Data Matching is the discipline within data quality management where you deal with the probably most frequent data quality issue that you meet in almost every organization, which is duplicates in master data. This is duplicates in customer master data, duplicates in supplier master data, duplicates in combined / other business partner master data, duplicates in product master data and duplicates in other master data repositories.
A duplicate (or duplicate group) is where two (or more) records in a system or across multiple systems represent the same real-world entity.
Typically, you can use a tool to identify these duplicates. It can be as inexpensive as using Excel, it can be a module in a CRM or other application, it can be a capability in a Master Data Management (MDM) platform, or it can be a dedicated Data Quality Management (DQM) solution.
Over the years there have been developed numerous tools and embedded capabilities to tackle the data matching challenge. Some solutions focus on party (customer/supplier) master data and some solutions focus on product master data. Within party master data many solutions focus on person master data. Many solutions are optimized for a given geography or a few major geographies.
In my experience you can classify available tools / capabilities into the below 5 levels of efficiency:
The efficiency percentage here is an empirical measure of the percentage of actual duplicates the solution can identify automatically.
In more detail, the levels are:
1: Simple deterministic
Here you compare exact values between two duplicate candidate records or use simple transformed values as upper-case conversion or simple phonetic codes as for example soundex.
Don’t expect to catch every duplicate using this approach. If you have good, standardized master data 50 % is achievable. However, with a normal cleanliness, it will be lower.
Surprisingly many organizations still start here as a first step of reinventing the wheel in a Do-It-Yourself (DIY) approach.
2: Synonyms / standardization
In this more comprehensive approach you can replace, substitute, or remove values or words in values based on synonym lists. Examples are replacing person nicknames with guessed formal names, replacing common abbreviations in street names with a standardized term and removing legal forms in company names.
Enrichment / verification with external data can also be used, for example by standardizing addresses or classifying products.
3: Algorithms
Here you will use an algorithm as part of the comparison. Edit distance algorithms, as we know from autocorrection, are popular here. One frequently used one is the Levenshtein distance algorithm. But there are plenty out there to choose from each with their pros and cons.
Many data matching tools simply let you choose from using one of these algorithms in each scenario.
4: Combined traditional
If your DIY approach didn’t stop when encompassing more and more synonyms it will probably be here where you realize that further quest for raising efficiency includes combining several methodologies and doing dynamic combined algorithm utilization.
A minor selection of commercial data matching tools and embedded capabilities can do that for you so you avoid reinventing the wheel one more time.
This will yield high efficiency, but not perfection.
5: AI Enabled
Using Artificial Intelligence (AI) in data matching has been practiced for decades as told in the post The Art in Data Matching. With the general rise of AI in recent years there is renewed interest both at tool vendors and at users of data matching to industrialize this.
The results are still sparse out there. With limited training of models, it can be less efficient than traditional methodology. However, it can for sure also limit the gap between traditional efficiency and perfection.
More on Data Matching
There is of course much more to data matching than comparing duplicate candidates. Learn some more about The Art of Data Matching.
The impact of poor data quality issues in different industries such as healthcare, banking, and telecom cannot be overemphasized. It can lead to financial losses, customer churn, real-life impact on users, waste of resources, and conflicts within the data team. When a data quality issue arises, data managers, Chief Data Officers, and Data team leads are often the primary targets. Therefore, all data stakeholders must be as thorough and inquisitive as possible in their search for the right data quality tool that can solve their data problems. With numerous options in the market, it can be challenging to select the right tool that meets your unique data needs. In this article, four promising modern data quality tools are explored, each with its distinctive features, to help you make an informed decision.
Known for its user-friendly interface, Soda.io is a top pick for teams seeking an agile approach to data quality. It offers customizable checks and alerts, enabling businesses to maintain control over their data health. Soda.io excels in providing real-time insights, making it an excellent choice for dynamic data environments.
UNIQUE FEATURES
– User-friendly interface: Easy for teams to use and manage.
– Customizable checks and alerts: Tailor the tool to specific data health needs.
– Real-time insights: Immediate feedback on data quality issues.
