysis with Alteryx and Melissa

 

Organizations are under more pressure than ever to gain accurate contact data for their customers. When your consumer base ranges from Los Angeles to Tokyo, it can be challenging. Poor data quality has a critical impact on both the financial stability as well as the operations of a business. Verifying and maintaining vast quantities of accurate contact data is often inefficient and falls short of the mark. According to IBM, the yearly cost of poor data quality is estimated at 3.1 trillion in the U.S. alone.

 

Melissa's Global Address Verification and Predictive Analysis for Alteryx are the tools your business needs to grow. Download this whitepaper to find out how to achieve marketing success, while reducing the cost of doing business overall.

 

Learn how to:

  • ·         Better understand and utilize your big data for marketing success
  • ·         Build better relationships with customers with clean data
  • ·         Target the customers most likely to buy
  • ·         Cut down on undeliverable mail and save on costs

 

Download free whitepaper now:

http://www.melissa.com/resources/whitepapers/alteryx-better-marketing-data.html

 

Flagship SSIS Developer Suite Now Enables Data Assessment and Continuous Monitoring Over Time; Webinar Adds Detail for SSIS Experts


Rancho Santa Margarita, CALIF - March 17, 2015 - Melissa Data, a leading provider of contact data quality and address management solutions, today announced its new Profiler tool added to the company's flagship developer suite, Data Quality Components for SQL Server Integration Services (SSIS). Profiler completes the data quality circle by enabling users to analyze data records before they enter the data warehouse and continuously monitor level of data quality over time. Developers and database administrators (DBAs) benefit by identifying data quality issues for immediate attention, and by monitoring ongoing conformance to established data governance and business rules.

Register here to attend a Live Product Demo on Wednesday, March 18 from 11:00 am to 11:30 am PDT. This session will explore the ways you can use Profiler to identify problems in your data.

"Profiler is a smart, sharp tool that readily integrates into established business processes to improve overall and ongoing data quality. Users can discover database weaknesses such as duplicates or badly fielded data - and manage these issues before records enter the master data system," said Bud Walker, director of data quality solutions, Melissa Data. "Profiler also enforces established data governance and business rules on incoming records at point-of-entry, essential for systems that support multiple methods of access. Continuous data monitoring means the process comes full circle, and data standardization is maintained even after records are merged into the data warehouse."

Profiler leverages sophisticated parsing technology to identify, extract, and understand data, and offers users three levels of data analysis. General formatting determines if data such as names, emails and postal codes are input as expected; content analysis applies reference data to determine consistency of expected content and field analysis determines the presence of duplicates.

Profiler brings data quality analysis to data contained in individual columns and incorporates every available general profiling count on the market today; sophisticated matching capabilities output both fuzzy and exact match counts. Regular expressions (regexes) and error thresholds can be customized for full-fledged monitoring. In addition to being available as a tool within Melissa Data's Data Quality Components for SSIS, Profiler is also available as an API that can be integrated into custom applications or OEM solutions.

Request a free trial of Data Quality Components for SSIS or the Profiler API.
Call 1-800-MELISSA (635-4772) for more information.

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Melissa Data Enrichers Enable Clean, Global Contact Data for Semarchy Users; Webinar Demonstrates Best of Breed Strategy for Fast, Optimized MDM Operations


Rancho Santa Margarita, CALIF - November 12, 2014 - Melissa Data, a leading provider of contact data quality and data integration solutions, today formally announced its partnership with Semarchy, a developer of innovative Evolutionary Master Data Management (MDM) software and solutions. Together the two firms are facilitating sophisticated data quality as a key performance enabler of effective MDM operations. Through this partnership, Semarchy offers its users a fast and easy way to perform worldwide address enrichment, standardization, geocoding and verification using Melissa Data's proven data quality tools and services. In turn, Melissa Data enables its users to upgrade data quality projects, moving beyond tactical processes to engage in a more comprehensive strategy combining data quality, data governance and master data management. Data integrators can learn more in a joint webinar presented by the two firms; click here to access the presentation on demand on Melissa Data's website.

"Clean, enhanced contact data is essential to enterprise MDM - maximizing the value of applications, empowering sales and marketing, and assuring trusted information as the basis for analysis and reporting," said Bud Walker, Director of Data Quality Solutions, Melissa Data. "By integrating data quality operations within the Semarchy Convergence ™ platform, we're supporting users in easily unlocking this kind of high level business value, increasing the quality of supplier, location and customer data within a single de-duplicated 360° view."

Semarchy Convergence™ for MDM is an integrated platform, available in the cloud or on-premise, enabling ground-up design and implementation of master data management initiatives; using a single tool, developers and data managers can create data models, manage quality, set up match/merge and transformation rules, and build data management applications and workflows. Semarchy Convergence™ now supports Melissa Data Enrichers, validated plug-ins that uniquely enable Semarchy users to verify, clean, correct, standardize, and enrich global customer records including full contact validation for global addresses, phones and emails.

