CIO Business Intelligence

AUGUST 9, 2024

If after anonymization the level of information in the data is the same, the data is still useful. But once personal or sensitive references are removed, and the data is no longer effective, a problem arises. Synthetic data avoids these difficulties, but they’re not exempt from the need of a trade-off.

Risk 138

Risk Measurement Data Lake Data Collection 138

IBM Big Data Hub

FEBRUARY 9, 2023

Consider the following four key building blocks of data governance: People refers to the organizational structure, roles, and responsibilities of those involved in data governance, including those who own, collect, store, manage, and use data. So where are you in your data governance journey?

Data Governance 76

Data Governance Business Objectives Data Quality Measurement 76

erwin

FEBRUARY 28, 2019

The smart cities movement refers to the broad effort of municipal governments to incorporate sensors, data collection and analysis to improve responses to everything from rush-hour traffic to air quality to crime prevention. This can be accomplished with dashboards and constituent portals.

Digital Transformation 106

Digital Transformation Data Governance Data-driven Data Warehouse 106

datapine

FEBRUARY 22, 2022

A business intelligence strategy refers to the process of implementing a BI system in your company. 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. It’s that simple.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Dashboards 218

datapine

JANUARY 31, 2022

BI users analyze and present data in the form of dashboards and various types of reports to visualize complex information in an easier, more approachable way. Business intelligence can also be referred to as “descriptive analytics”, as it only shows past and current state: it doesn’t say what to do, but what is or was.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214

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 78

Data Strategy Strategy Data Warehouse Prescriptive Analytics 78

DataKitchen

MARCH 16, 2023

Under Efficiency, the Number of Data Product Owners metric measures the value of the business’s data products. Under Quality, the Data Quality Incidents metric measures the average data quality of datasets, while the Active Daily Users metric measures user activity across data platforms.

Metrics 130

Metrics Data Analytics Analytics Measurement 130

erwin

JUNE 11, 2020

Data Intelligence is the analysis of multifaceted data to be used by companies to improve products and services offered and better support investments and business strategies in place. Data intelligence can encompass both internal and external business data and information. Data quality management.

Key Performance Indicator 103

Key Performance Indicator KPI Data-driven Machine Learning 103

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 69

Data Lake Data Governance Data Warehouse Data Processing 69

Jet Global

FEBRUARY 25, 2025

Data cleansing is the process of identifying and correcting errors, inconsistencies, and inaccuracies in a dataset to ensure its quality, accuracy, and reliability. This process is crucial for businesses that rely on data-driven decision-making, as poor data quality can lead to costly mistakes and inefficiencies.

Cost-Benefit 52

Cost-Benefit Data Collection Finance Reporting 52

Alation

OCTOBER 12, 2021

Folks can work faster, and with more agility, unearthing insights from their data instantly to stay competitive. Yet the explosion of data collection and volume presents new challenges. Like many, the team at Cbus wanted to use data to more effectively drive the business. Evaluate and monitor data quality.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

CIO Business Intelligence

DECEMBER 10, 2024

And Article 3 is important in reference to GDPR. If companies share data with each other, they must protect privacy and cybersecurity another task for the CIO, he says. The CIO must establish, together with other company functions, retention times, which must be scheduled based on the actual use of data collected by connected devices.

Manufacturing 78

Manufacturing Data-driven Data Governance Marketing 78

Cloudera

FEBRUARY 14, 2018

Now I get to put my money where my mouth is – and turn my focus internally on how we at Cloudera can become more data-driven. We aspire to and are on the journey to be the best-run company on data, and to be our own best reference. operations, and our CISO’s team while we invest in and form a stronger data and analytics team.

Data-driven 44

Data-driven Data Quality Risk Interactive 44

Alation

NOVEMBER 11, 2021

Let’s take a look at some of the key principles for governing your data in the cloud: What is Cloud Data Governance? Cloud data governance is a set of policies, rules, and processes that streamline data collection, storage, and use within the cloud. This framework maintains compliance and democratizes data.

