Data Management Insights
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.