Recent advances in data science—particularly parallel processing platforms capable of interrogating massive datasets in near-real time—are changing what’s possible. Here’s how DevOps teams can take advantage of these capabilities to increase the value of organizational data.

Why Time-to-Insight Matters

Parallel processing improves analytics along two important dimensions: speed and curiosity.

Speed: When organizations can process billions of rows of data in milliseconds, the value of that data changes fundamentally. In financial services, for example, the difference between a query answered in seconds versus one answered in a fraction of a second can have a direct impact on business outcomes. Latency is not just a technical concern—it has real bottom-line consequences.

Curiosity: Conventional analytics follows a simple loop: ask a question, get an answer. But when query response times are near-instantaneous, analysts can ask a question, receive an answer, and immediately ask three follow-up questions—without any time penalty. This transforms the analytics experience. Without speed and agility, decision-makers often fall into the habit of asking only the questions they already know the system can handle quickly, rather than exploring harder, more valuable questions.

DevOps teams that leverage parallel processing can empower their organizations to explore data more deeply and raise its overall value. Three approaches stand out.

1. Use Multiple Processors to Scale Analytics Workloads

A central data repository enables analytics across the organization, especially where business intelligence (BI) tools are heavily used. But centralization places significant performance demands on DevOps teams. Parallel processing is a practical solution to this challenge.

Parallelized code allows a processor to compute multiple data items simultaneously. This is essential for getting optimal performance from GPUs, which contain thousands of execution units. However, the benefits extend to hybrid CPU/GPU environments and even CPU-only systems—modern CPUs increasingly include wide execution units capable of processing multiple data items at once, and parallelized computation can meaningfully improve query performance in these environments as well.

Accelerated analytics platforms can also distribute single queries across more than one physical host when datasets are too large for a single machine. These platforms can scale to tens or hundreds of billions of rows on a single node and can run across multiple nodes. Because data loading can be done concurrently, load times improve roughly linearly with the number of nodes added.

2. Connect Data Silos for Deeper Organizational Understanding

Some of the most valuable insights come not from analyzing one dataset in isolation, but from understanding how datasets relate to each other. When data silos are connected, decision-makers can treat the organization’s entire data estate as a single, interconnected resource—rather than investigating each silo separately and trying to mentally reconcile the results.

The advantages extend further when internal datasets are combined with external sources. Analysts might want to understand how demographic shifts affect retail performance, or how weather patterns influence public safety metrics. These cross-domain questions are only practical to explore when data from disparate sources can be queried together quickly and reliably.

DevOps teams play a critical role here by building the pipelines, APIs, and data integration layers that make connected analytics possible. Selecting platforms that support federated queries or in-database analytics across heterogeneous sources is a key architectural decision.

3. Use Advanced Analytics to Strengthen Datacenter Security

Cybersecurity is a well-understood priority in DevOps, but keeping pace with the volume and speed of security data is a persistent challenge. Log files alone can grow to billions of rows per day, making real-time analysis with conventional tools impractical.

Pulling security data into an accelerated analytics environment allows security teams to query log files and other security datasets in near-real time. Dashboards built on this capability let staff identify anomalies and respond to incidents far faster than batch-processing approaches allow.

Spatiotemporal analysis—the ability to combine and analyze time and location data simultaneously—is particularly valuable for intrusion forensics. Understanding not just when an attack occurred but where it originated gives security teams actionable context to contain or prevent further damage. DevOps engineers can support this by ensuring security telemetry is collected in structured, queryable formats and routed to analytics platforms that support geospatial and time-series queries.

Rethinking What Organizational Data Can Do

Despite significant investments in big data infrastructure, many organizations still don’t turn to their data repositories to answer their hardest questions. Teams often assume their platforms are too slow, their datasets too unwieldy, or the effort too high to be worthwhile. As a result, they limit themselves to asking only the questions they already know can be answered quickly.

The opportunity for DevOps is to restore confidence in data as a strategic asset. By selecting and building platforms that offer the speed, scalability, and interoperability needed for advanced analytics, DevOps teams can help their organizations move from asking safe questions to asking the right ones—even when the answers are harder to find.

The data is almost certainly more capable than most organizations assume. The limiting factor is rarely the data itself—it’s whether the infrastructure and culture exist to ask ambitious questions of it.

This article is based on a piece originally published on devops.com.