Remove Business Intelligence Remove Data Governance Remove Data Strategy Remove Data Warehouse
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Questions to ask before building a Data Strategy

Data Science 101

Building a data strategy is a great idea. It helps to avoid many of the Challenges of a Data Science Projects. General Questions Before Starting a Data Strategy. Do you have a process for solving problems involving data? What are the biggest challenges in your business? What data do you collect?

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Why optimize your warehouse with a data lakehouse strategy

IBM Big Data Hub

In a prior blog , we pointed out that warehouses, known for high-performance data processing for business intelligence, can quickly become expensive for new data and evolving workloads. To do so, Presto and Spark need to readily work with existing and modern data warehouse infrastructures.

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What you don’t know about data management could kill your business

CIO Business Intelligence

Still, to truly create lasting value with data, organizations must develop data management mastery. This means excelling in the under-the-radar disciplines of data architecture and data governance. The knock-on impact of this lack of analyst coverage is a paucity of data about monies being spent on data management.

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What is a data architect? Skills, salaries, and how to become a data framework master

CIO Business Intelligence

Solutions data architect: These individuals design and implement data solutions for specific business needs, including data warehouses, data marts, and data lakes. Application data architect: The application data architect designs and implements data models for specific software applications.

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Data architecture strategy for data quality

IBM Big Data Hub

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Practice proper data hygiene across interfaces.

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Straumann Group is transforming dentistry with data, AI

CIO Business Intelligence

Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.

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Build a serverless transactional data lake with Apache Iceberg, Amazon EMR Serverless, and Amazon Athena

AWS Big Data

However, as data processing at scale solutions grow, organizations need to build more and more features on top of their data lakes. They enable transactions on top of data lakes and can simplify data storage, management, ingestion, and processing. Dimension-based models have been used extensively to build data warehouses.

Data Lake 108