TO CURE OR TO OBSERVE? HOW DATA OBSERVABILITY DIFFERS FROM DATA CURATION

In the world of data management, there are many terms and concepts that can be confusing. Two such concepts are data observability and data curation. While both are important for ensuring data accuracy and reliability, they have distinct differences. In this article, we will explore the key differences between data observability and data curation.

What is Data Observability?

Data observability refers to the ability to monitor and understand the behavior of data in real-time. It is the process of tracking, collecting, and analyzing data to identify any anomalies or issues. Data observability is often used in the context of monitoring data pipelines, where it can be used to identify issues such as data loss, data corruption, or unexpected changes in data patterns.

Data observability relies on metrics, logs, and other data sources to provide visibility into the behavior of data. By analyzing this data, it is possible to identify patterns and trends that can be used to optimize data pipelines and improve data quality.

What is Data Curation?

Data curation, on the other hand, refers to the process of managing and maintaining data over its entire lifecycle. It is the process of collecting, organizing, and managing data to ensure its accuracy, completeness, and reliability. Data curation involves tasks such as data cleaning, data validation, and data enrichment.

Data curation is essential for ensuring that data is accurate and reliable. It involves the use of automated tools and manual processes to ensure that data is properly labeled, formatted, and stored. Data curation is particularly important for organizations that rely heavily on data analytics, as inaccurate or incomplete data can lead to faulty insights and poor decision-making.

Key Differences Between Data Observability and Data Curation

While data observability and data curation share some similarities, there are key differences between the two concepts. The main differences are as follows:

  • Focus: Data observability focuses on monitoring data in real-time, while data curation focuses on managing data over its entire lifecycle.

  • Purpose: Data observability is used to identify and troubleshoot issues in data pipelines, while data curation is used to ensure data accuracy and reliability.

  • Approach: Data observability relies on monitoring tools and real-time analysis, while data curation relies on automated tools and manual processes.

Conclusion

In summary, data observability and data curation are two important concepts in the world of data management. While they share some similarities, they have distinct differences. Data observability is focused on real-time monitoring and troubleshooting, while data curation is focused on ensuring data accuracy and reliability over its entire lifecycle. Both concepts are important for ensuring that data is accurate, reliable, and useful for making informed decisions.

TRANSLYTICAL DATA PLATFORMS: THE FUTURE OF DATA MANAGEMENT?

As data continues to proliferate at an unprecedented rate, organizations require a powerful and flexible solution to manage, store, and analyze their data. Translytical data platforms are a new type of database management system that combines the capabilities of transactional and analytical databases. They enable businesses to perform transactional processing and analytics on the same data simultaneously in real-time or near real-time, without complex and costly ETL processes.

What are Translytical Data Platforms?

Translytical data platforms are a new class of database management systems that combine the capabilities of transactional and analytical databases. They provide the ability to process transactions and analytics simultaneously in real-time or near real-time, without the need for complex and costly ETL (Extract, Transform, Load) processes.

In other words, translytical data platforms enable businesses to perform transactional processing and analytics on the same data at the same time, resulting in faster insights and improved decision-making. These platforms are designed to handle the complexity of modern data, including structured, semi-structured, and unstructured data.

How are Translytical Data Platforms Different from Traditional Databases?

Traditional databases are designed for either transactional processing or analytics. Transactional databases are optimized for storing and processing large volumes of data related to business transactions, such as sales, inventory, and customer interactions. They ensure data consistency, accuracy, and reliability, but are not suitable for complex queries and analytics.

On the other hand, analytical databases are optimized for complex queries and reporting. They provide fast access to historical data for analysis and decision-making. However, they are not optimized for transactional processing and may require ETL processes to combine data from multiple sources.

Translytical data platforms bridge the gap between transactional and analytical databases by providing a single platform for processing transactions and analytics simultaneously. They enable businesses to perform real-time analytics on transactional data, eliminate the need for separate transactional and analytical databases, and reduce data duplication and latency.

Benefits of Translytical Data Platforms

      1. Real-Time Analytics: Translytical data platforms enable businesses to perform real-time analytics on transactional data. This means that they can get faster insights, make decisions quickly, and respond to changing business conditions.

      2. Flexible AI Foundation: Overall, translytical data platforms can provide a powerful foundation for AI applications, enabling organizations to process large amounts of data quickly and efficiently, and to gain real-time insights that can improve the accuracy and effectiveness of AI models.

