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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.

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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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Create an end-to-end data strategy for Customer 360 on AWS

AWS Big Data

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data.

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As insurers look to be more agile, data mesh strategies take centerstage

CIO Business Intelligence

The data mesh debate This is not to say that there is a consensus that data mesh is a universal solution. Stakeholders are currently waging an open debate across the industry of centralization versus federated data strategies. Data Center, Data Management, Digital Transformation To learn more, visit us here.

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3 Steps to Faster Insights in Data Analytics

Data Virtualization

In recent years, we have seen wide adoption of data analytics. Some issues that have been most often cited for this include: Poor data quality: While preparing. However, most organizations continue to find it challenging to quickly yield actionable insights.

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Power of ETL: Transforming Business Decision Making with Data Insights

Smart Data Collective

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. By understanding the power of ETL, organisations can harness the potential of their data and gain valuable insights that drive informed choices. Both approaches aim to improve data quality and enable accurate analysis.

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How AWS helped Altron Group accelerate their vision for optimized customer engagement

AWS Big Data

This allows for transparency, speed to action, and collaboration across the group while enabling the platform team to evangelize the use of data: Altron engaged with AWS to seek advice on their data strategy and cloud modernization to bring their vision to fruition.