Melissa Data Enrichers enhance the capture of new master records at the point of entry; deployed on-site or via the cloud, these tools eliminate the need for users to build integration between internal MDM systems and data quality processes. Customer records can be enhanced with missing contact data and master data can be appended to include attributes such as geographic coordinates. Users of the Semarchy Convergence™ platform can model any domain, managing data via collaboration workflows, generated user interfaces, or through simple batch processing to clean, standardize and enrich legacy data.

"Our perspective on master data management as an evolving business function recognizes the essential correlation between reliable data and authoritative analytics. By capitalizing on Melissa Data's proven technologies, Semarchy users can more effectively manage increasingly intelligent applications of global data - consolidating unstructured sources and enriching master data across all their business domains," said Matthew Dahlman, Technical Director Americas, Semarchy.

Click here for Semarchy Convergence for MDM as a 30-day trial version, and here to begin your free trials of Melissa Data Enrichers for the Semarchy platform. For more information, contact info@semarchy.com, sales@melissadata.com, or call 1-800-MELISSA (635-4772).

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Powerful Features Support Developers with Enriched, De-Duplicated Customer Records; Combats Exponential Increase in Costly Data Decay, Highlighted in Company Magazine


Rancho Santa Margarita, CALIF. - October 29, 2014 - Melissa Data, a leading provider of global contact data quality and data enrichment solutions, today announced significant enhancements to its flagship Data Quality Components for SQL Server Integration Services (SSIS), including the addition of three new services: global email, global phone verification, and U.S. property data enrichment. Data Quality Components for SSIS is a suite of custom data cleansing transformation components for Microsoft SSIS, used to standardize, verify, correct, consolidate and update contact data. With these new features, developers and DBAs are positioned to verify, enrich and retain all the best pieces of global customer data, using a single comprehensive and proven data quality tool.

New to the suite of tools is global email verification, which includes real-time email mailbox verification to eliminate up to 95 percent of invalid emails, so emails get delivered and don't bounce. Global phone numbers from over 230 countries can be verified and appended with geographic information, such as latitude and longitude coordinates, administrative area and predominant language spoken in that region. In addition, global phone features include the ability to return the digits necessary to dial out of your country and into the country of the phone number that was input. For users seeking to enrich U.S. address data, the property feature will provide up-to-date property and mortgage information on more than 140 million properties to improve overall customer intelligence.

"Contact data is always in flux, in fact, half of the customer records held in the average database are invalid or out-of-date in just 45 months - what we call the half-life of data," said Bud Walker, director of data quality solutions, Melissa Data. "Particularly as data becomes more global and increasingly includes email as a critical path of contact, database design must incorporate flexible, scalable tools that rely on a comprehensive approach to managing constant changes in customer data."

Melissa Data's research into the half-life of data, including the operation and long-term costs associated with undeliverable shipments, low customer retention and unsuccessful CRM initiatives, along with other SQL-based data quality challenges are featured in the current issue of Melissa Data Magazine, the company's quarterly resource for DBAs and data quality developers.

Melissa Data Magazine will be available at PASS Summit 2014, Booth #407, starting November 4 in Seattle, Washington. Click here to download the SQL Server edition of Melissa Data Magazine, or call 1-800-MELISSA (635-4772) for more information.

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By David Loshin

In the past few entries in this series we have basically been looking at an approach to understanding customer behavior at particular contextual interactions that are informed by information pulled from customer profiles.


But if the focal point is the knowledge from the profile that influences behavior, you must be able to recognize the individual, rapidly access that individual's profile, and then feed the data from the profile into the right analytical models that can help increase value.

The biggest issue is the natural variance in customer data collected at different touch points in different processes for different business functions. A search for the exact representation provided may not always result in a match, and at worse, may lead to the creation of a new record for the same individual, even one or potentially more records already exist.

In the best scenario, the ability to rapidly access the customer's profile is enabled through the combination of smart matching routines that are tolerant to some variance along with the creation of a master index.

That master index contains the right amount of identifying information about customers to be able to link two similar records together when they can be determined to represent the same individual while differentiating records that do not represent the same individual.

Once the right record is found in the index, a pointer can be followed to the data warehouse that contains the customer profile information.

This approach is often called master data management (MDM), and the technology behind it is called identity resolution. Despite the relative newness of MDM, much of the capability has been available for many years in data quality and data cleansing tools, particularly those suited to customer data integration for direct marketing, mergers, acquisitions, data warehousing, and other cross-enterprise consolidation.

In other words, customer profiles and integrated analytics builds on a level of master data competency that is likely to already be established within the organization.


Rancho Santa Margarita, CALIF- January 14, 2014 - Melissa Data, a leading provider of global contact data quality and integration solutions, today announced its strategic alliance with Blu Sky to solve growing data management challengesin healthcare markets. Melissa Data offers a comprehensive platform for data integration and data quality, and Blu Sky provides data capture technologies optimized for EpicCare software deployments used to administer mid-size and large medical groups, hospitals, and integrated healthcare organizations. By partnering with Melissa Data and its extensive suite of established data quality solutions, healthcare providers have a comprehensive single source for superior data management and compliance.