      3. Simplified Data Architecture: By eliminating the need for separate transactional and analytical databases, translytical data platforms simplify data architecture and reduce data duplication and latency

      4. Improved Data Quality: Translytical data platforms ensure data consistency, accuracy, and reliability by processing transactions and analytics on the same data.

      5. Cost Savings: Translytical data platforms eliminate the need for complex ETL processes and multiple databases, reducing the cost of infrastructure and maintenance.

Conclusion

Translytical data platforms are the future of data management in general. They provide businesses with the ability to process transactions and analytics simultaneously, in real-time or near real-time, without the need for complex and costly ETL processes. With the ability to handle structured, semi-structured, and unstructured data, translytical data platforms provide faster insights, simplified data architecture, improved data quality, and cost savings. As the volume and complexity of data continue to grow, translytical data platforms will become essential for businesses to stay competitive and make informed decisions.




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DATA FABRIC VS DATA MESH: AN APPLES & ORANGES STORY?

Data fabric and data mesh are two concepts that have gained a lot of attention in the world of data management. While they share some similarities, they have some fundamental differences that are important to understand. In this article, we will explain the difference between data fabric vs data mesh.

What is a Data Fabric?

A data fabric is an architecture that provides a unified and consistent view of an organization’s data, regardless of where it resides, how it’s stored, or how it’s accessed. It allows data to flow seamlessly between different systems and applications, while maintaining data integrity, security, and governance. It provides a way to seamlessly integrate and access data from different systems, applications, databases, and clouds, making it easier for organizations to derive insights and make decisions.

What is a Data Mesh?

A data mesh is a decentralized approach to data management that empowers teams to own and manage their own data domains. It recognizes that data is a product and treats it as such, with individual teams responsible for their own data products. The goal of a data mesh is to enable faster and more efficient data delivery by allowing teams to work independently and iterate quickly.

Difference between Data Fabric and Data Mesh

The fundamental difference between data fabric and data mesh is in their approach to data management. A data fabric is a centralized approach, while a data mesh is a decentralized approach. A data fabric provides a unified and consistent view of an organization’s data, while a data mesh enables teams to own and manage their own data domains.

Another key difference between data fabric vs data mesh is in their focus. A data fabric focuses on providing a seamless and consistent view of data across an organization, while a data mesh focuses on empowering teams to own and manage their own data domains. A data mesh typically creates more room for innovation.

Conclusion

In conclusion, while data fabric and data mesh share some similarities, they have fundamental differences in their approach to data management. A data fabric is a centralized approach that provides a unified and consistent view of an organization’s data, while a data mesh is a decentralized approach that empowers teams to own and manage their own data domains. Both approaches have their own advantages and disadvantages, and the choice between the two will depend on the specific needs of the organization. 

It is worth noting that choosing between data mesh and fabric is not always a binary decision. Some parts of an organization may choose to implement data mesh, while others may prefer the data fabric approach.


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UNDERSTANDING DATA LAKEHOUSES: THE HIGHWAY TO MODERN BIG DATA MANAGEMENT OR A U-TURN TO YOUR DATA SWAMP?



As the volume and complexity of data continue to grow, traditional data management solutions are struggling to keep up. This is where data lakehouses come in. In this article, we’ll take a closer look at what data lakehouses are, how they differ from traditional data warehouses, and their benefits for businesses.

What is a Data Lakehouse?

A data lakehouse is a modern data management architecture that combines the best features of data lakes and data warehouses. It provides a unified platform for storing, managing, and processing structured and unstructured data in real-time or near real-time. Unlike traditional data warehouses, which require data to be pre-processed before storage, data lakehouses allow businesses to store raw data in its native format.

In other words, data lakehouses are designed to handle the complexity of modern data, including structured, semi-structured, and unstructured data. They provide a single platform for storing and processing data, eliminating the need for complex ETL (Extract, Transform, Load) processes.

How are Data Lakehouses Different from Traditional Data Warehouses?

Traditional data warehouses are designed to store structured data in a pre-defined schema. Data is pre-processed and organized into tables, which can be queried using SQL (Structured Query Language). Data warehouses are optimized for reporting and analysis, but require significant data modeling and schema design effort, which can slow down the data ingestion process.

On the other hand, data lakehouses store data in its raw form, without the need for pre-processing or modeling of the data. Data is organized into data lakes, which can be queried using SQL or other query languages. Data lakehouses are optimized for data processing, enabling businesses to ingest, store, and process data in real-time or near real-time, without the need for complex ETL processes.

