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This Ten Mistakes to Avoid focuses on helping organizations make the transition from on-premises data and analytics platforms to cloud-based deployments more efficiently and thoughtfully.
Despite enterprises' attempts to adjust to a rapidly changing business landscape, many of yesterday’s problems remain front and center. What worked in the past is often outdated today. In this issue, we focus on modern-day solutions for age-old problems, from staffing and executive support to teamwork and skills development.
This Ten Mistakes to Avoid focuses on helping organizations sidestep QA problems that many DW projects experienced.
This Ten Mistakes to Avoid focuses on key issues facing organizations as they determine strategies for generating value from IoT data.
In 2018, the average salary dropped for the first time since 2015, but nearly all individual employees saw their salaries rise from the previous year. Read more in the 2019 TDWI Salary, Roles, and Responsibilities Report.
This issue of the Business Intelligence Journal looks at a variety of ways that speed—speed of response through data sampling, speed of design through agile data governance, and more—can contribute to the health and welfare of your enterprise.
TDWI’s annual Teams, Skills, and Budgets Report (formerly known as the TDWI BI Benchmark Report) enables business data and analytics teams to compare themselves to their peers on a series of organizational and performance metrics. This year’s report is based on a Web survey of 158 BI professionals conducted worldwide in spring and early summer 2018.
The data lake came seemingly out of nowhere in 2016 and quickly became a common approach to capturing, managing, and presenting extremely large quantities of highly diverse data. Today, data lakes are in production in several data-driven business use cases, including modern data warehouse environments, analytics
programs, omnichannel marketing data ecosystems, and digital supply chains. Though data lakes are still quite new, TDWI has seen enough implementations to know what works and what doesn’t. And The mistakes of data lakes are mostly about mindset.
By Patty Haines
Data quality is essential to getting more value from your organization’s data assets. Analysts, data scientists, and managers must know and understand the quality of the data they are using to make decisions and to set direction for their organizations if they are to make the best decisions.
Best seller lists have long been populated by business books explaining how to be a better leader or how to get things done. In this issue of the Business Intelligence Journal, we look at the characteristics of a good analytics leader and the new technologies these leaders must understand so users can be more efficient and enterprises can achieve more.
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