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Overcome these six data consumption challenges for a more data-driven enterprise

IBM Big Data Hub

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. Here are a few common data management challenges: Regulatory compliance on data use. Data quality. Data silos.

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ChatGPT: le nuove sfide della strategia sui dati nell’era dell’IA generativa

CIO Business Intelligence

Le aziende italiane investono in infrastrutture, software e servizi per la gestione e l’analisi dei dati (+18% nel 2023, pari a 2,85 miliardi di euro, secondo l’Osservatorio Big Data & Business Analytics della School of Management del Politecnico di Milano), ma quante sono giunte alla data maturity?

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AWS Lake Formation 2022 year in review

AWS Big Data

Data governance is the collection of policies, processes, and systems that organizations use to ensure the quality and appropriate handling of their data throughout its lifecycle for the purpose of generating business value.

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CIO Ryan Snyder on the benefits of interpreting data as a layer cake

CIO Business Intelligence

A data and analytics capability cannot emerge from an IT or business strategy alone. With both technology and business organization deeply involved in the what, why, and how of data, companies need to create cross-functional data teams to get the most out of it. That strategy is doomed to fail. What are the layers?

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Data architecture strategy for data quality

IBM Big Data Hub

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

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The Data Marketplace is On the Rise

TDAN

For most organizations, the process of becoming more data-driven starts with better understanding and using their own data. But internal data is just the tip of the iceberg. Underneath the surface of the (data) lake is the untapped value of external data, which has given rise to the data marketplace.

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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. Some use case examples will help.