Digna, with its AI-driven approach, stands out in how data quality issues are predicted, detected, and addressed. It not only flags data quality issues but also offers insights into their implications, helping businesses understand the impact on their operations. Digna’s unique selling points include its seamless integration, real-time monitoring, and the ability to provide reports on past data quality issues within three days – a process that typically takes months. It’s adaptable across various domains and ensures data privacy compliance while being scalable for any business size.
UNIQUE FEATURES
– AI-powered capabilities: Advanced predictive analysis and anomaly detection.
– Real-time monitoring: Immediate detection and notification of data quality issues.
– Automated Machine Learning: Efficiently corrects data irregularities.
– Scalability: Suitable for both startups and large enterprises.
– Flexible Installation: Cloud or On-prem installation, your choice.
– Automated Rule Validation: Say Goodbye to manually defining technical data quality rules. See the use case here
This tool offers a unique approach to data quality by focusing on data observability. Monte Carlo helps businesses monitor the health of their data pipelines, providing alerts for anomalies and breakdowns in data flow. It is particularly useful for companies with complex data systems, ensuring data reliability across the board.
UNIQUE FEATURES
– Focus on data observability: Monitors the health of data pipelines.
– Anomaly and breakdown alerts: Notifies about issues in data flow.
– Useful for complex data systems: Ensures reliability across all data.
Specializing in automatic data validation, Anomalo is ideal for businesses that deal with large volumes of data. It quickly identifies inconsistencies and errors, streamlining the data validation process. Anomalo’s machine learning algorithms adapt to your data, continually improving the detection of data quality issues.
UNIQUE FEATURES
– Automatic data validation: Ideal for handling large volumes of data.
– Machine learning algorithms: Adapt and improve issue detection over time.
– Quick identification of inconsistencies and errors: Streamlines the data validation process.
What You Should Know Before Choosing an Emerging Modern Data Quality Tool
Selecting the right data quality tool requires an understanding of your specific data challenges and goals. Consider how well it integrates with your existing infrastructure, the ease of setup and use, and the tool’s ability to scale as your data grows. Additionally, evaluate the tool’s ability to provide actionable insights and not just data alerts. The tool should be agile enough to adapt to various data types and formats while ensuring compliance with data privacy regulations.
In conclusion, whether you’re inclined towards the user-friendly approach of Soda.io, the observability focus of Monte Carlo, the automatic validation of Anomalo, or the AI-driven versatility of Digna, 2024 offers a range of top-tier data quality tools.
Digna in particular offers a comprehensive solution that stands out for its simplicity and effectiveness in data observability. Digna’s AI-driven approach not only predicts and detects data quality issues but also provides detailed alerts to users, ensuring that data problems are addressed promptly. With its ability to inspect a subset of customer data and provide rapid analysis, Digna saves costs and mitigates risks associated with data quality issues. Its seamless integration and real-time monitoring make it a user-friendly tool that fits effortlessly into any data infrastructure.
Make an informed choice today and steer your business toward data excellence with the right tool in hand.
Today’s guest blog post is from Marcin Chudeusz of DEXT.AI. a company specializing in creating Artificial Intelligence-powered Software for Data Platforms.
Have you ever experienced the frustration of missing crucial pieces in your data puzzle? The feeling of the weight of responsibility on your shoulders when data issues suddenly arise and the entire organization looks to you to save the day? It can be overwhelming, especially when the damage has already been done. In the constantly evolving world of data management, where data warehouses, data lakes, and data lakehouses form the backbone of organizational decision-making, maintaining high-quality data is crucial. Although the challenges of managing data quality in these environments are many, the solutions, while not always straightforward, are within reach.
Data warehouses, data lakes, and lakehouses each encounter their own unique data quality challenges. These challenges range from integrating data from various sources, ensuring consistency, and managing outdated or irrelevant data, to handling the massive volume and variety of unstructured data in data lakes, which makes standardizing, cleaning, and organizing data a daunting task.
Today, I would like to introduce you to Digna, your AI-powered guardian for data quality that’s about to revolutionize the game! Get ready for a journey into the world of modern data management, where every twist and turn holds the promise of seamless insights and transformative efficiency.
Digna: A New Dawn in Data Quality Management
Picture this: you’re at the helm of a data-driven organization, where every byte of data can pivot your business strategy, fuel your growth, and steer you away from potential pitfalls. Now, imagine a tool that understands your data and respects its complexity and nuances. That’s Digna for you – your AI-powered guardian for data quality.