Benefits of Data Lakehouses

    1. Scalability: Data lakehouses are designed to scale to handle large volumes of data. They can store structured, semi-structured, and unstructured data, providing businesses with the ability to ingest and process data at scale.

    2. Flexibility: Data lakehouses allow businesses to store data in its raw form, without the need for pre-processing. This provides the flexibility to query and analyze data in real-time or near real-time.

    3. Cost Savings: Data lakehouses eliminate the need for complex ETL processes, reducing the cost of data ingestion and storage. They also provide a unified platform for storing and processing data, eliminating the need for multiple data management systems.

    4. Improved Data Quality: Data lakehouses enable businesses to store data in its native format, reducing the risk of data loss or incorrect transformed data during the ETL process.

Conclusion

Data lakehouses are the future of big data management. They provide businesses with a unified platform for storing, managing, and processing structured and unstructured data in real-time or near real-time. Unlike traditional data warehouses, data lakehouses allow businesses to store raw data in its native format, eliminating the need for complex ETL processes. With scalability, flexibility, cost savings, and improved data quality, data lakehouses are essential for businesses to stay competitive and make informed decisions in the era of big data.


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DATA FABRIC IN A NUTSCHELL

In today’s digital age, data has become one of the most valuable assets for organizations of all sizes and across all industries. However, with data being generated, stored, and accessed from multiple sources and locations, managing and analyzing it has become increasingly complex.

To address this challenge, a concept called a data fabric has emerged in the field of data management. A data fabric is an architecture that provides a unified and consistent view of an organization’s data, regardless of where it resides, how it’s stored, or how it’s accessed.

The goal of a data fabric is to seamlessly integrate and access data from different systems, applications, databases, and clouds, making it easier for organizations to derive insights and make decisions. By allowing data to flow seamlessly between different systems and applications while maintaining data integrity, security, and governance, a data fabric enables users to access and analyze data in real-time, regardless of its location, format, or type.

A typical data fabric consists of multiple layers, including:

  • data discovery
  • data integration
  • data processing
  • data delivery

 

These layers work together to provide a complete view of an organization’s data, from its origin to its destination. In addition, a data fabric can provide advanced capabilities such as data lineage, data quality, and data governance, ensuring that data is accurate, consistent, and secure throughout its lifecycle.Overall, a data fabric is designed to simplify the complexities of managing and analyzing data in today’s modern data landscape, enabling organizations to be more agile, responsive, and competitive. By implementing a data fabric, organizations can eliminate the need for manual data integration, reduce the risk of errors, and derive insights from data more quickly and effectively. This allows organizations to make better decisions, optimize their operations.


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COLLIBRA DATA CITIZENS 22 – INNOVATIONS TO SIMPLIFY AND SCALE DATA INTELLIGENCE ACROSS ORGANIZATIONS WITH RICH USER EXPERIENCES

Collibra has introduced a range of new innovations at the Data Citizens ’22 conference, aimed at making data intelligence easier and more accessible to users.

Collibra Data Intelligence Cloud has introduced various advancements to improve search, collaboration, business process automation, and analytics capabilities. Additionally, it has also launched new products to provide data access governance and enhance data quality and observability in the cloud. Collibra Data Intelligence Cloud merges an enterprise-level data catalog, data lineage, adaptable governance, uninterrupted quality, and in-built data privacy to deliver a comprehensive solution.

Let’s have a look at the new announced functionality:

Simple and Rich Experience is the key message

Marketplace

Frequently, teams face difficulty in locating dependable data for their use. With the introduction of the Collibra Data Marketplace, this task has become simpler and quicker than ever before. Teams can now access pre-selected and sanctioned data through this platform, enabling them to make informed decisions with greater confidence and reliability. By leveraging the capabilities of the Collibra metadata graph, the Data Marketplace facilitates the swift and effortless search, comprehension, and collaboration with data within the Collibra Data Catalog, akin to performing a speedy Google search.

Usage analytics

To encourage data literacy and encourage user engagement, it’s important to have a clear understanding of user behavior within any data intelligence platform. The Usage Analytics dashboard is a new feature that offers organizations real-time, useful insights into which domains, communities, and assets are being used most frequently by users, allowing teams to monitor adoption rates and take steps to optimize their data intelligence investments.

