Modern Data Quality at Scale using Digna

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 These challenges range from integrating data from various sources, ensuring consistency, and managing outdated or irrelevant data, to handling the massive volume and variety of unstructured data in data lakes, which makes standardizing, cleaning, and organizing data a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.

Read also: Navigating the Landscape – Moden Data Quality with Digna

Digna’s Holistic Observability with Minimal Setup

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.

Watch the product demo

Kickstart your Modern Data Quality Journey

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.

Six AI and ML Use Cases Within MDM

One of the hottest trends in the Master Data Management (MDM) world today is how to exploit Artificial Intelligence (AI) and ignite that with Machine Learning (ML).

This aspiration is not new. It has been something that have been going on for years and you may argue about when computerized decision support and automation goes from being applying advanced algorithms to being AI. However, the AI and ML theme is getting traction today as part of digital transformation and whatever we call it, there are substantial business outcomes to pursue.

As told in the post Machine Learning, Artificial Intelligence and Data Quality perhaps all use cases for applying AI is dependent on data quality and MDM is playing a crucial role in sustaining data quality efforts.

Here are six use cases that are commonly being addressed by AI and ML capabilities:

AI MDM DQ Use Cases

  • Translating between taxonomies: As reported in the post Artificial Intelligence (AI) and Multienterprise MDM emerging technologies can help in translating between the taxonomies in use when digital transformation sets a new bar for utilizing master data in business ecosystems.
  • Transforming unstructured to structured: A lot of data is kept in an unstructured way and to in order to systematically exploit these data in AI supported business process we need make data more structured. AI and ML can help with that too.
  • Data quality issue prevention: Simple rules for checking integrity and validating data is good – but unfortunately not good enough for ensuring data quality. AI is a way to exploit statistical methods and complex relationships.
  • Categorizing data: Digital transformation, spiced up with increasing compliance requirements, has made data categorization a must and AI and ML can be an effective way to solve this task that usually is not possible for humans to cover across an enterprise.
  • Data matching: Establishing a link between multiple descriptions of the same real-world entity across an enterprise and out to third party reference data has always been a pain. AI and ML can help as examined in the post The Art in Data Matching.
  • Improving insight: The scope of MDM can be enlarged to Extended MDM Platforms where other data as transactions and big data is used to build a 360-degree view of the master data entities. AI and ML is a prerequisite to do that.

What is PxM?

Product experience Management (PxM or PXM) is a trending term in the Master Data Management (MDM) and Product Information Management (PIM) world.

As reported in the post Product Something Management the acronym PxM is one of several three letter acronyms starting with P and ending with M in play in this space.

Some of the topics handled in a PxM solution that can be seen as a development of traditional PIM solution are:

  • Analysis of product content including encompassing other data domains
  • Emphasis on product content that is search engine optimized (SEO)
  • Context aware presentation of product content
  • Personalization of product content
  • Exploiting Machine Learning (ML) and Artificial Intelligence (AI) in the above-mentioned scenarios

Solution vendors on the market are positioning themselves in relation to PxM as some vendors are going in the direction of being a PxM vendor while other vendors embrace the span from MDM over PIM to PxM. This urges solution buyers on the market to focus on implementing PxM from a best of breed vendor in this realm or to look for one-stop-shopping.

You can meet both kind of solution vendors on this list.

PxM wordle

Cloud multi-domain MDM as the foundation for Digital Transformation

Upen-200x300

Upen Varanasi

This guest blog post is an interview by Katie Fabiszak with CEO & Founder Upen Varanasi on the new MDM release from Riversand Technologies.

Riversand is pleased to announce the newest release of our cloud-native suite of Master Data Management solutions. Helping organizations create a data and analytics foundation to drive successful digital transformation was the driving factor behind Riversand’s decision to completely rethink and rebuild a master data platform that would meet the future needs of the digital era. According to Riversand CEO & Founder Upen Varanasi, “We knew we had to make an aggressive move to envision a new kind of data platform that was capable of handling the forces shaping our industry.” Katie Fabiszak, Riversand Global Vice President of Marketing, sat down with Upen to talk about Riversand’s vision of the future and how the bold decisions he had made several years ago led to the company’s own transformational journey and a new MDM solution:

KF:        Where do you think the market is heading?

