Data Management Insights

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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.
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.
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.
5 ways to become data-drivenSuccessful data-driven businesses foster collaborative, goal-oriented cultures, have leaders who believe in data and are governance-oriented. Read more in this summary of TDWI research that uncovers best practices for becoming data-driven.
Personal data: Getting it right with GDPRTo learn more about the definition of personal data, why it’s in the news and why it’s being tightly regulated by laws like the General Data Protection Regulation (GDPR), we interviewed Jay Exum, Privacy Counsel at SAS.
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 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.
The opportunity of smart grid analyticsWith smart grid analytics, utility companies can control operating costs, improve grid reliability and deliver personalized energy services.
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.
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.
5 steps to sustainable GDPR complianceFollow these steps to achieve GDPR compliance by the May 2018 deadline – and get added benefits along the way.
How openness can supercharge event stream analyticsWhat does openness do for event stream analytics? David Loshin shows how it helps you speed and govern the full streaming analytics life cycle.
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.
How to improve data prep for analytics: TDWI shares best practicesGetting good results from your data means doing a good job of preparing the data for analytics. Find out what TDWI says it takes to get there.
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.
Tips for successful data integration modernizationOrganizations today have to capture, process and move all types of new and big data. Get some tips for data integration modernization from TDWI.
Coming soon: The Industrial Internet and IoT standardsFrom smart farms to connected water meters, the IIoT party is happening – with consensus around IoT standards playing host.
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.
Chief data officer role shakes up traditional data governanceDoes every organization need someone in the chief data officer role to manage data governance?
8 ways an enterprise data strategy enables big data analyticsAnalyzing big data can reveal new insights and strengthen business decisions. Here's why you need an enterprise data strategy to help you get there.
Big data management: 5 things you need to knowBig data management demands new tools and processes. Learn about 5 things that can help you manage big data better and get consistent analytic results.
Clear-cut big data strategy tied to strong financial performanceWondering how important your big data strategy is? Find out from this 2015 report that’s based on a survey of more than 500 global executives.
Streaming data: The ins and outs of this technology buzzwordLearn what streaming data is, why it matters to your business and how it relates to big data, event stream processing, data management and the IoT.
Big data integration: Go beyond 'just add data'Every business-focused technology requires users to "just add data." This article by Matt Magne explores why that's not as easy as it sounds, especially in a big data age.
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.
Sensing a disturbance in the dataAs IoT data unites with event stream processing (ESP), these combined forces will automatically sense data pattern deviations and trigger immediate response.
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.
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.
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.
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?
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.”
Five steps that can save your data analytics – and help you save faceThere’s nothing more awkward than watching analysts struggle to defend their results. Even if you think your process is rock-solid, things can go awry – unless you keep these milestones in mind.

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