DataKitchen

MARCH 12, 2024

Our vision was to create a flexible, state-of-the-art data infrastructure that would allow our analysts to transform the data rapidly with a very low risk of error. After working with DataKitchen for a while, we noticed almost an absolute absence of data errors we didn’t catch earlier. That was amazing for the team.”

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Metrics Software Cost-Benefit Testing 117

Jim Harris

JANUARY 1, 2016

Lest my pith be misunderstood aplenty, this blog post provides more detail, plus links to related posts, about what I meant. 1 — Investigate Data quality is not exactly a riddle wrapped in a mystery inside an enigma. However, understanding your data is essential to using it effectively and improving its quality.

Data Quality 61

Data Quality Data Governance Cost-Benefit ROI 61

Alation

SEPTEMBER 7, 2021

This is the last of the 4-part blog series. In the previous blog , we discussed how Alation provides a platform for data scientists and analysts to complete projects and analysis at speed. In this blog we will discuss how Alation helps minimize risk with active data governance. Find Trusted Data.

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

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

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 Interactive 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

IBM Big Data Hub

MAY 9, 2024

Conversely, confidence in the accuracy and consistency of your data can minimize the risk of adverse health outcomes, rather than merely reacting to or causing them. Also, using predictive analytics can help identify trends, patterns and potential future health risks in your patients.

Data Governance 66

Data Governance Data Quality Insurance Predictive Analytics 66

IBM Big Data Hub

FEBRUARY 28, 2024

Successful strategic sourcing often results in process optimization, cost management, customer satisfaction, risk management , increased sustainability and other benefits. Sourcing teams are automating processes like data analysis as well as supplier relationship management and transaction management.

Strategy 95

Strategy Technology Cost-Benefit Risk Management 95

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

Smart Data Collective

SEPTEMBER 28, 2022

Data engineering services can analyze large amounts of data and identify trends that would otherwise be missed. If you’re looking for ways to increase your profits and improve customer satisfaction, then you should consider investing in a data management solution. Information management mitigates the risk of errors.

Big Data 105

Big Data Management Data Quality Cost-Benefit 105

IBM Big Data Hub

NOVEMBER 29, 2023

To build that trust and drive broad adoption, vendors of synthetic data generation tools will need to address two critical questions that many business leaders ask: Will synthetic data expose my business to additional data privacy risks? How accurately does synthetic data reflect my existing data?

Metrics 80

Metrics Machine Learning Statistics Risk 80

IBM Big Data Hub

APRIL 20, 2023

When consumers lose trust in a bank’s ability to manage risk, the system stops working. Banks and their employees place trust in their risk models to help ensure the bank maintains liquidity even in the worst of times. This can ensure that the decisions made are reliable and of high quality.

Risk 68

Risk Risk Management Reporting Metadata 68

IBM Big Data Hub

NOVEMBER 28, 2022

Provide early indicators of data quality. Poor data quality is one of the top barriers faced by organizations aspiring to be data-driven. Most data quality management approaches are reactive, triggered only when consumers complain to data teams about the integrity of datasets.

Metadata 56

Metadata Data Quality Data-driven Data Governance 56

Alation

AUGUST 9, 2022

But almost all industries across the world face the same challenge: they aren’t sure if their data is accurate and consistent, which means it’s not trustworthy. This can cause anything from day-to-day issues to significant business problems and risks. Many confuse data integrity with data quality.

Data Integration 52

Data Integration Data Quality Measurement Strategy 52

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

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)? I have developed a few rules to help drive quick wins and facilitate success in data-intensive and AI ( e.g., Generative AI and ChatGPT) deployments.

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

Laminar Security

DECEMBER 13, 2023

Close to 70% of respondents in an ISC report indicated that they believe their organization lacks requisite cybersecurity staff to handle cloud data risk effectively. Learn in this article how Laminar harnesses AI for data discovery and classification and reduces public cloud data risks.

Data-driven 119

Data-driven Machine Learning Risk Deep Learning 119

Cloudera

MARCH 3, 2022

In 2017, Anthem reported a data breach that exposed thousands of its Medicare members. The medical insurance company wasn’t hacked, but its customers’ data was compromised through a third-party vendor’s employee. 86% of Experian survey respondents’, for instance, are prioritizing moving their data to the cloud in 2022.

Data Governance 100

Data Governance Enterprise Data Quality Metadata 100

Alation

AUGUST 17, 2022

We’ve seen the emergence of new, less invasive approaches, yet the question remains: What do CIOs think is most required to deliver a successful data governance program? The Risks of Early Data Governance Programs. The biggest risk was a lack of ownership. If your definitions are bad, so is your governance/risk/security.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

datapine

MAY 31, 2022

Like many other branches of technology, security is a pressing concern in the world of cloud-based computing, as you are unable to see the exact location where your data is stored or being processed. This increases the risks that can arise during the implementation or management process. Cost management and containment. Compliance.

