IBM Big Data Hub

NOVEMBER 24, 2023

Application modernization starts with assessment of current legacy applications, data and infrastructure and applying the right modernization strategy (rehost, re-platform, refactor or rebuild) to achieve the desired result. Duplicative capabilities across applications and channels give rise to duplicative IT resources (e.g.,

Cost-Benefit 94

Cost-Benefit Testing Data-driven Enterprise 94

CIO Business Intelligence

APRIL 8, 2024

Nearly 10 years ago, Bill James, a pioneer in sports analytics methodology, said if there’s one thing he wished more people understood about sabermetrics, pertaining to baseball, it’s that the data is not the point. The point is to use the data like a razor to cut through false convictions to find the truth.

Analytics 112

Analytics Broadcasting Predictive Analytics Metrics 112

erwin

JULY 12, 2019

In light of recent, high-profile data breaches, it’s past-time we re-examined strategic data governance and its role in managing regulatory requirements. for alleged violations of the European Union’s General Data Protection Regulation (GDPR). Complexity. Five Steps to GDPR/CCPA Compliance. Govern PII “at rest”.

Data Governance 110

Data Governance Management Metadata Risk Management 110

Ontotext

MAY 2, 2024

Data leaders today are faced with an almost impossible challenge. They are expected to understand the entire data landscape and generate business-moving insights while facing the voracious needs of different teams and the constraints of technology architecture and compliance.

Metadata 52

Metadata Data Lake Data-driven Enterprise 52

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Fair warning: if the business lacks metrics, it probably also lacks discipline about data infrastructure, collection, governance, and much more.) Agreeing on metrics. Fault Tolerant Versus Fault Intolerant AI Problems.

Marketing 362

Marketing Experimentation Metrics Testing 362

IBM Big Data Hub

FEBRUARY 9, 2023

In our last blog , we delved into the seven most prevalent data challenges that can be addressed with effective data governance. Today we will share our approach to developing a data governance program to drive data transformation and fuel a data-driven culture. Don’t try to do everything at once!

Data Governance 79

Data Governance Business Objectives Strategy Data Quality 79

datapine

JANUARY 24, 2021

In our cutthroat digital age, the importance of setting the right data analysis questions can define the overall success of a business. That being said, it seems like we’re in the midst of a data analysis crisis. Your Chance: Want to perform advanced data analysis with a few clicks? Data Is Only As Good As The Questions You Ask.

IT 317

IT Statistics KPI Data-driven 317

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 84

Metadata Data Governance Digital Transformation Data Quality 84

Alation

AUGUST 17, 2022

According to analysts, data governance programs have not shown a high success rate. According to CIOs , historical data governance programs were invasive and suffered from one of two defects: They were either forced on the rank and file — who grew to dislike IT as a result. The Risks of Early Data Governance Programs.

Data Governance 92

Data Governance Risk Data-driven Measurement 92

Juice Analytics

APRIL 6, 2021

With our book , resources and workshops, we’ve shared guidance about what it takes to become a data fluent organization. Most of all, it starts with cultural habits that get people focused on using data in their decision-making. Habit 2: Create a shared vocabulary for your data What is an “active user”?

Metrics 119

Metrics Dashboards Data-driven Data Quality 119

erwin

JANUARY 30, 2020

I’m excited to share the results of our new study with Dataversity that examines how data governance attitudes and practices continue to evolve. Defining Data Governance: What Is Data Governance? . 1 reason to implement data governance. Constructing a Digital Transformation Strategy: How Data Drives Digital.

Data Governance 145

Data Governance Metadata Cost-Benefit Digital Transformation 145

Alation

MARCH 14, 2023

Data gathering and use pervades almost every business function these days — and it’s widely acknowledged that businesses with a clear strategy around data are best placed to succeed in competitive, challenging markets such as defence. What is a data strategy? Why is a data strategy important?

