There's a common thread uniting many of the most notable data analytics trends—namely, they center around technologies or practices that help enterprises optimally leverage their data faster. That's exactly what can be achieved with in-database analytics, which bring more efficiency, consistency, and effectiveness to enterprise data management.
Understanding in-database analytics
If you're capable of running complex analytics operations on a database or a data warehouse without relying on a separate analytics application outside of the database, you're using in-database analytics. Sophisticated algorithms, such as those found within machine learning (ML), drive the analytics logic that exists within the database technology of the warehouse.
It's incredibly time intensive to move data from a database to an outside application, and in-database analytics removes this obstacle. There is no need to wait for source data to be transformed in any way, as would often be necessary if using a standalone analytics app to read large data sets. Essentially, this means that high-level analysis can begin within the database or warehousing tool as soon as data is ingested.
Two functions within data warehouses and database management system (DBMS) platforms are essential to the success of in-database analytics:
- Partitioning: Data is broken up into a series of individual parts.
- Parallel processing: Each of those parts is then assigned to specific processing segments, to speed up overall transaction processing and deliver faster query results.
These two steps have a symbiotic relationship. Similarly, with in-database analytics, data marts—which, traditionally, are separate from databases, and devoted to very specific departments or subjects—can be consolidated within the data warehousing platform. As a result, the platform's analytics capabilities can reach even the most far-flung elements of your enterprise's data ecosystem and mine them for valuable insights.
Benefits of internal database analytics
Massive time savings is arguably the most compelling benefit of in-database analytics. Instead of bringing data to the analytics, you're bringing the analytics to the data, allowing for simplified and consolidated data management.
Gone are the hassles of data wrangling, in which sets of raw data have to be manually cleaned, deduplicated, and transformed into new formats just to be cataloged and analyzed. Because it's not necessary for you to make copies of data while using a warehouse that's equipped with in-database analytics, you aren't allowing redundant silos to form or creating data waste of any kind.
Using a platform equipped with in-database analytics also allows you to establish a standardized approach to analytics, one that is just as compatible with business users' data tools as it is with the more advanced analytics technologies of data scientists. The data warehousing tool can thus genuinely function as a single source of truth throughout the enterprise.
In-database analytics use cases
Because in-database analytics allow for the analysis of large data sets that would ordinarily be impractical to extract from other sources, the process can be integral to numerous operations.
- Pattern recognition: Driven by cutting-edge ML algorithms, pattern recognition searches for patterns in structured and unstructured data—everything from text and numerical tables to images and sounds. It's useful in image processing, document analysis, and many other applications.
- Fraud detection: The processing speed that's possible through in-database analytics allows for real- or near-real-time analysis, which is crucial in identifying payment card transactions that may be fraudulent.
- Balanced scorecard analysis: This method examines an enterprise's financial metrics alongside measurements of customer satisfaction, innovation, and internal business performance. Such a nuanced view can be realized more quickly than ever through in-database analytics.
- Ad hoc reporting: Using a data management platform capable of in-database analytics, business users without data science expertise can speedily analyze data that's gathered from numerous sources and create reports on specific pressing issues.
Improve warehousing and analytics with the right technology
To realize the greatest possible advantage from in-database analytics, your enterprise needs a solution that unites data warehousing functionality and analytics capabilities under one roof—and that's exactly what Teradata VantageTM offers. The platform integrates data from across all sources, ranging from the data lake to your newest applications at the edge, and uses multiple data processing engines to deliver actionable insights faster, more reliably, and at scale. You can use 100% of your data for in-database analytics with Vantage.
Vantage is compatible with cloud solutions from major providers like AWS, Microsoft Azure, and Google Cloud. It's built for the burgeoning cloud-first era of enterprise analytics but also offers support for those who maintain on-premises infrastructure as part of a hybrid cloud deployment.
To learn more about the analytics strengths of Vantage, review the 2022 Gartner Critical Capabilities report, in which Teradata ranks highest in all four analytical use cases examined in the research.