Poor data quality has such significant implications that many people find it difficult to believe the statistics. According to Gartner, poor data quality costs an average firm $15M annually. It may even be more disastrous for certain businesses.
Finding creative ways to offer large-scale, automated data testing has become a top concern for many businesses as business intelligence architects and operations teams struggle with the difficulty of maintaining numerous analytics systems. To provide you with the quality assurance required to maximize the value of your data, DataOps process incorporates testing into your data-analytics pipeline.
It permits precise measurement and outcomes transparency, which may be applied to competitive business decisions. In essence, the DataOps process is the art of automating the lifecycle of analytics to enhance data quality and foster agility.
In this article, we will examine the domain of DataOps and examine a problem that is becoming more and more prevalent: how to design the DataOps process for data testing in complicated data warehouse systems.
Table of Contents
What is DataOps?
DataOps process (data operations) combines people, procedures, and technology to facilitate flexible, automated, and highly secure data management.
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Many people assume that DataOps is a product that you can buy to magically solve your data problems. Or that DataOps process is nothing more than DevOps for data pipelines.
This leads to another myth: DataOps process is just the duty of your data engineers (quick answer: it is the responsibility of the whole business, not just a select few). So, let’s refute these myths (and any others you may have) by looking at the definition of the DataOps process.
“DataOps is a data management approach that emphasizes communication, integration, automation, & cooperation between data engineers, data scientists, and other data professionals.”
Notice how all the keywords extend beyond technology? It emphasizes concepts like teamwork, communication, integration, and experience. Additionally, have you seen the different functions that data teams play?
Reason being:
DataOps is all about integrating your favorite technologies, processes, and people in one location for improved data management within your business.
What Factors Have Contributed to the Emergence of DataOps?
Given that this decade has been dubbed the “decade of data,” firms are inevitably making investments to enable data teams to keep up with technological advancements in terms of productivity, efficiency, and creativity. To maximize data efficiency and value creation, DataOps process enters the picture in this situation.
Another aspect is the growing number of data consumers within an organization, each with their own set of abilities, resources, and knowledge. The volume, velocity, and variety of data are increasing, and companies need a new way to manage this complexity.
Heads of data teams, in particular chief data officers (CDOs), are required to use data to provide value to the company, respond to ad hoc requests, and make sure their teams are productive while overseeing all data management-related activities.
Indeed, that’s a big task!
Let’s examine each of these challenges in more detail.
1. Enormous Amounts of Complicated Data
Big data’s ascent was the catalyst for everything. Most organizations today deal with vast amounts of data that come from numerous sources and in a variety of formats. There are tens of thousands of different data sources and formats in major enterprises, making the data landscape way more complicated. A few instances are:
- CRM data about financial transactions
- Online comments and reviews
- Information about customers (including sensitive data covered by data compliance rules and privacy regulations).
However, you cannot utilize this data in its current form to find the answers to your strategic concerns, such as where to locate your next shop, or what kinds of goods your target market is interested in, or which international markets you ought to focus on.
2. Businesses Overwhelmed with Technology
The ultimate goal of the DataOps process is to drive business value, and business users need to have access to data. The data must be presented in a way such that these data and business teams can comprehend and utilize it for analysis with their preferred tools. Because of this, all the data your business collects must be transformed in different ways (i.e., using data and analytics pipelines).
To guarantee data quality, integrity, and relevance, the data is profiled, cleaned, converted, and stored in a safe location. For compliance with data protection laws and policies, this final clause is crucial (aka data governance).
As a result of employing a variety of tools for each of the aforementioned processes—from analytics and reporting tools to tools for data profiling and cataloging—your data and business teams may experience a technological overwhelm.
3. Different Mandates & Roles
The individuals employing the technology and tools to work on your data (often known as the data’s humans) are likewise diverse.
- Data engineers concentrate on preparing and transforming the data.
- Data scientists are concerned about finding the appropriate data for their algorithms.
- Business Analysts are concerned with producing daily/weekly reports and data visualizations.
- IT users are concerned with upholding data access standards and ensuring the integrity, security, and quality of data.
- Data managers are eager to learn whether the company is thriving.
Combining various technologies, procedures, and individuals with various objectives increases cooperation overhead and team conflict. Sounds difficult? It is. And for that reason, we require a DataOps framework.
How is your Data Team Benefited from DataOps?
Data people are a heterogeneous group, as we have said. See how DataOps process helps them achieve their goals and makes life simpler for them.
- True Data Democratization: All organization members who could profit from the data have access to it.
- Shorter Time to Insight: Because everyone has equal access to and visibility of the data, they can quickly conclude and make improvements.
- Powerful Data Governance: DataOps process guarantees uniform data generation, consumption, and deletion procedures, guaranteeing central data governance.
