DataKitchen

FEBRUARY 27, 2024

Challenges in Developing Reliable LLMs Organizations venturing into LLM development encounter several hurdles: Data Location: Critical data often resides in spreadsheets, characterized by a blend of text, logic, and mathematics.

Data Quality 117

Data Quality Unstructured Data Testing Modeling 117

CIO Business Intelligence

FEBRUARY 20, 2024

In this way, manufacturers would be able to reduce risk, increase resilience and agility, boost productivity, and minimise their environmental footprint. Industrial knowledge graphs employ industry-standard metadata to contextualize and structure data so it can be used in large language models.

Data Architecture 85

Data Architecture Unstructured Data Manufacturing IT 85

IBM Big Data Hub

JULY 26, 2023

AI solutions work by collecting asset performance data and feeding it into machine learning models, which can predict asset health and risk of failure. This use case shows how AI can help by processing unstructured image and video data in addition to structured data in the previous examples.

Optimization 81

Optimization Manufacturing Cost-Benefit Reporting 81

Rocket-Powered Data Science

JULY 19, 2023

The results showed that (among those surveyed) approximately 90% of enterprise analytics applications are being built on tabular data. The ease with which such structured data can be stored, understood, indexed, searched, accessed, and incorporated into business models could explain this high percentage.

Data-driven 186

Data-driven Enterprise Analytics Machine Learning 186

Smarten

MAY 17, 2023

Whether you are looking at ChatGPT or Open AI and wondering how it might be applied to your business environment, it is important to understand the current state of this technology, the inherent risks and the possible opportunities. Creating Summary Data for Unstructured Documents (PDFs, HTML, Websites, etc.)

Digital Transformation 52

Digital Transformation Experimentation Risk Testing 52

CIO Business Intelligence

APRIL 4, 2024

This involves rigorous evaluation of potential benefits, risks, and costs associated with each AI initiative to ensure investments are prudent and aligned with our risk-return profile.” Our biggest risk is if our employees don’t use AI as much as they could.” On-time delivery has improved substantially,” she says.

Enterprise 131

Enterprise Cost-Benefit Forecasting Sales 131

Laminar Security

APRIL 18, 2023

What’s needed is a technology solution that can see into and across the various, disparate clouds, giving the user one, unified view into all cloud data risk. “Data stakeholders need to understand data exposure and mitigate risk no matter how diversified the data environment is.

Data Warehouse 52

Data Warehouse Risk Cost-Benefit Structured Data 52

CIO Business Intelligence

JANUARY 24, 2024

At Vanguard, “data and analytics enable us to fulfill on our mission to provide investors with the best chance for investment success by enabling us to glean actionable insights to drive personalized client experiences, scale advice, optimize investment and business operations, and reduce risk,” Swann says.

Data Strategy 135

Data Strategy Strategy Unstructured Data Data Governance 135

IBM Big Data Hub

APRIL 9, 2024

Preparing and annotating data IBM watsonx.data helps organizations put their data to work, curating and preparing data for use in AI models and applications. “Being able to organize the data around that structure helps us to efficiently query, retrieve and use the information downstream, for example for AI narration.”

Management 88

Management IT Machine Learning Metrics 88

Laminar Security

AUGUST 21, 2023

The Shifting Data Security Landscape Cloud service providers (CSPs) have revolutionized data analytics and data pipelines, presenting data security teams with novel challenges. The Importance of DSPM As data proliferates across the cloud, the need to identify and address privacy and security risks becomes paramount.

Management 40

Management Risk Unstructured Data Data Strategy 40

Cloudera

OCTOBER 28, 2021

Since the beginning of Commercial insurance as we know it today, insurers have been using data generated by other industries to assess and rate risks. In the days of Lloyd’s Coffee House , insurers gathered data about cargo, voyages, seasonal weather and the performance history of vessels and mariners to underwrite risks.

Insurance 94

Insurance Risk Unstructured Data Data-driven 94

DataRobot Blog

JANUARY 17, 2023

establishing an appropriate price illiquid securities, predicting where liquidity will be located, and determining appropriate hedge ratios) as well as more generally: the existence of good historical trade data on the assets to be priced (e.g., The post A Data Scientist Explains: When Does Machine Learning Work Well in Financial Markets?

