As examined in the post The Intersection Between MDM, PIM and ESG, environmental data management is becoming an important aspect of the offerings provided in solutions for Master Data Management (MDM) and Product Information Management (PIM). Consequentially, this will also apply to the Data Quality Management (DQM) capabilities that are either offered as part of these solutions or as standalone solutions for data quality.
This site has an interactive Select Your Solution service for potential buyers of MDM / PIM / DQM solutions. The service has a questionnaire and an undelaying model for creating a longlist, shortlist or direct PoC suggestion for the best candidate(s) according to the context, scope, and requirements of an intended solution.
In line with the rise of the Environmental, Social and Governance (ESG) theme, the questionnaire and the underlying selection model must in the first place be enhanced with environmental data management aspects, as the environmental part of ESG is the one with the currently most frequently and comprehensive experienced data management challenges.
There is already established a good basis for this.
However, if you as either one from a solution end user organization with environmental data management challenges or a solution provider have input to environmental data management aspects to be covered, you are more than welcome to make a comment here on the blog or use the below contact form:
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 These challenges range from integrating data from various sources, ensuring consistency, and managing outdated or irrelevant data, to handling the massive volume and variety of unstructured data in data lakes, which makes standardizing, cleaning, and organizing data a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.
Master Data Management (MDM) and the overlapping Product Information Management (PIM) discipline is the centre of which the end-to-end data supply chain revolves around in enterprises that produce and/or sell goods.
Nine essential master data processes are:
1: Onboard Customer Data
It starts and ends with the King: The Customer. Your organization will probably have several touchpoints where customer data is captured. MDM was born out of the Customer Data Integration (CDI) discipline and a main reason of being for MDM is still to be a place where all customer data is gathered as exemplified in the post Direct Customers vs Indirect Customers.
2: Onboard Vendor Data
Every organization has vendors/suppliers who delivers direct and indirect products as office supplies, Maintenance, Repair and Operation (MRO) parts, raw materials, packing materials, resell products and services as well. As told in a post on this blog, you have to Know Your Supplier.
3: Enrich Party Data
There are good options for not having to collect all data about your customers and vendors yourself, as there are 3rd party sources available for enriching these data preferable as close to capture as possible. This topic was examined in the post Third-Party Data and MDM.
4: Onboard Product Data
While a small portion of product data for a small portion of product groups can be obtained via product data pools, the predominant way is to have product data coming in as second party data from each vendor/supplier. This process is elaborated in the post 4 Supplier Product Data Onboarding Scenarios.
5: Transform Product Data
As your organization probably do not use the same standard, taxonomy, and structure for product data as all your suppliers, you have to transform the data into your standard, taxonomy, and structure. You may do the onboarding and transformation in one go as pondered in the post The Role of Product Data Syndication in Interenterprise MDM.
6: Consolidate Product Data
If your organization produce products or you combine external and internal products and services in other ways you must consolidate the data describing your finished products and services.
7: Enrich Product Data
Besides the hard facts about the products and services you sell you must also apply competitive descriptions of the products and services that makes you stand out from the crowd and ensure that the customer will buy from you when looking for products and services for a given purpose of use.
8: Customize Product Data
Product data will optimally have to be tailored for a given geography, market and/or channel. This includes language and culture considerations and adhering to relevant regulations.
9: Personalize Product Data
Personalization is one step deeper than market and channel customization. Here you at point-of-sale seek to deliver the right Customer Experience (CX) by exercising Product eXperience Management (PXM). Here you combine customer data and product data. This quest was touched in the post What is Contextual MDM?
When working with the product domain in Master Data Management (MDM) and with Product Information Management (PIM) we have traditionally been working with the product model meaning that we manage data about how a product that can be produced many times in exactly the same way and resulting in having exactly the same features. In other words, we are creating a digital twin of the product model.
As told in the post Spectre vs James Bond and the Unique Product Identifier the next level in product data management is working with each product instance meaning each produced thing that have a set of data attached that is unique to that thing. Such data can be:
Serial number or other identification as for example the Unique Device Identification (UDI) known in healthcare
Manufacturing date and time
Specific configuration
Current and historical position
Current and historical owner
Current and historical installer, maintainer and other caretaker
Produced sensor data if it is a smart device / machine
There is a substantial business potential in being better than your competitor in managing product instances. This boils down to that data is power – if you use the data.
When managing this data, we are building a digital twin of the product instance.
Maintaining that digital twin is a collaborative effort involving the manufacturer, the logistic service provider, the owner, the caretaker, and other roles. For that you need some degree of Interenterprise MDM.
When blueprinting a Master Data Management (MDM) solution one aspect is if – or in what degree – you should unify customer MDM and supplier MDM.
In theory, you should unify the concept for these two master domains in some degree. The reasons are:
There is always an overlap of the real-world entities that has both a customer and a supplier role to your organization. The overlap is often bigger than you think not at least if you include the overlap of company family trees that have members in one of these roles.
The basic master data for these master data domains are the same: Identification numbers, names, addresses, means of communication and more.
The third-party enrichment opportunities are the same. The most predominant possibilities are integration with business directories (as Dun & Bradstreet and national registries) and address validation (as Loqate and national postal services).
In practice, the problem is that the business case for customer MDM and supplier MDM may not be realized at the same time. So, one domain will typically be implemented before the other depending on your organization’s business model.
Solution Considerations
Most MDM solutions must coexist with an – or several – ERP solutions. Many popular enterprise grade ERP solutions have adapted the business partner view with a common data model for basic customer and supplier data. This is the case with SAP S/4HANA and for example the address book in Microsoft Dynamics AX and Oracle JD Edwards.
MDM solutions themselves does also provide for a common structure. If you model one domain before the other, it is imperative that you consider all business partner roles in that model.
Data Governance Considerations
A data governance framework may typically be rolled out one master data domain at the time or in parallel. It is here essential that the data policies, data standards and business glossary for basic customer master data and basic supplier master data is coordinated.
Business Case Considerations
The business case for customer MDM will be stronger if the joint advantages with supplier MDM is incorporated – and vice versa.
This includes improvement in customer/supplier engagement and the derived supply/value chain effectiveness, cost sharing of third-party data enrichment service expenses and shared gains in risk assessment.
Available Solutions
Check the list of innovative solutions in the MDM space here.
The Disruptive MDM / PIM / DQM List 