Analysis and reporting with DataLever™

DataLever is not primarily an analytic tool. Yet DataLever users often find themselves replacing their custom C programs, PERL scripts, and AWK scripts with DataLever engines that run faster by orders of magnitude, require no coding, and handle millions and millions of records. Using DataLever's predefined functions, you can generate frequency counts, rankings, summaries, RFM analyses, and many other useful metrics.

Summarizing

DataLever makes it simple to summarize profiled data. For example, suppose you have a sales history file containing Customer, Territory, Product, and Amount fields. You could group by Customer and Product, while totaling Amount, giving you a summary of sales by customer and product.

Summaries can take many forms, including frequency counts and pre-aggregated tables to assist in reports on large databases. You can group on multiple fields, produce running summaries, and even handle transpositions!

Validating data

Verify your assumptions and validate your data by obtaining quick counts and totals. For example, you might summarize a sales history by territory. This gives you immediate feedback about the quality of your sales data, with answers to such questions as: "Are there any sales assigned to non-existent territories?" "Do all my totals look right?" "How many sales were made in each territory?" "What was the average sale amount in each territory?", and so on.

Reporting

DataLever lets you create structured reports that incorporate formatted records, organized by outline level and heading. Because it can pull many pieces of information together into a coherent whole, the reporting tool is especially useful for creating an integrated report of a data quality assessment. By combining multiple report tools in a project, you create one or more formatted reports, pulling together multiple pieces of information in an attractive and easy-to-read format.