Six Data Reliability Trends to keep your Eyes on

In the past decade, data reliability has grown from a minor technical detail to a major concern at the boardroom level. As we look ahead, data management strategies will gain momentum, propelled by technical advances and innovation. Here are six pivotal trends likely to influence the landscape of data reliability over the next few years.

DataOps, or the practice of marrying data engineering to data quality management, is about to take center stage. As data processes expand, so do the challenges related to managing and ensuring data’s reliability. This paves the way for Data Reliability Engineering (DRE), a specialized segment within DataOps. DRE emphasizes the fusion of best practices, tools, and automation to enhance quality and reduce manual work. Businesses will need to invest in training and upskilling their workforce on DRE best practices.

Machine learning’s (ML) role in data processes is on the rise. ML can pinpoint anomalies, discern patterns, and rectify inconsistencies. As these models become more advanced, they’ll adeptly manage diverse data, including multimedia and raw text. Advancements in ML promise unforeseen levels of data reliability across different data sets. Moreover, we anticipate the creation of ML tools specifically designed for data reliability.

Real data reliability requires tracing data’s origin and trajectory. As data volumes soar, the importance of understanding data lineage and provenance grows. Organizations increasingly need a way to map data’s journey from source to consumption. The demand for such a solution on the market will drive a plethora of accessible data reliability features and products, so that data reliability is an option for organizations that don’t necessarily have enterprise-level budgets.

In the upcoming years, trustworthy data will be synonymous with data that is not just current and accurate, but also compliant and secure. The distinctions between data controllers and processors will become even more critical. Reliability tools, which typically interface with data storage solutions, are strategically placed to introduce more stringent privacy measures, ensuring data is used and retained with proper consent.

Instant decision-making necessitates rapid data processing and analysis. But this swift pace brings its own set of challenges in ensuring data quality. Thus, novel strategies and tools for real-time data quality assurance, like on-the-fly data validation and anomaly detection, will become more prevalent.

Also, Large Language Models (LLMs), like GPT-4, are set to revolutionize interactions with databases. Rather than intricate manual operations, users can instruct LLMs in simple language. For instance, instead of a manual deep dive, an LLM can be directed to perform data checks. Acting as a smart bridge between users and databases, LLMs make data processes straightforward and accessible, allowing non-specialists to take charge of data quality.

Introduced by thought leaders like Chad Sanderson and Andrew Jones, data contracts outline the structure, nature, and quality of data when produced. Viewing data akin to a versioned API, these contracts shift some data quality responsibilities to data creators or app teams. They streamline data quality expectations, boosting confidence in the data being used. Their prevalence is likely to surge in the future.

In essence, as the landscape of data evolves, so do the methods and technologies that ensure its reliability. Businesses and tech professionals alike must stay abreast of these trends to make the most of their data assets.