The role of data observability when data is strategic
Going beyond the data quality ecosystem, data observability is becoming a crucial component of data landscapes that speeds data problem resolution, facilitates communication and collaboration among data professionals, strengthens data of data and increases the competitiveness of organizations.
Over the last few decades, we have seen companies move from data-driven strategies to data becoming the core strategy. Companies that were “data-driven” are now looking to evolve into data companies, where data is seen as an asset that can directly generate revenue. To achieve this, these organizations must undergo various transformations and strengthen their data operations to scale the value generated by data. Increasingly, the observability of data plays a key role.
Scaling data teams
To support the expansion of data operations, organizations are building data teams where specialized roles have emerged. This transformation is comparable to how IT teams evolved in the 1950s as companies created dedicated roles to maximize IT value creation. Thus, we now find similar segmentations in modern data teams. Data engineers focus on identifying, extracting, and transforming data. Scientists design and maintain data models that provide business recommendations. Analysts create reports to generate insights and visibility for business stakeholders, etc.
As a result, roles like data engineer are one or more steps away from direct communication with end users and lose connection to their requirements. At the same time, data scientists and analysts are cut off from data sources and lose sight of some technical aspects of data collection and transformation. Over time, as projects accumulate and team sizes grow, silos begin to appear, causing less end-to-end visibility to different stakeholders, spreading knowledge, and dispersing responsibility.
See also: Data Observability: A Modern Solution for Big Data
The growing number of data problems
Scaling value generation from data has also increased the number of data issues these teams have to deal with for a variety of reasons. For example, human errors, which can cause columns to be removed that make data incomplete, now have a much more significant impact and their propagation is more difficult to control. Additionally, regulatory changes (eg, biometric information can no longer be recorded at the CRM level) may require modifications to the way data is collected and processed, becoming a major source of unforeseen problems. .
In other cases, a specific business case (for example, redefinition of customer categories) may require changing data settings. While this change may initially seem insignificant, its impact on other reports and models that are based on the same data source will be more significant in a growing environment.
The combination of siled data teams and a growing number of data problems has catastrophic consequences for organizations. As team members struggle and waste time understanding where problems come from, who is responsible for them, and how to fix them, business stakeholders make the wrong decisions, the customer experience suffers, and the organization loses revenue.
The Drawbacks of Data Quality Solutions
However, after decades of using data quality solutions, they are known to be providing a partial solution to this challenge. Data quality solutions are designed to scan data at scheduled intervals to indicate whether it meets user requirements at some stages of the data value chain, but do not provide the contextual insights needed to understand where data quality problems are coming from. data and how the data team. could handle them in a timely manner. So, every time a problem is discovered, data teams must find when, where and how the problem occurred, which is days of work to find the root cause and fix the problem.
In the long run, frustration builds within data teams and their productivity declines. At the same time, business stakeholders, affected by the long time required to resolve data problems, lose confidence in the reliability of the data, and the ROI of data quality solutions plunges, given the amount of resources required. to configure and maintain them.
See also: Arguments in favor of a small data observability strategy
3 Ways Data Observability Solves Data Problems
Data observability provides data teams with insight into where problems are coming from and who is responsible for them. Data observability has three main characteristics.
- Data analysis in real time, so there is no delay between monitoring and use. Data teams can identify problems as they occur, reducing time to detection and preventing data users from dealing with them before producers know they exist.
- contextual information on data issues (eg, application, owner) to speed resolution time.
- Continuous data validation as data observability is part of the development to production lifecycle. This aspect significantly improves application reliability and prevents data incidents, lowering the total cost of ownership.
In addition to making it easier to manage data issues across the data landscape, data insights also improve communication within data teams. It provides information that simplifies exchanges between data producers and users who can clearly understand where the data comes from and its use in the data value chain. It also makes it easier to define SLAs at a granular level, breaking down silos and reinforcing a culture of accountability where roles and responsibilities are clearly defined.
Automatically update the data catalog
In parallel, data observability has the ability to provide information that complements the capabilities of the data catalog. Especially in complex data environments, the adoption and maintenance of the data catalog is one of the main challenges for the data management department and CDOs. The information automatically collected in a Data Observability platform is continuously synchronized with the data catalog, which reduces its maintenance costs and improves its accuracy, so users have the assurance that their decisions are made with reliable data.
In today’s data management landscape, data observability is critical for companies to put data at the center of their strategy. Going beyond the data quality ecosystem, it is becoming a crucial component of data landscapes that speeds data problem resolution, facilitates communication and collaboration among data professionals, strengthens data stacks, and increases the competitiveness of organizations.