Learn More About Data Quality Software

You know, at TechDogs, we are complete movie buffs. So, to explain a topic as complicated as Data Quality Software, we are going to piggyback on movie references as well. We will use the 1993 movie, The Fugitive, in which making assumptions based on flawed data nearly ends the life and career of Dr. Richard Kimble. In the film, Dr. Kimble is framed and arrested for the murder of his wife based on false evidence. In a very Shawshank style, Kimble escapes from prison and begins the quest of proving the data that lead to his arrest was false. Throughout the movie, he collects the correct data—phone transcripts, photographs of the actual killer, hospital records and more—connecting the dots to ultimately prove his innocence.
 
Good, reliable data is what saved Dr. Kimble, in The Fugitive. It is also what powers successful businesses—from driving personalization and top-notch customer experience to helping marketers understand what strategies work and what don't. All of it hinges on maintaining high-quality, accurate data and on the contrary, analyzing false trends from inadequate data can cost companies up to a quarter of their annual revenue. Luckily, Data Quality Software exists to clean up your data act.

 

What Is Data Quality Software?

To understand what Data Quality Software is, it’s important to know the principles behind good data quality. Dr. Kimble might define “good data” as truthful data but from a data-science perspective, data quality is a measure of your data’s “fitness” to accurately fulfill its intended purpose. Data scientists use the following five dimensions which are part of something called the data quality assessment framework (DQAF):
 

• Integrity

  Statistics are objectively collected, processed and shared.

• Soundness Of Methodology

  Data is collected within internally accepted standards, good practices and guidelines.

• Reliability/Accuracy

  Source data is timely, with comprehensive data collection programs used to obtain data. Data must also reflect country-specific conditions.

• Serviceability

  Data is revised on a regular basis, consistent within the dataset and follows a predictable revision policy.

• Accessibility

  Data/metadata is easily available and accessible to users, with assistance/support provided to users as needed.

Any tool that improves data completeness, accuracy, relevance, security and consistency falls within the category of Data Quality Software (DQS). Unlike Dr. Kimble who had to do it all manually, we have Data Quality Software to do the work using three specific tactics—data cleansing, data auditing and data migration. We’ll review each one of them but first, let’s take a brief detour down a historical data quality rabbit hole.
 

A History Of Data Quality Software

Gather round, data lovers, number crunchers and movie lovers everywhere! You’re about to learn how early database management systems progressed into the nimble and powerful tools we use today.

In the 1980s, decision support systems (DSS) emerged as the earliest database management systems. They included a broad range of applications whose purpose was to efficiently collect and organize business data and make it simpler to access. Tools like online analytical processing (OLAP) systems, the technology behind many Business Intelligence (BI) applications and Data Warehouses are examples of early DSS tools.

Data quality-as-a-service (DQaaS) arose to meet the need for maintaining large databases. DQaaS systems operated on mainframe computers and were designed to correct common data errors (e.g., misspellings, omissions, duplicates) and keep track of customer profile changes. (e.g., customers who died, got married, moved, etc.)

The 1990s ushered in the era of relational databases — databases that store and provide access to data points related to each other such as customer, sales and inventory data. The massive flow of data made it difficult for companies to maintain their databases. The solution was to use non-relational databases (NoSQL), which can translate multiple data types quickly and are more flexible. NoSQL databases use multiple computers on a Distributed System.

By the 1990s and beyond, businesses had begun recognizing the value of data and the importance of data analysis. The Data Quality Software we have today focuses on helping organizations manage, utilize and protect their data (a process known as “data governance”). These tools were developed over the past decade in response to the growing need to maintain data quality within a rising tide of data volume and complexity.

This brings us to our next question, does anybody really know how a DQS works?
 

How Does A Data Quality Software Work?

Data Quality Software has three key functions/ flavors: data profiling, data stewardship and data preparation.  These tools go by a few different names including “data scrubbing” or “data cleaning” software but as Shakespeare said, what’s in a name?

The job of these tools is to remove or correct low-quality data, a process that typically occurs in the intermediate staging area during the extract-transform-load (ETL) process. The ETL process is what happens when data is pulled from one source and put into another. Data Quality Software ensures the reliability of this data. Some common features/capabilities include:
 

• Connectivity

  Connects two or more data sources and facilitates the swift transfer of data

• Data Profiling/ Auditing

  Locates anomalies and hidden relationships within data elements

• Integration

  Integrates with an organization’s Master Data Management (MDM) system

• Parsing/ Standardization

  Converts and standardizes (makes consistent) data elements according to pre-defined rules

• Match And Merge Capability

  Finds and combines duplicates when data is merged from different sources

• Data Format/ Valid Address Checking

  Checks that addresses match the approved format of the national postal authority/ updates addresses appropriately

Richard Kimble manually addressed his bad data problem by first identifying his wife’s murderer (data profiling/ auditing), he then combined this knowledge with data from other sources like colleagues, hospital records and phone records (connectivity/ integration), to ultimately pointing a clean, data-supported line to the actual murderer while making a case for his innocence.

There are several types of Data Quality Software that will do this for you:
 

Types Of Data Quality Software

There are several types of Data Quality Software that will do this for you:
 

• Data Cleansing Software

  automates the process of converting or mapping data from one form to another, fixing low quality data by removing duplicates and appending and/or maintaining records.

• Data Auditing Software

  focuses on compliance, helping organizations detect fraud, comply with data regulatory standards and support data discovery and modeling. These tools enable data visualization, providing reports for analysis by multiple stakeholders.

• Data Migration Software

  aggregates various datasets, putting them into a single data warehouse, so the data can be further manipulated (stored, cleaned, analyzed). This process is also called data integration.

Why Do We Need Data Quality Software?

Good, clean, correct data is essential for accurate business intelligence. You can’t fully understand your customers when your data is incomplete. Data Quality Software automates and facilitates the entire process of data collection, auditing and analysis. It helps ensure that the conclusions for your business are based on reliable data. Remember, bad data is what got Richard Kimble into trouble. Good data is what saved him.

Spoiler alert: Dr. Richard Kimble doesn’t go to jail for the false accusation that he murdered his wife because he fixes his bad data problem. His life, quite literally, depends on it. Bad data can be just as destructive for your business.
 
The key benefits of Data Quality Software are:
 

• It makes high-quality data available for business projects and initiatives (while facilitating the process of master data management).

• It saves time by automating data governance strategies and data compliance audits.

• It enables consolidated views of an organization’s data (leading to more effective business strategies).

• It supports data mining for fraud detection and planning.

It supports data mining for fraud detection and planning.
 

The Future Of Data Quality Software

The future of DQS is all about ensuring companies achieve data compliance but is expanding to include other data needs, such as automating manual data processing tasks and delivering more accurate results using Artificial Intelligence and Machine Learning.

Gartner predicts that data quality vendors will expand their features to include enhanced data services including data integration, metadata management and master data management. The usability of these tools makes them more accessible to more people. Data management is no longer relegated to the IT department but bridges many corporate departments including marketing, sales and executive teams.

Businesses are increasingly adopting Data Quality Software to address the shift to digital transformation which produces large amounts of data. Good data is required for good decision-making (Richard Kimble and we wholeheartedly agree!) and Data Quality Software helps businesses connect all the dots.