FED Up With Big Data Hype? How Central Banks Struggle with Digital Disruption

November 24, 2016 • Big Data & Analytics, Surdak on Technology, TECHNOLOGY

By Christopher Surdak

Since releasing his latest book, Jerk: Twelve Steps to Rule the World,” Christopher Surdak has spoken with thousands of business and governmental leaders about Big Data, Analytics and Digital Transformation. With this experience, he tries to answer in this article, all the important questions revolving how central banks struggle with digital disruption.


Do any of these sound familiar to you:

“I need to monetise my data.”

“How do I get value from my analytics efforts?”

“My data scientist finds lots of interesting relationships, now what?”

“How will Hadoop increase my revenues?”

“Why don’t my forecasts and models work anymore?”


If so, you’re not alone. Since releasing my latest book, “Jerk”, earlier this year I have spoken with thousands of business and governmental leaders about Big Data, Analytics and Digital Transformation. From startups to Fortune 50 goliaths, from government agencies across the world, to global NGO’s such as the World Bank, everyone is struggling to figure out how to better put data to use, while they still have time.

Some organisations actually gain new insights by analysing new sources of data, but they also find that it’s difficult to benefit from their efforts. In these cases, organisations haven’t changed how they operate to take advantage of these insights.

Many organisations have invested heavily in Big Data technology, deploying a virtual “Noah’s Ark” of new software tools such as Hadoop, Hive, Pig, Impala and so on. Each new tool attempts to address the shortcomings of its predecessors, and the Big Data ecosystem is evolving very rapidly. Perhaps it is no surprise that the open source group Apache has created a software platform called Zookeeper to help manage this growing herd of software tools. This rapid evolution, combined with the enormous hype around Big Data, is driving many organisations to build expansive Big Data platforms. These initiatives are loaded with potential, but frequently short in actual business value.


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