Goodbye to Manually Defining Technical Data Quality Rules
Gone are the days when defining technical data quality rules was a laborious, manual process. You can forget the hassle of manually setting thresholds for data quality metrics. Digna’s AI algorithm does it for you, defining acceptable ranges and adapting as your data evolves. Digna’s AI learns your data, understands it, and sets the rules for you. It’s like having a data scientist in your pocket, always working, always analyzing.
Figure 1: Learn how Digna’s AI algorithm defines acceptable ranges for data quality metrics like missing values. Here, the ideal count of missing values should be between 242 and 483, and how do you manually define technical rules for that?
Seamless Integration and Real-time Monitoring
Imagine logging into your data quality tool and being greeted with a comprehensive overview of your week’s data quality. Instant insights, anomalies flagged, and trends highlighted – all at your fingertips. Digna doesn’t just flag issues; it helps you understand them. Drill down into specific days, examine anomalies, and understand the impact on your datasets.
Whether you’re dealing with data warehouses, data lakes, or lakehouses, Digna slips in like a missing puzzle piece. It connects effortlessly to your preferred database, offering a suite of features that make data quality management a breeze. Digna’s integration with your current data infrastructure is seamless. Choose your data tables, set up data retrieval, and you’re good to go.
Figure 2: Connect seamlessly to your preferred database. Select specific tables from your database for detailed analysis by Digna.
Navigate Through Time and Visualize Data Discrepancies
With Digna, the journey through your data’s past is as simple as a click. Understand how your data has evolved, identify patterns, and make informed decisions with ease. Digna’s charts are not just visually appealing; they’re insightful. They show you exactly where your data deviated from expectations, helping you pinpoint issues accurately.
With Digna, every column in your data table gets attention. Switch between columns, unravel anomalies, and gain a holistic view of your data’s health. It doesn’t just monitor data values; it keeps an eye on the number of records, offering comprehensive analysis and deep insights with minimal configuration. Digna’s user-friendly interface ensures that you’re not bogged down by complex setups.
Figure 3: Observe how Digna tracks not just data values but also the number of records for comprehensive analysis. Transition seamlessly to Dataset Checks and witness Digna’s learning capabilities in recognizing patterns.
Real-time Personalized Alert Preferences
Digna’s alerts are intuitive and immediate, ensuring you’re always in the loop. These alerts are easy to understand and come in different colors to indicate the quality of the data. You can customize your alert preferences to match your needs, ensuring that you never miss important updates. With this simple yet effective system, you can quickly assess the health of your data and stay ahead of any potential issues. This way, you can avoid real-life impacts of data challenges.
Whether you prefer inspecting your data directly from the dashboard or integrating it into your workflow, I invite you to commence your data quality journey. It’s more than an inspection; it’s an exploration—an adventure into the heart of your data with a suite of features that considers your data privacy, security, scalability, and flexibility.
Automated Machine Learning
Digna leverages advanced machine learning algorithms to automatically identify and correct anomalies, trends, and patterns in data. This level of automation means that Digna can efficiently process large volumes of data without human intervention, erasing errors and increasing the speed of data analysis.
The system’s ability to detect subtle and complex patterns goes beyond traditional data analysis methods. It can uncover insights that would typically be missed, thus providing a more comprehensive understanding of the data.
This feature is particularly useful for organizations dealing with dynamic and evolving data sets, where new trends and patterns can emerge rapidly.
Domain Agnostic
Digna’s domain-agnostic approach means it is versatile and adaptable across various industries, such as finance, healthcare, and telcos. This versatility is essential for organizations that operate in multiple domains or those that deal with diverse data types.
The platform is designed to understand and integrate the unique characteristics and nuances of different industry data, ensuring that the analysis is relevant and accurate for each specific domain.
This adaptability is crucial for maintaining accuracy and relevance in data analysis, especially in industries with unique data structures or regulatory requirements.
Data Privacy
In today’s world, where data privacy is paramount, Digna places a strong emphasis on ensuring that data quality initiatives are compliant with the latest data protection regulations.
