Remove Data Warehouse Remove Reporting Remove Risk Remove Uncertainty
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Banking on mainframe-led digital transformation for financial services

IBM Big Data Hub

Banks have the most to gain if they succeed (and the most to lose if they fail) at bringing their mainframe application and data estates up to modern standards of cloud-like flexibility, agility and innovation to meet customer demand. Couldn’t execs have run better analyses to spot risks within the data?

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Quantitative and Qualitative Data: A Vital Combination

Sisense

All descriptive statistics can be calculated using quantitative data. It’s analyzed through numerical comparisons and statistical inferences and is reported through statistical analyses. Consequently, using quantitative data, you can make strategic and tactical decisions that will benefit your organization and drive growth.

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New Thinking, Old Thinking and a Fairytale

Peter James Thomas

An obvious parallel in my world is to consider another business activity that reached peak popularity in the 2000s, Data Warehouse programmes [4]. Figures suggest that both BPR and Data Warehouse programmes have a failure rate of 60 – 70% [5]. King was a wise King, but now he was gripped with uncertainty.

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Two Birds, One Stone: How to Get Better AX Reporting and Prepare for Future D365 Migration Today

Jet Global

Although Microsoft’s rollout of its two ERP cloud products (D365 F&SCM, and for smaller businesses, D365 Business Central) has been going on for some time, the current climate of economic uncertainty has prompted a lot of companies to hit the pause button on migration, choosing instead to stay the course with their existing Dynamics AX systems.

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Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability

DataKitchen

Bridging the Gap: How ‘Data in Place’ and ‘Data in Use’ Define Complete Data Observability In a world where 97% of data engineers report burnout and crisis mode seems to be the default setting for data teams, a Zen-like calm feels like an unattainable dream. What is Data in Use?

Testing 176
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Data Science, Past & Future

Domino Data Lab

The data governance, however, is still pretty much over on the data warehouse. Toward the end of the 2000s is when you first started getting teams and industry, as Josh Willis was showing really brilliantly last night, you first started getting some teams identified as “data science” teams. You know what?

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Cloudera + Hortonworks, from the Edge to AI

Cloudera

The tremendous growth in both unstructured and structured data overwhelms traditional data warehouses. We are both convinced that a scale-out, shared-nothing architecture — the foundation of Hadoop — is essential for IoT, data warehousing and ML. We have each innovated separately in those areas.