A few years ago, Qlik ran a campaign called the Whole Story. The related video is still posted (fun fact: It was filmed in Qlik’s office near San Francisco). Our message was that you don’t know the whole story in your data if you a.) aren’t able to look at all the data while b.) understanding all the relationships between data.
Today, we are in the information age with a tremendous amount of data being created (as much as 90% of data being created in the last two years alone). This data comes from a wide range of sources and takes many different forms: human-generated documents and social media communications; transactional data that we use to run our businesses; and there is an ever-increasing proliferation of sensors producing streams of data.
One big mistake I see organizations make when starting out on their data governance journey is forgetting the rationale behind data. So don’t just govern to govern. Whether you need to minimize risks or maximize your benefits, link your data governance projects to clear and measurable outcomes. As data governance is a non-departmental initiative, but rather a company-wide initiative, you will need to prove its value from the start to convince leaders to prioritize and allocate some resources.
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.
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.
Sabre Airline Solutions (Sabre) supplies applications to airlines that enable them to manage a variety of planning tasks and strategic operations, including crew schedules, flight paths, and weight and balance for aircraft.
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?