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

Ontotext

JULY 6, 2023

This shift of both a technical and an outcome mindset allows them to establish a centralized metadata hub for their data assets and effortlessly access information from diverse systems that previously had limited interaction. There are four groups of data that are naturally siloed: Structured data (e.g.,

Cost-Benefit 52

Cost-Benefit Metadata Experimentation Risk 52

erwin

SEPTEMBER 10, 2020

Yet given this era of digital transformation and fierce competition, understanding what data you have, where it came from, how it’s changed since creation or acquisition, and whether it poses any risks is paramount to optimizing its value. Data Lineage Tells an Important Origin Story.

Data Governance 131

Data Governance Key Performance Indicator Digital Transformation Metadata 131

CIO Business Intelligence

DECEMBER 4, 2023

A modern information lifecycle management approach Today’s ILM approach recognizes the enterprise value of all digitized and enriched assets , avoiding the habituated, narrow reliance ontraditional structured data. Preserve/Dispose.

Unstructured Data 52

Unstructured Data Data Lake Metadata Business Objectives 52

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

Cloudera

SEPTEMBER 29, 2021

Through processing vast amounts of structured and semi-structured data, AI and machine learning enabled effective fraud prevention in real-time on a national scale. . This resulted in staff spending more time on more complex tasks while also reducing human errors and security risks.

Digital Transformation 79

Digital Transformation Data Governance Data-driven Uncertainty 79

Ontotext

MARCH 8, 2023

Specifically, the increasing amount of data being generated and collected, and the need to make sense of it, and its use in artificial intelligence and machine learning, which can benefit from the structured data and context provided by knowledge graphs. Which financial institutions have filed similar risk compliance issues?

Enterprise 52

Enterprise Knowledge Discovery Risk Data-driven 52

Ontotext

JUNE 22, 2023

This will minimize the risk of investing resources in fundamentally wrong assumptions and will give researchers more confidence to choose top targets for validation. Its user-friendly and customizable nature enables users to explore the data visually and provides metrics that automate target ranking and reduce risks.

Metrics 52

Metrics Statistics Visualization Metadata 52

Octopai

JUNE 26, 2020

It provides the value and purpose of the data content, thereby becoming an extremely effective tool for quickly locating information – a necessity for BI groups dealing with analytics and business user reporting. According to Donna Burbank from Dataversity , “metadata helps both IT and business users understand the data they are working with.

Metadata 84

Metadata Data Governance Enterprise Reporting 84

Sisense

JUNE 8, 2020

“This ability to pull data sets together from a cloud-native environment for better transparency and faster decision making, in turn, can give analysts the speed they need to answer questions quickly and more accurately with more data collaboration,” Lee added. Deeper insights from bigger data sets.

Data Warehouse 89

Data Warehouse Data-driven Dashboards Sales 89

CIO Business Intelligence

NOVEMBER 16, 2023

That’s where another level of risk mitigation comes in. It’s all about employee training,” he says, “and making sure they understand what they need to do, and they’re well trained on data security.” We’re concerned where the data from the prompting might end up,” she says. “We We don’t want to take those risks.”

Modeling 134

Modeling Enterprise Marketing Sales 134

IBM Big Data Hub

AUGUST 28, 2023

Data extraction Once you’ve assigned numerical values, you will apply one or more text-mining techniques to the structured data to extract insights from social media data. Crisis management and risk management: Text mining serves as an invaluable tool for identifying potential crises and managing risks.

Data mining 59

Data mining Machine Learning Deep Learning Marketing 59

Octopai

MARCH 22, 2023

This can be more cost-effective than traditional data warehousing solutions that require a significant upfront investment. Support for multiple data structures. Unlike traditional data warehouse platforms, snowflake supports both structured and semi-structured data.

Cost-Benefit 78

Cost-Benefit Data Warehouse Unstructured Data Optimization 78

Ontotext

MARCH 18, 2020

According to an article in Harvard Business Review , cross-industry studies show that, on average, big enterprises actively use less than half of their structured data and sometimes about 1% of their unstructured data.

