A Quick-Fire Guide to Better Data Lineage and Process Transparency


Without knowing where your data has come from, or how it passes through your systems, you’ll never truly understand your data governance framework and what insights it holds. That’s why ensuring data lineage is crucial for your business; it offers complete transparency and a foundation for informed change.

What is data lineage?

Data Lineage describes data origins, movements, characteristics, and quality of data. It's important to understand this data journey so that you can maintain transparency over your data. Data Lineage typically describes where Big Data begins, and how it is changed when presented in its final outcome.

Specifically, data lineage helps you:

  • Improve your organization’s overall business intelligence, as you can better trace process success and data insights
  • Identify data sources, which can ease the development process for your technical teams
  • Gain better transparency into your data processing activities, allowing you to ensure your activities are in-line with regulatory needs

However, as much as these benefits are enticing enough to get you started, you’ll first have to navigate the challenges of data lineage.

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1. Understanding your data lineage challenges

With regulators needing to know how your sensitive data is being handled throughout its lifecycle, it’s never been more important to track your data lineage.

But, for businesses who don’t yet have a data lineage process in place, it can be difficult to know where to start. Especially when you have thousands of data sources and integrations, isolated data sets and an outdated, manual data management process. However, without overcoming these challenges, you run the risk of facing further problems that’ll only cost your data teams more time and money.

To achieve better transparency, your organization should plan a strategic, step-by-step data lineage approach.

2. Defining your approach

Fortunately, with the right planning, there are many ways to overcome your data lineage challenges:

  • Pinpoint goals. Whether you want to improve your data lineage process to gain better business intelligence, or you’d like more transparency to make data governance and achieving compliance easier, it’s important to define your goals. This keeps you accountable to your goals and will help you evangelise and recruit other teams for support.
  • Anticipate scope. How much data does your organization currently process? And what data flows are you hoping to better track? You’ll need to understand the size of the task at hand before you dive in.
  • List required processes. Before you start the data lineage process, you’ll need to find where your data resides and map it together. This ensures you don’t miss a single dataset that may be crucial for a data flow. You’ll also need to determine how your sensitive data is processed and protected.
  • Educate teams. Going forward, each team handling data must be aligned. From defining standardized data vocabulary, to assigning responsibilities, it’s essential everything is transparent and collaborative. Without buy-in from everyone, you can end up with siloed data or badly formatted datasets.
  • Document processes. Data lineage should tell a story of where data has come from, what alterations it’s had, who handles it, and where it ends up. Documenting these key points is necessary for data regulation best practices and to consolidate your entire data lifecycle.

By creating a data lineage roadmap and covering all your bases, you’ll be able to piece together your data flows efficiently.

3. Streamlining the data lineage process

Manually creating data lineage flows is a difficult task. The effort it takes to discover your data, let alone assemble it, can be overwhelming for businesses without the time or expertise.

To help ease the process:

  • Consider using tools, such as CloverDX’s Harvester, that find your data for you. This saves time once spent manually sifting through disparate data sets.
  • Translate your rules and data flows into standardized data models. To make these data lineage processes automated, consider turning these models into building blocks, too. This makes assembling future data workflows quicker and more realistic, as you can repeat models and their rules with a few clicks.

4. Get your data in line

A good data lineage process aligns your integrations and allows you to find valuable insights amongst all the noise. However, with the large scale of integrations and sources your organization manages, it’s tricky to map out your workflows.

By following a strategic approach, determining goals, listing processes, assigning responsibilities, and documenting your practices, your data lineage process will stand in better stead. And, by using modern approaches such as data models, you’ll streamline the process, too.

So, are you ready to piece together your data lineage puzzle? If you’d like more advice, please get in touch.

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Posted on October 17, 2019
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