The platform uses state-of-the-art security measures to safeguard sensitive information, ensuring that data is handled responsibly and ethically.
Digna’s commitment to data privacy means that organizations can trust the platform to manage their data without compromising on compliance or risking data breaches.
Built to Scale
Digna is designed to be scalable, accommodating the evolving needs of businesses ranging from startups to large enterprises. This scalability ensures that as a company grows and its data infrastructure becomes more complex, Digna can continue to provide effective data quality management.
The platform’s ability to scale helps organizations maintain sustainable and reliable data practices throughout their growth, avoiding the need for frequent system changes or upgrades.
Scalability is crucial for long-term data management strategies, especially for organizations that anticipate rapid growth or significant changes in their data needs.
Real-time Radar
With Digna’s real-time monitoring capabilities, data issues are identified and addressed immediately. This prompt response prevents minor issues from escalating into major problems, thus maintaining the integrity of the decision-making process.
Real-time monitoring is particularly beneficial in fast-paced environments where data-driven decisions need to be made quickly and accurately.
This feature ensures that organizations always have the most current and accurate data at their disposal, enabling them to make informed decisions swiftly.
Choose Your Installation
Digna offers flexible deployment options, allowing organizations to choose between cloud-based or on-premises installations. This flexibility is key for organizations with specific needs or constraints related to data security and IT infrastructure.
Cloud deployment can offer benefits like reduced IT overhead, scalability, and accessibility, while on-premises installation can provide enhanced control and security for sensitive data.
This choice enables organizations to align their data quality initiatives with their broader IT and security strategies, ensuring a seamless integration into their existing systems.
Conclusion
Addressing data quality challenges in data warehouses, lakes, and lakehouses requires a multifaceted approach. It involves the integration of cutting-edge technology like AI-powered tools, robust data governance, regular audits, and a culture that values data quality.
Digna is not just a solution; it’s a revolution in data quality management. It’s an intelligent, intuitive, and indispensable tool that turns data challenges into opportunities.
I’m not just proud of what we’ve created at DEXT.AI; I’m most excited about the potential it holds for businesses worldwide. Join us on this journey, schedule a call with us, and let Digna transform your data into a reliable asset that drives growth and efficiency.
Cheers to modern data quality at scale with Digna!
This article was written by Marcin Chudeusz, CEO and Co-Founder of DEXT.AI. a company specializing in creating Artificial Intelligence-powered Software for Data Platforms. Our first product, Digna offers cutting-edge solutions through the power of AI to modern data quality issues.
Contact me to discover how Digna can revolutionize your approach to data quality and kickstart your journey to data excellence.
Today’s guest blog post is from Marcin Chudeusz of DEXT.AI. a company specializing in creating Artificial Intelligence-powered Software for Data Platforms.
Data quality isn’t just a technical issue; it’s a journey full of challenges that can affect not only the operational efficiency of an organization but also its morale. As an experienced data warehouse consultant, my journey through the data landscape has been marked with groundbreaking achievements and formidable challenges. The latter, particularly in the realm of data quality in some of the most data-intensive industries: banks, and telcos, have given me profound insights into the intricacies of data management. My story isn’t unique in data analytics, but it highlights the evolution necessary for businesses to thrive in the modern data environment.
Let me share with you a part of my story that has shaped my perspective on the importance of robust data quality solutions.
The Daily Battles with Data Quality
In the intricate data environments of banks and telcos, where I spent much of my professional life, data quality issues were not just frequent; they were the norm.
The Never-Ending Cycle of Reloads
Each morning would start with the hope that our overnight data loads had gone smoothly, only to find that yet again, data discrepancies necessitated numerous reloads, consuming precious time and resources. Reloads were not just a technical nuisance; they were symptomatic of deeper data quality issues that needed immediate attention.
Delayed Reports and Dwindling Trust in Data
Nothing diminishes trust in a data team like the infamous phrase “The report will be delayed due to data quality issues.” Stakeholders don’t necessarily understand the intricacies of what goes wrong—they just see repeated failures. With every delay, the IT team’s credibility took a hit.
Team Conflicts: Whose Mistake Is It Anyway?
