Peter James Thomas

OCTOBER 30, 2018

Note: Not all of the organisations I have worked with or for have had a C-level Executive accountable primarily for Marketing. Where they have, I have normally found the people holding these roles to be better informed about data matters than their peers. The same goes in general for Marketing Managers.

Marketing 51

Marketing Strategy Data Architecture Data Strategy 51

erwin

SEPTEMBER 17, 2020

Replace manual and recurring tasks for fast, reliable data lineage and overall data governance. It’s paramount that organizations understand the benefits of automating end-to-end data lineage. The importance of end-to-end data lineage is widely understood and ignoring it is risky business.

Cost-Benefit 143

Cost-Benefit Data Governance Metadata Reporting 143

AWS Big Data

DECEMBER 21, 2023

smava believes in and takes advantage of data-driven decisions in order to become the market leader. The Data Platform team is responsible for supporting data-driven decisions at smava by providing data products across all departments and branches of the company.

Data Lake 91

Data Lake Data Warehouse Data-driven B2B 91

Birst BI

FEBRUARY 25, 2020

However, analytic silos can still be a huge problem if the business intelligence platform paired with Snowflake does not offer the right balance of IT governance and end-user self-service. Customers such as Crossmark , DJO Global and others use Birst with Snowflake to deliver the ultimate modern data architecture.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Architecture Enterprise 52

Anmut

MARCH 24, 2022

We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching data transformation in a way that addresses the root causes of data problems and leads to tangible results and business success. It doesn’t have to be this way.

ROI 52

ROI Measurement Data-driven Data Transformation 52

IBM Big Data Hub

JULY 5, 2023

This was, without a question, a significant departure from traditional analytic environments, which often meant vendor-lock in and the inability to work with data at scale. Another unexpected challenge was the introduction of Spark as a processing framework for big data. Comprehensive data security and data governance (i.e.

Data Lake 63

Data Lake Metadata Cost-Benefit Data Warehouse 63