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.
- 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.
- 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.
- 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.
- You don’t know me. Or do you? Data meets anthropologyLaw and medicine. Anthropology and data management. And so on. What new advances can happen when fields of study converge?
- Components of an information management strategyBefore starting a data management strategy for your business, you need to understand each component. Data expert David Loshin breaks them down.
- Goooooal! How data stewards score with data visualizationWhen it comes to data visualization, the role a data steward plays is not so different from that of a referee. They both enforce rules, stay true to the game -- and are critical to success.
- Understanding data in motionLearn how to analyze fast moving data streams on the fly with event stream processing.
- Charlie Brown's Teacher Speaks Hadoop. Do you?Ever felt like you and your big data specialist were speaking different languages? Learn how a non-geek can speak big data.
- Canada Post on the (careful) commercialization of dataAs a common data point across databases, address data is an integral part to any master data management strategy. It’s powerful when it’s right; frustrating when it’s not. Could Canada Post turn a seemingly ordinary data point into a profitable business line?
- Supercharging capital markets with real-time data and visualizationBy performing deeper analyses on data captured in-stream, and then injecting the results back into the business in real time, firms can better manage market risk, liquidity and counterparty credit risk during the trading day.
- You can’t have that data! It’s not perfect yetShould you have complete confidence in the quality of your data before handing it over for use in processes or analytics? Not necessarily. Find out why it’s okay for your data to be “good enough.”
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