How can data architects ensure accurate and up-to-date data?

I do think that Data Observability is important in establishing Data Quality. Work being done by Monte Carlo in this space is interesting. AI enabled monitoring should definitely be used for improving the data quality of the Lakehouse solution. A combination of People, Process and Tools is required for an end to end DQ and Data Governance solution. Data Lineage also is important to identify where the root cause of identified data discrepancy can be traced to

Data Observability is going to be the driving force behind data quality, metrics around data usability and data usage. Traditional & established data cleansing practices, data quality checks, data audit checks, data governance also have role to play. But unless they work together and drive to action and visibility these are just practices. Today's need is to integrate these traditional approaches and make them more manageable, democratize their usage, encourage their visibility/understanding and create a true Data Observability 360 for data footprint. None above replaces Data Leaders engagements during change management, review process or ARB process but above 360 view is needed to automate and close the leaks in the current process.

Data can be changed and evolve over time which can stale data quality rules. Therefore regular monitoring and continuous improvement can contribute to a robust data quality management framework within an organization. Aside from the technical approach of ensuring data quality, it's crucial to pay attention to metrics that might not be commonly emphasized like Consider how reliable data is from external sources? Are organizations incorporating feedback from end users? Evaluate the percentage of bias in data. Building a data-driven culture in an organization is also vital, ensuring employees can grasp the importance and sensitivity of data.

Data architects secure accurate, current data by establishing clear data quality standards and metrics. Define criteria for accuracy, completeness, consistency, and timeliness. Regularly monitor and measure data against these benchmarks, implementing corrective actions as needed. This proactive approach maintains data integrity, essential for informed decision-making and trust in the data ecosystem.

The best way is to ask front end developers where data entry occurs to improve the data quality but that rarely happens because application developers are distant from data analytics and are focused on speed to market more of the time. When ingesting data from a data source to raw staging of an analytics data store, you need to ensure there's no data loss. With tool providers it just involves looking at dashboard monitoring / analysing logs. For file ingestion, checksums can be used. Once data is in raw staging, you can write SQL or use data Profiling tools to examine which table columns are missing data, if they're normalised correctly etc. You can add these data quality tests at the end of your data pipeline #dataarchitecture

From my industry experience, there's a prevalent tendency to overemphasise automation. While automation is indispensable for validation and cleansing, I've observed instances where organisations automate processes that could benefit from prior streamlining. It's crucial to prioritise simplification and scrutinise requirements before delving into automation. This sequential approach ensures that automation enhances streamlined processes rather than perpetuating complexities.

Validation acts as the gatekeeper, ensuring that incoming data fits the expected formats and meets predefined quality criteria before it's ever used. Cleansing is the next critical step, scrubbing the data clean of inaccuracies, duplicates, and irrelevant details that can skew analytics and insights. These processes are vital for upholding data reliability, as they prevent "garbage-in-garbage-out" scenarios that can lead to costly missteps.

In IBM, following data validation and cleansing practices are followed: ~ Utilizing data validation tools such as Talend Data Quality or Informatica Data Quality to ensure data accuracy and consistency. ~ Implementing data cleansing techniques using tools like IBM InfoSphere Information Server to standardize and enrich the data. ~ Establishing automated data monitoring processes to regularly check and validate the integrity of the data, leveraging tools like Apache Griffin ~ Collaborating with data stewards and stakeholders to define and enforce data quality standards and best practices ~ Conducting regular data quality audits and assessments to identify and rectify any inconsistencies or discrepancies in the data.

To ensure data integrity, implement robust validation rules during ingestion—rejecting malformed entries—and cleanse by de-duplicating and enriching data. For instance, utilize regex for format validation and link customer records with external databases to update addresses. Automate with tools for efficiency.

Implement strict validation rules at the point of data entry to ensure that only accurate and properly formatted data is accepted. Utilize real-time validation mechanisms to immediately identify and correct errors as users input data. This can include checks for data types, ranges, and consistency. Establish comprehensive quality assurance processes to systematically validate data against predefined standards and business rules. Implement MDM strategies to manage and maintain a single, authoritative source of master data. This helps prevent duplication and ensures uniformity across the organization.

