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Data workflows have become increasingly critical in an information-driven, location-independent workforce. To highlight just how important they are, consider that 100% of enterprises are either actively pursuing or planning to pursue better data management initiatives. Despite the importance of these strategies, they are often viewed as tedious processes that enable critical workflows rather than a valuable pipeline of insight. 

Businesses need to understand how data observability can help extract insight from previously untapped sources. This article will explain what data observability is and how businesses can achieve it with an effective data management strategy.

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What Is Data Observability?

Data observability describes the extent to which businesses understand and manage the health of their data across their organization. This can refer to stored operational data, newly collected unstructured data, recently transferred data, and all other types of data that is collected, analyzed, and used for important decision-making. Undetected issues or errors can have widespread effects on the business, ranging from unintentional and unexpected downtime to loss of productivity. Data observability gives businesses a holistic overview of their data to highlight and resolve issues with the most reliable, high-quality data. 

Figure 1 Data Observability

How Data Observability Improves Critical Data Workflows and Increases Data Quality

Get the Insights You Need to Improve Data Quality

Data observability allows you to see data as it moves through your pipeline and understand how it changes. This is done by recording where data comes from, monitoring its quality, and logging how applications process it. Data teams can use this information to prevent issues such as inconsistency, inaccuracy, incompleteness, duplication, or obsolescence. With the level of understanding that comes from data observability, businesses can detect issues with data early and prevent them from becoming bigger problems.

Data observability also helps to automate data quality management tasks. Businesses that don’t have large data teams to manage the information they generate can often find themselves dealing with a significant backlog of data management tasks. These can include time intensive tasks such as cleaning large data sets and managing replicated data points. Despite their resource intensive nature, these tasks must be done to maintain data quality, which is critical for any organization that intends to extract actionable insights from its data. Data observability allows businesses to automate data quality management and easily identify when these automations are not performing at the optimal level. 

Augment Performance and Production Management

One key reason businesses introduce widespread data collection and analysis is to ensure that their organization is performing at optimal levels at all times. Fully observable operational data can aid business leaders in identifying inefficiencies. Analyzing such data can also recommend the right course of action that will help teams reach higher levels of performance and output. 

Analyze Trends to Understand the Underlying Causes of Data Issues and Outages

As more businesses rely on data to conduct daily activities, high quality data management is crucial for business continuity. Any outages or data issues can have widespread effects on business operations and customer experience—businesses that provide public services can even be fined should outages affect the end consumer. Data observability allows businesses to understand underlying problems and either proactively solve them or prevent them from occurring again. 

Building the Critical Components of a Data Management Strategy

Provide the Right Tools to Allow Non-technical Staff to Contribute to Data Processes

Successful data programs rely heavily on the active participation of team members across the organization. However, it can be difficult to achieve buy-in from non-technical team members who might find data processes complicated and cumbersome. Modern tools and automated data pipelines can make it easy for everyone in an organization to contribute to a holistic overview of their business. Business leaders must make these tools available and easily accessible for everyone who should be contributing information to common data repositories. 

A Series of Techniques to Analyze Data

Data teams can help business leaders collect, store, and analyze important operational and administrative data. However, data is only as valuable as the insights it can generate. This is why it is important for organizations to consider the techniques they use to extract and deliver these insights. Businesses must evaluate their priorities and choose the techniques that most align with their needs. 

Proper Auditing Processes to Ensure High Data Quality

Once key data processes have been established, businesses must avoid complacency. Businesses that adopt a “set it and forget it” approach to data management can miss data quality issues that can escalate quickly and lead to business decision-making being performed with incomplete or erroneous data. Effective data auditing processes help businesses avoid this by proactively identifying potential issues and ensuring that critical information is free from mistakes or inaccuracies. 

 

Data observability can help businesses go beyond simply collecting and storing operational and administrative information. Organizations that build and maintain a holistic view of their operation are significantly more effective and efficient and are better able to identify and resolve data issues quickly. This often translates to doing the same for operational blind spots and inefficiencies. With data tools becoming more sophisticated, data observability has never been more achievable for businesses of all sizes. 

However, achieving data observability requires work, necessitating that businesses take a proactive and holistic approach to data management. This includes putting the right tools in place to allow non-technical staff to contribute, using the right techniques to extract valuable insights, and having proper auditing processes in place. By taking these steps, businesses can improve their data quality, increase their efficiency, and make better decisions.

 

Written by Loretta Jones

Loretta Jones is VP growth at Acceldata.io with extensive experience marketing to SMBs, mid market companies and enterprise organizations. She is a self proclaimed 'startup junkie’ and enjoys growing early stage startups. She studied Psychology at Brown University and credits this major to successful marketing as well as navigating a career in Silicon Valley. She’s a nature lover and typically schedules her vacations around the migratory patterns of whales and large ocean creatures.

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