"Integrated data quality is essential to advancing universal healthcare options, yet the complexities of healthcare data management are evident in today's headlines," said Gary Van Roekel, COO, Melissa Data. "As government initiatives catalyze change in the market, our alliance with Blu Sky provides a significant technical and competitive advantage for healthcare CTOs - offering a comprehensive, proven resource for data quality, integration, and capture. Improved and integrated patient data quality will not only help providers reduce the cost of care, but also facilitate better diagnosis and treatment options for patients."

With this alliance, Melissa Data provides global data quality solutions that verify, standardize, consolidate, and enhance U.S. and international contact data, in combination with a comprehensive Data Integration Suite in Contact Zone, enabling cleansed and enhanced patient data to be transformed and shared securely within a healthcare network. Blu Sky adds subject matter experts to the equation - with deep expertise in the EpicCare software used extensively in healthcare networks to facilitate a "one patient, one record" approach; patient data capture, storage, and management is assured of compliance with a growing range of healthcare regulations, including CASS certification of address results, and HIPAA privacy and security policies.

"Mobile healthcare, connected pharmacy applications, and electronic medical records represent tangible advancements in healthcare accessibility," said Rick O'Connor, President, Blu Sky. "The same advances increase complexity of data management in the context of HIPAA confidentiality and other industry standards. With a single source to address compliance network-wide, providers are poised for healthcare innovations based on secure, high quality patient information."

For more information about the healthcare data management alliance between Melissa Data and Blu Sky, contact Annie Shannahan at 360-527-9111, or call 1-800-MELISSA (635-4772).


Performance Scalability

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By David Loshin

In my last post I noted that there is a growing need for continuous entity identification and identity resolution as part of the information architecture for most businesses, and that the need for these tools is only growing in proportion to the types and volumes of data that are absorbed from different sources and analyzed.

While I have discussed the methods used for parsing, standardization, and matching is past blog series, one thing I alluded to a few notes back was the need for increased performance of these methods as the data volumes grow.

Let's think about this for a second. Assume we have 1,000 records, each with a set of data attributes that are selected to be compared for similarity and matching. In the worst case, if we were looking to determine duplicates in that data set, we would need to compare each records against the remaining records. That means doing 999 comparisons 1,000 times, for a total of 999,000 comparisons.

Now assume that we have 1,000, 000 records. Again, in the worst case we compare each record against all the others, and that means 999,999 comparisons performed 1,000,000 times, for a total of 999,999,000,000 potential comparisons. So if we scale up the number of records by a factor of 1,000, the number of total comparisons increases by a factor of 1,000,000!

Of course, our algorithms are going to be smart enough top figure out ways to reduce the computation complexity, but you get the idea - the number of comparisons grows in a geometric way. And even with algorithmic optimizations, the need for computational performance remains, especially when you realize that 1,000,000 records is no longer considered to be a large number of records - more often we look at data sets with tens or hundreds of millions of records, if not billions.

In the best scenario, performance scales with the size of the input. New technologies enable the use of high performance platforms, through hardware appliances, software that exploits massive parallelism and data distribution, and innovative methods for data layouts and exchanges.

In my early projects on large-scale entity recognition and master data management, we designed algorithms that would operate in parallel on a network of workstations. Today, these methods have been absorbed into the operational fabric, in which software layers adapt in an elastic manner to existing computing resources.

Either way, the demand is real, and the need for performance will only grow more acute as more data with greater variety and diversity is subjected to analysis. You can't always just throw more hardware at a problem - you need to understand its complexity and adapt the solutions accordingly. In future blog series, we will look at some of these issues and ways that new tools can be adopted to address the growing performance need.


Reflections: The Challenges of Master Data Resolution

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By David Loshin

I have worked for almost fifteen years on what would today be called master data management. I recall the first significant project involved unique identification of individuals based on records pulled from about five different sources, and there were three specific challenges:

  1. Determination of identifying attributes - specifying the data elements that, when composed together, provide enough information to differentiate between records representing different entities;

  2. Identity resolution in the presence of variation-having the right algorithms, tools, and techniques for using the identifying attribute values to search for and find matching records among a collection of source data sets; and

  3. Performance management- tuning the algorithms and tools properly to ensure (as close to) linear scalability as the volumes of data grow.

When I first was on a team that attacked these problems in the mid-1990s, the data set sizes were an order of magnitude greater than organizations typically analyzed. And while today those same data volumes would seem puny by comparison, the lessons learned remain very pertinent, since organizations continue to struggle with the same challenges. In fact, one might say that the issues have only become more acute, as the increased volumes magnify the challenges.

For one thing, even if the number of records grows, the widths of the tables typically do not. That means that the variety of the values assigned to sets of data elements may seem to decrease, making it more difficult to find the right combination of <attribute, value>pairs to be used for unique identification and differentiation.

On the other hand, the increased number of records does open the possibility for introduction of errors, especially during manual data entry, highlighting the importance of good algorithms and tools for matching and linkage.

And of course, the larger the data sets, the greater the need for scalability.

In each of the next set of posts, we will look at these issues in much greater detail, as well as consider how those specific challenges have changed in the past twenty years.


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