IBM Big Data Hub

APRIL 24, 2024

Some common principles and practices organizations use to bolster data privacy include: Data visibility For effective data governance, an organization needs to know the types of data it has, where the data resides, and how it is used. The company conducts regular audits to keep the data inventory up to date.

Risk 79

Risk Measurement Data Governance Insurance 79

IBM Big Data Hub

FEBRUARY 23, 2024

The GDPR applies to any organization that processes the personal data of European residents, regardless of where that organization is based. Second, a comprehensive inventory makes it easier to comply with user requests to share, update, or delete their data. Organizations may need to update consent forms to meet these requirements.

Measurement 76

Measurement Risk Data Collection Data Processing 76

IBM Big Data Hub

MAY 28, 2024

While every data protection strategy is unique, below are several key components and best practices to consider when building one for your organization. What is a data protection strategy? Its principles are the same as those of data protection—to protect data and support data availability.

Strategy 83

Strategy Measurement Risk Cost-Benefit 83

O'Reilly on Data

JULY 28, 2020

These measures are commonly referred to as guardrail metrics , and they ensure that the product analytics aren’t giving decision-makers the wrong signal about what’s actually important to the business. When a measure becomes a target, it ceases to be a good measure ( Goodhart’s Law ). Data Wrangling and Feature Engineering.

Marketing 363

Marketing Experimentation Metrics Testing 363

IBM Big Data Hub

JANUARY 22, 2024

The practical steps an organization takes to meet these requirements will depend on its location, resources and data processing activities, among other factors. The GDPR defines the circumstances under which companies can legally process personal data. It cannot change the basis after the fact unless it has user consent to do so.

Measurement 81

Measurement Risk Data Processing Data Collection 81

Smart Data Collective

DECEMBER 21, 2022

Data that is unsystematic and includes unnecessary information can not only needlessly strain IT systems but can also attract cyber attackers who seek out weaknesses in network infrastructures. Tips for successful data cleansing. Data cleansing isn’t a one-time activity.

Business Analytics 109

Business Analytics Analytics Data Quality Big Data 109

Cloudera

MAY 9, 2019

The number one challenge that enterprises struggle with their IoT implementation is not being able to measure if they are successful or not with it. Data readiness – These set of metrics help you measure if your organization is geared up to handle the sheer volume, variety and velocity of IoT data.

IoT 56

IoT Modeling Metrics Measurement 56

Jet Global

SEPTEMBER 29, 2021

Provide Access to Quality Data. Q uality data is accurate, relevant, and complete. Although ensuring data collection is accurate and reliable is important, leveraging data for strategic decision-making is essential. Make data accessible to all employees who need it to make decisions. Avoid “Vanity Metrics’.

Data-driven 52

Data-driven Finance Data Collection Metrics 52

FineReport

MAY 31, 2023

Return on assets measures the net profit generated per unit of asset, while return on equity (ROE) signifies the return on shareholders’ equity, indicating the efficiency of the company’s own capital. Inaccurate or inconsistent data can lead to erroneous conclusions and decisions.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

IBM Big Data Hub

SEPTEMBER 27, 2022

AI model governance introduces technology to implement guardrails at each stage of the AI/ML lifecycle. This includes data collection, instrumenting processes and transparent reporting to make needed information available for all the stakeholders. Recently a French banking group was faced with new compliance measures.

Consulting 52

Consulting Machine Learning Modeling Strategy 52

Cloudera

FEBRUARY 16, 2022

blueberry spacing) is a measure of the model’s interpretability. Model interpretability is one of five main components of model governance. We can think of model lineage as the specific combination of data and transformations on that data that create a model. The complete list is shown below: Model Lineage .

Machine Learning 85

Machine Learning Modeling Metadata Recreation/Entertainment 85

IBM Big Data Hub

MARCH 26, 2024

While ESG seeks to provide standard methods and approaches to measuring across environmental, social and governance KPIs, and holds organizations accountable for that performance, sustainability is far broader. Sustainability and ESG: An opportunity for synergy Sustainability and ESG are not synonymous.

Measurement 61

Measurement Metrics Strategy Reporting 61

Alation

MAY 16, 2023

Sources can include analytics data regarding user behavior, transactional data from ecommerce websites, and third-party data from other organizations. It’s worth noting that a data pipeline may have more than one data source. Ingestion tools are connected to various data sources.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

erwin

APRIL 21, 2020

By combining physical system catalogs, critical data elements, and key performance measures with clearly defined product and sales goals, you can manage the effectiveness of your business and ensure you understand what critical systems are for business continuity and measuring corporate performance.