If yours is anything like the 1000+ data-driven companies that use Hevo, more than 70% of the business apps you use are SaaS applications Integrating the data from these sources in a timely way is crucial to fuel analytics and the decisions that are taken from it. But given how fast API endpoints etc can change, creating and managing these pipelines can be a soul-sucking exercise.
Hevo’s no-code data pipeline platform lets you connect over 150+ sources in a matter of minutes to deliver data in near real-time to your warehouse. What’s more, the in-built transformation capabilities and the intuitive UI means even non-engineers can set up pipelines and achieve analytics-ready data in minutes.
All of this combined with transparent pricing and 24×7 support makes us the most loved data pipeline software in terms of user reviews.
Take our 14-day free trial to experience a better way to manage data pipelines.
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What is Data Quality Testing?
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Data quality testing shields your company from inaccurate data. If the quality of your business’s data assets is affecting revenue, it’s time to think about a solution.
Accurate data is essential for analysis and data-driven insights for businesses across all sectors. Without this knowledge, firms find it difficult to stay productive, competitive, and lucrative in their market. For instance, in a Dun & Bradstreet survey, 39% of marketers said poor contact data quality was the biggest obstacle to successful marketing automation.
Data is used by marketers to create marketing efforts that attract and keep consumers, who are the lifeblood of almost every business. Potentially successful marketing tactics are choked off by this unreliable data.
Why do Data Quality Tests Matter?
Over 95% of a company’s data is referred to be “dark data,” claims FirstEigen, a Chicago-based big data reconciliation, and analytics company. Dark data is untrustworthy, unwatched, and unvalidated.
It grows dramatically as it circulates through your business. The longer this dark data goes undetected, the more expensive a repair will be. Data testing is required if you want to get dependable and consistent datasets.
Executives cannot trust the data or make educated judgments without accuracy and dependability in data quality. This might lead to an increase in operating expenses and chaos for users farther down the hierarchy. Analysts end up relying on incomplete reports and drawing the wrong inferences from them. And since there are bad methods in place, end users’ productivity will drop.
The Data Quality Assessment Framework (DQAF), which comprises data quality aspects arranged into six key categories—completeness, timeliness, validity, integrity, uniqueness, and consistency— is used to solve these problems.
When assessing the quality of a certain dataset at any given time, these dimensions are helpful. Most data managers give each dimension a score between 0 and 100, or an average DQAF.
How to Design DataOps Process for Data Testing?
To get us started, a few definitions will help because we’re dealing with data quality here, so it’s important to be accurate:
- Data sources: Objects that continuously produce data and make it usable as a data entity. It may be a sensor, a sophisticated data warehouse, a third-party API, or an operational application.
- Data products: Data entities that meet specific needs and are often created by merging and modifying data from data sources.
- Data pipeline: A set of transformations via which data is passed from data sources to data products.
- Data definition: Metadata that describes a specific data entity.
DataOps controls all the tools, processes, and teams that need to be activated to move data from data sources to value creation. To realize the full potential of DataOps, your data and business users, with the assistance of the chief data officer, can design DataOps process for data testing using the following steps:
1. Make Data Quality Testing Everyone’s Responsibility
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If data quality is to transition from an after-the-fact data steward operation into routine practice, then all roles that deal with the data must participate in the process of keeping the tests updated in order to keep it clean.
This implies that the duties normally carried out by data stewards must be considered by all roles that produce data products. It does not, however, imply that the function of data steward should be completely eliminated, as there would still be several data sources and data sets within the company that are not actively being developed.
2. Testing-Driven Development with DataOps Process
In DataOps process, the data definition (i.e., schema and set of rules for potential values) must appropriately describe the restrictions that are required on the input data when creating new code that utilizes data as an input (i.e., data pipeline code). Python libraries like Great Expectations may be used to build constraints on the data definition using sample data in order to kickstart this process.
An analyst or data engineer can then adjust and add further restrictions. Next, possible data inputs can be generated using a data fuzzing library, such as Google’s LibFuzz Structured Fuzzing or Faker, based on the restrictions in the data description. The inputs aid in hardening the processing code and the data specification based on the failures produced by these tests.
When data tests fail, they leave behind faulty data that must be cleaned up. This is the only technique to guarantee that the data pipeline really functions in conventional data development designs. Data product developers should thus operate in safe development settings and employ what DataKitchen refers to as the “Right to Repair” architecture when developing tests to assess data quality. This enables them to produce data products or alter existing ones without adding faulty data to the mix.
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Data testing is sometimes made more challenging by the fact that the given data products are only representations of the data inputs that produced them (for example, the charts and graphs that populate a dashboard). In this situation, it is challenging to create code-based unit tests that detail every restriction that applies to a dataset.
