In October 2018, TDWI and Talend asked over 200 architects, IT and Analytics managers, directors and VPs, and a mix of data professionals about their cloud data warehouse strategy in a survey conducted in October 2018. We wanted to get real answers about how companies are moving to the cloud, especially with the recent rise of Cloud Data Warehouse technologies. For instance, we wanted to know if a cloud data warehouse (CDW) is seen as a key driver of digital transformation.

I started my Qlik journey as a customer and there was a reason why I fall in love with the technology; its one-of-a-kind Associative technology that helped me ask the unknown questions by surfacing the hidden associations and connections between data values.

APIs securely expose key enterprise data and services to internal stakeholders and external developers. They can also generate a goldmine of data. As you grow your API programs to reinvent operations, build modern applications, and create ecosystems, you can also use key API data to answer some important questions: Which customers are using my APIs? How do I categorize my customers? Should I monetize my APIs? How should I build my API revenue model and rate plan?

As the popularity of home automation and the cost of electricity grow around the world, energy conservation has become a higher priority for many consumers. With a number of smart meter devices available for your home, you can now measure and record overall household power draw, and then with the output of a machine learning model, accurately predict individual appliance behavior simply by analyzing meter data.

Machine learning is in the ascendancy. Particularly when it comes to pattern recognition, machine learning is the method of choice. Tangible examples of its applications include fraud detection, image recognition, predictive maintenance, and train delay prediction systems. In day-to-day machine learning (ML) and the quest to deploy the knowledge gained, we typically encounter these three main problems (but not the only ones).

The 2019 Gartner Magic Quadrant for Analytics and Business Intelligence Platforms report is out. And I am sure that the Marketing departments of many analytics vendors are in a frenzy of activity to get their story out with as much spin as possible.

In the modern SaaS world, observability is key to running software reliability, managing risks and deriving business value out of the code that you’re shipping. To measure how your service is performing, you record Service Level Indicators (SLIs) or metrics, and alert whenever performance, correctness or availability is affected.

Getting the insights you need quickly is essential to understanding and improving your QA process. With that in mind, we’re rolling out a few new features and updates to help teams gain insight into the state of software testing for their team quickly and efficiently.

Today’s always a big day in the analytics world – the release of the annual Gartner Magic Quadrant for Analytics and Business Intelligence. Qlik continues its streak of being positioned in the leader’s quadrant, with today marking our ninth consecutive year. That is consistency in vision and execution a customer can trust. So how did we get here?

When it comes to the supply chain, it can be a complex and challenging process and one which is difficult to manage because you will be relying on other companies.