Jet Global

SEPTEMBER 1, 2023

But we’re also seeing its use expand in other industries, like Financial Services applications for credit risk assessment or Human Resources applications to identify employee trends. Analysts can use predictive analytics to foresee if a change will help them reduce risks, improve operations, and/or increase revenue.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52

datapine

APRIL 15, 2020

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. Working software over comprehensive documentation. Discover the available data sources.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

CIO Business Intelligence

JANUARY 23, 2024

Quality metrics can be used to measure the improvements that come from reducing defects, lowering the impacts of human errors, improving data quality, and other program outcomes that illustrate how increasing quality connects to business impact.

Measurement 134

Measurement Digital Transformation KPI Metrics 134

DataRobot Blog

JULY 21, 2022

Inquire whether there is sufficient data to support machine learning. Document assumptions and risks to develop a risk management strategy. For a credit risk model, the target could be defined as “fully repays loan” or “payments in first 2 years are current” or or “collateral is repossessed.”. Define project scope.

Machine Learning 98

Machine Learning Metrics Modeling Testing 98

datapine

FEBRUARY 17, 2023

That said, data and analytics are only valuable if you know how to use them to your advantage. Poor-quality data or the mishandling of data can leave businesses at risk of monumental failure. In fact, poor data quality management currently costs businesses a combined total of $9.7 million per year.

Analytics 191

Analytics Cost-Benefit Dashboards Business Intelligence 191

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. They can govern the implementation with a documented business case and be responsible for changes in scope. On the flip side, document everything that isn’t working. Clean data in, clean analytics out.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Cloudera

SEPTEMBER 7, 2023

But it’s also fraught with risk. This June, for example, the European Union (EU) passed the world’s first regulatory framework for AI, the AI Act , which categorizes AI applications into “banned practices,” “high-risk systems,” and “other AI systems,” with stringent assessment requirements for “high-risk” AI systems.

Insurance 81

Insurance Risk Data-driven Finance 81

O'Reilly on Data

MARCH 24, 2020

Why is high-quality and accessible data foundational? If you’re basing business decisions on dashboards or the results of online experiments, you need to have the right data. The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data.

Machine Learning 343

Machine Learning Statistics Data Quality Modeling 343

datapine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. If nothing can be changed, there is no point of analyzing data.

IT 317

IT Statistics KPI Data-driven 317

Jet Global

MAY 1, 2020

In Gartner’s report, an analyst goes to great pains to say that there is “much more risk associated to non-technology issues than there is to deploying the infrastructure, tools, and apps.”. We can almost guarantee you different results from each, and you end up with no data integrity whatsoever. Risk to the business.

Data Warehouse 64

Data Warehouse Data Quality Risk Reporting 64

FineReport

APRIL 26, 2023

” This type of Analytics includes traditional query and reporting settings with scorecards and dashboards. The tool is designed to be intuitive, so even users with limited technical expertise can create reports and dashboards quickly and easily. It supports master-slave replication for data backup.

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Jet Global

FEBRUARY 23, 2024

The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality.

Data Warehouse 52

Data Warehouse Reporting Data Transformation Sales 52

Ontotext

MAY 2, 2024

Data-centric approach In the data-centric approach, metadata serves as a layer of interoperability between the data sources. This powers numerous applications, insight generations, dashboards, and tools. It also enables business teams to have their data at their fingertips and have a uniform view of it.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

Alation

MARCH 21, 2023

Data security The travel industry collects, transmits, processes, and stores a wide range of personally identifiable information (PII) from customers, which are of interest to bad actors, as cybercriminals target this valuable data. Otherwise, they risk a data privacy violation.

Data Analytics 52

Data Analytics Analytics Data-driven Big Data 52

Sisense

FEBRUARY 10, 2021

We do this throughout a variety of industries, and as the head of the electric grid ecosystem and product center at DNV, my focus is on helping optimize risk assessment, remediation, and overall reliability of critical assets at our electric utility customers. At DNV , our mission is to safeguard life, property, and the environment.

Dashboards 52

Dashboards Enterprise Risk Software 52

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. Machine learning developers are beginning to look at an even broader set of risk factors.

Machine Learning 37

Machine Learning Management Enterprise Risk Management 37

datapine

AUGUST 29, 2022

And with that understanding, you’ll be able to tap into the potential of data analysis to create strategic advantages, exploit your metrics to shape them into stunning business dashboards , and identify new opportunities or at least participate in the process. Product/market fit is THE most important factor to get right.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Jet Global

MARCH 4, 2020

While this is important, what if you could grow your bottom line without having to take on expansion risks? How to Build Useful KPI Dashboards. Now it is time to look at some data management best practices. How to Keep Track of Your KPI Data. Consolidating the data into a central location can take a long time.

Manufacturing 80

Manufacturing Metrics Reporting KPI 80

Jet Global

OCTOBER 4, 2023

Finance teams are under pressure to slash costs while playing a key role in data strategy, yet they are still bogged down by manual tasks, overreliance on IT, and low visibility on company data. This expansion of responsibilities is exacerbating the well-documented trend of finance team burnout, leading to undesirable turnover.

Finance 52

Finance Reporting Cost-Benefit Management 52

DataRobot Blog

DECEMBER 20, 2022

The use of artificial intelligence (AI) in the investment sector is proving to be a significant disruptor, catalyzing the connection between the different players and delivering a more vivid picture of the future risk and opportunities across all different market segments. You can understand the data and model’s behavior at any time.

Management 59

Management Machine Learning Optimization Modeling 59

Grooper

AUGUST 19, 2019

It includes the reports, charts, dashboards, and terminology unique to your organization. ISL helps today's business leaders understand how data answers business questions. Master data management. Data governance. Scoring – i.e. profitability or risk. Structured, semi-structured, and unstructured data.

Analytics 54

Analytics Machine Learning Data Science Data-driven 54

Andrew White

MARCH 22, 2019

The result was that both end users (non IT companies) in public and private organizations, and vendors, changed direction: clients looked for quick fixes to help avoid risk; vendors refocused away from their current trajectory of developing solid information stewardship solutions toward the ‘easy money’ of GDPR. It is a great question.

Analytics 68

Analytics Metadata Data Governance Management 68

Andrew White

JANUARY 9, 2022

There are many tactics to employ that might help – including data literacy; storytelling; use of our value pyramid/business value model; use of our decision intelligence model. Is there a good map that shows the connections between data, advanced analytics, digital, innovation, etc. That is the key. – Yes and no.

Analytics 64

Analytics Measurement Modeling Data-driven 64