Risk 237

Risk Cost-Benefit Business Intelligence Data-driven 237

IBM Big Data Hub

APRIL 10, 2023

A well-designed data architecture should support business intelligence and analysis, automation, and AI—all of which can help organizations to quickly seize market opportunities, build customer value, drive major efficiencies, and respond to risks such as supply chain disruptions.

Strategy 86

Strategy Data Architecture Cost-Benefit Reporting 86

IBM Big Data Hub

JANUARY 25, 2024

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. Master data enrichment to enhance categorization and materials attributes. Results may vary.

Optimization 73

Optimization Cost-Benefit Prescriptive Analytics Manufacturing 73

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

IBM Big Data Hub

JANUARY 25, 2023

In our last blog , we introduced Data Governance: what it is and why it is so important. In this blog, we will explore the challenges that organizations face as they start their governance journey. Organizations have long struggled with data management and understanding data in a complex and ever-growing data landscape.

Data Governance 52

Data Governance Data-driven Strategy Management 52

erwin

MARCH 25, 2021

Risk Management and Regulatory Compliance. Risk management, specifically around regulatory compliance, is an important use case to demonstrate the true value of data governance. According to Pörschmann, risk management asks two main questions. Strengthen data security. How likely is a specific event to happen? “You

Data Governance 109

Data Governance IT Cost-Benefit Risk 109

DataKitchen

OCTOBER 2, 2023

One of the most pressing issues is the ownership of databases by multiple data teams, each with its governance protocols, leading to a volatile data environment rife with inconsistencies and errors. The Hub Data Journey provides the raw data and adds value through a ‘contract.

Data Quality 100

Data Quality Testing Uncertainty Data Enablement 100

erwin

FEBRUARY 13, 2020

Therefore, most enterprises have encountered difficulty trying to master data governance and metadata management, but they need a solid data infrastructure on which to build their applications and initiatives. Data Governance Attitudes Are Shifting. Metadata Management Takes Time.

Metadata 135

Metadata Data Governance Management Cost-Benefit 135

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

IBM Big Data Hub

DECEMBER 16, 2022

An enterprise data catalog does all that a library inventory system does – namely streamlining data discovery and access across data sources – and a lot more. For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance.

Metadata 52

Metadata Data Quality Data-driven Data Governance 52

erwin

APRIL 29, 2020

Regulatory compliance places greater transparency demands on firms when it comes to tracing and auditing data. For example, capital markets trading firms must understand their data’s origins and history to support risk management, data governance and reporting for various regulations such as BCBS 239 and MiFID II.

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Metadata Key Performance Indicator Data Governance Data Quality 111

IBM Big Data Hub

AUGUST 23, 2023

This data governance requires us to understand the origin, sensitivity, and lifecycle of all the data that we use. It is the foundation for any AI Governance practice and is crucial in mitigating a number of enterprise risks. The problem is that these use cases require training LLMs on sensitive proprietary data.

Data Governance 52

Data Governance Enterprise Modeling Risk 52

Alation

APRIL 13, 2021

Much of his work focuses on democratising data and breaking down data silos to drive better business outcomes. In this blog, Chris shows how Snowflake and Alation together accelerate data culture. He shows how Texas Mutual Insurance Company has embraced data governance to build trust in data.

Insurance 98

Insurance Data Governance Data-driven Metrics 98

IBM Big Data Hub

MAY 9, 2023

We can use foundation models to quickly perform tasks with limited annotated data and minimal effort; in some cases, we need only to describe the task at hand to coax the model into solving it. But these powerful technologies also introduce new risks and challenges for enterprises. Learn more about watsonx.ai

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Enterprise Technology Modeling Cost-Benefit 128

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. appeared first on IBM Blog.

Risk 72

Risk Reporting Data-driven Publishing 72

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. Using automated data validation tests, you can ensure that the data stored within your systems is accurate, complete, consistent, and relevant to the problem at hand. Data engineers are unable to make these business judgments.

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Testing Cost-Benefit Metrics Data Quality 130

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. For instance, JPMorgan Chase & Co.

Data Quality 69

Data Quality Data-driven Data Lake Data Governance 69

Ontotext

SEPTEMBER 1, 2023

They run the risk of using trademarked, copyrighted, or protected data as they scour public data and can be easily exploited and manipulated to ignore previous instructions. Additionally, data is the fulcrum of AI, and the data used to train LLMs must be properly governed and controlled.

Risk 52

Risk Modeling Data Quality Data Governance 52