Alation

JULY 15, 2022

In the Cambridge Analytica case, the company went from a data strategy focused on monetisation by increased revenue to company closure due to the reputational damage from the negative media and public response. Clearly, using private Facebook data collected in a nefarious manner to sway political elections is not ethical.

Cost-Benefit 89

Cost-Benefit Data Governance ROI Risk 89

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 Marketing 52

Sisense

SEPTEMBER 3, 2020

Programming and statistics are two fundamental technical skills for data analysts, as well as data wrangling and data visualization. Data analysts in one organization might be called data scientists or statisticians in another. Database design is often an important part of the business analyst role.

Statistics 97

Statistics Metrics Visualization Finance 97

Alation

FEBRUARY 13, 2020

A Data Catalog is a collection of metadata, combined with data management and search tools, that helps analysts and other data users to find the data that they need, serves as an inventory of available data, and provides information to evaluate fitness data for intended uses. Conclusion.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

O'Reilly on Data

OCTOBER 22, 2020

Since I work in the AI space, people sometimes have a preconceived notion that I’ll only talk about data and models. So it’s definitely a double-edged sword. When it doesn’t work so well: This is a question a company definitely wants to answer before they’ve provided data or services to a counterparty.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

Domino Data Lab

JULY 22, 2019

There’s a really nice comfortable blend here of what’s important in business, in engineering, in data science, etc. I definitely want to provide some shout-outs. In data science, definitely, there are other people who’ve talked more about that and we’ll point to them. You know what? All righty.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

erwin

MARCH 4, 2021

Led by Frank Pörschmann of iDIGMA GmbH, an IT industry veteran and data governance strategist, it examined “ The What & Why of Data Governance.”. The What: Data Governance Defined. Data governance has no standard definition. Virginia residents also would be able to opt out of data collection.

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

O'Reilly on Data

MARCH 18, 2020

By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data. The logic in this case partakes of garbage-in, garbage out : data scientists and ML engineers need quality data to train their models. This is consistent with the results of our data quality survey.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Jedox

NOVEMBER 17, 2022

Manual, repetitive tasks being replaced by automated processes is definitely not new, but the impact of this on the accounting field leveraged through Artificial Intelligence (AI) and Machine Learning (ML) is still relatively new. The demands on the accounting industry have changed considerably in recent years.

Finance 75

Finance Cost-Benefit Forecasting Reporting 75

The Unofficial Google Data Science Blog

JULY 18, 2023

Reliability and validity: Definitions and measurement Two fundamental concepts emerged from this research: reliability and validity. Measurement challenges Assessing reliability is essentially a process of data collection and analysis. We derive our measurement of data quality, ICC, from the variance parameters in the model.$$

Measurement 52

Measurement Metrics Uncertainty Slice and Dice 52

Occam's Razor

AUGUST 13, 2012

The questions reveal a bunch of things we used to worry about, and continue to, like data quality and creating data driven cultures. Yehoshua I've covered this topic in detail in this blog post: Multi-Channel Attribution: Definitions, Models and a Reality Check. EU Cookies!) What's possible to measure.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

Domino Data Lab

JUNE 23, 2019

The biggest time sink is often around data collection, labeling and cleaning. Coming up with those creative signals you can extract from the raw data to use as better inputs to your machine learning models is something to definitely invest in. I think definitely in consumer companies they are real.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

AUGUST 29, 2022

Companies, both big and small, are seeking the finest ways to leverage their data into a competitive advantage. With that in mind, we have prepared a list of the top 19 definitive data analytics and big data books, along with magazines and authentic readers’ reviews upvoted by the Goodreads community. trillion each year.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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 Modeling Data-driven 64

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. Brace yourselves for impact.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. :). And you can definitely do brand vs non-brand analysis using these options. Six years go by.

Marketing Analytics 131

Marketing Analytics Marketing Analytics Measurement 131

datapine

FEBRUARY 22, 2022

Before going all-in with data collection, cleaning, and analysis, it is important to consider the topics of security, privacy, and most importantly, compliance. Businesses deal with massive amounts of data from their users that can be sensitive and needs to be protected. Clean data in, clean analytics out.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

DataKitchen

JUNE 24, 2021

Their definition of DataOps was that we do some automation and check a record count. Initially, they didn’t understand the definition and they let alone understand the potential. And by doing that you can very simply draw attention to areas and provide data, KPIs, and metrics that people will actually believe.

ROI 211

ROI Metrics Measurement Cost-Benefit 211

Alation

MARCH 5, 2021

How Alation Activates Data Governance. Why is Data Governance Important? As data collection and storage grow, so too does the need for data governance. Where data governance once focused primarily on compliance, the age of big data has broadened its applications. Data Governance Roles.

Data Governance 59

Data Governance Data Quality Dashboards Risk 59

Alation

FEBRUARY 17, 2023

Reichental describes data governance as the overarching layer that empowers people to manage data well ; as such, it is focused on roles & responsibilities, policies, definitions, metrics, and the lifecycle of the data. In this way, data governance is the business or process side. This is a very good thing.

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

O'Reilly on Data

MARCH 24, 2020

Beyond the autonomous driving example described, the “garbage in” side of the equation can take many forms—for example, incorrectly entered data, poorly packaged data, and data collected incorrectly, more of which we’ll address below. The model and the data specification become more important than the code.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Alation

JULY 6, 2021

But first, they need to understand the top challenges to data governance, unique to their organization. Source: Gartner : Adaptive Data and Analytics Governance to Achieve Digital Business Success. As data collection and volume surges, so too does the need for data strategy. Why Do Data Silos Happen?

Data Governance 52

Data Governance Metadata Data Quality Risk 52

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

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. The third challenge was around trusting the data. The fourth challenge was around using the data. Set consistent data policies.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

Alation

AUGUST 26, 2021

Data intelligence first emerged to support search & discovery, largely in service of analyst productivity. For years, analysts in enterprises had struggled to find the data they needed to build reports. This problem was only exacerbated by explosive growth in data collection and volume. Data lineage features.

Metadata 64

Metadata Data Governance Dashboards Software 64

Jet Global

MARCH 4, 2024

What is the best way to collect the data required for CSRD disclosure? The best way to collect the data required for CSRD disclosure is to use a system that can automate and streamline the data collection process, ensure the data quality and consistency, and facilitate the data analysis and reporting.

Reporting 52

Reporting Management Cost-Benefit Enterprise 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. Direct Multi-Data Source Connection.

Finance 52

Finance Reporting Management Software 52