FineReport

MARCH 24, 2024

They identify and interpret trends in complex datasets, optimize statistical results, and maintain databases while devising new data collection processes. Additionally, they facilitate organizational risk assessments, provide consulting services to leadership, and mentor junior analysts. JPMorgan Chase & Co.:

Statistics 52

Statistics Data mining Visualization Reporting 52

CIO Business Intelligence

SEPTEMBER 2, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98

CIO Business Intelligence

AUGUST 31, 2022

Finance companies collect massive amounts of data, and data engineers are vital in ensuring that data is maintained and that there’s a high level of data quality, efficiency, and reliability around data collection.

Finance 98

Finance IT Software Reporting 98

bridgei2i

MARCH 11, 2021

From eliminating the need for human assistance in repetitive tasks to reducing the risk of human errors in manual processes – AI can do a lot for large-scale businesses. With improved data cataloging functionality, their systems can become responsive. The post How Can Small Businesses Benefit from an AI Data Company?

Key Performance Indicator 52

Key Performance Indicator Data Governance Data Lake Metadata 52

O'Reilly on Data

MARCH 31, 2020

Because it’s so different from traditional software development, where the risks are more or less well-known and predictable, AI rewards people and companies that are willing to take intelligent risks, and that have (or can develop) an experimental culture. If you can’t walk, you’re unlikely to run.

Management 332

Management Machine Learning Experimentation Metrics 332

O'Reilly on Data

MARCH 18, 2020

The update sheds light on what AI adoption looks like in the enterprise— hint: deployments are shifting from prototype to production—the popularity of specific techniques and tools, the challenges experienced by adopters, and so on. By contrast, AI adopters are about one-third more likely to cite problems with missing or inconsistent data.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Sisense

SEPTEMBER 3, 2020

As you can imagine, data scientists with all these aptitudes can be of considerable value to their employers. They are the link between the data resources available to an enterprise and executives looking for opportunities to make the business better, faster, and stronger. See an example: Explore Dashboard.

Statistics 97

Statistics Metrics Visualization Finance 97

Cloudera

FEBRUARY 14, 2018

operations, and our CISO’s team while we invest in and form a stronger data and analytics team. their customer insights, connected products, risk mitigation). Mitigate risk to Cloudera’s business – for example in the area of Cybersecurity, and to ensure compliance with regulatory and consumer privacy policies and concerns.

Data-driven 44

Data-driven Data Quality Risk Interactive 44

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

FineReport

MAY 31, 2023

On the right side of the dashboard, the balance sheet serves as the primary financial statement that captures the financial position of an enterprise, encompassing assets, liabilities, and owners’ equity, on a specific date. These indicators indirectly shed light on the enterprise’s operational conditions.

Dashboards 52

Dashboards Key Performance Indicator Metrics Visualization 52

Alation

FEBRUARY 13, 2020

Benefits of a Data Catalog. What Does a Data Catalog Do? A modern data catalog includes many features and functions that all depend on the core capability of cataloging data—collecting the metadata that identifies and describes the inventory of shareable data. Benefits of a Data Catalog. Conclusion.

Metadata 64

Metadata Data Lake Recreation/Entertainment Big Data 64

Peter James Thomas

AUGUST 29, 2018

I know you have a strong background in Enterprise Data Management , how does the CDO role differ from this area? I guess that depends on how you determine the scope of Enterprise Data Management. My role today boils down to 4 key objectives – data availability, data transparency, data quality and data control.

Data-driven 61

Data-driven Cost-Benefit Metadata Technology 61

CIO Business Intelligence

JANUARY 30, 2024

Birgit Fridrich, who joined Allianz as sustainability manager responsible for ESG reporting in late 2022, spends many hours validating data in the company’s Microsoft Sustainability Manager tool. Data quality is key, but if we’re doing it manually there’s the potential for mistakes.

Reporting 115

Reporting Data Quality Strategy Data-driven 115

IBM Big Data Hub

MAY 15, 2024

Business leaders risk compromising their competitive edge if they do not proactively implement generative AI (gen AI). Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges.

Cost-Benefit 50

Cost-Benefit Data Integration Data Architecture Manufacturing 50

Domino Data Lab

JULY 22, 2019

But for most enterprise, using machine learning…not really. What I’m trying to say is this evolution of system architecture, the hardware driving the software layers, and also, the whole landscape with regard to threats and risks, it changes things. You see these drivers involving risk and cost, but also opportunity.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

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 70

Data Strategy Strategy Data Warehouse Prescriptive Analytics 70

erwin

MARCH 4, 2021

Modern data governance is a strategic, ongoing and collaborative practice that enables organizations to discover and track their data, understand what it means within a business context, and maximize its security, quality and value. Or “How would you recognize having effective data governance in place?”. Where is it?