Data issues often sparked conflicts within teams. The blame game became a routine. Was it the fault of the data engineers, the analysts, or an external data source? This endless search for a scapegoat created a toxic atmosphere that hampered productivity and satisfaction.
Data quality issues aren’t just a technical problem; they’re a people problem. The complexity of these problems meant long hours, tedious work, and a general sense of frustration pervading the team. The frustration and difficulty in resolving these issues created a bad atmosphere and made the job thankless and annoying.
Decisions Built on Quicksand
Imagine making decisions that could influence millions in revenue based on faulty reports. We found ourselves in this precarious position more often than I care to admit. Discovering data issues late meant that critical business decisions were sometimes made on unstable foundations.
High Turnover: A Symptom of Data Discontent
The relentless cycle of addressing data quality issues began to wear down even the most dedicated team members. The job was not satisfying, leading to high turnover rates. It wasn’t just about losing employees; it was about losing institutional knowledge, which often exacerbated the very issues we were trying to solve.
The Domino Effect of Data Inaccuracies
Metrics are the lifeblood of decision-making, and in the banking and telecom sectors, year-to-month and year-to-date metrics are crucial. A single day’s worth of bad data could trigger a domino effect, necessitating recalculations that spanned back days, sometimes weeks. This was not just time-consuming—it was a drain on resources amongst other consequences of poor data quality.
The Manual Approach to Data Quality Validation Rules
As an experienced data warehouse consultant, I initially tried to address these issues through the manual definition of validation rules. We believed that creating a comprehensive set of rules to validate data at every stage of the data pipeline would be the solution. However, this approach proved to be unsustainable and ineffective in the long run.
The problem with manual rule definition was its inherent inflexibility and inability to adapt to the constantly evolving data landscape. It was a static solution in a dynamic world. As new data sources, data transformations, and data requirements emerged, our manual rules were always a step behind, and keeping the rules up-to-date and relevant became an arduous and never-ending task.
Moreover, as the volume of data grew, manually defined rules could not keep pace with the sheer amount of data being processed. This often resulted in false positives and negatives, requiring extensive human intervention to sort out the issues. The cost and time involved in maintaining and refining these rules soon became untenable.
Comparison between Human, Rule, and AI-based Anomaly Detection
Embracing Automation: The Path Forward
This realization was the catalyst for the foundation of dext.ai. Danijel (Co-founder at Dext.ai) and I combined our AI and IT Know-How to create AI-powered software for Data Warehouses. This led to our first product Digna, we needed intelligent, automated systems that could adapt, learn, and preemptively address data quality issues before they escalated. By employing machine learning and automation, we could move from reactive to proactive, from guesswork to precision.
Automated data quality tools don’t just catch errors—they anticipate them. They adapt to the ever-changing data landscape, ensuring that the data warehouse is not just a repository of information, but a dependable asset for the organization.
Today, we’re pioneering the automation of data quality to help businesses navigate the data quality landscape with confidence. We’re not just solving technical issues; we’re transforming organizational cultures. No more blame games, no more relentless cycles of reloads—just clean, reliable data that businesses can trust.
In the end, navigating the data quality landscape isn’t just about overcoming technical challenges; it’s about setting the foundation for a more insightful, efficient, and harmonious future. This is the lesson my journey has taught me, and it is the mission that drives us forward at dext.ai.
This article was written by Marcin Chudeusz, CEO and Co-Founder of DEXT.AI. a company specializing in creating Artificial Intelligence-powered Software for Data Platforms. Our first product, Digna offers cutting-edge solutions through the power of AI to modern data quality issues.
Contact us to discover how Digna can revolutionize your approach to data quality and kickstart your journey to data excellence.
The next vendor to be included on The Disruptive MDM / PIM / DQM List 2022 is Informatica.
Informatica has been a leader on the Master Data Management (MDM), Product Information Management (PIM) and Data Quality Management (DQM) space for many years latest as seen in their 6th time front position on the Gartner MDM quadrant.
Data quality dimensions are some of the most used terms when explaining why data quality is important, what data quality issues can be and how you can measure data quality. Ironically, we sometimes use the same data quality dimension term for two different things or use two different data quality dimension terms for the same thing. Some of the troubling terms are:
Validity / Conformity – same same but different
Validity is most often used to describe if data filled in a data field obeys a required format or are among a list of accepted values. Databases are usually well in doing this like ensuring that an entered date has the day-month-year sequence asked for and is a date in the calendar or to cross check data values against another table and see if the value exist there.
