Boosting value of DataOps through enhanced monitoring

By Thibaut Gourdel, Technical Product Marketing Manager, Talend.

  • 1 year ago Posted in

As the business landscape changes, data availability and an enterprise’s data needs change along with it. The ability to respond swiftly and decisively to problems and opportunities is critical to success, and data observability is a key part of this capability that must not be overlooked. According to Gartner research, bad, inaccurate or obfuscating data costs businesses an average of $12.9 million every year.

To understand the importance of data observability, we must first understand DataOps. DataOps is vital to any modern business of any size, even if smaller companies take a less formal approach. Whichever way DataOps fits into the organisation, a proactive and hands-on approach to the data best practices is key to maximising data ROI.

The field of DataOps is parallel to DevOps; the integration and streamlining of disparate teams and groups within software development pipelines. DevOps improves efficiency and responsiveness within the development process and maximizes the quality of the final product. DataOps brings this approach to an enterprise’s means of gathering, selecting, monitoring and using data. DevOps and DataOps both seek to remove the silo effect; teams operating without open intercommunication, leaving different aspects of the business unable to coordinate effectively. DataOps improves the visibility, reliability and actionability of data across the whole organisation.

Every company depends on detailed and reliable data to make important business decisions. Market statistics, resource monitoring and internal performance information are all sources of data that can show where a company needs to improve, which strengths to focus on and which threats need to be addressed. Although there is no such thing as too much good data, there are such things as inaccurate data, untimely data, superfluous data, misleading data or badly presented data.

DataOps sifts good data from bad, starting with the initial discovery of sources and the process of gathering data. The next step is to select data from these sources, prioritize it, and find the most effective way of presenting that data to the people who need to see it. Thus, we can see DataOps has well-defined data processes, just as DevOps does. Managing these processes and unlocking the full potential of DataOps relies on data observability.

Data observability in DataOps

Data observability gives data teams the resources to monitor and manage the business’ data and processes. Workplace culture, best practices and technological solutions such as automated monitoring systems and dashboards all play a role in this. The role of DataOps is to select the most useful data and maintain reliable sources, but with an ever-increasing surfeit of data available today, analysts effectively need data about the DataOps infrastructure itself.

Good data observability empowers DataOps to monitor the flow, the quality and the presentation of data for quick and effective responses to emerging issues, from source to point of use. Leadership and data technicians will have insight into data quality, accuracy and relevance; the lineage of the data from its original source through the DataOps pipeline; and how it is stored, moved and used within the organization. Observability facilitates truly effective data monitoring and provides team leaders awareness of uptime and issues in the data pipeline.

Most importantly, good data observability includes automatic notifications to all relevant staff when a problem begins to emerge, and guidance towards the best course of action. A trust score and other detailed metrics can allow for more effective automation and notification in data monitoring. Even ten minutes of downtime in the data pipeline can be devastating to an enterprise, and observability is the key to minimizing damage and preventing data disasters.

The importance of data observability today

Data operatives cannot afford to take a laissez-faire or reactive approach to monitoring data pipelines. If data professionals wait for a problem to present itself before taking any action, the damage has already been done. Proactive monitoring of the data flow will allow technicians to respond to emerging problems swiftly, fixing many issues before they can cause any damage to the company. Typical alerts issued by data observability could be that data has not arrived at the intended destination, such as Snowflake, or that data schema drift has been detected.

This can only be achieved in real-time with excellent data observability. While an enterprise may once have had hours or even days to respond to a fault in its data infrastructure, today’s businesses live and die by their ability to stay ahead of the competition. Every second of downtime in any business process can be costly, and losing access to data – or worse, relying on flawed data – can be catastrophic.

For example, data observability can manage schema drifts where changes are operated within the data sources that drive errors in the pipelines. Data observability enables data teams to monitor the schema and detect the changes when they happen so that they can prevent the impact on the business with data fed through the BI dashboards or visualisation tools. We can take the example of a change happening in a reference product series in the retail industry; if not monitored properly the change will impact the inventory and sales dashboards.

The big advantage of data observability is its ability to monitor data and infrastructures at the same time. Historically, data and IT teams were monitoring either the data or the infrastructure; however, now that data has become such critical in the processes, the entire environment will be monitored – data, processes and machines, guaranteeing a service level agreement of 99%.

In conclusion

Businesses have always needed data to inform decisions at every level of the organisation. External data – demographics, resources, trends and obstacles – gives leadership the information to determine long-term vision and business strategy. Internal data – employee productivity and wellbeing, the health of the company’s infrastructure and performance metrics – allows the organization to address its flaws, capitalize on its strengths and operate at its best.

DataOps is a relatively young field in business, and data observability is an even newer aspect of that field, but neither can be dismissed as a trend or buzzword. DataOps arose to answer a clear need for businesses to take a more collaborative approach to managing their data and to think more holistically about how data is communicated within the company. In turn, data observability is a response to the growing and urgent need for proactive management and monitoring within DataOps.

Today data observability is more deployed and adopted by young organisations on modern data stacks. Digital natives are also early adopters of data observability as they run business on data and with data. Larger organisations tend to do more traditional data quality monitoring but the market is evolving where more and more data players are adding data observability to their capabilities.

Every business must understand this need, even if the organisation is not yet able to quantify it. A Gartner survey found that at least 60% of businesses don’t measure exactly how much they lose due to bad or misused data, leaving management in the dark about what DataOps can really offer. Research by Forrester indicates that 40% of a data analyst’s time goes to dealing with data issues. Many businesses have finally woken up to the need for DataOps, but without observability, DataOps will always be hamstrung. The key to a business’ success is well-informed decision-making; the key to making decisions is DataOps; the key to accurate DataOps is data observability.

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