DataOps plays a significant role in modern-day organizations where artificial intelligence, IoT, ML, and Generative AI are driving innovation and powering business. However, DataOps can become a very costly exercise. But fear not, you can have a successful DataOps strategy without breaking the bank. With powerful data lineage solutions, you can make DataOps more efficient, get the results organizations demand, and keep the costs low at the same time. 

Download the Gartner Quick Answer: What is Active Metadata?

What are DataOps Costs?

Before we explore how data lineage can reduce costs, let’s have a look at some of the biggest expenses in DataOps. These include:

Infrastructure and hardware: Be it in private or public cloud, there are significant costs associated with storing and retrieving data. 

Compliance: Ensuring that the data is managed according to applicable rules and regulations and being audit-ready can be expensive. 

Human resources: DataOps requires significant talent and expertise to balance performance with efficiency. 

Data quality costs: Poor data quality can affect processes that rely on it. It can affect their output, require reworks, and in some cases affect long-term decisions. 

Investing in Data Lineage to Improve Operating Costs

Data lineage helps organizations gain better visibility into their data pipelines and make more informed cost-saving decisions. Going a step further, data lineage can also help analyze the storage and accessibility requirements of different data sets and determine better storage strategies. This has major benefits that go beyond simply keeping your data in compliance – you can also save costs on storage space and contribute to sustainability efforts that have real-world physical impacts (for example, less data to store relies on fewer physical servers that consume large amounts of electricity). The following three methods can help you achieve these goals and level up your data strategy. 

1. Automate Data Lineage Processes

While data lineage can reduce DataOps costs, the process itself can take up a lot of resources and may be difficult for organizations to implement. But automation can help. 

Automating the data lineage process can reduce the errors and the resources associated with it. It can reduce the resources required to find and fix inaccurate or incomplete data, provide real-time reporting on data, offer enhanced visibility, and get impact analysis information easily. 

Here’s how organizations can automate data lineage processes: 

Tracking Tools

Data tracking tools offer organizations better visibility into the data sources, pipelines, and transformations. These tools can help organizations understand the source of data and errors, transformations, and help with impact analysis. Tracking the data manually will be resource-intensive and maybe even a futile exercise due to the volume and velocity of data in large enterprises. Tracking tools can help organizations implement data lineage with minimal resources. 

Data Pipeline Orchestration

DataOps teams often spend significant time and resources setting up data flows and pipelines for analysis, transformation, and other tasks in accordance with the security and scalability requirements. 

By automating data pipeline orchestration, DataOps teams can enhance data visibility, deploy faster, and enhance data governance. It can help organizations run their data lineage processes efficiently. 

Reduced Manual Documentation

Manual documentation is resource-intensive and in many instances, they’re prone to errors. Teams regularly come up with new data pipelines and workflows and keeping track of every data source and transformation manually will take a lot of resources. As the organization grows, manual documentation won’t be scalable. 

By automating documentation, organizations can reduce resources and errors and keep their systems scalable. 

2. Optimize Metadata Sources

Up until the past couple of years, metadata (or data about data) wasn’t of much concern to organizations. Metadata represented a very small portion of an organization’s data and it didn’t cost much to just let it grow unmanaged. But now it makes up a significant chunk of an organization’s data and DataOps teams have realized that there is a ton of value to be gained by managing metadata effectively. 

In some ways, data lineage and metadata are interdependent. Metadata has become a crucial element in data lineage because it can help organizations understand data sources, transformations, and pipelines. 

At the same time, metadata has to be managed effectively and efficiently and the solution is data lineage. Data lineage can help manage metadata and reduce their growth. Data lineage can help identify redundant and unused metadata sources. 

Here are some of the other ways in which data lineage can help optimize metadata sources. 

Metadata Consolidation

As organizations and their data grow, the same or very similar pieces of data tend to have multiple copies of metadata. By consolidating this metadata, organizations can save a lot in terms of DataOps t and even data storage costs.   

By tracking metadata sources, their transformations, and their destinations, data lineage tools can help bring together similar or related data and remove redundant pieces. 

Metadata Sprawl

Over the last decade, we have seen the ratio of data to metadata grow from 1000:1 to 1000:1 for large objects and 10:1 for small objects. Organizations have to analyze metadata sources and remove the unnecessary ones so that it is easier and cost-effective to manage. 

