Remove Cost-Benefit Remove Data Strategy Remove Data Transformation
article thumbnail

8 data strategy mistakes to avoid

CIO Business Intelligence

Organizations can’t afford to mess up their data strategies, because too much is at stake in the digital economy. How enterprises gather, store, cleanse, access, and secure their data can be a major factor in their ability to meet corporate goals. Here are some data strategy mistakes IT leaders would be wise to avoid.

article thumbnail

The Journey to DataOps Success: Key Takeaways from Transformation Trailblazers

DataKitchen

In early April 2021, DataKItchen sat down with Jonathan Hodges, VP Data Management & Analytics, at Workiva ; Chuck Smith, VP of R&D Data Strategy at GlaxoSmithKline (GSK) ; and Chris Bergh, CEO and Head Chef at DataKitchen, to find out about their enterprise DataOps transformation journey, including key successes and lessons learned.

Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Data’s dark secret: Why poor quality cripples AI and growth

CIO Business Intelligence

In healthcare, missing treatment data or inconsistent coding undermines clinical AI models and affects patient safety. In retail, poor product master data skews demand forecasts and disrupts fulfillment. In the public sector, fragmented citizen data impairs service delivery, delays benefits and leads to audit failures.

article thumbnail

How ANZ Institutional Division built a federated data platform to enable their domain teams to build data products to support business outcomes

AWS Big Data

Inspired by these global trends and driven by its own unique challenges, ANZ’s Institutional Division decided to pivot from viewing data as a byproduct of projects to treating it as a valuable product in its own right. For instance, one enhancement involves integrating cross-functional squads to support data literacy.

Metadata 105
article thumbnail

Unlock scalability, cost-efficiency, and faster insights with large-scale data migration to Amazon Redshift

AWS Big Data

These challenges can range from ensuring data quality and integrity during the migration process to addressing technical complexities related to data transformation, schema mapping, performance, and compatibility issues between the source and target data warehouses.

article thumbnail

How CFM built a well-governed and scalable data-engineering platform using Amazon EMR for financial features generation

AWS Big Data

In this post, we share how we built a well-governed and scalable data engineering platform using Amazon EMR for financial features generation. Having the requirement to use our own notebooks, we initially didn’t benefit from this integration. At this stage, CFM data scientists can perform analytics and extract value from raw data.

article thumbnail

A step-by-step guide to setting up a data governance program

IBM Big Data Hub

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture.