Relationship between Data Quality and MDM

Melissa IN Team | Data Quality, India, Master Data Management | , , ,

When businesses need to make decisions about their future, they rely on data. Data is what determines where the next store should be opened, how to segregate customers on email lists, etc. Businesses deal with a plethora of data and it doesn’t help that the data is varied and formatted in different ways. Decisions taken based on poor quality data will have a poor outcome. So, to make data useful, you need high-quality data and an efficient Master Data Management system.

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5 Ways To Use A CDP To Optimize Data Quality

Melissa IN Team | Customer Data Platform, Data Quality, India | , , , ,

In today’s customer-centric environment, businesses must be able to predict customer needs and offer personalized service. Your ability to do this is determined by the quality of customer data held. Poor data quality can keep even the best marketing campaigns from succeeding. According to a study, sales teams lose approximately $32,000 per sales rep and 550 hours just because of bad data. Read more “5 Ways To Use A CDP To Optimize Data Quality”

The Evolution Of Data Quality

Melissa AU Team | Data Quality | , , ,

How do brands decide where to open a store? How does an entrepreneur decide where to advertise his new company? What makes a company decide to promote one product and not the others? Data – all of these decisions are based on data.

Today, finding data is easy but this is not enough. To support good short-term and long-term decision making, businesses must ensure that they rely on good quality data. This means that it must be an accurate representation of reality. It must be:

• Accessible
• Accurate
• Complete
• Timely
• Unique
• Reliable
• Relevant

These criteria developed over time as businesses grew more and more reliant on data. Let’s take a look at this evolution.

Establishing The Importance Of Data Quality

The term ‘Business Intelligence’ (BI) was established by Professor Richard Millar Devens in 1865. It was first published in his Cyclopædia of Commercial and Business Anecdotes. The term was used to describe how Sir Henry Furnese gathered data and acted on it in a bid to boost profits.

In 1958, Hans Peter Luhn published an article about the potential of collecting and analyzing business information with the help of technology to make it useful. His articles mentioned that using this information ‘first’ could offer significant advantages.

Until the late 1960s, the only people who could translate data into useful information were specialists. At the time, data was typically stored in silos and remained fragmented and disjointed. Thus, the results were often questionable. The problem was recognized by Edgar Codd in 1970. He presented a solution in the form of a “relational database model” that was soon adopted worldwide.

Early Data Management

The earliest form of a database management system can be described as the decision support system (DSS). Modern business intelligence has its roots in this system. By the 1980s, the number of BI vendors had grown considerably. By now, the value of data-driven decisions had been realized by many businesses. Several tools were also developed to help make data more accessible and organized. This included data warehouses, OLAP and Executive Information Systems. This further led to the development of relational databases.

Data Quality-As-A-Service (DQaaS)

At this time, data was stored in large mainframe computers that held the name and address data for data delivery. They were designed to track customer accounts that were invalid because the person had moved, got married or divorced, died or any other such reason. They could also correct errors in common spellings, names and addresses.
Around 1986, governmental agencies allowed companies to reference postal data by cross-checking their data with the NCOA (National Change of Address) registry. This move dramatically improved data quality and accuracy and was initially sold as a service.

The Internet And Data Availability

By the early 1990s, the internet was beginning to make its presence felt and it brought with it a flood of data. At the same time, reliance on data analysis for business decisions was also increasing. The relational databases in use could no longer keep up with the data available to them. This was compounded by the different data types that were being developed.

A solution emerged in the form of Non-relational databases or NoSQL. These databases used multiple computer systems and could translate many different types of data and made data management more flexible. Large unstructured data sets handled by NoSQL began to be referred to as big data and the term became official by 2005.

Data Governance

By 2010, businesses developed means to store, combine, manipulate and present information in different ways to cater to their different needs. This marked the beginning of data governance. Pioneering companies in the field designed governance organizations to find the best way to maintain data and develop collaborative processes.