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Alation

OCTOBER 5, 2021

What is data governance and how do you measure success? Data governance is a system for answering core questions about data. It begins with establishing key parameters: What is data, who can use it, how can they use it, and why? Why is your data governance strategy failing? Common data governance challenges.

Data Governance 96

Data Governance Strategy Data Quality Metrics 96

erwin

DECEMBER 12, 2019

Metadata is an important part of data governance, and as a result, most nascent data governance programs are rife with project plans for assessing and documenting metadata. But in many scenarios, it seems that the underlying driver of metadata collection projects is that it’s just something you do for data governance.

Metadata 52

Metadata Data Governance Digital Transformation Data Quality 52

erwin

JANUARY 11, 2019

Organizations have spent a lot of time and money trying to harmonize data across diverse platforms , including cleansing, uploading metadata, converting code, defining business glossaries, tracking data transformations and so on. But the attempts to standardize data across the entire enterprise haven’t produced the desired results.

Data Governance 40

Data Governance Risk Metadata Management 40

O'Reilly on Data

OCTOBER 22, 2020

Taking the time to work this out is like building a mathematical model: if you understand what a company truly does, you don’t just get a better understanding of the present, but you can also predict the future. Since I work in the AI space, people sometimes have a preconceived notion that I’ll only talk about data and models.

Consulting 296

Consulting Risk Cost-Benefit Modeling 296

AWS Big Data

APRIL 19, 2023

Data mesh is a new approach to data management. Companies across industries are using a data mesh to decentralize data management to improve data agility and get value from data. This is especially true in a large enterprise with thousands of data products.

Technology 85

Technology Data-driven Machine Learning Sales 85

Ontotext

JUNE 10, 2021

And yeah, the real-world relationships among the entities represented in the data had to be fudged a bit to fit in the counterintuitive model of tabular data, but, in trade, you get reliability and speed. Ironically, relational databases only imply relationships between data points by whatever row or column they exist in.

Metadata 119

Metadata Cost-Benefit OLAP Modeling 119

Ontotext

MARCH 8, 2023

Gartner predicts that graph technologies will be used in 80% of data and analytics innovations by 2025, up from 10% in 2021. Use Case #1: Customer 360 / Enterprise 360 Customer data is typically spread across multiple applications, departments, and regions. Several factors are driving the adoption of knowledge graphs. million users.

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

Data Warehouse 81

Data Warehouse Cost-Benefit Metadata Data-driven 81

Alation

SEPTEMBER 7, 2021

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. But governance is a time-consuming process (for users and data stewards alike).

Data Governance 59

Data Governance Risk Data Quality Dashboards 59

DataRobot Blog

MARCH 10, 2022

These initiatives utilize interconnected devices and automated machines that create a hyperbolic increase in data volumes. This type of growth has stressed legacy data management systems and makes it nearly impossible to implement a profitable data-centered solution. High-level example of a common machine learning lifecycle.

Manufacturing 52

Manufacturing IoT Machine Learning Forecasting 52

erwin

JANUARY 17, 2019

Data governance is more important to the enterprise than ever before. It ensures everyone in the organization can discover and analyze high-quality data to quickly deliver business value. A mature and sustainable data governance initiative must include data integration.

Metadata 63

Metadata Data Governance Data Quality Data-driven 63

FineReport

APRIL 26, 2023

As the world becomes increasingly digitized, the amount of data being generated on a daily basis is growing at an unprecedented rate. This has led to the emergence of the field of Big Data, which refers to the collection, processing, and analysis of vast amounts of data. What is Big Data? What is Big Data?

Big Data 52

Big Data Data Analytics Analytics Cost-Benefit 52

Ontotext

JUNE 22, 2023

Another problem in drug development is that although companies gather a lot of data from multiple databases, they struggle to derive key insights about safety, drugability, etc. They need some kind of compass to find their way in the data jungle and identify information that is useful and relevant to their specific use case.

