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erwin Expert Blog

Aug
1
2019

It’s important we recognize the benefits of data lineage. As corporate data governance programs have matured, the inventory of agreed-to data policies has grown rapidly. These include guidelines for data quality assurance, regulatory compliance and data democratization, among other information utilization initiatives. Organizations that are challenged by translating their defined data policies into implemented processes...

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Enterprise architecture (EA) isn’t dead, you’re just using it wrong. Part three of erwin’s digital transformation blog series.   I’ll let you in on a little secret: the rumor of enterprise architecture’s demise has been greatly exaggerated. However, the truth for many of today’s fast-moving businesses is that enterprise architecture fails. But why? Enterprise architecture...

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Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a digital transformation strategy viable? Part Two of the Digital Transformation Journey … Part one In our last blog on driving digital transformation, we explored how business architecture and process (BP) modeling are pivotal factors in...

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In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. News broke earlier this week of British Airways being fined 183 million pounds – or $228 million – by the U.K. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). While...

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At a fundamental level, digital transformation is about further synthesizing an organization’s operations and technology, so involving business architecture and process modeling is a best practice organizations cannot ignore. This post outlines how business architecture and process modeling come together to facilitate efficient and successful digital transformation efforts. Business Process Modeling: The First Step to...

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The need for an effective data modeling tool is more significant than ever. For decades, data modeling has provided the optimal way to design and deploy new relational databases with high-quality data sources and support application development. But it provides even greater value for modern enterprises where critical data exists in both structured and unstructured...

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Over the past few weeks several huge mergers and acquisitions (M&A) have been announced, including Raytheon and United Technologies, the Salesforce acquisition of Tableau and the Merck acquisition of Tilos Therapeutics. According to collated research and a Harvard Business Review report, the M&A failure rate sits between 70 and 90 percent. Additionally, McKinsey estimates that...

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Digital transformation examples can be found almost anywhere, in almost any industry. Its past successes – and future potential – are well documented, chronicled in the billion-dollar valuations of the frontrunners in the practice. Amazon began as a disruptor to brick-and-mortar bookstores, eventually becoming one of the most obvious digital transformation examples as it went...

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Data innovation is flourishing, driven by the confluence of exploding data production, a lowered barrier to entry for big data, as well as advanced analytics, artificial intelligence and machine learning. Additionally, the ability to access and analyze all of this information has given rise to the “citizen analyst” – a business-oriented problem-solver with enough technical...

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It’s time to consider data-driven enterprise architecture. The traditional approach to enterprise architecture – the analysis, design, planning and implementation of IT capabilities for the successful execution of enterprise strategy – seems to be missing something … data. I’m not saying that enterprise architects only worry about business structure and high-level processes without regard for...

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