IBM Big Data Hub

JANUARY 5, 2023

Poor data quality is one of the top barriers faced by organizations aspiring to be more data-driven. Ill-timed business decisions and misinformed business processes, missed revenue opportunities, failed business initiatives and complex data systems can all stem from data quality issues.

Data Architecture 52

Data Architecture Data Quality Strategy Data Lake 52

Rocket-Powered Data Science

FEBRUARY 15, 2023

First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients).

Strategy 290

Strategy Experimentation Uncertainty Machine Learning 290

O'Reilly on Data

JULY 28, 2020

Internally, AI PMs must engage stakeholders to ensure alignment with the most important decision-makers and top-line business metrics. Put simply, no AI product will be successful if it never launches, and no AI product will launch unless the project is sponsored, funded, and connected to important business objectives.

Marketing 364

Marketing Experimentation Metrics Testing 364

CIO Business Intelligence

MAY 7, 2024

That’s according to a recent report based on a survey of CDOs by AWS in conjunction with the Chief Data Officer and Information Quality (CDOIQ) Symposium. The CDO position first gained momentum around 2008, to ensure data quality and transparency to comply with regulations following the housing credit crisis of that era.

Data Strategy 131

Data Strategy Data-driven Data Quality Data Governance 131

erwin

JULY 12, 2019

The complexity of regulatory requirements in and of themselves is aggravated by the complexity of the business and data landscapes within most enterprises. Creating and automating a curated enterprise data catalog , complete with physical assets, data models, data movement, data quality and on-demand lineage.

Data Governance 110

Data Governance Management Metadata Risk Management 110

IBM Big Data Hub

FEBRUARY 9, 2023

The primary goal of any data governance program is to deliver against prioritized business objectives and unlock the value of your data across your organization. Realize that a data governance program cannot exist on its own – it must solve business problems and deliver outcomes.

Data Governance 76

Data Governance Business Objectives Data Quality Measurement 76

datapine

DECEMBER 17, 2019

If what you are reporting does not align with the wider business objectives, you might end up driving the IT department – and sometimes even the rest of the business – further apart. Quality over quantity: Data quality is an essential part of reporting, particularly when it comes to IT.

Reporting 173

Reporting IT Key Performance Indicator Dashboards 173

BizAcuity

NOVEMBER 13, 2024

Challenges in Achieving Data-Driven Decision-Making While the benefits are clear, many organizations struggle to become fully data-driven. Challenges such as data silos, inconsistent data quality, and a lack of skilled personnel can create significant barriers.

Consulting 52

Consulting Data-driven Cost-Benefit Business Intelligence 52

CIO Business Intelligence

AUGUST 13, 2024

This includes regular security audits of automated systems and ensuring compliance with data protection regulations. Prioritize data quality to ensure accurate automation outcomes. Develop holistic metrics aligned with business objectives, integrating KPIs and OKRs into automated systems.

Data-driven 130

Data-driven Metrics Optimization Deep Learning 130

CIO Business Intelligence

NOVEMBER 13, 2023

Failure to align technology capabilities with business goals can result in a wasted investment in technology that doesn’t support business objectives. Transformational leaders must ensure their organizations have the right systems and processes in place to collect, store, and analyze data effectively.

Digital Transformation 143

Digital Transformation Cost-Benefit Experimentation Measurement 143

BI-Survey

FEBRUARY 17, 2020

From operational systems to support “smart processes”, to the data warehouse for enterprise management, to exploring new use cases through advanced analytics : all of these environments incorporate disparate systems, each containing data fragments optimized for their own specific task. .

Data Governance 89

Data Governance Data Warehouse Data Quality Data Strategy 89

CIO Business Intelligence

NOVEMBER 2, 2023

Still, 94% of technical leaders say they should be getting more value from their data and 78% say their organizations struggle to drive business priorities with data. But if that’s not tied to the IT team and they’re not thinking the way that I think, it’s very hard to actually have an aligned strategy.”

