Business Objectives Data Strategy Measurement Technology 
CIO Business Intelligence
JUNE 23, 2023
According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital business objectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.
Paul Blogs on BI
NOVEMBER 13, 2023
I have a had a lot of conversations about data strategy this year. With both the rise in organizations looking to move their data to the cloud and the increasing awareness of the power of BI and generative AI, data strategy has become a top priority. This is where the infamous “How do you eat an elephant?”
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Alation
MARCH 14, 2023
Data gathering and use pervades almost every business function these days — and it’s widely acknowledged that businesses with a clear strategy around data are best placed to succeed in competitive, challenging markets such as defence. What is a data strategy? Why is a data strategy important?
IBM Big Data Hub
FEBRUARY 9, 2023
The primary goal of any data governance program is to deliver against prioritized business objectives and unlock the value of your data across your organization. Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes.
Cloudera
SEPTEMBER 19, 2023
Adding another position may not be terribly appealing, but there is one C-suite role every company should consider—chief data and analytics officer (CDO or CDAO). Data is the lifeblood of modern business, the fuel that powers digital transformation, and every company should have a data strategy.
Anmut
MAY 12, 2022
Executive teams want results fast, and without tangible proof that data strategies and investments are making a difference, they often have to move onto the next thing, and sometimes the next CDO. Data investment drives tremendous business value. Build a differentiated, prioritised data strategy.
IBM Big Data Hub
JANUARY 5, 2023
Both approaches were typically monolithic and centralized architectures organized around mechanical functions of data ingestion, processing, cleansing, aggregation, and serving. Monitor and identify data quality issues closer to the source to mitigate the potential impact on downstream processes or workloads.
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