How can data governance frameworks enable self-service analytics in data engineering projects?

I worked in finance and personally experienced the impact of data categorization. A centralized metadata system was essential for credit judgment involving large datasets. It reduces dependency on IT by enabling our analysts to locate and use datasets easily.

Having data lineage and dictionaries built in the overall data governance platforms can enable self service analytics teams to be educated on where the data came from, what it means etc and help them work better. This bridges the gap between functional teams and the self service analytics teams if they are operating differently.

A data catalog is not just for data engineers. Business analysts and non-technical users would also benefit from having a single place to go to search and discover if data exists that may suit their needs. This can save them countless hours of research and frustration. Data catalogs also reduce complexity and minimize the potential for duplicate efforts. This is because they make the existence of data assets more transparent. Without a central data catalog, data engineers will find it easier to build a new data asset from scratch versus spend the time to see if a similar asset already exists. This just creates additional layers of complexity for future efforts.

Here's how data governance frameworks enable self-service analytics: > It provides the two key pillars "trust and reliability" to analysts which is key to a successful self-service platform >Analysts can discover, understand, request, and connect to enterprise data to derive insights in a secure and compliant way. > Helps in agile development and improves SDLC which saves a lot of delivery time, money and reduces scope.

Data governance frameworks are essential for enabling self-service analytics in data engineering projects. They provide a structured and secure environment that empowers business users to access and analyze data while ensuring data quality, security, and compliance. These frameworks offer features like data cataloging, access control, data quality assessment, data lineage tracking, and auditing to make self-service analytics effective and reliable. Additionally, they promote collaboration and documentation, helping users share insights and follow data usage policies and guidelines.

At last an easy question to answer! By definition, DG stands for promoting the availability, quality, and security of enterprise information. Apply an interface on top of that and you get an instant self-service tool. The only requirement to be met is that all enterprise data must follow data standards and become homogeneous across the company to be easily understood and reachable. And the icing of the cake - produce a canonical model of your data along with governance standards.

In my experience 6 Key benefits of data lineage are: 1. Better data governance 2. Improved data compliance and risk management 3. Faster root-cause analysis 4. Understanding downstream impact 5. Drive automatic data quality 6. Facilitate auditing and documentation

A comprehensive comprehension of the origin and evolution of data prior to its arrival at the analytical platform is of utmost importance for analysts. Data lineage is an indispensable resource for analytical teams, offering transparency regarding the data's origin and transformations, as well as providing essential information to remain informed and alerted about any schema changes that may impact their analytical activities. In the event of data inconsistencies or failures, data lineage serves as a catalyst, expediting the troubleshooting process. It enables analysts to swiftly identify the precise point of failure and resolve the issue with maximum efficiency.

Data lineage along with log transformations published on UI can provide a self-service platform for business support issues. It can help business self-analyse why a report shows unexpected values and more so provide transparency for the actual data value change from A-B-C instead if A-C - uncovering the transformation B within the black-box!

Enabling data access via APIs within a data governance framework, can facilitate self-service analytics: 1. Data Discovery & Access - Users can discover data assets through API endpoints to access and retrieve curated data from various sources, e.g. databases, cloud services, or 3rd-party applications. 2. Data Security - APIs can enforce access controls and authentication mechanisms, ensuring that only authorized users can access specific data endpoints. Role-based access control can be implemented to define who can access which data through the APIs. 3. Data Catalog & Metadata - API endpoints describe the data they expose, the format of data, and any required parameters, which help users understand the APIs and the data they provide.

In the realm of data engineering, self-service analytics is the golden ticket to unleashing the power of information. Data governance frameworks serve as the guiding hand, shaping the path for both data engineers and consumers. They lay the foundation for data access, safeguarding sensitive information and ensuring regulatory compliance. But beyond these vital tasks, data access also bestows autonomy upon data users, enabling them to navigate, explore, and share insights, fostering a culture of self-service analytics. Through robust governance, data engineering projects find the perfect harmony between security, compliance, and user empowerment, unlocking the true potential of data.

It's pivotal not only in ensuring the integrity of data through stages of ingestion, storage, and processing but also in harmonizing and consolidating the data, which is crucial for MDM. In an MDM framework, data quality transcends the role of accuracy; it's about presenting a unified view of data entities while respecting the variations stemming from different data sources. This unique challenge necessitates advanced data quality strategies, such as entity resolution, deduplication, standardization, and lineage tracing, all tailored to an MDM environment. In the context of MDM, DQ is not a one-off process but an ongoing journey. It involves continual monitoring and governance to adapt to the evolving nature of business data and rules.

The linchpin for self-service analytics? It's the Critical Data Elements (CDE) standard. By building our data governance framework around these CDEs, we create an environment where data engineering teams readily embrace high-quality data sources. This, in turn, fuels the effectiveness of data science work. It's the pivot point that elevates our self-service analytics to a whole new level.

Some of the data quality metrics are very straightforward and easy to set, while in various domains these metrics tend to change dynamically over time. Therefore it is important that beyond the automated data quality monitoring, there is a collaborative platform, where data consumers can raise concerns and can propose changes and/or enhancements about the data quality metrics. Having said that it is key, that the data enablement team is integrated with the business operations (as well), so the pain-points can be easily understood. Integrated data enablement and business teams also foster the usage of common definitions and vocabulary, leading to more accurate data quality metrics definitions.

Having worked predominately in banking and financial services data quality is paramount as it could have significant impact on a consumers ability to secure a loan etc. imagine if your credit score was inaccurate in the impact that one number would have on your ability to secure an auto, home loan or even a credit card.

The two most important questions to determine if data is valuable? 1. Does the data possess value? 2. Will the data be valuable? This exercise is excellent for those who are uncertain about how to begin identifying critical data elements or data assets. It helps limit the scope of your data projects, ensuring your focus is on the highest-value data.

To truly empower self service analytics, an internal data portal is essential to seamlessly connect all the necessary components. Envision a user-friendly consumer portal where analysts can effortlessly explore datasets and data products in a contextual manner. They can also visualize the lineage and aggregated quality of the data before subscribing to it. This portal will automatically orchestrate the provisioning of the appropriate data, allowing analysts to choose their preferred location and ensuring specific data controls are in place. These controls are based on the data classification, as well as other properties or tags from the catalog.

When talking about self service analytics, one key point to consider is the importance of the reliability of the data. To ensure this, a strong collaboration between IT, data and business teams is mandatory to put in place rules, labels, access, cataloguing, ... It also implies to put in place easy ways to process the data. If the data producers in business find it difficult to share their data and maintain data catalogs, APIs or so on, the change will fail. That is why companies must involve all actors including businesses in their data governance efforts. And even more when self service engineering is concerned.

Reference data: can be critical when the ecosystem is built up of different sub systems, and each sub system uses a different data standard, yet share data among each other, often, the error in one sub system can propogate and can be hard to identify, thus its critical to create reference data and crosswalk among systems for standardising data.

Most of the times we forget that to embrace any data analytics project we need to be sure that we have a decent data quality. In my experience Master Data management from DAMA framework ,can really help with because it provides a solid definition of critical data, it’s policies and quality standards, that in my opinion are a must before start thinking about any analytics project.