Measurement 97

Measurement Sales Data Governance Metadata 97

erwin

APRIL 21, 2020

So one of the biggest lessons we’re learning from COVID-19 is the need for data collection, management and governance. What’s the best way to organize data and ensure it is supported by business policies and well-defined, governed systems, data elements and performance measures?

Measurement 96

Measurement Sales Data Governance Metadata 96

Alation

MAY 16, 2023

Sources can include analytics data regarding user behavior, transactional data from ecommerce websites, and third-party data from other organizations. It’s worth noting that a data pipeline may have more than one data source. Ingestion tools are connected to various data sources.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

AWS Big Data

MARCH 26, 2024

In this post, we discuss how you can use purpose-built AWS services to create an end-to-end data strategy for C360 to unify and govern customer data that address these challenges. We recommend building your data strategy around five pillars of C360, as shown in the following figure.

Data Strategy 69

Data Strategy Strategy Data Warehouse Prescriptive Analytics 69

Andrew White

JANUARY 9, 2022

First, how we measure emissions and carbon footprint is about data design and policy. In other words, D&A plays a key role in the foundational measuring angle. Link to item 6 on slide 27 is broken, [link] , for Dashboard to measure business impact, can you provide a current link? This was from 2020.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Alation

JANUARY 17, 2023

Data would be pulled from various sources, organized into, say, a table, and loaded into a data warehouse for mass consumption. This was not only time-consuming, but the growing popularity of cloud data warehouses compelled people to rethink this process. Data governance is a key use case of the modern data stack.

Data Warehouse 52

Data Warehouse Cost-Benefit Data Transformation Data Science 52

Alation

DECEMBER 15, 2021

Yet high-volume collection makes keeping that foundation sound a challenge, as the amount of data collected by businesses is greater than ever before. An effective data governance strategy is critical for unlocking the full benefits of this information. Data governance requires a system.

Data Governance 52

Data Governance Strategy Metadata Data Strategy 52

Alation

FEBRUARY 13, 2020

More often than not, today, the key to unlocking that accomplishment sits within a tsunami of data: data collected from consumers, applications, and sensors. Each of your organizations has something impossible to accomplish. I don’t know exactly what it is, but I know it’s there. – John F.

Machine Learning 52

Machine Learning Data-driven Big Data Technology 52

Alation

NOVEMBER 11, 2021

As IT leaders oversee migration, it’s critical they do not overlook data governance. Data governance is essential because it ensures people can access useful, high-quality data. Therefore, the question is not if a business should implement cloud data management and governance, but which framework is best for them.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

Alation

MARCH 5, 2021

Data governance defines how data should be gathered and used within an organization. It address core questions, such as: How does the business define data? How accurate must the data be for use? Organizations have much to gain from learning about and implementing a data governance framework.

Data Governance 59

Data Governance Data Quality Dashboards Risk 59

datapine

FEBRUARY 22, 2022

This includes defining the main stakeholders, assessing the situation, defining the goals, and finding the KPIs that will measure your efforts to achieve these goals. A planned BI strategy will point your business in the right direction to meet its goals by making strategic decisions based on real-time data.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Alation

FEBRUARY 17, 2023

This past week, I had the pleasure of hosting Data Governance for Dummies author Jonathan Reichental for a fireside chat , along with Denise Swanson , Data Governance lead at Alation. Can you have proper data management without establishing a formal data governance program?

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

DataKitchen

JANUARY 23, 2023

Data lineage can also be used for compliance, auditing, and data governance purposes. DataOps Observability Five on data lineage: Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. What is missing in data lineage?

Testing 130

Testing Data Governance Data Quality Data-driven 130

Alation

AUGUST 26, 2021

It asks much larger questions, which flesh out an organization’s relationship with data: Why do we have data? Why keep data at all? Answering these questions can improve operational efficiencies and inform a number of data intelligence use cases, which include data governance, self-service analytics, and more.

Metadata 64

Metadata Data Governance Dashboards Software 64

Alation

AUGUST 2, 2022

Legacy technologies, siloed data, and manual processes make securing data and protecting privacy much more expensive and risky. After investing to develop these critical customer insights, a security breach can quickly damage trust and compromise the value of that data. 75% faster onboarding of analysts and data scientists.

Strategy 52

Strategy Cost-Benefit Metrics Data Lake 52