Why invest in data quality and observability


The business landscape gets routinely disrupted by new technologies, social shifts, environmental issues, and constant upheavals in connected global enterprises. Add the pressure of economic uncertainties, and you know why organizations are fighting to keep pace with these changes.

The question is how? Today, data is at the heart of every business decision. If the data is of good quality, it will drive sound business decisions. On the other hand, bad data can lead to poor analysis and wrong decisions. The impact of data quality compounds over time, be it positive or negative, and it reflects in the value of the organization.

Building trust in data is a challenge. The traditional data quality solutions may not meet the demands of modern data ecosystems while the cost of bad decisions can multiply in no time. Delivering trusted data in real-time is a necessity now, and data observability is emerging as the right technology to address it.

Why data quality and observability?

Data quality indicates if data is fit for use to drive trusted business decisions. Assessing this fitness is complex and depends on the needs of the data consumers. What’s critical is that these assessments quickly drive the improvement of data quality. Traditionally, data quality rules help you locate the issues, and efficient workflows help fix them. But modern businesses demand more. Instead of fixing the issues, they want to prevent the issues from reaching the enterprise applications. 

That is where data observability scores by extending the focus to data systems. Data observability is the ability to understand the health of an organization’s data landscape, data pipelines, and data infrastructure. It uses a set of tools to proactively monitor, track, alert, analyze, and troubleshoot incidents to deliver data quality at scale. With ML-driven data observability, you can predict errors and enable finding the root cause to fix the issues before they hurt.

Data downtime is a serious pain point that can cost you anywhere from $140K to $540K per hour, not to mention compliance and operational risks. Data quality and observability can minimize your data downtime by proactively detecting anomalies, making it the ultimate stack for you to build trust in data.

The following five points discuss why investing in data quality and observability is the best decision you will make today.

1. Prevents costly decision mistakes

Poor-quality data can permeate deep into the enterprise before you realize it. It can directly impact your product development, marketing, operations, sales, and customer service decisions, driving down the value of your business. When data powers automated systems or mission-critical decisions, the stakes can multiply and lead to disastrous results.

Nearly 60% of organizations don’t measure the annual financial cost of poor-quality data, according to the Gartner survey. But in reality, wrong decisions can cost organizations an average of $12.9 million annually. For example, incorrect contact details of some customers can affect the success of marketing campaigns. Or product design based on incomplete data can turn into a failure in the market. Data quality can often be the difference between a successful entry into new markets and a failed one.

According to the rule of ten coined by Dr. Thomas Redman, it costs ten times as much to complete a unit of work when the data is flawed as it does when they are perfect. And it does not account for non-financial costs, such as lost opportunities or loss of brand value. Data quality and observability can prevent data quality issues sooner and save you from costly decision mistakes.

2. Supports modern data ecosystems  

Gartner predicts that by 2024, enterprises leveraging a data and analytics ecosystem will outperform competitors. The modern data ecosystem is no doubt a growing trend. It spans data sources, enterprise data repositories in the cloud, on-prem, hybrid, tools, applications, infrastructure, and stakeholders. As the number of data points within an enterprise grows, so does the challenge to maintain them. That’s why most of these systems now take the cloud-first approach for delivery at scale and responsive data architecture.

The requirements of these systems are not simple. They demand high-quality data pipelines to support multiple delivery channels and a huge number of enterprise applications. They work with large volumes of data arriving at high speed, which need to be immediately available to support agile decisions.

A Gartner survey notes that the need to strive for data quality across data sources and landscapes is the biggest challenge to data management practice. ML-driven data quality and observability can support the diverse sources and formats of data in modern data ecosystems. With quick rule adaptation, it can deliver trusted data in near-real-time, reducing errors by more than 50%. It predicts errors and helps prevent them from reaching the downstream applications.

3. Improves data engineering efficiency

Data engineering teams have a tough life. They spend a lot of time fixing data quality issues that should not have been there at all. Some issues creep back again, and it can be frustrating to repeat the work. Moreover, with constant work on production-level errors, they hardly get time to innovate and improve the data systems.

“By 2025, 463 exabytes of data will be created each day globally, which means huge amounts of data in different formats and time frames to process, thereby resulting in a significant increase in the data engineering effort.” – Forbes (Jan 2021): Will Data Engineering Efforts Reduce In The Future?

