Has Google’s Gemini AI Just Killed ChatGPT? A Comparison
According to research from Exploding Topics, approximately 328.77 million terabytes of data is created every day. This data resides in multiple locations, such as legacy on-premises systems, cloud applications, and hybrid environments. It also includes streaming data from smart devices, Internet of Things (IoT) sensors, mobile trace data, and more.
Today, systems and data sources are more connected than ever before, which can often lead to complexity and risk. What may seem like a simple change to one data source can result in serious consequences and a broken data pipeline. It can also bring operational systems to a halt or cause executive dashboards to fail. To avoid this and ensure data is successfully managed at scale, organisations should adopt data observability as part of an overall data integrity strategy. This enables businesses to rely on the accuracy, consistency, and context of their data – empowering confident decision-making across the organisation.
Defining data observability
Traditional methods of managing data quality simply do not work in today’s digital age. Manually identifying and solving problems is too time-consuming; especially given the ever-growing volume of data many businesses are dealing with.
Business leaders may find themselves wondering whether the data flowing throughout the organisation is ready to be used. This is an important question to ask, as every department relies on data for different reasons. For example, operations managers need to be able to rely on downstream analytics to drive key business decisions. Top executives who want an overview of how the company is performing for key stakeholders, need to know they can trust the information they have been given.
The term ‘observability’ itself has been a key element of numerous process methodologies within sectors, such as manufacturing and software development. Essentially, data observability ensures the reliability of an organisation’s processes and analytics by alerting any problems as soon as they occur. This prevents further issues from happening and enables users to visualise data processes to quickly identify deviations from typical patterns.
Data observability can be broken down into three key capabilities: discovery, analysis, and action. ‘Discovery’ is the collection of information about data assets using a variety of techniques and tools. ‘Analysis’ is identifying any events that have the potential to adversely affect data integrity. ‘Action’ is proactively resolving data issues to maintain and improve data integrity at scale. The best data observability tools incorporate artificial intelligence (AI) to identify and prioritise potential issues.
Data observability and data quality
Although data observability and data quality complement one another, there are some important differentiations. Data quality focuses on clearly defined business rules and analysing individual records and data sets to determine whether or not they conform to said rules. For example, customer records should be consistent across all systems and databases, especially if they hold sensitive or personal information.
On the other hand, data observability incorporates a proactive element of anomaly detection before data quality rules are applied. For example, if the volume of data changes suddenly and unexpectedly, it is important to know how and why it has happened. A sudden spike in certain values could indicate an upstream issue with the data and also display longer-term trends in the data. This enables recommending targeted data quality business rules for ongoing data integrity actions.
To get the best value from data observability, organisations should implement products that include an integrated data catalogue. This will often provide a single searchable inventory of data assets and allow technical users to easily search, explore, and understand their data. Additionally, it enables key users to visualise the relationships among various data sets and to clearly understand data lineage. It has never been more important for organisations to have an integrated data catalogue which provides collaboration tools such as commenting capabilities, monitoring, auditing, certifying, and tracking data across its entire lifecycle.
By introducing data observability, organisations can understand the overall health of their data, reduce the risks associated with erroneous analytics, and proactively solve problems by addressing their root causes. As businesses increasingly take steps towards cloud transformation - modernising their data environments to support advanced analytics and drive more powerful decision-making - the ability to trust their data will become ever more critical. Organisations that adopt data observability as part of a robust data integrity strategy will, ultimately, be the ones ahead of the curve.