Data lineage can help with this. It can help DataOps teams identify the relevant and irrelevant metadata and their sources and help remove them and stop storing them. The enhanced visibility from the data lineage tools can help prevent unnecessary duplication of metadata and help organizations manage their storage more efficiently. 

Metadata Management

Besides performing routine metadata cleanup, organizations need a metadata management plan. They need to actively monitor metadata sources and their sprawl, identify irrelevant or similar pieces of metadata, and manage them constantly. 

Data lineage can help here. It can automate metadata management to a large extent and prevent their unrestricted growth. DataOps teams can configure data lineage tools to automatically detect unnecessary metadata creation or metadata errors and rectify them automatically. 

The enhanced visibility from these tools can also help metadata teams come up with a comprehensive metadata management strategy. 

scanners copy 2

3. Prioritize and Resolve Data Quality Problems

When attempting to optimize DataOps, it may be tough to know where you should get started. Data lineage solutions can help DataOps teams get more visibility into the state of their data, identify the source of data quality problems, and how much it is costing the business, and prioritize solving the problems. 

Cleaning Data

Data errors and noise can affect the quality of output and can make DataOps more expensive. Data lineage can identify data errors and their sources and help DataOps teams clean them. It can also help them proactively prevent errors and noise within the data. 

Bottlenecks and Inefficiencies

Bottlenecks in data pipelines, unnecessary processes and transformations, and other inefficiencies can slow down DataOps and make them more costly. The enhanced visibility from the data lineage systems can help DataOps teams identify bottlenecks and inefficiencies in the pipelines and workflows and remove them. 

End-to-End Data Lineage

By incorporating end-to-end data lineage, organizations can track their data flows in real time, identify sources of errors and inefficiencies, and prevent them from affecting the systems. It helps organizations actively prevent data and pipeline problems before they cause issues downstream and become more costly to fix. 

How Data Lineage Helps in Reducing DataOps Costs

By tracking the origin, transformation, and usage of data assets, data lineage offers organizations better visibility into the state of data and their data handling requirements. It can help DataOps teams choose the right tools and solutions to meet the organization’s performance requirements and at the same time keep the costs to a minimum. 

Resource Allocation

Inefficient resource allocation can make your organization’s DataOps very costly. For instance, instant access storage systems for data that should be archived can become an unnecessary expense for the organization. 

Data lineage offers enhanced visibility allowing organizations to keep the costs low without affecting their requirements. 

Compliance and Auditing

Organizations in all industries are under strict rules to keep their data secure and can face severe penalties in the event of a data breach. But often, organizations need significant resources to be audit-ready. 

Data lineage can help cut down costs here. With data lineage, DataOps teams have a clear picture of all the data sources and destinations. They can ensure that no data is lost and that they’re stored as required, according to regulations. 

Data Transformation

Data lineage solutions can help mitigate poor data quality and optimize data processes creating better systems for managing data. It can help DataOps teams understand the transformation the data is going through and make them more efficient. 

Optimization and Automation

Data lineage tools can reduce manual processes in DataOps. It can automate routine tasks like testing data quality and monitoring data pipelines with minimal input from the team. 

Data Quality

Poor data quality can affect systems and outputs downstream and can be costly in the long run. Data lineage can verify and review data sources - including metadata - and ensure their accuracy and completeness. 

Issue Detection and Resolution

By enhancing visibility into the state of data, data pipelines, and data storage solutions, data lineage can help DataOps teams stay on top of issues and in some cases prevent them. Without this, data-related issues may pile up and become very expensive to fix. 

Start Optimizing Your DataOps Costs with Manta

DataOps is a necessity in any organization dealing with large amounts of data. As an organization grows, its data starts spreading over multiple systems and silos. This can increase the cost of data storage and management. 

DataOps can prevent this and make it cost-effective. That’s where Manta comes in. Manta is a proven data lineage platform that has helped DataOps teams enhance their productivity by 60% and reduce costs by 30%. 

Improve Trust in Your Data with Manta.  Improve data quality throughout the pipelines and workflows with Manta. Ready to jump into the new age of DataOps? Get started with Manta now

Manta Gartner Report Quick Answer What is Active Metadata next to graphic representation of blue data boxes connecting

P.S. This post was written by a human!

Related Resources