DATA MANAGEMENT INSIGHTS
Turn big data into big value
Recent Data Management articles
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IoT: The customer experience accelerator you can't afford to ignoreIoT represents a powerful source of data that, when combined with analytics, can yield insights on everything from behavior to emotions to health. And that's why it's key to improving customer experience.
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Data lineage: Making artificial intelligence smarterFor AI to reach its full potential, the data feeding its algorithms and models needs to be well-understood. Data lineage plays a vital role in understanding data, making it a foundational principle of AI.
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Key questions to kick off your data analytics projectsThere’s no single blueprint for starting a data analytics project. Technology expert Phil Simon suggests considering these ten questions as a preliminary guide.
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The five D's of data preparationFrom discovering which data is best to use, to delivering it in the right format to users, learn why these 5 D’s are essential to data preparation.
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Data management backgrounderFrom data integration to data quality and data preparation, find out what these terms mean and why they’re so important for your analytics projects.
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Data quality management What you need to knowData quality isn’t simply good or bad. Data quality management puts quality in context to improve fitness of the data you use for analysis and decision-making.
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The future of IoT: On the edgeFrom cows to factory floors, the IoT promises intriguing opportunities for business. Find out how three experts envision the future of IoT.
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Data lake and data warehouse – know the differenceData lake – is it just marketing hype or a new name for a data warehouse? Find out what a data lake is, how it works and when you might need one.
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What is data profiling and how does it make big data easier?Data profiling, the act of monitoring and cleansing data, is an important tool organizations can use to make better data decisions.
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Three C’s of the connected customer in the IoTTo optimize the connected customer experience, Blue Hill Research says organizations should build an IoT model based on three key features.
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IoT success depends on data governance, security and privacyThe IoT puts intense demands on the data management life cycle. Learn from 10 common mistakes organizations have made with IoT endeavors.
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Data management for cybersecurity: Know the essentialsSecurity teams tend to underestimate the importance of data management for cybersecurity. See the seven key reasons data management has been so difficult and five steps to getting it right.
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The importance of data quality: A sustainable approachBad data wrecks countless business ventures. Here’s a data quality plan to help you get it right.
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How does one of the largest cities in the world use data for social good? From data privacy to data quality, what are the challenges in using data for social good, and how does one large organization in New York City address them? Hear from a research scientist at the Center for Innovation through Data Intelligence about the data they have, the questions they ask of it, and the data they’d like to see in the future.
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5 data management best practices to help you do data rightFollow these 5 data management best practices to make sure your business data gives you great results from analytics.
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Data governance: The case for self-validationLearn why you should redefine data governance policies to empower customers to be accountable for keeping their personal data accurate, consistent and up-to-date.
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What was your data doing during the financial crisis?Financial institutions usually survive a crisis, then react to prevent it in the future. SAS' Mazhar LeGhari explains how data can help you break that cycle.
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Data governance framework: What is it and do I already have one?A data governance framework encompasses a holistic approach to how you collect, manage and archive data.
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Soccer versus baseball: which is the best analogy for data governance?Is data governance more like baseball, featuring individual effort, or like soccer, where a team approach wins? Carol Newcomb evaluates the best sports analogy for data governance.
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People, process, culture – and technologyData governance requires measurement and constant improvements of data quality. That’s a mountain of a job without clearly defined roles and responsibilities. Peyman Mestchian, Managing Partner at Chartis Research, and Tom Kimner, Head of Americas Risk at SAS, talk about data governance and the need for specialized departments, technology and skills.
Data Management: The Basics
Data Management
Data management can often mean the difference between a business that will succeed and one that will drown in too much disorganized information. With high-quality, well-managed data, you can gain a clearer picture of your business. Access the right data when you need it. And make better, more strategic decisions.
From data quality to data integration to master data management, data management solutions help you stop reacting to bad data – and start using your data to make a difference.
What is big data?
For most organizations, big data is the reality of doing business. It’s the proliferation of structured and unstructured data that floods your organization on a daily basis – and if managed well, it can deliver powerful insights.
Imagine being able to analyze data to determine the root cause of failures. Or detect fraudulent behavior before it affects revenue. Implementing the right solutions to make the most of your big data – from data management to analytics – can be key to your business success.
SAS & Hadoop
Organizations with massive volumes of data are turning to Hadoop to extract value from all that information. But what is Hadoop, exactly – and how can it help your organization? Learn how Hadoop is evolving to meet the big data challenges of today and tomorrow. And how you can take advantage of data management, data visualization and analytics to get faster, more meaningful insights from your Hadoop environment.
ETL (extract, transform, load)
Older technologies don't always die out and get replaced with newer technologies. Sometimes they adapt to the times and retain their relevance. That’s what has happened with ETL (extract, transform, load) – a decades-old technology that many organizations still rely on as part of their larger data integration strategy. Learn the history of ETL and how it works, then find out where it fits in a world of big data, Hadoop and streaming.
What is a data warehouse?
With large amounts of data readily available to organizations, the question is no longer how businesses collect it – but how they store it. Data warehouses are a reliable solution. They serve as a repository for data that's been collected and integrated from multiple sources. And unlike data lakes, which store raw data, data warehouses contain data that's consistently formatted so it's ready for analytics or reporting purposes.
Blockchain
What can you store in blockchain besides Bitcoin transactions? Insurance claims, medical records and even voting records can all be stored in blockchain. Simply put, blockchain is a distributed data architecture that stores data and transactions – and validates the accuracy of those transactions by consensus. Learn more about blockchain and how you can analyze blockchain data.
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