DataKitchen

MAY 10, 2024

Data Evaluation Before new data sets are introduced into production environments, they must be thoroughly evaluated and cleaned. This initial stage of data observability ensures that data quality is maintained from the start, preventing errors that could affect downstream processes and decisions.

Data Quality 141

Data Quality Dashboards Strategy Testing 141

Jim Harris

JANUARY 1, 2018

The Chicken Littles of Data Quality use sound bites like “data quality problems cost businesses more than $600 billion a year!” or “poor data quality costs organizations 35% of their revenue!” Furthermore, the reason that citing specific examples of poor data quality (e.g.,

Data Quality 77

Data Quality Cost-Benefit Consulting Dashboards 77

DataKitchen

FEBRUARY 27, 2024

However, the foundation of their success rests not just on sophisticated algorithms or computational power but on the quality and integrity of the data they are trained on and interact with. The Imperative of Data Quality Validation Testing Data quality validation testing is not just a best practice; it’s imperative.

Data Quality 117

Data Quality Unstructured Data Testing Modeling 117

DataKitchen

MARCH 5, 2024

The Stakeholder Confidence Crisis Relying on hope as a data accuracy and integrity strategy is fraught with risks. Stakeholders, from internal teams to external clients, seek confidence in the data they use and depend on. You will be in trouble if you are not measuring data quality or delivering error rates and SLAs.

Data Quality 146

Data Quality Measurement Testing Data-driven 146

DataRobot Blog

MAY 26, 2022

To start with, SR 11-7 lays out the criticality of model validation in an effective model risk management practice: Model validation is the set of processes and activities intended to verify that models are performing as expected, in line with their design objectives and business uses.

Risk 52

Risk Modeling Metrics Business Objectives 52

CIO Business Intelligence

MAY 8, 2024

So depending on where the data comes from, it can be terrible or very easy and can take half an hour if you’re lucky, or the whole day if you’re not. All of this stems from understanding where your data is. The risk profile was very different to what it is now. You need to have a clear view of all your information assets.

Risk 108

Risk Testing Strategy Technology 108

DataKitchen

JUNE 19, 2023

Another way to look at the five pillars is to see them in the context of a typical complex data estate. .” – Take A Bow, Rihanna (I may have heard it wrong) Validating data quality at rest is critica l to the overall success of any Data Journey. Start checking data at rest with a strong data profile.

Testing 130

Testing Cost-Benefit Metrics Data Quality 130

Rocket-Powered Data Science

FEBRUARY 15, 2023

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? Clean it, annotate it, catalog it, and bring it into the data family (connect the dots and see what happens). Test early and often. Test and refine the chatbot.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

DataKitchen

OCTOBER 20, 2023

Deploying a Data Journey Instance unique to each customer’s payload is vital to fill this gap. Such an instance answers the critical question of ‘Dude, Where is my data?’ ’ while maintaining operational efficiency and ensuring data quality—thus preserving customer satisfaction and the team’s credibility.

Insurance 169

Insurance Metadata Data-driven Data Quality 169

DataKitchen

NOVEMBER 18, 2022

As he thinks through the various journeys that data take in his company, Jason sees that his dashboard idea would require extracting or testing for events along the way. So, the only way for a data journey to truly observe what’s happening is to get his tools and pipelines to auto-report events. Data and tool tests.

Testing 130

Testing Statistics Measurement Metrics 130

DataKitchen

OCTOBER 2, 2023

.’ What’s a Data Journey? Data Journeys track and monitor all levels of the data stack, from data to tools to code to tests across all critical dimensions. A Data Journey supplies real-time statuses and alerts on start times, processing durations, test results, and infrastructure events, among other metrics.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

CIO Business Intelligence

DECEMBER 19, 2023

Third, in the CDO Agenda: 2024: Navigating Data and Generative AI Frontiers , 57% of respondents haven’t changed their data environments to support generative AI. CIOs should look for other operational and risk management practices to complement transformation programs.

