Analytic Data Processing -- What is it?

Although the end-game is a dashboard or scorecard, you only achieve a win if you show data that’s been processed analytically.

Analytic data processing starts by querying a database which then converts plain values into more refined values, fixes cryptic text values, and performs filtering based on the revisions. Typical operations include:

• Convert and normalize text values;
• Show values as comparisons to various forms of summary values;
• show values as ranks, quartiles, and variance from a larger set; and
• Filter and re-group revised values until data is small and focused, suitable for user consumption;

Now for the most important consideration: Analytic operations should be iterative and sequential, where the output of one operation forms the input to the next. This is necessary to produce a successful dashboard.

A limited subset of analytic data processing should be available to the end user while they are using the dashboard.

Dashboard Requirements

The dashboard itself should have the ability to sort and filter where applicable. Typically, the user will have some control over: geographic filters, date ranges, and organizational filters.

Dashboards should be able to launch a more in-depth user interface to allow the user to see the detail of what’s being shown on the dashboard.

Once in the more detailed mode, the user might use that as a starting point for creating a new metric that shows on the dashboard, or to change the appearance of the dashboard.

You can never be too thin

At one megabyte total download size, nextanalytics is the leader in small-footprint business intelligence analytics, a perfect size for inclusion with other applications.

Small size translates to big cost savings in training, implementation, deployment, and support overhead.  

You can never be too rich

Despite being small size, nextanalytics has the data processing capabilities you require. Our many online examples and documentation are testimony to the breadth of what you can perform.

Specialized for the ISV and solution provider, our open source user interfaces can be delivered as-is, or customized.

How does it work?

Nextanalytics delivers speed and scalability through in-memory column-oriented data processing and proprietary disk caching.

• Column oriented processing provides olap without cube overhead, complex SQL and table proliferation, or high risk spreadsheets.
  
• In-memory analytics save unnecessary work in terms of not having to create and maintain many cubes, databases, and tables.

Proprietary disk caching delivers the ability to handle production data volumes on commodity class desktops with no need to pay high license fees to mainstream database and BI vendors.

Disk caching also reduces the need for reporting databases, table creation of subsets of data, and an overall reliance on DBAs and SQL authors to be involved in reporting tasks.