BI-Survey

APRIL 27, 2023

They plan to expand their use of this modeling technique and methodology. Lessons about data modeling, modernization, and automation include the following: Focus on fundamentals Companies place the highest priority on data quality, ease of use, analytics performance, and data governance.

Modeling 52

Modeling Data Warehouse Data Quality Business Driver 52

CIO Business Intelligence

OCTOBER 24, 2023

As CIOs prepare for the next wave of digital transformation, they must demonstrate shorter-term business impacts from technology investments and achieve larger innovation goals that evolve the organization’s business model.

Digital Transformation 144

Digital Transformation Data-driven Business Objectives Strategy 144

CIO Business Intelligence

MAY 4, 2022

Rasmussen and others acknowledge that this year’s list of business drivers may seem like a break from past years’ priorities, where transformation generally dominated. According to Gartner, the total of business-led IT spending averages up to 36% of the total formal IT budget. 13, respectively.

IT 135

IT Business Driver Reporting Uncertainty 135

CIO Business Intelligence

APRIL 20, 2022

After nearly 10 years at JetBlue, where he served as chief digital and technology officer, Eash Sundaram recently took on a new role: operating executive at Tailwind Capital, a private equity firm that focuses on mid-market companies in the business and industrial services markets. The model was ‘buy low, consolidate, and sell high.’

Business Driver 87

Business Driver Marketing Strategy Technology 87

BI-Survey

AUGUST 24, 2022

The primary technical drivers of increased usage are “self-service authoring tools” (73%), data preparation tools (48%), and “embedded BI/analytics” (38%). Business drivers. Focus on meeting the needs of power users first to develop useful data models and structures that other users can leverage. Key takeaways.

IT 52

IT Business Driver Data Quality Data-driven 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

Others argue that there will still be a unique role for the data scientist to deal with ambiguous objectives, messy data, and knowing the limits of any given model. With those stakes and the long forecast horizon, we do not rely on a single statistical model based on historical trends.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61