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This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. The new category is often called MLOps.
After countless open-source innovations ushered in the Big Data era, including the first commercial distribution of HDFS (Apache Hadoop Distributed File System), commonly referred to as Hadoop, the two companies joined forces, giving birth to an entire ecosystem of technology and tech companies.
Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. The solution is data intelligence.
Or we create a datalake, which quickly degenerates to a data swamp. Contextualdata understanding Data systems often cause major problems in manufacturing firms. IBM built a workforce advisor that uses summarization and contextualdata understanding with intent detection and multi-modal interaction.
To keep pace as banking becomes increasingly digitized in Southeast Asia, OCBC was looking to utilize AI/ML to make more data-driven decisions to improve customer experience and mitigate risks. While these are great proof points to demonstrate how business value can be driven by AI/ML, this was only made possible with trusted data.
OCBC also won a Cloudera Data Impact Award 2022 in the Transformation category for the project. Real-time data analysis for better business and customer solutions. The partnership has enabled OCBC to better store, manage and harness the power of our data.”.
It’s a truism that data is the most important asset in the 21 st century economy. But, today too many enterprises exist in a data fog, with poorly contextualizeddata scattered across millions of tables. Dispelling this data fog is one of the key challenges for the next generation enterprise.
How effectively and efficiently an organization can conduct data analytics is determined by its data strategy and data architecture , which allows an organization, its users and its applications to access different types of data regardless of where that data resides.
An enterprisedata catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.
IAM can enable enterprise transformation projects through automation that enables quicker access. It allows for movement of workloads into the cloud seamlessly and enables companies to externalize applications that are on-premises. – Protecting data from hackers is the critical task of CDOs. IAM brings value .
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