Homepage

Creating a user-friendly experience that allows users to quickly and easily find what they need is crucial. The revamped Collibra homepage offers a streamlined and personalized experience, featuring insights, links, widgets, and recommended datasets based on a user’s browsing history or popular items. This consistent and intuitive design ensures that users can navigate the platform seamlessly, providing a hassle-free experience every time they log into Collibra Data Intelligence Cloud.

Workflow designer

Data teams often find manual rules and processes to be challenging and prone to errors. Collibra Data Intelligence Cloud’s Workflow Designer, which is now in beta, addresses this issue by enabling teams to work together to develop and utilize new workflows to automate business processes. The Workflow Designer can be accessed within the Collibra Data Intelligence Cloud and now has a new App Model view, allowing users to quickly define, validate, and deploy a set of processes or forms to simplify tasks.

 

Improved performance, scalability, and security

Collibra Protect

Collibra Protect is a solution that offers smart data controls, allowing organizations to efficiently identify, describe, and safeguard data across various cloud platforms. Collibra has collaborated with Snowflake, the Data Cloud company, to offer this new integration that enables data stewards to define and execute data protection policies without any coding in just a matter of minutes. By using Collibra Protect, organizations gain greater visibility into the usage of sensitive and protected data, and when paired with data classification, it helps them protect data and comply with regulations at scale.

Data Quality & Observability in the Cloud

Collibra’s latest version of Data Quality & Observability provides enhanced scalability, agility, and security to streamline data quality operations across multiple cloud platforms. With the flexibility to deploy this solution in any cloud environment, organizations can reduce their IT overhead, receive real-time updates, and easily adjust their scaling to align with business requirements.

Data Quality Pushdown for Snowflake 

The new feature of Data Quality Pushdown for Snowflake empowers organizations to execute data quality operations within Snowflake. With this offering, organizations can leverage the advantages of cloud-based data quality management without the added concern of egress charges and reliance on Spark compute.

New Integrations

Nowadays, almost 77% of organizations are integrating up to five diverse types of data in pipelines, and up to 10 different types of data storage or management technologies. Collibra is pleased to collaborate with top technology organizations worldwide to provide reliable data across a larger number of sources for all users. With new integrations currently in beta, mutual Collibra customers utilizing Snowflake, Azure Data Factory, and Google Cloud Storage can acquire complete visibility into cloud data assets from source to destination and offer trustworthy data to all users throughout the organization.

 

Some of this functionality was announced as beta and is available to a number of existing customers for testing purposes.



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THE MARKETING DATA JUNGLE

Customer & household profiling, personalization, journey analysis, segmentation, funnel analytics, acquisition & conversion metrics, predictive analytics & forecasting, …  The marketing goal to deliver a trustworthy and complete insight in the customer across different channels can be quiet difficult to accomplish.

A substantial amount of marketing departments have chosen to rely on a mix of platforms going from CEM/CXM, CDP, CRM, eCommerce, Customer Service, Contact Center, Marketing Automation to Marketing Analytics. A lot of these platforms are best of breed and come from a diverse number of vendors who are leader in their specific market segment. Internal custom build solutions (Microsoft Excel, homebrew data environments, …) always complete this type of setup.

78% According to a Forrester study, although 78% of marketers claim that a data-driven marketing strategy is crucial, as many as 70% of them admit they have poor quality and inconsistent data.


The challenges

Creating a 360° customer view across this diverse landscape is not a walk in the park. All of these marketing platforms do provide added value but are basically separate silos. All of these environments use different data and the data that they have in common, is typically used in a different way. If you need to join all these pieces together, you need some magical super glue.  Reality is that none of the marketing platform vendors actually have this in house.

Another point of attention is your data scope. We don’t need to explain you that customer experience is the hot thing in marketing nowadays. However marketeers need to do much more than just analyze customer experience data in order to create real customer insight.

Creating insight also requires that the data that you analyze goes beyond the traditional customer data domain. Combining customer data with i.e. the proper product/service, supplier, financial, … data is rather fundamental for this type of exercises. This type of extended data domains is usually lacking or the required detail level is not present in one particular platform.

38% Recent research from  KPMG and Forrester Consulting shows that 38% of marketers claimed they have a high level of confidence in their data and analytics that drives their customer insights. That’s said, only a third of them seem to trust the analytics they generate from their business operations.


The foundations

Regardless of the mix of marketing platforms, many marketing leaders don’t succeed in taking full advantage of all their data. As a logical result they also fail to make a real impact with their data driven marketing initiatives. The underlying reason for this issue is that many marketing organizations lack a number of crucial data management building blocks that allow them to break out of these typical martech silos. The most important data capabilities that you should take into account are:

 

Capability

Description

Master Data Management (aka MDM)

Creating a single view or so called golden record is the essence of Master Data Management. This allows you to make sure that a customer, product, etc is consistent across different applications.