In left to right Data flow, the more you go left, data model should be as raw as the sources. As you move right, based on the usage of the data, the data model can be tweaked/ transformed, be it generic normalised model or specific denormalised model, model suited for specific tool, data vault based model etc.

From my experience, aligning data models with business processes is paramount. SMEs often encounter unique challenges, like evolving regulatory requirements. Addressing these nuances through adaptable schema design becomes a strategic necessity for sustainable data quality. From a practical standpoint, I think that using modern data engineering tools to maintain data and data models is critical. Data structures, data quality rules and data transformations should be always checked in as code to promote good discipline on testing, deployment and auditability.

In my recent experience with the Data Vault implementation, I learned that incorporating best practices and principles of data modeling is integral to this approach. The application of normalization ensures efficient organization of data, minimizing redundancy and dependency issues. Simultaneously, judicious de-normalization or separation of duties can be strategically applied to enhance query performance, striking a balance between storage efficiency and retrieval speed. Practically, It goes beyond mere structure; it's about creating an agile framework that anticipates and accommodates the fluid nature of data e.g. during out implementation we found Data Vault more flexible and agile in comparison to a dimensional model.

Establishing Standards: First things first, set the standards. Keep an eye on accuracy, completeness, and consistency. Proactive Mindset: Stay ahead of the game. Be proactive to prevent and tackle errors that might pop up when standards and quality take a hit. Tech Upgrade: Bring in the good stuff. Utilize top-notch tech to enhance processes, keep an eye on data lineage, and track logs and historical data of incidents. Continuous Improvement: It's a journey, not a destination. Always strive to get better, because there's always room for improvement. ??

From my experience, its mandatory to consider in your data stack modern data platforms, such as Lake house that solves these historical warehouse challenges, and reduces the time that engineering teams usually take to solve it. As an example, open formats as Delta address these challenges.

Data Quality KPIs and Reporting: As part of your data governance framework, establish KPIs specifically related to DQ. These KPIs should measure the accuracy, completeness, timeliness, and consistency of data. Create regular data quality reports(dashboards preferred) that provide insights into the state of data quality, making it visible to stakeholders and management. These reports can help identify areas where data may be outdated or inaccurate, allowing for improvements and immediate actions to maintain data quality over time.

Establecer políticas y procesos de gobernanza de datos para garantizar la calidad y la integridad. La administración efectiva de datos implica la asignación de roles y responsabilidades, así como la implementación de procedimientos para el control y la supervisión de los datos.

I was leading the Data forum at Barclays and I made it my rule to turn it into the gold standard of governance forums. What I put in place, and worked really well, was: * A clear agenda * Defined participants: who needed to be there from a data owner and a data consumer perspective * Strict time-keeping * Taking trailing conversations offline * Getting an official approval of every item, however small * Detailed and succinct minutes Why did I do this? Because the key is clarity and brevity. If you can tell each of the participants, why they are there (this project is likely to impact you) and what is expected of them (agree the timeline with the data owner), then you can achieve alignment across functions and reduce nasty impacts. Win win!

Achieving data democratization requires the implementation of a versatile data governance framework that considers not only tables, but all data assets (tables, models, functions, dashboards). The fundamental element lies in the adoption of a flexible tool that perfectly aligns with this framework. Many companies struggle to implement data management, mainly due to the complex nature and diversity of tools (data lakes focus on management files, data warehouses on columns and rows). The challenge lies in having a modern data platform that address all this challenges.

There are many tools and products that thrive upon the meta gathered from ticketing systems, problem tickets, knowledge articles, all simultaneously and fluidly. Training your personnel to use key words and identifiers therefor helps the next service person to find what they need in a timely fashion, reducing time to resolution of an issue and overall customer satisfaction. Since the meta is searched with every issue, one must ensure that their knowledgebase and what is crafted is done in a productive manner. A corporation must have personnel that "Check" the knowledge, the authenticity and the content post creation, correct it if need be and add key identifiers for people searching. If items are not properly tagged the meta is useless.

