Smart Data Collective

SEPTEMBER 21, 2021

The increased amounts and types of data, stored in various locations eventually made the management of data more challenging. Challenges in maintaining data. As organizations keep using several applications, the data collected becomes unmanageable and inaccessible in the long run. Data quality and governance.

Management 105

Management Internet of Things Data Quality Data Integration 105

AWS Big Data

MARCH 26, 2024

A Gartner Marketing survey found only 14% of organizations have successfully implemented a C360 solution, due to lack of consensus on what a 360-degree view means, challenges with data quality, and lack of cross-functional governance structure for customer data. This is aligned to the five pillars we discuss in this post.

Data Strategy 66

Data Strategy Strategy Data Warehouse Prescriptive Analytics 66

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Alation

MAY 16, 2023

Data Pipeline Use Cases Here are just a few examples of the goals you can achieve with a robust data pipeline: Data Prep for Visualization Data pipelines can facilitate easier data visualization by gathering and transforming the necessary data into a usable state.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

FineReport

JULY 18, 2022

Most data analysts are very familiar with Excel because of its simple operation and powerful data collection, storage, and analysis. Key features: Excel has basic features such as data calculation which is suitable for simple data analysis. Price: KNIME Analytics Platform is an analytics tool free of cost.

Forecasting 52

Forecasting Dashboards Visualization Statistics 52

Andrew White

JANUARY 9, 2022

Much as the analytics world shifted to augmented analytics, the same is happening in data management. You can find research published on the infusion of ML in data quality, and also data catalogs, data discovery, and data integration. A data fabric that can’t read or capture data would not work.

Analytics 64

Analytics Measurement Modeling Data-driven 64

datapine

FEBRUARY 22, 2022

IT should be involved to ensure governance, knowledge transfer, data integrity, and the actual implementation. Before going all-in with data collection, cleaning, and analysis, it is important to consider the topics of security, privacy, and most importantly, compliance. Clean data in, clean analytics out.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

IBM Big Data Hub

AUGUST 4, 2023

Here are some advantages—and potential risk—to consider during this organizational change: Productivity Many companies look to data democratization to eliminate silos and get more out of their data across departments. By recognizing data as a product, it creates greater incentive to properly manage data.

Data Architecture 64

Data Architecture Data Lake Machine Learning Data Governance 64

DataKitchen

JANUARY 23, 2023

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. Data lineage does not directly improve data quality.

Testing 130

Testing Data Governance Data Quality Data-driven 130

O'Reilly on Data

DECEMBER 19, 2018

Data management isn’t limited to issues like provenance and lineage; one of the most important things you can do with data is collect it. Given the rate at which data is created, data collection has to be automated. How do you do that without dropping data? Toward a sustainable ML practice.

Machine Learning 261

Machine Learning Software Testing Metadata 261

erwin

JULY 17, 2019

Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there. Constructing A Digital Transformation Strategy: Data Enablement. Many organizations prioritize data collection as part of their digital transformation strategy.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

Jet Global

APRIL 14, 2022

Having accurate data is crucial to this process, but finance teams struggle to easily access and connect with data. Improve data quality. Δ The post Automate Your Yardi Real Estate Data Collection and Management appeared first on insightsoftware. Near real-time information is vital to: Save time.

Data Collection 52

Data Collection Management Finance Reporting 52

Jet Global

MAY 9, 2024

Batch processing pipelines are designed to decrease workloads by handling large volumes of data efficiently and can be useful for tasks such as data transformation, data aggregation, data integration , and data loading into a destination system. How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MAY 16, 2022

And using data collected during a close to make smart company decisions outside of finance is an emerging expectation for the Office of the CFO. This means real-time validation on XBRL documents to instantly flag any errors to improve overall quality in first and subsequent filings.

Finance 52

Finance Reporting Management Software 52