O'Reilly on Data

JUNE 18, 2019

As model building become easier, the problem of high-quality data becomes more evident than ever. Even with advances in building robust models, the reality is that noisy data and incomplete data remain the biggest hurdles to effective end-to-end solutions. Data integration and cleaning.

Machine Learning 259

Machine Learning Data Quality Statistics Modeling 259

Smarten

MAY 13, 2022

Tech research and analysis firm, Gartner predicts that, ‘Through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives,’ and that prediction applies to all types of industries and vertical business sectors, including finance and accounting.

Analytics 76

Analytics Cost-Benefit Finance Data Quality 76

O'Reilly on Data

MARCH 24, 2020

You may picture data scientists building machine learning models all day, but the common trope that they spend 80% of their time on data preparation is closer to the truth. This definition of low-quality data defines quality as a function of how much work is required to get the data into an analysis-ready form.

Machine Learning 347

Machine Learning Statistics Data Quality Modeling 347

Smarten

APRIL 18, 2023

Digital Transformation and Citizen Data Scientists Go Hand-in-Hand! Gartner predicts that, ‘50% of organizations will adopt modern data quality solutions to better support their digital business initiatives.’

Digital Transformation 52

Digital Transformation Predictive Modeling Data Quality Enterprise 52

Andrew White

MAY 4, 2020

So conventional wisdom (see second example below) was that you needed to focus heavily on a broad data quality program. The issue for this first example however is not data quality; it’s about the data. The models are practically useless. As a result, the guidance for the decisions are useless too.

Uncertainty 90

Uncertainty Data Quality Consulting Predictive Modeling 90

Smarten

FEBRUARY 13, 2022

The world-renowned technology research firm, Gartner, predicts that, ‘through 2024, 50% of organizations will adopt modern data quality solutions to better support their digital business initiatives’. As businesses consider the options for data analytics, it is important to understand the impact of solution selection.

Analytics 52

Analytics Key Performance Indicator Business Intelligence ROI 52

Smarten

NOVEMBER 26, 2023

You may be interested to know that TechJury reports seven out of ten businesses rate data discovery as very important, and that the top three business intelligence trends are data visualization, data quality management and self-service business intelligence.

Marketing 40

Marketing Business Intelligence Key Performance Indicator Prescriptive Analytics 40

BI-Survey

NOVEMBER 24, 2020

It not only increases the speed and transparency of decisions and their quality, but it is also the foundation for the use of predictive planning and forecasting powered by statistical methods and machine learning. Faster information, digital change and data quality are the greatest challenges.

Forecasting 94

Forecasting Predictive Modeling Statistics Data-driven 94

Cloudera

SEPTEMBER 7, 2023

The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.

Insurance 85

Insurance Risk Data-driven Finance 85

Smarten

DECEMBER 3, 2023

‘Giving your team the right tools and a simple way to manage the overwhelming flow of data is crucial to business success.’ So, what does all this mean to your business? Why is augmented analytics an important factor in your success? The typical business will find it difficult to achieve approval for a new software solution.

Analytics 40

Analytics Cost-Benefit Data Quality Consulting 40

datapine

AUGUST 14, 2019

Therefore, there are numerous data science tools and techniques that provide scientists with an easier, more digestible workflow and powerful results. Our Top Data Science Tools. The tools for data science benefit both scientists and analysts in their data quality management and control processes.

Data Science 214

Data Science Statistics Business Intelligence Visualization 214

CIO Business Intelligence

APRIL 25, 2022

Data Science Dojo. Data Science Dojo is one of the shortest programs on this list, but in just five days, Data Science Dojo promises to train attendees on machine learning and predictive models as a service, and each student will complete a full IoT project and have the chance to enter a Kaggle competition.

Data Science 145

Data Science Machine Learning Deep Learning Statistics 145

AWS Big Data

MARCH 7, 2023

ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar. Unlike ingestion processes, data can be transformed as per business rules before loading. You can apply technical or business data quality rules and load raw data as well.

Analytics 72

Analytics Data Warehouse Data Lake Metadata 72

Smarten

NOVEMBER 3, 2017

These tools should include: Self-Serve Data Preparation – Allows business users to perform Advanced Data Discovery and auto-suggests relationships, reveals the impact and importance of key factors, recommends data type casts, data quality improvements and more!

Analytics 40

Analytics Visualization Experimentation Cost-Benefit 40

O'Reilly on Data

DECEMBER 12, 2019

Small residuals usually mean a model is right, and large residuals usually mean a model is wrong. Residual plots place input data and predictions into a two-dimensional visualization where influential outliers, data-quality problems, and other types of bugs often become plainly visible.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

Decision Management Solutions

NOVEMBER 13, 2018

This was the leading obstacle to high impact analytics, outscoring even poor data quality or a lack of strategic support or alignment. Following this approach, everyone can clearly see what analytic will fit in the decision, how precise it needs to be, what data is needed, how it can be deployed, measured, explained, and more.

Insurance 40

Insurance Analytics Modeling Visualization 40

Smart Data Collective

SEPTEMBER 26, 2021

Data visualization enables you to: Make sense of the distributional characteristics of variables Easily identify data entry issues Choose suitable variables for data analysis Assess the outcome of predictive models Communicate the results to those interested. It improves the data quality and system effectiveness.

Metadata 98

Metadata Visualization Unstructured Data Data mining 98

DataRobot Blog

DECEMBER 15, 2022

While AI-powered forecasting can help retailers implement sales and demand forecasting—this process is very complex, and even highly data-driven companies face key challenges: Scale: Thousands of item combinations make it difficult to manually build predictive models. Prepare your data for Time Series Forecasting.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

datapine

NOVEMBER 27, 2019

Companies are no longer wondering if data visualizations improve analyses but what is the best way to tell each data-story. 2020 will be the year of data quality management and data discovery: clean and secure data combined with a simple and powerful presentation. 1) Data Quality Management (DQM).

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

datapine

JANUARY 31, 2022

As Dan Jeavons Data Science Manager at Shell stated: “what we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business”.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

CIO Business Intelligence

JUNE 5, 2024

We were the go-to guys for any ML or predictive modeling at that time, but looking back it was very primitive.” “How do you maintain a single source of truth effectively if you have multiple people working on the same copy of a spreadsheet?” How do you know which version is the real one?

Optimization 90

Optimization Predictive Modeling Data-driven Modeling 90

datapine

DECEMBER 4, 2019

Moreover, as most predictive analytics capabilities available today are in their infancy — they have simply not been used for long enough by enough companies on enough sources of data – so the material to build predictive models on was quite scarce. Last but not least, there is the human factor again. Graph Analytics.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Jet Global

MAY 9, 2024

Machine Learning Pipelines : These pipelines support the entire lifecycle of a machine learning model, including data ingestion , data preprocessing, model training, evaluation, and deployment. API Data Pipelines : These pipelines retrieve data from various APIs and load it into a database or application for further use.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

JULY 17, 2023

One of the major challenges in most business intelligence (BI) projects is data quality (or lack thereof). In fact, most project teams spend 60 to 80 percent of total project time cleaning their data—and this goes for both BI and predictive analytics.

Predictive Analytics 52

Predictive Analytics Reporting Software Strategy 52

Jet Global

SEPTEMBER 1, 2023

Data-Driven Decision Making: Embedded predictive analytics empowers the development team to make informed decisions based on data insights. By integrating predictive models directly into the application, developers can provide real-time recommendations, forecasts, or insights to end-users.

Predictive Analytics 52

Predictive Analytics Risk Analytics Software 52