UV:       Well, we are truly in the digital era. The markets are rewarding companies that are leaders in this digital landscape. This is forcing all companies to truly embrace digital transformation. A key aspect of this transformation is the role of data. Data is the new “fuel” of digital transformation. What matters is how enterprises can leverage data and unlock its power to create better and faster business outcomes. Turning data into insights is a key factor. Another important aspect is to understand the complexities of the information supply chain. Simplifying the flow of data is critical to drive better and faster business outcomes. Let’s take an example from the retail/CPG space: Enterprises want to better understand their customers (utilizing customer insights through AI), create/sell products and services that meet and exceed customer expectations (product positioning through AI), meet their customers across all touch points (channel management), secure their customer’s information (privacy), and interact with trading partners to creating cutting edge product differentiation (cloud and AI).

KF:        Why did Riversand decide to shift gears and embark on what became a 2+ year transformational journey for the company?

UV:       At Riversand, we understood the need to envision a new kind of platform to handle the trends occurring in our industry. We focused on creating a data-driven multi-domain Master Data Management System that has the following properties:

  • A contextual master data modeling environment.
  • A platform that can handle scale, velocity and variety of data, built to handle master data as well as transaction and interaction data.
  • Embedded AI to drive better insights and outcomes.
  • Built on cloud, with the ability to integrate data and processes across hybrid clouds.
  • A highly business-friendly user experience.
  • Apps built on the platform that can solve the “final mile” problem for business users. PIM and customer domain solutions are each apps built on this same platform.

Our goal was to create a platform and solutions that can be quick to implement, provide insights and recommended actions to business users, and automates many of the more mundane data management and stewardship functions. We believe we can provide Enterprises with the critical capabilities from the Master Data Management and Product Information Management space to differentiate themselves in the marketplace.

KF:        What are the core values of Riversand?

UV:       Our core values are innovation, commitment & integrity. We have consistently strived to bring leading edge innovation to the market. We have dared to step back at times in order to take a bigger leap forward. What helps is that we have had a long-term view of our company and the industry we play in. We have been in business for 18 years and have been committed to this space and to our customers all along the way. Integrity in everything we do is key in dealing with our stakeholders – employees, customers and partners – and has been our focus since inception. We might not get things right all the time to begin with, but we will always make it right with our stakeholders over time.

KF:        What do you see as Riversand’s biggest strength?

UV:       Our people, our passion for this space and our long-term thinking give us an incredible edge against our competition. We are blessed to have a team who have shaped this company over many years and who are invested deeply in the company and this market space.

Riversand picKF:        What would you say sets Riversand apart?  How is our technology and approach different than the other MDM and PIM vendors in the market?

UV:       The path we have chosen provides clear benefits for our customers with respect to our competition. Some key points of differentiation are:

  • We provide a single platform to help implement MDM and PIM initiatives. No need to create further silos of data and processes.
  • We future-proof both business users and IT users with a platform that is flexible and future enabled.
  • We are built for the cloud and SaaS eras: Upgrades are easy and done by us, and customers can scale with pay as you go models.
  • Our AI engine is built on a big data stack to drive insights and actions for better business outcomes.
  • With our big data technology stack, enterprises have the ability to scale with data and handle both its variety and velocity.
  • A completely new and extremely business friendly user interface and experience that people love to use!
  • The app building toolkit (SDK) to help customers and partners build their own apps so that they can solve their final mile problems: This is in addition to the core apps we are actively building.

KF:        Where is Riversand today along this journey that we began over 2 years ago?

UV:       Our new solutions were introduced as a kind of soft launch with a select few customers over the past year. The feedback and insights we received during this year were extremely useful in becoming enterprise-ready. We are now in a position to launch our platform to the broader market. Over the next two quarters, we will be further enhancing our offerings including: the analytics/AI platform, app SDK for partners and customers, launching additional SaaS appsnd entering additional vertical markets. We also look forward to creating robust partner ecosystems for these vertical markets.

KF:        What’s next for Riversand – what do you envision for the future?

UV:       We are really excited about the potential of our new master data platform and we look forward to working with our current customers as well as new customers to help them with their digital transformation journey. We are disruptors in our space and we will continue to establish ourselves as a larger, bolder and leading global brand.

We hope you enjoyed the interview!  You can check out additional information by reading the press announcement here.

Katie Fabiszak oversees and directs global marketing efforts at Riversand.  She is an accomplished executive with more than 20 years of success in global marketing for high tech companies. She is responsible for leading the strategic evolution of the company’s branding and marketing strategy.  With proven success developing effective marketing strategies to drive revenue, Katie’s extensive career includes marketing leadership positions at Informatica, StrikeIron and DataFlux (a SAS company).