CIO Business Intelligence

SEPTEMBER 2, 2022

The average salary for a full stack software engineer is $115,818 per year, with a reported salary range of $85,000 to $171,000 per year, according to data from Glassdoor. Part of the role also includes continually improving the organization’s technology stack, while maintaining a priority for business continuity and risk management.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

The average salary for a full stack software engineer is $115,818 per year, with a reported salary range of $85,000 to $171,000 per year, according to data from Glassdoor. Part of the role also includes continually improving the organization’s technology stack, while maintaining a priority for business continuity and risk management.

Finance 98

Finance IT Software Reporting 98

AWS Big Data

AUGUST 14, 2023

When are data products deprecated, and who is accountable for the consequences to their consumers? How do we define “risk” and “value” in the context of data products, and how can we measure this? Whose responsibility is it to justify the existence of a given data product?

Management 79

Management Risk Measurement Optimization 79

CIO Business Intelligence

APRIL 29, 2022

Launching a data-first transformation means more than simply putting new hardware, software, and services into operation. True transformation can emerge only when an organization learns how to optimally acquire and act on data and use that data to architect new processes.

Digital Transformation 111

Digital Transformation Strategy Data-driven Data Strategy 111

Sisense

SEPTEMBER 3, 2020

Besides strong technical skills (for instance, use of Hadoop, programming in R and Python , math, statistics), data scientists should also be able to tackle open-ended questions and undirected research in ways that bring measurable business benefits to their organization. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

O'Reilly on Data

JULY 15, 2019

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management. AI projects in financial services and health care.

Machine Learning 38

Machine Learning Management Enterprise Risk Management 38

CIO Business Intelligence

AUGUST 7, 2023

According to Rocket Software’s survey, nearly half of respondents (42%) noted that they think about IT risk daily (31%) and even multiple times a day (11%). Sixty-three percent of IT leaders even measure success within their IT organization by their ability to reduce risk.

Risk 84

Risk IT Management Risk Management 84

erwin

FEBRUARY 4, 2021

However, organizations still encounter a number of bottlenecks that may hold them back from fully realizing the value of their data in producing timely and relevant business insights. So what’s holding organizations back from fully using their data to make better, smarter business decisions? Data Governance Bottlenecks.

Data Governance 89

Data Governance Metadata Data Quality Risk Management 89

Andrew White

JANUARY 11, 2021

What are you seeing as the differences between a Chief Analytics Officer and the Chief Data Officer? Value Management or monetization. Risk Management (most likely within context of governance). Product Management. measuring value, prioritizing (where to start), and data literacy? Governance.

Data Analytics 52

Data Analytics Analytics Data-driven Finance 52

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Outline clear metrics to measure success. Document assumptions and risks to develop a risk management strategy. Data aggregation such as from hourly to daily or from daily to weekly time steps may also be required. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66