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Data is fuel for artificial intelligence and machine learning applications and organizations with a wealth of legacy data are at an advantage in creating such applications because there’s more data to train them. Fed by more data, models and predictions are more accurate.
The challenge: Tools for AI and ML are targeted toward data in widely accessible modern formats; not legacy data structures. Additionally, many analytics teams are familiar with data in Hadoop and Spark, but are often much …
It’s no surprise that there are gaps between Big Iron (invented in the middle of the last century) and Big Data (invented a decade ago). But bridging them together can bring great business value: Customer 360, Real-time Fraud Detection, Active Archive to name a few.
Moving mainframe data into Hadoop storage formats is non-trivial, and processing that data in Hadoop is a challenge as well. Without the right tools, you may end up stalling your Big Data …
Emerging analytics platforms are laying the foundation for innovation and fundamentally changing the approach IT organizations take to managing their infrastructure. IT organizations are being challenged to do more with less staff – and that staff should be able to perform similar tasks across all the IT platforms using common tools and technologies. Because of this pressure, IT must identify ways to bring existing infrastructure and legacy systems into the modern world of analytics, and in doing so, …
Before regulations like GDPR, enterprise-grade Data Quality tools might have been viewed as “nice to have” or driven by pockets of the organizations, such as marketing or sales. Today, Data Quality is easily recognized as a critically important data-based challenge that can jeopardize regulatory compliance.
Download the eBook, Data Quality-Driven GDPR, to gain insight into the questions that your organization must be able to answer to comply with GDPR such as:
• How are …