The Gartner 2022 Leadership Vision for Data and Analytics Leaders highlights the trend of operationalizing ML to solve complex problems. A good ML-driven data quality and observability solution offers your data engineering teams an efficient way to prevent issues from reaching the data consumers. They can catch data errors proactively and reduce the frequency of the issues they need to fix. They can locate the root cause at upstream sources and resolve critical issues faster.  

4. Delivers healthier data pipelines

Modern data ecosystems are complex. They use data pipelines to automate the data movement and transformation from the source to the target. The performance of downstream applications or repositories depends on the pipelines. The health of your data pipelines thus drives smooth operations and trusted analytics. In other words, your data pipelines are critical to the success of your business.

A strong, trustworthy data pipeline that enables acceleration in decision-making is vital to developing a data-driven culture. But maintaining the health of data pipelines is challenging because of the increasing volumes and speeds of data.

Data quality and observability can address this challenge with the ability to detect and troubleshoot problems in data pipelines. With healthy pipelines, your data consumers can confidently access the right data to drive trusted business decisions. You can also minimize data downtime by not only detecting but also fixing or replacing broken deliveries and building new pipelines at an increased speed.

5.     Provides end-to-end visibility

Data moves and gets transformed in its journey through the enterprise data ecosystem. In most cases, lack of visibility into this journey is the cause of not catching errors or not taking timely action on them. Complete visibility into this journey helps you know where exactly data loses its quality and integrity.

Companies are not seizing the strategic potential in their data, observes Thomas C. Redman. He reasons that bad data, in turn, breeds mistrust in the data, further slowing efforts to create an advantage. A good data quality and observability solution provides you with end-to-end visibility into the state of your data quality. You can install quality checks at every step of the data journey, opening up opportunities to build trust in data.

Building your case

Gartner expects that by 2026, 70% of organizations successfully applying observability will achieve shorter latency for decision making, enabling competitive advantage for target business or IT processes.

When you trust your data, you can make smarter decisions faster. When your teams have access to high-quality data, they can focus on building great products or delivering better customer experiences. Data quality and observability is the ultimate stack that guarantees continuous delivery of healthy data in near-real-time, and it’s time to build your case for investing in it.

  1. Justifying the proposed investment begins with a clearly defined goal. Choose a recent issue of poor data quality and how it affected the organization. Support with concrete data of what it costs the company, including delays or missed opportunities. Add all possible cost leakage points that contribute towards the loss.
  2. Demonstrate how investing in a good data quality and observability solution can speed up the compliance process and maintain it continuously.  
  3. Add how you can use trusted data for strategic advantage. Discuss how data quality can help you improve customer experience, reduce risk, and drive growth by closing the gap between the corporate vision and the results. 
  4. Emphasize how your organization is growing and how a future-ready data ecosystem will help ensure that your organization will continue to be successful in the future.
  5. Project how the investment will generate better financial value and open up new opportunities.

Choosing the right solution

Once your top management is on board for investing in a good data quality and observability solution, it’s time to shop around. You can opt for building an in-house solution, but check if it works for your organization.  

When selecting a commercial enterprise solution, choose a comprehensive solution that has the following features.

  • Data pipeline and dataset monitoring
  • Predictive detection of anomalies
  • End-to-end visibility into the data quality
  • ML-generated adaptive data quality rules
  • Faster implementation with out-of-the-box support for popular databases 
  • Future-readiness

Collibra Data Quality & Observability helps you catch bad data before it can affect your business. With proactive monitoring and end-to-end visibility into data health, you can always stay on top of risks. You’ll never miss quality improvement opportunities, as the self-service solution empowers all stakeholders to view key metrics, catch errors,  and assign issues.

You can leverage Collibra Data Quality & Observability to 

  • Generate a competitive advantage and higher revenue with trusted data.
  • Mitigate compliance risk with high-quality data.
  • Increase team productivity with ML-generated adaptive rules.
  • Migrate data confidently with no loss of quality. 

Convert your data into an enterprise asset to drive value and performance. Choose Collibra.

What next?

With Collibra, data quality specialists report a 50% productivity improvement while BI analysts, data analysts, and data scientists report a 23% productivity improvement. What about you?

Request a demo or start a free trial today.

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