DATA MANAGEMENT INSIGHTS
Turn big data into big value
Recent Data Management articles
-
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.
-
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.
-
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.
-
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.
-
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.
-
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.
-
Cinq étapes pour une conformité durable à GDPRFollow these steps to achieve GDPR compliance by the May 2018 deadline – and get added benefits along the way.
-
Le Règlement général sur la protection des données : transformer la contrainte en opportunitéLe Règlement général sur la protection des données suscite des sentiments mitigés, mais Kalliopi Spyridaki explique comment en faire un avantage compétitif.
-
5 ways data monetization can inform data strategyData monetization promises more than monetary returns. Anne Buff explains how monetization drives strategy for data-driven organizations.
-
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.
-
3 things you need to know about event stream processingAs more data is generated from the Internet of Things, more of it will be analyzed as it is streaming. Learn how to analyze streaming data – and how it differs from traditional analysis.
-
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.
-
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.
-
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.
-
Big data privacy: Four ways your data governance strategy affects security, privacy and trustWith IT and business strategies converging, something that’s a little late to the party is the concept of big data privacy.
-
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.
-
Toyota Financial Services’ CIO is a model for change: part innovator, part gatekeeper CIO Ron Guerrier walks a fine line, gently snuffing out rogue IT activities activities without impinging on the innovation and real-time needs that drove business users to adopt them in the first place.
-
I see big data. All the time. It’s everywhere.Big data is rapidly creeping into every element of our life. In this article, Tamara Dull from SAS Best Practices explores some big data examples - and how it will affect you.
-
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.
-
Data quality: The Achilles' heel of risk managementGiven the tightly regulated environment banks face today, the importance of data quality cannot be overstated. Beyond the obvious benefits of staying one step ahead of regulatory mandates, having accurate, integrated and transparent data will drive confident, proactive decisions to support a solid risk management foundation.
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.
Send SAS Insights straight to your inbox
