With the advent of “big data” analytics, enhanced business intelligence software and complex online data processing, the ability to gather data that can be used to take immediate action is becoming a must have for most businesses. After all, making decisions on the fly is an important tactical advantage.
But companies that are looking to move from the world of batch processing to one where instant result tallying becomes the norm will have to plan accordingly. It will take forethought, technology and a good sense of deliverable value to breed success. Bringing real-time processing and data integration into corporate Oracle databases can get complicated. Building the business case can be straightforward, however, especially if IT professionals can fully explain to the business side the multitude of advantages real time offers.
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Examples include the following:
- The ability to instantly react to customer demands;
- Process intelligence related to fluid situations (such as financial transactions and stock-related purchases);
- Enhanced responsiveness during a manufacturing process; and
- Improved ability to deal with product routing (delivery issues, traffic, shipping concerns).
A little more detail is necessary before venturing down the path of wholesale change. IT pros can start with a simple question: Why is real-time data so crucial? The answer lies with need to support operational users and influence what should happen next. To accomplish that, the enterprise data warehouse needs to know what is happening right now, in real time.
While many data integration technologies serve the data acquisition needs of a data warehouse, IT organizations are still evaluating a range of approaches to meet the demand for low-latency data. Some of those approaches include intraday batch extract, transform and load, or ETL, processes; minibatches; enterprise application integration; and real-time change data capture techniques.
With so many approaches available, the challenge becomes how to determine what will meet the need for current data collection yet still support the anticipated move to operational data warehousing of real-time data.
Of course, real-time data processing has been around for some time, used almost exclusively in scientific research (weather forecasting, medical monitoring) and financial circles (market indexing, automated trading). But due to initial high deployment costs and complexity, real-time data collection and processing rarely went beyond those vertical markets.
Today the paradigm has shifted—real-time data collection is becoming affordable and manageable thanks to the commoditization of IT hardware such as servers and storage, server virtualization and a commitment by major vendors to ease the transition into the world of real-time data collection.
That shift has made calculating the value of real-time analytics much easier, and it’s providing the fodder to build an appropriate business case for integrating real-time data collection into the corporate knowledge store. What’s more, many vendors have greased the wheels of implementation by creating tools and offering options that can leverage the corporate investment already made in a large-scale, enterprise-level database.
The key element here is how to make that transformation and exemplify why that move to real-time data acquisition and analysis should become part of a business’ core competencies. It all comes down to business time increasingly moving toward real time. As organizations look to grow their competitive advantage, they are trying to uncover opportunities to capture and respond to business events faster and more rigorously than ever. Today competitive advantage comes from the effective use of IT.
That point of view dictates that achieving faster business intelligence requires a robust enterprise data warehouse combined with an analytics framework. Today a data warehouse relies on each element of the business gathering data through an assortment of activities. Typically, the data is captured, aggregated, analyzed and leveraged to improve decision making.
But much of that analysis relies on batch processing or at the very least delayed analytics. Nevertheless, that data has to be complete, accurate and trusted, requiring controls, filters and other algorithms to be in place so that the business can maintain that level of assurance. Yet achieving that level of assurance with current methodologies has a high price—one that is measured in timeliness and directly correlates to the value of the data.
Simply put, timely data ensures better-informed decisions, and that timeliness can be preserved using real-time data collection and analysis. The real business case for real-time data collection and analysis can be summed up in one word: value.
FRANK J. OHLHORST is an award-winning technology journalist, professional speaker and IT business consultant with more than 25 years of experience. He served as a network administrator and applications programmer at the U.S. Department of Energy before forming his own computer consulting firm, which can be found at ohlhorst.net.
This was first published in August 2012