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.
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:
- Reliability: Delivery should be trouble-free, on time, and high quality—the same as you’d expect from any other line of business, say manufacturing.
- Scalability: Eliminate bottlenecks to growth and speed, just as you’d want from your supply chain.
- Cost Effectiveness: Reduce inefficiencies, improve productivity, and minimize waste through better management, tooling, and automation.
Why Is Digital Transformation So Hard?
When you stop and think about all the people and processes across the business, 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.
What’s Needed: Data Observability
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:
- Monitor: Capture a wide array of information from data, processing, and pipelines to gain a 360-degree view of data operations.
- Analyze: There's a wealth of insight that can be inferred from these observations to make data more reliable, scalable, and cost effective. Many of these insights are difficult or impossible to capture using other approaches.
- Act: Insights can then inform or enable automated action through a data observability platform or other engineering approaches and technologies.
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.
- Monitor: The more you monitor, the more risk you can avoid. This includes things like processing performance metrics, data movement and reconciliation, and structural changes (schema drift). Recommendation engines can identify which things to monitor and how to eliminate blind spots.
- Analyze: Monitoring alone falls short in that it cannot explain why something failed or predict future incidents. Analytics can address these gaps:
- 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.
- Act: Thresholds can be defined to avoid over/under reporting of incidents. However, the best approach is to automate actions for self-healing and self-tuning, which minimize or avoid downtime altogether.
Here are examples of how Data observability helps organizations scale innovation with data by eliminating friction points in design, development, and deployment.
- Design-to-Cost: Compare the cost of different architectures running at scale. Save time and money by avoiding or refactoring solutions that cost too much.
- Data Democracy: Scale data usage and accelerate development with self-service data discovery to save time in collecting data for new solutions.
- Fail Fast & Scale Fast: Configuration recommendations, simulation, and bottleneck analysis simplify scaling for R&D (fail fast) and production (scale fast).
Analytics derived from data, processing, and pipelines can generate numerous insights with which an organization can optimize for resource planning, labor allocation, and strategy.
- Resource Utilization: Breaking down silos, archiving unused data, consolidating or eliminating redundant data and processes, overprovisioning, and misconfiguration.
- Labor Reduction: Machine learning automation can reduce labor costs for multiple functions, from platform management to data governance. This is accomplished by automating manual tasks or simplifying tasks to lower the skills required.
- Strategy: Comparing costs across data pipelines can ensure that data investments are optimized for the biggest business benefits—now and in the future. This can be achieved with integration and analytics on utilization and pricing data.
Take the Next Step To 'Up Your Data Game'
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.