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This article was co-authored by Duke Dyksterhouse , an Associate at Metis Strategy. Data & Analytics is delivering on its promise. Some are our clients—and more of them are asking our help with their datastrategy. So, they built a data-lake. We discourage that thinking.
Datalake is a newer IT term created for a new category of data store. But just what is a datalake? According to IBM, “a datalake is a storage repository that holds an enormous amount of raw or refined data in native format until it is accessed.” That makes sense. I think the […].
For a while now, vendors have been advocating that people put their data in a datalake when they put their data in the cloud. The DataLake The idea is that you put your data into a datalake. Then, at a later point in time, the end user analyst can come along and […].
This increase was driven in part by the launch of my new Maths & Science section , articles from which claimed no fewer than 6 slots in the 2018 top 10 articles, when measured by hits [1]. This is my selection of the articles that I enjoyed writing most, which does not always overlap with the most popular ones. May onwards.
Reading Time: 11 minutes The post DataStrategies for Getting Greater Business Value from Distributed Data appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.
Most current data architectures were designed for batch processing with analytics and machine learning models running on data warehouses and datalakes. In this article, I’ll share insights on aligning vision and leadership, as well as reducing complexity to make data actionable for delivering real-time AI solutions.
Various databases, plus one or more data warehouses, have been the state-of-the art data management infrastructure in companies for years. The emergence of various new concepts, technologies, and applications such as Hadoop, Tableau, R, Power BI, or DataLakes indicate that changes are under way.
With this first article of the two-part series on data product strategies, I am presenting some of the emerging themes in data product development and how they inform the prerequisites and foundational capabilities of an Enterprise data platform that would serve as the backbone for developing successful data product strategies.
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.
When companies embark on a journey of becoming data-driven, usually, this goes hand in and with using new technologies and concepts such as AI and datalakes or Hadoop and IoT. Suddenly, the data warehouse team and their software are not the only ones anymore that turn data […].
For anyone who is unaware, the title of the article echoes a 1953 Nature paper [1] , which was instead “of considerable biological interest” [2]. I have been very much focussing on the start of a data journey in a series of recent articles about DataStrategy [3]. Introduction. Follow @peterjthomas.
When I first started focussing on the data arena, Data Warehouses were state of the art. More recently Big Data architectures, including things like DataLakes , have appeared and – at least in some cases – begun to add significant value. DataStrategy. Watch this space. [2].
The reasons for this are simple: Before you can start analyzing data, huge datasets like datalakes must be modeled or transformed to be usable. According to a recent survey conducted by IDC , 43% of respondents were drawing intelligence from 10 to 30 data sources in 2020, with a jump to 64% in 2021! Discover why.
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By creating visual representations of data flows, organizations can gain a clear understanding of the lifecycle of personal data and identify potential vulnerabilities or compliance gaps. Note that putting a comprehensive datastrategy in place is not in scope for this post.
The post Navigating the New Data Landscape: Trends and Opportunities appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information. At TDWI, we see companies collecting traditional structured.
Data catalogs also seek to be the. The post Choosing a Data Catalog: Data Map or Data Delivery App? appeared first on Data Virtualization blog - Data Integration and Modern Data Management Articles, Analysis and Information.
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Reading Time: 3 minutes Join our conversation on All Things Data with Robin Tandon, Director of Product Marketing at Denodo (EMEA & LATAM), with a focus on how data virtualization helps customers realize true economic benefits in as little as six weeks.
Having been in business for over 50 years, ARC had accumulated a massive amount of data that was stored in siloed, on-premises servers across its 7 business domains. Using Alation, ARC automated the data curation and cataloging process. “So Subscribe to Alation's Blog Get the latest data cataloging news and trends in your inbox.
Become a “Data-First” Company The organizations that invest in data first and foremost are changing the business landscape. Apple had famously reached a trillion dollar valuation on August 2, 2018, and analysts predicted that Amazon wasn’t far behind.
451 Research begins its paper on the “unstoppable rise of the Data Catalog” with the following: Could the data catalog be the most important data management breakthrough to have emerged in the last decade? 1] The data catalog is indeed the […].
As data initiatives mature, the Alation data catalog is becoming central to an expanding set of use cases. Governing DataLakes to Find Opportunities for Customers. At Munich Re, our datastrategy is geared to offer new and better risk-related services to our customers. The Road Ahead.
The evolution of cloud-first strategies, real-time integration and AI-driven automation has set a new benchmark for data systems and heightened concerns over data privacy, regulatory compliance and ethical AI governance demand advanced solutions that are both robust and adaptive.
When workers get their hands on the right data, it not only gives them what they need to solve problems, but also prompts them to ask, “What else can I do with data?” ” through a truly data literate organization. What is data democratization? What are your data and AI objectives?
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