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

IBM Big Data Hub

AUGUST 4, 2023

In turn, they both must also have the data literacy skills to be able to verify the data’s accuracy, ensure its security, and provide or follow guidance on when and how it should be used. Make AI transparent Data democratization ensures data collection, model building, deploying, managing and monitoring are visible.

Data Architecture 59

Data Architecture Data Lake Machine Learning Data Governance 59

Alation

JANUARY 26, 2023

Offer the right tools Data stewardship is greatly simplified when the right tools are on hand. So ask yourself, does your steward have the software to spot issues with data quality, for example? 2) Always Remember Compliance Source: Unsplash There are now many different data privacy and security laws worldwide.

Data Governance 52

Data Governance Strategy Data Quality Data Collection 52

Alation

MAY 16, 2023

Machine Learning Data pipelines feed all the necessary data into machine learning algorithms, thereby making this branch of Artificial Intelligence (AI) possible. Data Quality When using a data pipeline, data consistency, quality, and reliability are often greatly improved.

Data Lake 40

Data Lake Data Governance Data Warehouse Data Processing 40

Peter James Thomas

AUGUST 29, 2018

I have since run and driven transformation in Reference Data, Master Data , KYC [3] , Customer Data, Data Warehousing and more recently Data Lakes and Analytics , constantly building experience and capability in the Data Governance , Quality and data services domains, both inside banks, as a consultant and as a vendor.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

Andrew White

JANUARY 9, 2022

– We see most, if not all, of data management being augmented with ML. 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.

Analytics 64

Analytics Measurement Data-driven Modeling 64

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. Again, it’s important to listen to data scientists, data engineers, software developers, and design team members when deciding on the MVP.

Marketing 364

Marketing Experimentation Metrics Testing 364

O'Reilly on Data

OCTOBER 22, 2020

The safest course of action is also the slowest and most expensive: obtain your training data as part of a collection strategy that includes efforts to obtain the correct representative sample under an explicit license for use as training data. Perhaps I can refer them to someone else in that case.).

Consulting 362

Consulting Risk Cost-Benefit Modeling 362

Occam's Razor

APRIL 21, 2014

The mistake we make is that we obsess about every big, small and insignificant analytics implementation challenge and try to fix it because we want 99.95% comfort with data quality. We wonder why data people are not loved. :). If there is nothing in the referring string, that visit is marked as Direct. Six years go by.

Marketing Analytics 132

Marketing Analytics Marketing Analytics Metrics 132

Depict Data Studio

MAY 12, 2020

Amanda went through some of the top considerations, from data quality, to data collection, to remembering the people behind the data, to color choices. COVID-19 Data Quality Issues. It’s really hard to make these apples to apples comparisons, as easy as it might seem since the data is so accessible.”.

Visualization 52

Visualization Uncertainty Data Collection Data Quality 52

The Unofficial Google Data Science Blog

JULY 18, 2023

While we can count the number of rods that measure a particular length, we struggle to provide a concrete reference value for something like the liberalness of a particular government. Measurement challenges Assessing reliability is essentially a process of data collection and analysis. We highly recommend you read that as well.

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Domino Data Lab

FEBRUARY 4, 2019

These two points provide a different kind of risk management mechanism which is effective for science, specifically data science. Of course, some questions in business cannot be answered with historical data. Instead they require investment, tooling, and time for data collection. Why does this matter?

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66

Jet Global

MAY 9, 2024

ETL is a specific type of data pipeline that focuses on the process of extracting data from sources, transforming it, and loading it into a destination, such as a data warehouse or data lake. ETL is primarily used for data warehousing and business intelligence applications. How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MARCH 4, 2024

To determine which elements of the CSRD and the ESRS you need to comply with, you will have to conduct a materiality assessment, which involves the following steps: Identify the ESG topics that are relevant for your sector and your business model, using the ESRS as a reference.

Reporting 52

Reporting Management Cost-Benefit Publishing 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. Use dynamic text based on variables you define to ensure all reports always reference the correct narrative or single data point.

Finance 52

Finance Reporting Management Software 52

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 observability and data lineage are complementary concepts.

Testing 130

Testing Data Governance Data Quality Data-driven 130