Data Governance 122

Data Governance Digital Transformation Data-driven Cost-Benefit 122

Cloudera

AUGUST 4, 2021

Data governance used to be considered a “nice to have” function within an enterprise, but it didn’t receive serious attention until the sheer volume of business and personal data started taking off with the introduction of smartphones in the mid-2000s. Security: It must serve data throughout a system.

Data Governance 87

Data Governance Metadata Data-driven Enterprise 87

IBM Big Data Hub

SEPTEMBER 11, 2023

Often, the reason digital government experiences lag behind commercial enterprises is not a lack of funding, but a lack of human-centered design. The pain point tracker clusters the foundational data in which value metrics are then applied. and quality (how does this impact service delivery, business process and data quality?).

Data-driven 91

Data-driven IT Measurement Cost-Benefit 91

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Domino Data Lab

JUNE 23, 2019

SkipFlag was a knowledge base that built itself from your enterprise communication, email, Slack, Wiki information and content. If you have a user facing product, the data that you had when you prototype the model may be very different from what you actually have in production.

Management 110

Management Machine Learning Experimentation Metrics 110

datapine

AUGUST 29, 2022

The book has three main ideas: The biggest risk your company faces is investing a lot of time and resources into building something that the market doesn’t want. Most people are aware that companies collect our GPS locale, text messages, credit card purchases, social media posts, Google search history, etc.,

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Andrew White

JANUARY 9, 2022

But I would search on our web site for AI and mid-size enterprise; or ask Anthony Mullen who leads our AI research in our data and analytics team. Most of D&A concerns and activities are done within EA in the Info/Data architecture domain/phases. – I am sorry but I am not aware of this myself. This is flat wrong.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Alation

NOVEMBER 11, 2021

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. It enables collaboration, even as your data landscape grows larger and more complex. Data Sovereignty and Cross?Border

Data Governance 52

Data Governance Data Quality Metrics Metadata 52

datapine

FEBRUARY 22, 2022

Improved risk management: Another great benefit from implementing a strategy for BI is risk management. 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.

Business Intelligence 218

Business Intelligence Strategy Cost-Benefit Key Performance Indicator 218

Alation

DECEMBER 15, 2021

Modern business is built on a foundation of trusted data. 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 52

Data Governance Strategy Metadata Data Strategy 52

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

JULY 6, 2021

Common Data Governance Challenges. Every enterprise runs into data governance challenges eventually. Issues like data visibility, quality, and security are common and complex. Data governance is often introduced as a potential solution. The world is collectively generating trillions of gigabytes of new data.

Data Governance 52

Data Governance Metadata Data Quality Risk 52

Alation

OCTOBER 12, 2021

Enterprise data analytics enables businesses to answer questions like these. Having a data analytics strategy is a key to delivering answers to these questions and enabling data to drive the success of your business. What is Enterprise Data Analytics? The fourth challenge was around using the data.

Strategy 52

Strategy Enterprise Analytics Data Governance 52

erwin

JULY 17, 2019

Part Two of the Digital Transformation Journey … In our last blog on driving digital transformation , we explored how enterprise architecture (EA) and business process (BP) modeling are pivotal factors in a viable digital transformation strategy. Constructing A Digital Transformation Strategy: Data Enablement.

Digital Transformation 98

Digital Transformation Strategy Metadata Data-driven 98

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. Cloud Data Migration.

Metadata 64

Metadata Data Governance Dashboards Software 64

CIO Business Intelligence

JANUARY 20, 2023

But to get maximum value out of data and analytics, companies need to have a data-driven culture permeating the entire organization, one in which every business unit gets full access to the data it needs in the way it needs it. This is called data democratization. Security and compliance risks also loom.

Data Lake 135

Data Lake Data-driven Finance Data Architecture 135

Jet Global

APRIL 14, 2022

Having accurate data is crucial to this process, but finance teams struggle to easily access and connect with data. Reduce the risk of human error. Improve data quality. Reducing the risk of using manual processes. Near real-time information is vital to: Save time. 30% Siloed. Enable cookies.

Data Collection 52

Data Collection Management Finance Reporting 52

Jet Global

MAY 9, 2024

Data pipelines can serve various purposes beyond ETL, such as real-time analytics, machine learning, and stream processing applications. ETL pipelines are designed to prepare data for analysis, reporting, or other business intelligence applications. What is an ETL pipeline? How is ELT different from ETL?

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52

Jet Global

MARCH 4, 2024

The CSRD is a phased directive that requires all large companies and listed companies in the EU to disclose information on their environmental, social, and governance (ESG) performance, risks, and impacts. What types of existing IT systems are commonly used to store data required for ESRS disclosures?

Reporting 52

Reporting Management Cost-Benefit Enterprise 52

Jet Global

MARCH 4, 2024

Moving data across siloed systems is time-consuming and prone to errors, hurting data quality and reliability. Built on proven technology trusted by thousands, it delivers investor-grade data with robust controls, audit trails, and security. It’s not just a solution, it’s a partnership for a greener future.

Reporting 52

Reporting Data-driven Software Risk 52