Real-Time and Big Data Challenge Data Management Best Practices
The incremental movement toward real-time operation is the most influential trend today in data-driven IT disciplines such as business intelligence (BI), data warehousing (DW), and data integration (DI). From a technology viewpoint, collecting, processing, and delivering data is hard enough; doing it in real time requires effort that is downright Herculean. Thanks to the big data phenomenon, the volume of data continues to swell, exacerbating the situation.
When faced with the challenge of making BI/DW/DI solutions operate in real time, the temptation among technical personnel is to omit or reduce some of the best practices of data management in hopes that the simplification will speed up processing. Likewise, new practices around analytics with big data are almost devoid of data management best practices such as data profiling, transformation, quality, and enhancement, as well as improvements to metadata and master data. The unfortunate consequence is that most data sets delivered in real time today are of minimal quality, richness, and auditability, as compared to data found in the average data warehouse. Data sets delivered as the output of big data analytics are in an even sorrier state.
In this Webinar, you’ll learn:
- How real-time and big data have evolved into compelling business requirements
- Why satisfying these requirements shouldn’t lead to scrimping on data management practices (with a few exceptions)
- Adverse effects on the business from the poor condition of data
- Technologies and tool types for managing real-time data and big data properly
- A data management strategy based on “doing it all,” instead of omissions and trade-offs
Philip Russom, Ph.D.