They also brought about a “policy-centric approach” to data models, data quality standards and data security. They designed processes that allowed data to be stored in multiple databases as long as it adhered to a common policy standard and thus data became a corporate asset.

Data Quality In The Present Day

Data governance is still evolving. There are a number of associations such as the Data Governance Institute, Data Governance Professionals Organization, etc. that are committed to promoting data governance practices.

Given that it reduces the risk of human error and offers faster processing, Machine Learning is becoming a popular way to implement data governance. Every company that uses data should have its own data governance teams and policy. Data governance teams comprise of business managers and data managers as well as clients who use the business services.

The policy should ideally cover the overall data management, how data is to be used, to whom it should be available and data security. There are countless tools available to enhance data quality but finding the tools best suited to a particular situation can be a challenge. If you’re looking for ways to enhance your data quality, choose workflow driven data quality tools that can be scaled easily while maintaining a system of trust.

What is Date of Death Data & Why Is It Useful?

Melissa Team | Direct Mail, Global Data Quality, Identity Verification, Mailing, Mailing Lists, Personator | , , , ,

Death-related data obtained from Social Security records, obituaries and other sources can be used for many purposes. When it comes to direct marketing, verifying death dates and removing deceased individuals from your mailing lists can benefit your business in several ways.

Protect Your Brand Image & Maintain Customer Respect

No matter how up-to-date your database is, there’s a good chance at least a few contacts on your mailing list are deceased. By identifying individuals who should be removed from your mailing list, you can more effectively target those who are able to utilize your goods and/or services.

Additionally, having to explain why marketing mail was sent to a deceased individual could prove to be an awkward situation for your business. Let’s say you’ve just launched a marketing campaign where a significant percentage of your mailings are unknowingly sent to deceased individuals. Not only does this make your company look bad from an organizational perspective, but you risk further damaging your brand image and upsetting customers by sending mail to a member of their household who has passed away. A deceased suppression service can help you avoid these sensitive situations.

Save Money on Postage & Production

When you can be sure your mailings are only reaching a responsive audience, you are maximizing your marketing dollars. List targeting allows you to keep your print and mail costs down and response rates high. A deceased suppression service helps you maintain a fresh, updated contact list – thereby reducing the amount of returned mail.

Prevent Fraud

As online activity has increased during the pandemic, many types of fraud have been on the rise – including scams involving death certificates. One use case where death data has been particularly beneficial is voter registration. Up to 1.8 million deceased individuals were listed as voters in 2019 – as voter data changes in real time due to life events such as moving, marriage, and death, it’s imperative that election boards keep voter data clean and up-to-date in order to maintain election integrity. In addition to mitigating fraud, election boards can utilize data capabilities such as appending addresses with apartment numbers and deceased suppression as part of their strategy to improve voter outreach efforts and reduce undeliverable mail and waste.

Why Melissa?

Melissa has been a leading provider of global identity verification and data quality solutions for more than 35 years. Personator, Melissa’s real-time Identity Verification solution, includes identity, ID and document verification, age authentication and global watchlist screening to establish the identity of a consumer or satisfy AML/KYC compliance. Personator World meets stringent international compliance obligations and includes tools to help businesses with fraud mitigation. Melissa also makes it easy to clean and update your direct mailing lists with our Deceased Suppression Service. Our deceased suppression database is compiled from Social Security records and other sources to match your records and identify those who should be removed from your contact list – so you can protect your brand image and reduce costs associated with wasted postage and production.

Sources:

https://mailingsystemstechnology.com/article-4675-Data-Quality-and-Election-Integrity-How-Voter-Registrations-Are-Validated.html

What is Data Profiling and Why Profile Your Data?

Melissa IN Team | Data Profiling | , , ,

Many times the presumptions regarding the data that we store and provide are not always precise. Despite taking all necessary precautions our systems are not always totally free of bugs. As a result, the quality of data provided gets compromised and this could lead to several negative outcomes.

