How can you use data lineage and metadata to drive effective decision making?

There are a number of tools out these which claim making data lineage and metadata management a breeze. However, effective data lineage and robust metadata requires discipline approach and a behavioral shift on part of development teams and business stakeholders. Doing both of these retroactively regardless of the tool is hard. In my team, we will be testing out approaches using AI to establish data lineage and augmenting metadata for data already in data lake environment.

Start by thinking about what you are trying to achieve with your metadata / data lineage strategy, dont just blindly "harvest" metadata into one big repository as it will only become a metadata swamp.

Actually, there are 2 aspects to data lineage: horizontal data lineage, that indeed tracks the data from source to destination (preferably with information on what happens with the dat along the way, like transformations) and vertical lineage, that tracks the semantic consistency of the data.

I would like to add some advantages to data lineage. When you link your data definitions to all the stages where that information is used, you have an advantage during projects with impact on that specific information. You easily spot the impacted reports, indicators and other data products where this information is linked to.

A critical aspect of leveraging more value from data lineage is understanding the business purpose and use cases. This understanding transforms data lineage from a mere tracking tool to a powerful asset for ensuring data quality, compliance, accuracy, and resolving data issues. It enables us to see not just where data has been, but also its significance and role in our business objectives.

I think there’s enough documentation out there on what metadata is, don’t you?! Data lineage is indeed data about your data and an important part of fully understanding your data landscape.

Data-driven culture can be nurtured by providing everyone in the organization with access to metadata. Then, if a particular team or individual needs access to the underlying data, they can raise a formal request. Curate the metadata by adding business context to make the underlying data easy to use.

Metadata is also about the restrictions from Business Rules applied on data. Defines how data entry to be made making Data Quality an integral part.

Un metadato que pocas veces se considera, pero es muy importante para los datos críticos son los metadatos transaccionales. El tener claridad de quien fue la persona que modifico el dato y donde nos permite tener el control en caso de tener una perdida de calidad en los datos y poder corregir los procesos desde el origen de estos.

Metadata is almost more important than the data it describes because it supports the ability to understand data in a much more simple and efficient manner. Sound understanding establishes confidence in analyzing and using data to facilitate wider alignment and use across teams and operating at a faster pace when needed.

If you're trying to use data to make decisions, you need to have data lineage and metadata in place. Otherwise, you're not making decisions on data with 100% confidence. Based on what the data is called, you can make a guess, even an educated guess, at what the data means. However, it wouldn't be the first time that data was named one thing and meant something completely different. People often misuse data. They might name it something and start using it for something else. There might be similarly named data that makes it confusing to know what the right data is to use. Decision making needs to be based on data you understand.

Leveraging data lineage and metadata for decision-making is pivotal. They serve as the backbone for fostering trust and transparency in your data. Understanding the origins and journey of your data not only validates its accuracy but also fortifies the reliability and relevance of your analysis. This clarity in data flow significantly enhances the efficiency and accuracy of reporting, amplifying the quality of your decisions.

The metadata approach culture is extending around all the Data culture. We need to have declarative metadata around the processes, data load, lineage, metrics, observability, security, data quality (including the data quality on the metadata itself!), data models relating each dataset and fields in your datamesh, and finally relate each dataset/fields/... and all the past points I have just mentioned with the corresponding ownership/stakeholder to provide a governance in your data architecture. It will allow the different applications in our data platform provide the answers reading the metadata and acting accord to it. It will provide the control about our dataplatform, in terms of Scalability, Confidence and Efficiency reducing TCO.

Ensure that the data lineage is available at a column-level. This will help you trace the KPIs used in the BI reports to the source system, building trust in data.

Very insightful! Thanks for your post. In a developing country like Ethiopia, where I am living now, data is poorly managed and even not used for decision making in most setting. It's a time to change this trend by cultivating a positive attitude toward data among the work force, particularly health sector. Data is a resource, and let's give value for data.

Number 1 best practice is to only do data lineage when you have a good business reason to do so. If you do it as an academic exercise to geek out on data in your data office, you might have fun but it won’t get used not adopted. Number 2: determine whether you need business/logical or technical/physical data lineage. Start small with a define use case and ensure you deliver value as you progress.

There are various approaches to lineage, such as scanning multiple domains to gather content for immediate or future use. However, the difficulty lies in use cases where the need for lineage is recognized only after a project or analysis has already progressed significantly. Recommendation: It is advisable to proactively consider and incorporate lineage in the early stages of a project or analysis to avoid complications later on.

Two more key points: Think about how to make your data lineage sustainable. A one-off effort is useful to support a project analysis, but that's no good in the long term unless you have mechanisms to keep the lineage up to date. Technology can only do so much. That normally means people and process have to fill in the blanks, provide business context, map the "hard to map" stuff, etc. Build with a human-in-the-loop mindset.

Manually building lineage is tedious and incredibly time-consuming. Check out governance tools that support auto-lineage functionality. It can take anywhere from few minutes to a few days to build lineage automatically using a governance tool. As a best practice, start with critical reports and data sources and build lineage only for that instead of boiling the ocean.

Understand that you need to focus on business critical data to demonstrate purpose and value to get buy-in. You can still rollup other dependent elements and so on.. But the 'sell' should be business critical.??

In my view both data lineage and metadata can help in two ways 1) Making informed decisions - Knowing where data is coming from helps to build trust around data and hence define how much this data impacts decisions. 2) Understanding risks- If the data lineage is not known then it should be used to document risk around the same

In a perfect world, our enterprise data landscape would have ''organically'' been documented whilst it was created and evolved over the years, but this is never the case. Typically the documentation is out of date or it can no longer be found. Therefore, in 99% of cases we need to start by retrospectively extracting data lineage and metadata either manually or automatically through a data discovery, profiling and extraction process. There are many automated tools in the market these days, however none would be considered a silver bullet. Due to the vast number of database & integration solutions, all with unique designs and implementations, not one tool will automatically complete this process. Finally, get a team with the right skills.

In a data-driven enterprise, the fusion of data lineage and metadata became a beacon for effective decision-making. By visualizing the journey of critical data elements and enriching it with comprehensive metadata, the team gained unparalleled insights. This strategic integration not only enhanced data reliability but also empowered decision-makers with a contextual understanding of information sources. The story underscores how leveraging data lineage and metadata can be a transformative force, illuminating the path to more informed and impactful decision-making within an organization.