In Yellowfin 8 we introduced two new products, Yellowfin Signals, to automatically discover the most critical changes in your business as they happen, and Yellowfin Stories, to provide context to the numbers and tell compelling stories with your data. We’ve received great feedback since then and implemented some of the requested enhancements to improve the user experience, increase governance and performance.

In my last post, I gave you the first six Do’s and Don’ts of Data Governance and promised to bring together an additional six to consider when making a data governance plan for your organization. Here are six more dos and don'ts when building your data governance framework.

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.