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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 future of data architecture is hybrid: choosing your hybrid-first data strategy starts at Cloudera Now 2022

Cloudera

With all of the buzz around cloud computing, many companies have overlooked the importance of hybrid data. The truth is, the future of data architecture is all about hybrid. As a leader in hybrid data, Cloudera is positioned to help organizations take on the challenge of managing and analyzing data wherever it resides.

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Critical Components of Big Data Architecture for a Translation Company

Smart Data Collective

Big data technology has been instrumental in helping organizations translate between different languages. We covered the benefits of using machine learning and other big data tools in translations in the past. How Does Big Data Architecture Fit with a Translation Company?

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6 strategic imperatives for your next data strategy

CIO Business Intelligence

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.

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The steep cost of a poor data management strategy

CIO Business Intelligence

Such is the case with a data management strategy. That gap is becoming increasingly apparent because of artificial intelligence’s (AI) dependence on effective data management. For many organizations, the real challenge is quantifying the ROI benefits of data management in terms of dollars and cents.

Strategy 114
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Data is essential: Building an effective generative AI marketing strategy

IBM Big Data Hub

According to the IBM survey, when CMOs were asked what they thought the primary challenges were in adopting generative AI, they listed three top concerns: managing the complexity of implementation, building the data set and brand and intellectual property (IP) risk. With the right generative AI strategy, marketers can mitigate these concerns.

Marketing 120
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How the right data and AI foundation can empower a successful ESG strategy

IBM Big Data Hub

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.