As a result, the set of tests frequently verifies that the pipeline does not malfunction unexpectedly, but they do not assure that the data is of excellent quality. This is analogous to your code not being checked for business-logic errors by the compiler.
3. Continuous Integration and Data Definitions
This makes the concept of data quality integral to the definition of data:
- Data definitions are versioned, tested, and published like first-class code.
- Pipeline stages that combine or blend several inputs specify the inputs as a group, enabling tests for connections between the input data to be written.
- As data is imported into the production data platform, a check is performed against the data definition. You can use these tests to implement statistical process control on the active platform.
Combining these ideas will enable the team to ensure that data problems are identified during ingestion.
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4. Troubleshooting and Root Cause Automation
End users will discover certain problems in any system. We want data and business users to be able to discuss any issues with the data products they are utilizing fast and simply in a self-service environment.
Building trust does not only consist of being able to report problems and have them documented for other data consumers, the data owner must act swiftly to identify the problem’s core cause and fix it. However, diagnosing data problems may sometimes be challenging and time-consuming. In such scenarios, the following questions are raised:
- What is the problem brought on by faulty input data at the source or a flaw in the code?
- Where was a defect in the code, and when was it put into the pipeline?
- How is our knowledge of the source data accurate if it was a source problem?
- Who contributed to faulty data if it was a source problem?
- How Big is the problem where other data products employ identically flawed (and hence misleading) input data?
- When was the data issue introduced (how much additional comparable faulty data exists)?
We can respond to these queries considerably fast if data and pipeline code versioning are in place, and data tests are continually running against the pipelined data.
The answers to the root cause (What, Where, and How) questions mentioned above could be achieved methodically with the DataOps process for data testing. We are able to employ the following procedure thanks to detailed data lineage:
- Understand the expectations of the data consumer.
- Update the definition of the data product to reflect these expectations.
- Create a data test that deviates from the predictions (potentially leverage fuzz testing tools to aid in this process)
- Determine which particular data inputs result in the test failing and whether they require an update to the data definition as they are outrageous.
- Return to the pipeline’s initial step and include tests for the new output expectation if the data inputs are appropriate.
- Move backward and add tests as you go until you reach a point where the issues with the output are unrelated to any given combination of inputs. In this situation, the pipeline stage in question is the fundamental source of the problem.
- Detect source data problems and correctly reject the data within the pipeline.
The introduction of the data issue is then determined via data profiling. The lineage graph may answer the “How Big” question by following the path of data issues from their source to other affected data products, while metadata on the source data can be utilized to identify “Who” triggered the problem.
5. Documenting & Resolving Data Issues
Using the data lineage graph, it is also possible to alert every user of data products who may be impacted by an issue with the data that has been identified.
Consider the following enrollment scenario for higher education, where various data pipelines have been set up to use enrollment numbers to aid in budgeting and marketing. As soon as a problem is found in a pipeline, the system may immediately notify the downstream consumers based on the lineage graphs connecting the different intermediate data products.
Now, there are several sorts of issues that may all be warned using a similar approach (diagram shows example problems across job failures and slow running jobs). These warnings provide data consumers with the option of choosing whether to utilize the impacted data product (dashboard, application, etc.) or hold off until a patch is available.
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The lineage graph can also be used to spread system-wide data quality documentation. Any of the aforementioned data quality problems can be automatically noted on the data definition in the data catalog when they are discovered.
Any other data product producers can then grasp the data quality concerns present in the data sources they are dealing with since the quality issue has been transmitted to all downstream data products.
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The metadata for records where the corrective code has not been applied additionally makes a note of this carryover of quality concerns. The data quality comment can then be deleted from the source and impacted data definitions when a remedy is implemented inside a data pipeline to compensate for faulty source data. This patch can also be propagated to the metadata.
Final Thoughts
DataOps process has gained acceptance among data teams of all sizes over the past few years as a paradigm that facilitates speedy deployment of data pipelines while still delivering immediately available, accurate, and trustworthy data.
To enhance data quality, businesses may use DataOps process throughout various pipelines. To do this, regular operations like testing must be automated, and end-to-end observability must be implemented with monitoring and alerting across all data stack layers, from ingestion to storage to transformation to BI tools.
In this post, we gained an understanding of what is DataOps process & the factors that led to its prevalence. Following that, we learned about data quality testing and how to establish DataOps process for Data Testing. With these DataOps processes in place, stakeholders have greater access to higher-quality data, encounter fewer data problems, and increase organizational belief in data-driven planning.
Give us your thoughts on ‘How to Create a DataOps process for Data Testing?’ in the comment section below!