 

Business Glossary

Having the correct terms & definitions might seem trivial but reality is that in the majority of the organizations noise on the line is reality. However having crystal clear terms and definitions is a basic requirement to have all stakeholders manage the data in the same way and prevent conflicts and waste down the data supply chain.

 

Data Catalog

Imagine Google-like functionality to search through your data assets. Find out what data you have, what’s the origin, how and where it is being used.

 

Data Quality

The why of proper data quality is obvious for any data consuming organization. If you have disconnected data landscape, data quality is even more important because it also facilitates the automatic match & merge glue exercise that you put in place to come to a common view on your data assets.

 

Data Virtualization

Getting real-time access to your data in an ad hoc and dynamic way is one of the missing pieces to get to your 360° view in time and budget. Forgot about traditional consumer headaches such as long waiting times, misunderstood requests, lack of agility, etc.

 

 

We intentionally use the term capability because this isn’t a IT story. All of these capabilities have a people, process and technology aspect and all of them should be driven by the business stakeholders. IT and technology is facilitating.


The results

If you manage to put in place the described data management capabilities you basically get in control. Your organization can find, understand and make data useful. You improve the efficiency of your people and processes, and reduce your data compliance risks. The benefits in a nutshell:

  1. Get full visibility of your data landscape by making data available and easily accessible across your organization. Deliver trusted data with documented definitions and certified data assets, so users feel confident using the data. Take back control using an approach that delivers everything you need to ensure data is accurate, consistent, complete and discoverable.
  2. Increase efficiency of your people and processes. Improve data transparency by establishing one enterprise-wide repository of assets, so every user can easily understand and discover data relevant to them. Increase efficiency using workflows to automate processes, helping improve collaboration and speed of task completion. Quickly understand your data’s history with automated business and technical lineage that help you clearly see how data transforms and flows from system to system and source to report.
  3. Reduce data and compliance risks. Mitigate compliance risk setting up data policies to control data retention and usage that can be applied across the organization, helping you meet your data compliance requirements. Reduce data risk by building and maintaining a business glossary of approved terms and definitions, helping ensure clarity and consistency of data assets for all users.

42% of data-driven marketers say their current technology in place is out of date and insufficient to help them do their jobs. Walker Sands Communications State of Marketing Technology report.



Conclusion

The data you need to be successful with your marketing efforts is there. You just have to transform it into useable data so that you can get accurate insights to make better decisions. The key in all of this is getting rid of your marketing platform silos by making sure that you have the proper data foundations in place. The data foundations to speed up and extend the capabilities of your datadriven marketing initiatives.


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CHANGE & DATA GOVERNANCE – TAKE A LEAP FORWARD

A successful data governance initiative is based on properly managing the People, Process, Data & Technology square. The most important element of these four is undoubtedly People. The reason for that is that at the end it boils down to people in your organization to act in a new business environment. This always implies change so make sure that you have an enabling framework for managing also the people side of change. Prepare, support and equip individuals at different levels in your organization to drive change and data governance success.

Change & the critical ingredient for data governance success.


Change is crucial in the success or failure of a data governance initiative for two reasons:

1First of all you should realize that with data governance you are going to tilt an organization. What we mean by this is that the situation before data governance is usually a silo-oriented organization. Individual employees, teams, departments, etc are the exclusive owner of their systems and associated data. With the implementation of data governance you will tilt that typical vertical data approach and align data flows with business processes that also run horizontally through an entire organization. This means that you need to help the organization to arrive at an environment where the data sharing & collaboration concept  is the new normal.

2The second important reason is the so-called data governance heartbeat. What we see in many organizations is that there is a lot of enthusiasm at the start of a program. However, without the necessary framework, read also a change management plan, you run the fundamental risk that such an initiative will eventually die a silent death. People lose interest, no longer feel involved, no longer see the point of it. From that perspective, it is necessary to create a framework that keeps data governance’s heart beating.

How to approach change?


Change goes beyond training & communication. To facilitate the necessary changes, ChangeLab and Datalumen designed the ADKAR-based LEAP approach. LEAP is an acronym that stands for Learn, Envision, Apply & Poll. Each of these important steps help realize successful and lasting change.


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CALCULATING DATA GOVERNANCE ROI