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Building Culturally Inclusive AI Models For us to build the most effective technology, we need to learn from our past and build our future with more comprehensive data sets with cultural intelligence. This means AI that recognizes your story, chatbots that speak to you more authentically, and smart assistants that have inclusive data about your ancestry. |
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Selecting a Database for Your Data Needs Data requirements vary, and with so many different types of databases, selecting which to use can be a tricky choice. It all depends on what your database will be used for, the data structure, and the scale of data. Let’s look at six popular database types so you can decide which would be best for your situation. |
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4 Key Factors Driving Digital Transformation There are so many strong reasons why digital transformation has become big, but many organizations are missing a major opportunity by simply running digital projects instead of fully transforming the organization itself—similar to doing some agile things without actually committing to being agile. |
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Data Means Nothing if You Don’t Know How to Use, Analyze, and Interpret It Simply having data stowed away and ready to use when needed is great and all, but if you don’t have a smart strategy for how to not only analyze and interpret it, but also put it to proper use, then you may end up creating a connected ecosystem without a real purpose. |
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Ensure That Your Current Cloud Solution Will Stand the Test of Time It’s still early in the lifecycle of cloud adoption. This means certain cloud vendors and technologies will fall by the wayside as adoption takes on critical mass. How, then, do you future-proof your cloud solution to make sure you don’t make a decision that you’ll regret later? Here are three ideas to consider. |
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Data-Driven Testing Skills in an Agile and DevOps World For agile and DevOps, an understanding of the role of data analysis in the test strategy is helping teams accelerate development, testing, and deployments. As we continue to enhance our testing effectiveness, data analytics skills are an important dimension in managing risks in a “continuous everything” world. |
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Test Your Data Quality to Increase the Return on Your QA Investment With the high volume of data coming into your organization, it’s important that it be complete, correct, and timely. But considering the velocity at which this data is moving, how do you measure its current quality? You must be able to test it wherever it sits still enough to be viewable, without altering it. |
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What You Should Consider to Make the Best Use of Your Collected Data We live in a world where data is constantly being recorded. In software, determining the timing of when to use that data is critical to making the most of the information. You should take into account data freshness, the data-gathering processes and any dependencies between them, and when to distribute information. |