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Taming copy sprawl with real-time analytics

The future of digital business will be built on synchronizing data across disparate infrastructures such as hybrid clouds, applications and edge computing in real-time to detect patterns and automatically act on insights.  Over the past several years, the availability of data and the technologies associated with analyzing it have exploded.  With the increase in analytics projects being started in public clouds, there is a direct increase in demand for copies of data. I invested in to help enterprises eliminate the need for copies and instead, utilize data from the single sources.

According to IDC, 60% of all data storage is a copy of some kind and it has become a $50 billion dollar problem.  As more sources of data (clicks, events, messages) are generated and combined with operational data from systems of record, analysis becomes a challenge with traditional data integration technologies. While there’s excitement about new technologies such as Kafka, to process streams of events, real-time analytics needs to combine that new data with trusted up-to-date copies from enterprise data silos while protecting data privacy. A typical database environment today has 5 – 12 copies for each production instance, creating many management and compliance challenges that can include:

  • Snapshot inaccuracy. Changes occurring between snapshots are almost impossible to track, pro rate and create analysis deeper than the time context.
  • Data inflexibility. An object or time context has already been applied to the data that you can’t change without replacing the entire data set with a new context.
  • Reporting complexity. Every snapshot period requires a report.   For example, three years of data with a month end snapshot means 36 reports to build and maintain.

Despite these challenges, the business imperative for synchronizing operational databases across hybrid clouds is essential to compete in the digital age.

Real-time Analytics Use Case: Detecting credit card fraud before it happens

Many industries are starting to rely more heavily on the ability to synchronize, analyze and act on data instantaneously. Generating real-time insights is particularly important to Financial Services, with fraud detection experiencing the fastest adoption. According to a report from Javelin Strategy & Research, thieves racked up $16 billion in credit card fraud in 2016. Banks and credit card companies can set their business apart by flagging unusual activity quickly, in turn saving their customers thousands of dollars. With real-time data analytics, these financial institutions can almost immediately identify any suspicious card activity, alert cardholders of possible theft and prevent the sale before the fraudsters make off with whatever they are trying to purchase. By establishing a baseline of normal transaction activity and using real-time analytics to identify anomalies that could signal fraud, they can streamline fraud detection to the point that it becomes instantaneous.

Winning in digital business

To win in today’s digital economy and ensure future viability, it is essential for organizations to capture the value of data quickly, change direction at pace, and capitalize on immediate opportunities. Griddable’s smart grid for enterprise data provides a platform for converging disparate forms of data, synchronizing, and then analyzing these insights to automate business decisions. Taming copy sprawl with data integration and real-time analytics gives businesses the ability to make meaningful, strategic adjustments that minimize costs and maximize the bottom line.

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