The problems arise when data is moved between databases with different rules and when data is captured in textual forms before being loaded into a database.
Conformity is often used to describe if data adheres to a given standard, like an industry or international standard. This standard may due to complexity and other circumstances not or only partly be implemented as database constraints or by other means. Therefore, a given piece of data may seem to be a valid database value but not being in compliance with a given standard.
Sometimes conformity is linked to the geography in question. For example a postal code will be conform depending on the country where the address is in. Therefore, a the postal code 12345 is conform in Germany, but not in United Kingdom.
In the data quality realm accuracy is most often used to describe if the data value corresponds correctly to a real-world entity. If we for example have a postal address of the person “Robert Smith” being “123 Main Street in Anytown” this data value may be accurate because this person (for the moment) lives at that address.
But if “123 Main Street in Anytown” has 3 different apartments each having its own mailbox, the value does not, for a given purpose, have the required precision.
If we work with geocoordinates we have the same challenge. A given accurate geocode may have the sufficient precision to tell the direction to the nearest supermarket is, but not precise enough to know in which apartment the out-of-milk smart refrigerator is.
Timeliness / Currency – when time matters
Timeliness is most often used to state if a given data value is present when it is needed. For example, you need the postal address of “Robert Smith” when you want to send a paper invoice or when you want to establish his demographic stereotype for a campaign.
Currency is most often used to state if the data value is accurate at a given time – for example if “123 Main Street in Anytown” is the current postal address of “Robert Smith”.
Uniqueness / Duplication – positive or negative
Uniqueness is the positive term where duplication is the negative term for the same issue.
We strive to have uniqueness by avoiding duplicates. In data quality lingo duplicates are two (or more) data values describing the same real-world entity. For example, we may assume that
“Robert Smith at 123 Main Street, Suite 2 in Anytown”
is the same person as
“Bob Smith at 123 Main Str in Anytown”
Completeness / Existence – to be, or not to be
Completeness is most often used to tell in what degree all required data elements are populated.
Existence can be used to tell if a given dataset has all the needed data elements for a given purpose defined.
So “Bob Smith at 123 Main Str in Anytown” is complete if we need name, street address and city, but only 75 % complete if we need name, street address, city and preferred colour and preferred colour is an existent data element in the dataset.
Data Quality Management
Master Data Management (MDM) solutions and specialized Data Quality Management (DQM) tools have capabilities to asses data quality dimensions and improve data quality within the different data quality dimensions.
Also, Tibco has acquired Information Builders and thus taken their position.
Again, this year Informatica is the most top-right positioned vendor. Good to know, as I am right now involved in some digital transformation programs where Informatica Data Quality (iDQ) is part of the technology stack.
You can get a free copy of the report from Ataccama here.
The previous acquisitions have strengthened the Precisely offerings around data quality for the customer master data domain and the adjacent location domain.
The Winshuttle take over will make Precisely a multidomain vendor adding cross domain capabilities and specific product domain capabilities.
The original Winshuttle capabilities revolves around process automation for predominately SAP environments covering all master data domains and further Application Data Management (ADM).
As Winshuttle recently took over the Product Information Management (PIM) solution provider Enterworks, this will bring capabilities around product master data management and thus make Precisely a provider for a broad spectrum of master data domains.
The interesting question will be in what degree Precisely over the time will be willing to and able to integrate these different solutions so a one-stop-shopping experience will become a one-stop digital experience for their clients.
Infogix is a four-decade old provider of solutions for data quality and adjacent disciplines as data governance, data catalog and data analytics.
Precisely was recently renamed from Syncsort. Under this brand they nabbed Pitney Bowes software two years ago as told in the post Syncsort Nabs Pitney Bowes Software Solutions. Back in time Pitney Bows nabbed veteran data quality solution provider Group1.
Before being Syncsort their data quality software solution was known as Trillium. This solution also goes a long way back.
So, it is worth noticing that the M&A activity revolves around data quality software that was born in the previous millennium.
Liliendahl on Data Quality 