Data Strategy 137

Data Strategy Strategy Sales Data Governance 137

CIO Business Intelligence

JUNE 23, 2023

According to the MIT Technology Review Insights Survey, an enterprise data strategy supports vital business objectives including expanding sales, improving operational efficiency, and reducing time to market. The problem is today, just 13% of organizations excel at delivering on their data strategy.

Data Strategy 116

Data Strategy Strategy Visualization Consulting 116

Andrew White

JUNE 11, 2019

Some might conclude this is a new trend; some might look back at the days when SAP acquired Business Objects and IBM acquired Cognos and Oracle acquired Siebel. Just managing data without effective governance won’t cut it; analyzing data and presenting a dashboard without trust in the data won’t cut it.

IT 82

IT Data Quality Data Integration Business Objectives 82

Jen Stirrup

SEPTEMBER 30, 2021

This initial phase focuses on understanding the business value-add from a business perspective, then translating this knowledge into a data mining problem definition. This may also involve the generation of a preliminary plan designed to deliver the business objectives. What are we trying to achieve?

Business Intelligence 135

Business Intelligence Data mining Machine Learning Testing 135

CIO Business Intelligence

OCTOBER 2, 2024

An AI policy serves as a framework to ensure that AI systems align with ethical standards, legal requirements and business objectives. While this leads to efficiency, it also raises questions about transparency and data usage. This includes regular audits to guarantee data quality and security throughout the AI lifecycle.

Gap analysis 119

Gap analysis Risk Manufacturing Data Governance 119

IBM Big Data Hub

DECEMBER 20, 2023

An AI strategy allows organizations to purposefully harness AI capabilities and align AI initiatives with overall business objectives. Define clear objectives What problems does the organization need to solve? Present the AI strategy Present the AI strategy to stakeholders, ensuring it aligns with business objectives.

Strategy 71

Strategy Business Objectives Cost-Benefit Consulting 71

AWS Big Data

AUGUST 1, 2024

Migrating to Amazon Redshift offers organizations the potential for improved price-performance, enhanced data processing, faster query response times, and better integration with technologies such as machine learning (ML) and artificial intelligence (AI).

Data Warehouse 107

Data Warehouse KPI Optimization Cost-Benefit 107

IBM Big Data Hub

JUNE 30, 2022

The rise of data strategy. There’s a renewed interest in reflecting on what can and should be done with data, how to accomplish those goals and how to check for data strategy alignment with business objectives. This requires a deep understanding of the organization’s strengths and weaknesses.

Data Strategy 56

Data Strategy Strategy Business Objectives Data-driven 56

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.

Modeling 98

Modeling Metadata Data Governance Visualization 98

BizAcuity

APRIL 1, 2023

However, many businesses seem to face a lot of challenges, which includes ensuring a ‘single source of truth’ across the organization. Several large organizations have faltered on different stages of BI implementation, from poor data quality to the inability to scale due to larger volumes of data and extremely complex BI architecture.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

BizAcuity

JANUARY 31, 2023

However, many businesses seem to face a lot of challenges, which includes ensuring a ‘single source of truth’ across the organization. Several large organizations have faltered on different stages of BI implementation, from poor data quality to the inability to scale due to larger volumes of data and extremely complex BI architecture.

Business Intelligence 52

Business Intelligence Consulting KPI Data Warehouse 52

Data Virtualization

SEPTEMBER 12, 2024

Reading Time: 2 minutes In today’s data-driven landscape, the integration of raw source data into usable business objects is a pivotal step in ensuring that organizations can make informed decisions and maximize the value of their data assets. To achieve these goals, a well-structured.

Data Integration 52

Data Integration Business Objectives Data-driven Management 52

CIO Business Intelligence

APRIL 4, 2024

To keep up, Redmond formed a steering committee to identify opportunities based on business objectives, and whittled a long list of prospective projects down to about a dozen that range from inventory and supply chain management to sales forecasting. “We We don’t want to just go off to the next shiny object,” she says.

