Big-data analytics, actionable insights, and powerful outcomes are the de facto expectations for data-analytics programs. Is your data strategy aligned to deliver those results?Organizations are seeking sophisticated analytical techniques and tools to gain more profound insights into how they can capitalize on the blue ocean of data analytics. Listen this week at your office and you’ll undoubtedly hear whisperings about harnessing the power of analytics. It might not be called data management or big-data analytics, and the questions might be more subtle, such as: How do we discover new insights into our products? Which operational capabilities will deliver the highest ROI? How do we leverage our data to generate better strategies and execute with improved confidence? Managers and leaders alike are searching for approaches to tap into the value of big-data analytics. What exactly is a big-data analytics strategy?A comprehensive data analysis foundationSet the frame mentally of the … [Read more...] about Do you have a data strategy to achieve better organizational analytics?
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The explosion of data in the modern world has brought on many novel business problems when It comes to the applications of modeling and analysis. Businesses are starting to recognize the value that mature, robust analytics practice can bring to both their understanding of the industry, and their bottom line.As I’ve detailed in previous articles, such as “Handing off models from data science to IT,” the relationship between IT teams and data scientists can lead to complications in model creation and deployment. One of the key themes I used to illustrate the needs between these two groups is collaboration. Which is quite the coincidence, because while coming together as an organized unit can be incredibly beneficial, so can focusing on the specific needs and wants for each group. In order for the IT and data science teams to collaborate, they need to be able to maintain performance in their lanes. This means letting the IT team work on IT and having the data scientists … [Read more...] about Key steps to model creation: data cleaning and data exploration
Artificial intelligence and machine learning promise to radically transform many industries, but they also pose significant risks — many of which are yet to be discovered, given that the technology is only now beginning to be rolled out in force.There have already been a number of public, and embarrassing, examples of AI gone bad. Microsoft's Tay went from innocent chatbot to a crazed racist in just a day, corrupted by Twitter trolls. Two years ago, Google had to censor image searches for keywords like "gorilla" and "chimp" because it returned photos of African-Americans — and the problem still hasn't been fully fixed in its Google Photos app.As businesses increasingly embrace AI, the stakes will only get higher."We wake up sweating," says Ankur Teredesai, head of AI at Seattle-based KenSci, a startup that applies AI to health care data. "At the end of the day, we're talking about real patients, real lives."KenSci’s AI platform makes health care recommendations to … [Read more...] about AI’s biggest risk factor: Data gone wrong
One of the great things about R is the thousands of packages users have written to solve specific problems in various disciplines -- analyzing everything from weather or financial data to the human genome -- not to mention analyzing computer security-breach data.Some tasks are common to almost all users, though, regardless of subject area: data import, data wrangling and data visualization. The table below show my favorite go-to packages for one of these three tasks (plus a few miscellaneous ones tossed in). The package names in the table are clickable if you want more information. To find out more about a package once you've installed it, type help(package = "packagename") in your R console (of course substituting the actual package name ).My favorite R packages for data visualization and munging Package Category Description Sample Use Author dplyr data wrangling, data analysis The essential data-munging R package when working with data frames. Especially useful for operating … [Read more...] about Best R packages for data import, data wrangling & data visualization
Data is a human invention. Humans define the phenomenon they want to measure, design systems to collect data about it, clean and pre-process it before analysis, and finally, choose how to interpret the results. Even with the same dataset, two people can form vastly different conclusions. This is because data alone is not “ground truth” — observable, provable, and objective data that reflects reality. If researchers infer data from other information, rely on subjective judgment, do not collect data in a rigorous and careful manner, or use sources that are of questionable authenticity, then the data they produce it is not ground truth. How you choose to conceptualize a phenomenon, determine what to measure, and decide how to take measurements will affect the data that you collect. Your ability to solve a problem with artificial intelligence depends heavily on how you frame your problem and whether you can establish ground truth without ambiguity. We use ground truth as … [Read more...] about 9 common mistakes executives make with data