Modern companies are under intense pressure to accelerate digital transformation (DX). Simply put, to compete and win, organizations need to “up their data game” by leveraging analytics to optimize all aspects of business. They are rapidly turning to data observability to help them.
So how do you up your data game? Take the same approach as you take with other lines of business: leverage analytics to improve data operations so data operations can deliver superior analytics for the rest of the company. Operational data excellence can be achieved by focusing on objectives that are common to other lines of business, including:
When you think about all the elements of a data environment, three words come to mind: volume, variety, and velocity. The “3 Vs” that are so essential to the effective use of data can be applied to a business context, too. There’s the volume of people, including employees, customers, suppliers and partners. Then there’s the variety of individual processes and decisions that each of them makes every day. Finally, there’s the velocity of people and processes executing in real-time. In that sense, digital transformation is not one thing—it’s more like a million events, processes, and artifacts all trying to work together.
Complex data and analytics environments are needed to inform and automate all of this activity, and complexity makes it challenging to operate reliably, at scale, and under budget. If done well, you transform faster with superior outcomes, beating the competition. If done poorly, millions of things can be negatively impacted, budgets get blown, or both.
While data observability is a fairly recent concept, observability, in general, is not new. Data observability approaches have modernized the Application Performance Monitoring (APM) space in recent years. Observability outside of IT is much older than that. It comes from control theory and provides a scientific approach to managing complex, dynamic, or opaque systems (and data operations are all three!).
Manufacturing provides an interesting example of observability. Imagine pointing a heat-sensing smart camera at a piece of machinery to detect when any visible (observable) part in the system is running hot. Contrast this with installing a heat sensor in every part of the system, which is expensive and actually introduces more potential points of failure (many heat sensors vs. one smart camera). It’s clearly not practical to put a heat sensor in every part, and you don’t always know which part may overheat and fail. The smart camera can observe the known and the “unknown unknowns.” In sum, observe everything and engineer as needed.
Data observability takes a similar approach by combining monitoring, analytics, and automation to drive improvements in data operations by helping teams observe the ”unknown unknowns”. It follows these key actions:
Next, let’s walk through examples of how data observability supports objectives of improving reliability, increasing scalability, and realizing cost effectiveness.
Reliability requires comprehensive risk coverage and the ability to predict and prevent incidents. Unfortunately, many organizations have gaps in risk coverage, and they operate in a reactive break-fix mode versus a preventative mode. Observability helps on both fronts.
- Correlation of information simplifies troubleshooting to minimize downtime.
- Recommendation engines prescribe corrective actions.
- Trending analysis can predict future incidents, including performance trends, throughput, or even trends in data content (data drift) that affect the accuracy of AI and ML.
Here are examples of how Data observability helps organizations scale innovation with data by eliminating friction points in design, development, and deployment.
Analytics derived from data, processing, and pipelines can generate numerous insights with which an organization can optimize for resource planning, labor allocation, and strategy.
Get a demo of the Acceldata platform to see how you can “up your data game” and help your data ops teams deliver trusted, reliable data across your organization.