So, what can be done to prevent such situations? You need to profile your data.

Data Profiling

Data profiling refers to the procedure where the data sources get evaluated for their structure and quality to be sure of the accuracy of your data.

• Your data gets evaluated by comparing it to an existing data source.
• This can help you arrive at the right conclusion on its accuracy.
• Profiling your data helps in determining its completeness, precision, and validity.

Data profiling is done by combining it with the Extract, Transform, and Load process (ETL Process) most of the time. This helps in moving data from one location to another. Combining ETL and Data profiling helps to cleanse the data, fix the issues, and move quality data to the desired location. Profiling your data helps to identify the quality issues that require correction and the particular issues that can be fixed during the ETL process.

Why Is Data Profiling Important?

Using compromised data puts your entire project at risk. The problems and challenges that are faced by projects of data integration are similar to the ones faced by the IT industry. They include:

• Compromising quality to meet deadlines
• Lack of time
• Budget overrun
• Incorrect and insufficient understanding of the data source

These challenges and problems could be the result of certain issues including the following

• The difficulty in unwinding data due to its huge volumes
• The complexity of databases and applications
• The process is challenging and time-consuming
• This is also subject to errors

The quality, structure, and content of data need to be understood before getting it integrated or used in an application.

To understand the preciseness and quality of data most of the initiatives for data integration depend on external sources of information. This includes relying on the experience of staff, depending on source programs and documentation.

The external information could be wrong, outdated, or incomplete most of the time. This means you’ll have to put in more time, effort, and money to get these issues fixed and validate your data. You’ll be compromising the entire project in case you fail to do it.

Data Profiling is necessary for the following.

• To understand the data
• To organize it
• To compare and verify if your data matches with its source
• To ensure that the data match the statistical measure standards
• To make sure data is per the company’s business rules and regulations

Proper data profiling helps you to answer the following questions.

• Do you have the required data?
• Will that data be sufficient to complete your project in time?
• Is your data complete? or are there any blank values?
• How unique is your data?
• Does it support the requirements of your company?
• Does it accurately represent the needs of your organization?
• Is it possible to integrate, cross-refer, or consolidate the data for usability?
• What data requires cleaning?
• Has there been a duplication of data?
• Are the data patterns anomalous?
• What data requires transformation?
• Can you be sure of its correctness and consistency?

Being able to answer these questions correctly will ensure the quality of your data which is necessary for the overall growth and success of your business.

Data Profiling: The Different Techniques

In general data, profiling is done using 3 different techniques. They are the following.

1. Column Profiling Technique

Using this technique of profiling the number of times each value appears within each of the columns in the table is counted. This technique helps to discover the patterns in your data as well as to understand the frequency distribution.

2. Cross-Column Profiling Technique

There are two different processes under this technique of data profiling. They are:

• Key Analysis
• Dependency Analysis

Key Analysis is a process where a group of values within a table is scanned to trace out a prospective primary key.
Dependency analysis is carried out to identify the structures built/dependent relationships within the data set. Compared to Key Analysis the process of Dependency Analysis is more complex.
Both these techniques are used to identify dependencies and relationships among the attributes of data within a table.

3. Cross-Table Profiling Technique

This technique of profiling searches the entire table to identify possible foreign keys. This technique also helps to identify the differences and similarities in data and syntax between the tables. This will help in removing data redundancy and in locating data sets that can be charted together.
There is an additional step which is often considered as the final step in profiling data- Data Rule Validation. This proactive method verifies to understand the authenticity and accuracy of the data entered using a set of predefined rules.

The above-mentioned techniques of data profiling may be carried out using automated services or can be done by an analyst manually.

The data profiling process helps to verify whether the rows in the table are filled with accurate and valid data as well as to understand its quality. Once a problem is detected you need to get it fixed by mentioning the steps in your project for data quality. Data profiling helps in governing your data properly.