In my experience, data lineage and metadata is one of those topics which is much talked about but is very rarely done well. With the increase of volume, variety, and velocity of data, the traditional/legacy approach of documenting lineage manually is not sustainable. Inferring the lineage from the code itself is the modern way to do it, and today's problem is how multiple lineage systems can be integrated together to create a wholistic end to end view. In my mind a open standard which allows to integrate and unify various lineage systems is what the industry requires.

One of the tools that is adept at building and managing data lineage is definitely DBT. By generating DAGs of your data models automatically, you are able to visualize the dependicies between models and how data flows from source to destination.

Utilizar herramientas que rastreen el linaje de datos y proporcionen metadatos detallados sobre la procedencia y transformación de los datos. Esto facilitará la auditoría, la resolución de problemas y la comprensión completa del ciclo de vida de los datos.

In my experience, data architects don’t define the data quality and governance practices. They are enablers but not accountable. They define how data should be stored and design the data model. On the other hand, Data engineers are responsible to set up the data sourcing processes and data governance teams are accountable for the data health.

Agree, data architects define what data important to enable business strategy and design data flow between information systems and where it is source or consumed or manipulated. Data quality will be impacted if this design is not built on the right way. Other aspects need to be considered in the design are whom accessing the data and which process activities use the data.

As a data architect, my perspective lies in recognizing data architecture as more than just a blueprint; it's a living framework that must adapt to the evolving nature of data, business goals, and technological advancements. Warehouse implementations. The review process delves into the nuanced considerations of data quality, security, and performance—essential components, especially in the Cloud Data Warehouse context. Given the dynamic nature of cloud environments, continuous adjustments are imperative to optimize performance and fortify security measures. The Data Solution Architect, drawing on a wealth of experience, ensures that the architecture aligns with the specific requirements and challenges posed by Cloud Data Warehousing.

One thing I have found helpful is having a data dictionary of your data architecture. This has to be updated regularly to incorporate any changes that may occur to either source systems or even the data warehouse. Taking advantage of modern tools that allow for auto documentation should be a consideration. I personally use dbt to do not only data transformation , but testing and auto documentation as well. With published documentation, members of my team are able to easily know where to find what and what it means.

Mantenerse al tanto de las tendencias tecnológicas y actualizar la arquitectura de datos según sea necesario. Incorporar nuevas tecnologías, como inteligencia artificial, para mejorar la calidad y la eficiencia en la gestión de datos.

Understand business Goals & Values: Align data quality initiatives and timeliness of data with business objectives to drive informed decision-making, enhance customer experiences, and optimize operational efficiency, ultimately contributing to business growth and competitive advantage.

Ensuring up-to-date data is crucial for informed decision-making, including automated ETL processes for regular data integration and real-time data processing for immediate updates to correct data quality inaccuracies. Utilizing CDC tracks alterations in source data while monitoring systems and scheduled audits maintain data accuracy, strong data governance for compliance, and policy updates. Feedback loops with business users identify outdated data and use cloud technologies to offer scalable solutions. Continuous review and updates of data processes adapt to evolving business needs; combining these efforts ensures data remains current, reliable, and supportive of organizational objectives in maintaining data integrity and relevance.

All the above points are valid. I also believe that a sturdy data governance should also be in place. GDPR and other compliances cannot be implemented on data without a good governance system. The data governance should be updated on a regular basis to ensure the correct treatment (extraction, distribution, storage and transformation) of data.

It's important for Data Architects to be part of change management process to assess any changes related to systems, application or business processes if any potential impact on the data flows, data structures with the goal to minimize/reduce risks to data integrity, and comply with data protection and regulations.

Destacar la importancia de la colaboración y la comunicación entre los equipos de datos y las partes interesadas. Fomentar un enfoque holístico que abarque desde la captura hasta la entrega de datos, asegurando que todos los aspectos de la gestión de datos se aborden de manera integral.