The notion of just-in-time (JIT) analytics is one of the latest yet most significant developments throughout the data landscape. This novel concept not only eliminates many of the conventional limits that have hampered enterprise use of analytics, but also spurs a newfound capability for continuous data intelligence that drastically increases the value derived from analyzing data.
JIT analytics enables organizations to connect to, query, search, integrate and analyze their data wherever it is without moving or copying it. As the just-in-time name implies, firms can do so at the moment business processes demand such insight in a dynamic, seamless fashion.
Bolstered by a small but rapidly growing series of startups that directly support some of the biggest data management trends at the moment (including data fabrics and data mesh), just-in-time analytics revolutionizes traditional analytics with a more comprehensive approach.
Consequently, they can forsake many of the laborious, time-consuming and expensive infrastructural investments in elaborate data pipelines predicated on replicating data throughout their respective ecosystems. Instead, just-in-time analytics lets users increase flexibility, speed of analysis and, most tellingly, input business logic at the data layer instead of locking it into the storage layer in endless silos.
Since data-driven decision-making and analytics are at the core of competing in today’s knowledge economy, software vendors are racing to see who can drive down the time and cost of generating insights based on data. As a result, the just-in-time analytics phenomenon is outdistancing older techniques by leaps and bounds.
The End of Data Consolidation
JIT analytics signals an end to the era of data consolidation in which data management was based on organizations moving data into warehouses, data lakes and data lakehouses.
By leveraging a variety of approaches, including data materialization, query tool abstractions, and data virtualization, this new analytics era obsoletes the need to move data to store it in one place for analysis.
JIT represents a substantial evolution in fundamental data strategy that not only results in better, timelier analytics, but protects the enterprise from numerous shortfalls. Costs (related to data pipelines, manual code generation, etc.) are reduced, boosting resource conservation.
Moreover, organizations decrease a considerable amount of risk related to regulations and data privacy by not constantly copying data from one setting to another, which potentially exposes it to pricy noncompliance penalties.
JIT represents a substantial evolution in fundamental data strategy that not only results in better, timelier analytics, but protects the enterprise from numerous shortfalls.
With so much data scattered throughout multicloud, hybrid cloud and poly cloud deployments, this benefit is invaluable. Best of all, the business logic — schema, definitions, end-user understanding of data’s relation to business objectives — is no longer locked in silos but is transported to the data layer for greater transparency and usefulness.
With most just-in-time analytics approaches, there’s still some data movement. But it’s minimal, well documented, and only happens when a business process warrants it — as opposed to copying all data from place to place before it’s used.
Overall Analytics Improvement
Perhaps the most noticeable distinction about connecting to and analyzing data is both the amount of data involved in (and the quality of) the results.
There’s a causal relationship between these effects because the more data users have to analyze about a particular situation or use case, the easier the analytics become. This is one of the reasons data virtualization techniques are becoming more popular for JIT analytics.
With this approach, users can connect to all their sources in a holistic data fabric to expand the amount of data required for a use case (like building machine learning models), the variety of that data, and the number of features that can be derived for real-time insights.
When there’s an abundance of diverse data, users don’t need a fancy algorithm to accurately train machine learning models for attrition. In fact, when data virtualization techniques are bolstered by query federation and a semantic knowledge graph data model, organizations can broaden the array of forms their analysis takes.
Access to enough clean, timely data can reduce a lot of analytics problems to simply query answering. In life sciences, for instance, just by connecting to enough data, even global vendors can manage their worldwide supply chain needs with a query that greatly reduces the analytics complexity of this time-honored problem.
Additionally, the semantic standards of the knowledge graph data model and its business terminology support the semantic search for rapidly sifting through data for data discovery and other use cases.
Time to Value
In addition to the quality of analytics produced and the different forms of analysis JIT analytics supports, its chief value proposition is its time to value.
When users are continuously replicating data from place to place, they’re essentially creating silos that require more time (and make it harder) to integrate sources for loading applications or analytics. Changing business requirements and new sources can break existing schema, forcing data modelers to spend lengthy time periods recalibrating models while fleeting business opportunities pass.
The combination of data virtualization, query federation, and graph data models allow organizations to swiftly combine different schema via techniques for automating the logical inferences required to do so at the moment integration is needed.
Thus, organizations can integrate data in real-time for their analytics or application needs to get answers faster and in the hands of business users who profit from them.
The current focus of the investor and data vendor startup communities on just-in-time analytics may be relatively small now, but it’s moving at a rapid pace that’s equal to that of business in today’s knowledge economy.
These analytics approaches reduce costs and time to value while increasing the tangible business utility that analytics produces. Consequently, organizations can profit from analytics more than ever before while making the overall process more affordable with much faster time to value.
Feature image via Pixabay.