Metrics 52

Metrics Statistics Visualization Metadata 52

O'Reilly on Data

NOVEMBER 28, 2023

And everyone has opinions about how these language models and art generation programs are going to change the nature of work, usher in the singularity, or perhaps even doom the human race. AI users say that AI programming (66%) and data analysis (59%) are the most needed skills. Many AI adopters are still in the early stages.

Enterprise 271

Enterprise Modeling Testing Reporting 271

Sisense

FEBRUARY 10, 2021

In order to do this, my team uses data to identify problem areas and potential issues for our customers (ideally before they happen). Like many organizations, our utility company clients are sitting on copious amounts of data from a variety of sources, including mobile applications and streaming data captured by grid-edge technologies.

Dashboards 52

Dashboards Enterprise Risk Software 52

datapine

APRIL 15, 2020

It’s necessary to say that these processes are recurrent and require continuous evolution of reports, online data visualization , dashboards, and new functionalities to adapt current processes and develop new ones. Working software over comprehensive documentation. Discover the available data sources.

Business Intelligence 276

Business Intelligence Analytics Testing Dashboards 276

Occam's Razor

AUGUST 22, 2011

Be data driven?" Key To Your Digital Success: Web Analytics Measurement Model. Six Rules For Creating A Data Driven Boss! Be data driven?" Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model.

KPI 124

KPI Metrics Measurement ROI 124

CIO Business Intelligence

JANUARY 23, 2024

Keep the number of metrics small and manageable, ideally three or four, and at most seven key ones because people cannot focus on multiple pages of data.” Efficiency metrics might show the impacts of automation and data-driven decision-making. He suggests, “Choose what you measure carefully to achieve the desired results.

Measurement 139

Measurement Digital Transformation KPI Metrics 139

Ontotext

JANUARY 10, 2024

In Computer Science, we are trained to use the Okham razor – the simplest model of reality that can get the job done is the best one. Next, I will explain how knowledge graphs help them to get a unified view to data derived from multiple sources and get richer insights in less time. So, they become very data-driven.

Metadata 72

Metadata Slice and Dice Data Integration Enterprise 72

DataRobot Blog

DECEMBER 15, 2022

Working from datasets you already have, a Time Series Forecasting model can help you better understand seasonality and cyclical behavior and make future-facing decisions, such as reducing inventory or staff planning. Let’s run through the process and see exactly how you can go from data to predictions. Setting up a Time Series Project.

Forecasting 81

Forecasting Modeling ROI Machine Learning 81

erwin

MAY 27, 2020

Data modeling supports collaboration among business stakeholders – with different job roles and skills – to coordinate with business objectives. Data resides everywhere in a business , on-premise and in private or public clouds. A single source of data truth helps companies begin to leverage data as a strategic asset.

Modeling 98

Modeling Metadata Data Governance Visualization 98

O'Reilly on Data

OCTOBER 13, 2020

Similarly, in “ Building Machine Learning Powered Applications: Going from Idea to Product ,” Emmanuel Ameisen states: “Indeed, exposing a model to users in production comes with a set of challenges that mirrors the ones that come with debugging a model.”.

Management 244

Management Metrics Machine Learning Modeling 244

Cloudera

SEPTEMBER 7, 2023

With the emergence of new creative AI algorithms like large language models (LLM) fromOpenAI’s ChatGPT, Google’s Bard, Meta’s LLaMa, and Bloomberg’s BloombergGPT—awareness, interest and adoption of AI use cases across industries is at an all time high.

Insurance 78

Insurance Risk Data-driven Finance 78

erwin

FEBRUARY 19, 2021

Metadata management is key to wringing all the value possible from data assets. However, most organizations don’t use all the data at their disposal to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or accomplish other strategic objectives. Quite simply, metadata is data about data.

Metadata 110

Metadata Management Data Quality Cost-Benefit 110

Domino Data Lab

JUNE 15, 2021

Over the next decade, the companies that will beat competitors will be “model-driven” businesses. These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69