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Big Data and AI Blame Failures on Bad Data

Guest post by Bill Inmon “Bill Inmon is an American computer scientist, recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” Source: Wikipedia. An article headline — “BIG DATA/AI BLAME FAILURES ON BAD DATA” — caught my attention.
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How to innovate faster with API Management - Why API utilisation must improve to meet transformation demands

Without effective management and reuse, APIs will not deliver their digital transformation potential. There is an urgent need to make application programming interfaces (APIs) efficient and utilised more effectively. This need is being driven by the rapidly increased rate of digitisation that customers and business lines are demanding from CIOs and CTOs. APIs are a powerful tool that can be employed to deliver competitive advantage and market differentiation, the two biggest demands being placed on the technology team.

10 Best Rapid Prototyping Tools

Prototyping is made easier with the software. In order to create a prototype as close as possible to your final vision, the best prototyping tools use design features, navigation elements, and interactions. During product development, this can help you avoid costly reworks. Rapid prototyping should be part of any project, whether it is a mobile app or a website design.

These 6 Process Mining Benefits Can Help You Build a Business Case for Investment

How can you be sure your technology investment will have real, positive business impact? When it comes to tech purchases, organizations, like individual consumers, often experience buyer's remorse. When consumers buy apps or gadgets that don't deliver on the promise of making our lives better/easier/more glamorous, we relegate the unused tech to a drawer, some miscellaneous folder on our computer desktops, or worse, a landfill.

When is it Time to Upgrade Your Data Visualization Software?

Analytics and data visualization are essential parts of modern business intelligence (BI). These tools help you better understand your business and improve overall decision-making. However, as your data grows and your users' analysis needs evolve, it may be time to upgrade your visualization solution to achieve better capability and reporting.

Using Webhooks and ThoughtSpot Custom Actions

One of the most significant benefits of the modern data stack is the loosely coupled nature of each layer to help you adapt to change and capitalize on new business opportunities. You can choose the best solution which fits your need without long-term vendor commitments, and the risk of introducing complex integrations and IT management. One of the ways to achieve this loose coupling is through webhooks.

An Overview of API Lifecycle Management

Understanding the stages of API lifecycle management offers an overhead look at application programming interfaces so you can find opportunities for improvement. Below, you will find the three major stages of an API lifecycle. Each section offers a closer look at the steps professionals often address when optimizing API strategy, functionality, access control, workflows, and other critical features.

How to Test Autoscaling in Kubernetes

In an ideal world, you want to have precisely the capacity to manage the requests of your users, from peak periods to off-peak hours. If you need three servers to attend to all the requests at peak periods and just one server at off-peak hours, running three servers all the time is going to drive up expenses, and running just one server all the time is going to mean that during peak periods, your systems will be overwhelmed and some clients will be denied service.

API-first development and the case for API mocking

One morning, you realize you have a great idea for an API. You discuss it with your team, then start building out the business case and technical requirements. Where do you go from there? You could write out the business requirements for the API and then code it. Or you could describe your API in a specification language, like OpenAPI, and use that definition to improve your team's understanding of the API and do some early testing. But are either of these the best solution?