When it comes to today’s hottest business currency — that is, data — certain processes are an absolute necessity to raise its value to your organization. On the top of the list are the processes that allow you to mine the assets — buried insights and indicators — that are hidden among those diverse data stores scattered throughout your enterprise.

ETL, or ‘Extract, Transform and Load’, describes the steps it takes to connect relevant data, transform it into a usable whole, and contain it within a unified, homogeneous data warehouse to provide usability for reporting, analysis, summaries, calculations, trend-watching, and more. Since many of these are the keys to driving a business forward in as straight a line as possible, the results often boost profitability in a very short time.

ETL begins by extracting data from a variety of internal and external sources, on an assortment of platforms. The data is transformed through joining, filtering, standardizing, and merging as necessary so that the resulting collection can be accessed uniformly, consistently and reliably. Once loaded, the result is a single data warehouse that can supply new insights, metrics and key indicators to executive and team dashboards so they can monitor and control everything from strategy to operations.

Ideally, the transform stage includes the following operations:

Data Standardization
This feature ensures that your data is consistent and that you can rectify the inevitable data-entry errors or discrepancies in content or format from one department to the next. For example, data standardization would convert every entry of “UK,” “U.K.,”or “uk” to “United Kingdom.”

Data Normalization
The feature limits parameters of your data by re-aligning data within certain ranges, date periods and values.
For example, empty data fields and dates written in different formats are filled and formatted uniformly.

Data Transformation
Using familiar functions and formulas to transform your data any way you want, this features allows you to add calculated columns and filters, and to group or change the data structure. You won’t need to perform formulas on your raw data source anymore.

Data Merging
The ability to combine data from multiple data sources into a single resource allows the ability to view data in a variety of scenarios such as cause –and-effect, actual-versus-target, among others.

If you were able go back in time, and bring your wisdom and today’s technology with you, surely you would collect your data with conventions for consistency so you could easily access and quickly view the most actionable, insightful metrics possible. ETL lets you do that starting now.

ClicData Data Warehouse and Dashboard Software includes standardization, normalization, transformation and merging features that are as easy to use as cut-and-paste. Discover the intuitive design and sophisticated power of ClicData.