Enterprise 134

Enterprise Cost-Benefit Forecasting Sales 134

Wayne Yaddow

FEBRUARY 18, 2025

Introduction Data transformations and data conversions are crucial to ensure that raw data is organized, processed, and ready for useful analysis. However, these two processes are essentially distinct, and their testing needs differ in manyways.

Testing 52

Testing Data Transformation Data-driven Data Quality 52

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.

Data Strategy 84

Data Strategy Strategy Data Warehouse Prescriptive Analytics 84

AWS Big Data

NOVEMBER 15, 2023

As the organization receives data from multiple external vendors, it often arrives in different formats, typically Excel or CSV files, with each vendor using their own unique data layout and structure. DataBrew is an excellent tool for data quality and preprocessing.

Metadata 105

Metadata Sales Data Lake Big Data 105

Anmut

AUGUST 18, 2022

The goal of data investment. What is the goal when your business invests in data? There are several responses you might give: maybe you want to improve data quality, to enable better analytics, or to support better decision-making. The rise of (in)effective data investment.

Business Objectives 52

Business Objectives Risk Data Quality Strategy 52

bridgei2i

JUNE 23, 2021

These include:lack of understanding of the business-centric use cases of AI, IT gaps,lack of skilled employees, issues in data quality, and resistance to incorporate new technologies into the framework. It enables them to identify how their business can best use AI. Identify KPIs.

Consulting 52

Consulting Key Performance Indicator Business Objectives Data Strategy 52

AWS Big Data

MARCH 7, 2023

This post also discusses the art of the possible with newer innovations in AWS services around streaming, machine learning (ML), data sharing, and serverless capabilities. ETL (extract, transform, and load) technologies, streaming services, APIs, and data exchange interfaces are the core components of this pillar.

Analytics 86

Analytics Data Warehouse Data Lake Metadata 86

FineReport

APRIL 9, 2024

Implementing BI Tools Successfully Best Practices for BI Tool Implementation Deploying business intelligence tools successfully involves adhering to best practices that align the software with business objectives and user needs.

Business Intelligence 52

Business Intelligence Software Data-driven Predictive Modeling 52

Alation

MARCH 14, 2023

It should make data available, maintain data consistency and accuracy, and support data security. Gartner describes it as ‘ a highly dynamic process employed to support the acquisition, organisation, analysis, and delivery of data in support of business objectives ’. Why is a data strategy important?

Data Strategy 52

Data Strategy Strategy Metadata Data Quality 52

Ontotext

SEPTEMBER 1, 2023

Otherwise, they are like a black box, where very little is known as to how they arrive at answers and responses and organizations can lose control of private data, GenAI pipelines can get compromised, or applications can be attacked in subtle ways by hackers.

Risk 52

Risk Modeling Data Quality Data Governance 52

Sisense

MARCH 19, 2021

Combining data, domain expertise, and an analytics platform opens up opportunities for “new revenue for your company and a ton of new value for your existing customers,” according to Sisense Managing Director of Data Monetization and Strategy Consulting Charles Holive. Data quality, availability, and security.

Data-driven 52

Data-driven Data Strategy Strategy Business Objectives 52

DataRobot Blog

MAY 26, 2022

Evaluating ML models for their conceptual soundness requires the validator to assess the quality of the model design and ensure it is fit for its business objective. Conceptual Soundness of the Model. Figure 6: Model lift chart showing model predictions against actual outcomes, sorted by increasing predicted value.

Risk 52

Risk Modeling Metrics Business Objectives 52

Ontotext

MAY 16, 2024

For companies who are ready to make the leap from being applications-centric to data-centric – and for companies that have successfully deployed single-purpose graphs in business silos – the CoE can become the foundation for ensuring data quality, interoperability and reusability.

Enterprise 52

Enterprise Data-driven Cost-Benefit Business Objectives 52

Ontotext

JULY 12, 2024

The automatic tagging specifically helps ensure consistency, which generates better data quality and deeper analytics and reporting. Among the main benefits of this bundle is the ability to manage all digital assets in one place, avoiding intensive data migrations.

Enterprise 59

Enterprise Cost-Benefit Metadata Data Integration 59