Digital Transformation 142

Digital Transformation Unstructured Data Machine Learning Risk Management 142

O'Reilly on Data

JULY 28, 2020

Product Managers are responsible for the successful development, testing, release, and adoption of a product, and for leading the team that implements those milestones. This isn’t always simple, since it doesn’t just take into account technical risk; it also has to account for social risk and reputational damage.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

JANUARY 9, 2024

ChatGPT caused quite a stir after it launched in late 2022, with people clamoring to put the new tech to the test. Our firm’s leaders] wanted to make sure there were guidelines in place to protect the company, its data, and its people.” 1 among the top three risks — followed by statistical validity and model accuracy.

IT 131

IT Risk Cost-Benefit Enterprise 131

DataKitchen

FEBRUARY 21, 2023

are more efficient in prioritizing data delivery demands.” Release New Data Engineering Work Often With Low Risk: “Testing and release processes are heavily manual tasks… automate these processes.” Learn, improve, and iterate quickly (with feedback from the customer) with low risk.

Data-driven 130

Data-driven Testing Risk Cost-Benefit 130

CIO Business Intelligence

MAY 15, 2024

“If you have a data center that happens to have capacity, why pay someone else?” According to Synopsys’ open source security and risk analysis released in February, 96% of all commercial code bases contained open source components. It also focuses largely on risk and governance issues.

Modeling 135

Modeling Risk Software Enterprise 135

IBM Big Data Hub

NOVEMBER 29, 2023

They are already identifying and exploring several real-life use cases for synthetic data, such as: Generating synthetic tabular data to increase sample size and edge cases. You can combine this data with real datasets to improve AI model training and predictive accuracy. This is partially true for synthetic data.

Metrics 88

Metrics Machine Learning Risk Statistics 88

O'Reilly on Data

JUNE 19, 2019

More precisely, models built or tuned for specific applications (in reality, this means models + data ) will need to be managed and protected : A database for authorization and security: who has read/write access to certain models. A catalog or a database that lists models, including when they were tested, trained, and deployed.

Modeling 194

Modeling Machine Learning Testing Metrics 194

CIO Business Intelligence

MARCH 24, 2023

The Business Application Research Center (BARC) warns that data governance is a highly complex, ongoing program, not a “big bang initiative,” and it runs the risk of participants losing trust and interest over time.

Data Governance 142

Data Governance Management Metadata Data Quality 142

IBM Big Data Hub

NOVEMBER 24, 2023

Subsequent phases are build and test and deploy to production. Overall, Generative AI helps de-risk modernization programs via ensuring adequate visibility to legacy applications as well as dependencies. Let us explore the Generative AI possibilities across these lifecycle areas.

Cost-Benefit 99

Cost-Benefit Testing Data-driven Enterprise 99

CIO Business Intelligence

JUNE 9, 2023

It’s a time-tested truth: Getting a head start improves outcomes. A few years ago, Gartner found that “organizations estimate the average cost of poor data quality at $12.8 million per year.’” Beyond lost revenue, data quality issues can also result in wasted resources and a damaged reputation.

Strategy 114

Strategy Management Key Performance Indicator ROI 114

O'Reilly on Data

DECEMBER 12, 2019

1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. These recommendations are based on our experience, both as a data scientist and as a lawyer, focused on managing the risks of deploying ML. 6] Debugging may focus on a variety of failure modes (i.e.,

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

O'Reilly on Data

OCTOBER 22, 2020

It’s also a good indirect measure of training data quality: a team that does not know where their data originated is likely to not know other important details about the data as well. ML models directly involved in critical safety systems are generally easy to identify as areas of concern.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

O'Reilly on Data

MARCH 24, 2020

The assumed value of data is a myth leading to inflated valuations of start-ups capturing said data. John Myles White , data scientist and engineering manager at Facebook, wrote: “The biggest risk I see with data science projects is that analyzing data per se is generally a bad thing.

Machine Learning 346

Machine Learning Statistics Data Quality Modeling 346

Ontotext

FEBRUARY 12, 2024

Domain teams should continually monitor for data errors with data validation checks and incorporate data lineage to track usage. Establish and enforce data governance by ensuring all data used is accurate, complete, and compliant with regulations. This calls for additional planning, documentation, and testing.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Sisense

AUGUST 28, 2020

Uncomfortable truth incoming: Most people in your organization don’t think about the quality of their data from intake to production of insights. However, as a data team member, you know how important data integrity (and a whole host of other aspects of data management) is. Data integrity risks.

Data Integration 101

Data Integration Testing Data Quality Data-driven 101

datapine

APRIL 15, 2020

Your Chance: Want to test an agile business intelligence solution? Business intelligence is moving away from the traditional engineering model: analysis, design, construction, testing, and implementation. Test BI in a small group and deploy the software internally. Finalize testing. Without further ado, let’s begin.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

IBM Big Data Hub

JANUARY 25, 2024

Over time, inventory managers have tested different approaches to determine the best fit for their organizations. Considering that IDC surveyed 37% of companies that manage spare parts inventory by using spreadsheets, email, shared folders or an uncertain approach, it becomes evident that this practice carries more risk than it might seem.

Optimization 81

Optimization Cost-Benefit Prescriptive Analytics Manufacturing 81

erwin

JANUARY 11, 2019

It’s only when companies take their first stab at manually cataloging and documenting operational systems, processes and the associated data, both at rest and in motion, that they realize how time-consuming the entire data prepping and mapping effort is, and why that work is sure to be compounded by human error and data quality issues.

Data Governance 40

Data Governance Risk Metadata Management 40

CIO Business Intelligence

APRIL 11, 2024

Oracle’s cloud pitch Kevin Thompson, CEO at Tricentis, a provider of testing and engineering products, sees Oracle as a solid contender in the enterprise cloud space, thanks to its key differentiation from more established rivals. The inherent risk is trust. Oracle requires all Fusion customers to upgrade every quarter,” he says.

Enterprise 125

Enterprise IT Data Processing Strategy 125

CIO Business Intelligence

MARCH 7, 2024

Public LLMs in the sandbox: Safely test publicly available Large Language Models (LLMs) in a sandbox environment, separate from the production setting, to assess their impact without risking operational disruptions. Many businesses are in the exploratory phases, creating unique business cases and testing their theory on uses.

Digital Transformation 73

Digital Transformation Machine Learning Risk Testing 73

IBM Big Data Hub

SEPTEMBER 15, 2023

These requirements will likely mandate publicly traded companies to disclose their greenhouse gas (GHG) emissions footprint, climate-related goals, and progress, as well as climate-risk related financial impact and expenditures. for emissions inventories) and the multitude of data that needs integration and conversion.

Risk 79

Risk Reporting Data-driven Publishing 79

Alation

JULY 15, 2022

I recently led an online session, Data Monetisation and Governance , looking at the evolution of data governance , defining data ethics (from the Turing Institute ), and touching on the balancing act between using data to monetise (by increasing revenue, decreasing spend, or mitigating risk) and meeting ethical obligations.

Cost-Benefit 89

Cost-Benefit Data Governance ROI Risk 89

CIO Business Intelligence

NOVEMBER 13, 2023

Transformational leaders must ensure their organizations have the expertise to integrate new technologies effectively and the follow-through to test and troubleshoot thoroughly before going live. Ensuring data quality, privacy, and security is essential.

Digital Transformation 142

Digital Transformation Cost-Benefit Experimentation Measurement 142

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

Alation

APRIL 18, 2023

For any data user in an enterprise today, data profiling is a key tool for resolving data quality issues and building new data solutions. In this blog, we’ll cover the definition of data profiling, top use cases, and share important techniques and best practices for data profiling today.

IT 52

IT Metadata Data Quality Data Governance 52

Anmut

APRIL 6, 2023

You can think of a data maturity assessment as a health check-up for your organisation’s data practices, just like how a doctor evaluates your physical health by checking your vitals and running tests, a data maturity assessment evaluates your organisation’s data management.

Cost-Benefit 52

Cost-Benefit Data Quality Data Governance Measurement 52