Enterprise 59

Enterprise B2B Unstructured Data Machine Learning 59

erwin

JUNE 26, 2020

A data catalog uses metadata, data that describes or summarizes data, to create an informative and searchable inventory of all data assets in an organization. Clearly documents data catalog policies, rules and shares information assets. Managing a remote workforce creates new challenges and risks.

Metadata 132

Metadata Cost-Benefit Measurement Data-driven 132

bridgei2i

FEBRUARY 21, 2019

Traditional algorithmic solutions around structured data have gained and continue to gain traction. A textbook example of t raditional analytics techniques revolving around structured data in global enterprise sales organizations. Examples of AI Solutions Usage. and deliver incremental value for the most part.

Sales 40

Sales Structured Data Forecasting Enterprise 40

bridgei2i

FEBRUARY 8, 2019

Fortunately, AI is here to help, with algorithms that predict opportunities with a high chance of closing and those that are at high risk. Current voice-based sales assistants mostly include products that help the sales rep fill in more of the structured data we have already discussed in various contexts.

Sales 40

Sales B2B Cost-Benefit Structured Data 40

IBM Big Data Hub

JULY 19, 2023

Supply Chain: Demand forecasting, supply chain optimization, risk assessment and mitigation. Robustness : AI systems should be able to withstand attacks to the training data. We started with prepping the proprietary structured data to clean and make it ready in a format to be able to pass within prompts for fine-tuning and inferencing.

Consulting 66

Consulting Machine Learning Manufacturing Optimization 66

Jet Global

FEBRUARY 18, 2020

People often forget his next statement: “90 percent of all that new data is unstructured.” So if we think historically about companies with an ERP, they’re typically using structured data (strictly defined and classified), and they’re not very proactive about pushing insights toward users.

Data-driven 64

Data-driven Machine Learning Cost-Benefit ROI 64

Jet Global

SEPTEMBER 4, 2020

Jet Analytics is a data warehouse automation platform that enables D365F&SCM customers to accelerate and de-risk their BI projects without requiring specialist skills. Jet Analytics provides for a one-to-one match with source tables and a set of pre-defined data entities out of the box. Another Alternative to BYOD.

Data Lake 88

Data Lake OLAP Data Warehouse Unstructured Data 88

Ontotext

NOVEMBER 2, 2023

In spite of all the activity, the data paradigm hasn’t evolved much. Organizations are still managing data using relational technology invented in the 1970’s. While relational databases are the best fit for managing structured data workloads, they are not good for ad hoc inquiry and scenario-based analysis.

IT 69

IT Cost-Benefit Data-driven Technology 69

Juice Analytics

MAY 1, 2021

It is a skill that combines elements of artistic expression and structured methods. Then we will discuss how to structure data stories to guide your audience through data. Part 1: Lessons in Data Storytelling from Pixar Pixar is the gold standard in storytelling.

Slice and Dice 133

Slice and Dice Recreation/Entertainment Structured Data Dashboards 133

Sisense

OCTOBER 6, 2020

Most commonly, we think of data as numbers that show information such as sales figures, marketing data, payroll totals, financial statistics, and other data that can be counted and measured objectively. This is quantitative data. It’s “hard,” structured data that answers questions such as “how many?”

Statistics 98

Statistics Unstructured Data Data-driven Visualization 98

datapine

MAY 9, 2019

Business intelligence concepts refer to the usage of digital computing technologies in the form of data warehouses, analytics and visualization with the aim of identifying and analyzing essential business-based data to generate new, actionable corporate insights. They prevent you from drowning in data.

Business Intelligence 270

Business Intelligence Dashboards Data Warehouse Sales 270

BizAcuity

MARCH 1, 2019

Data is only useful when it is actionable for which it needs to be supplemented with context and creativity. Traditional methods of analyzing structured data are not designed to efficiently process these large amounts of real-time data that is collected from IoT devices. to make better decisions and risk assessments.

IoT 56

IoT Internet of Things Big Data Data-driven 56

Smart Data Collective

SEPTEMBER 26, 2019

Unstructured data lacks a specific format or structure. As a result, processing and analyzing unstructured data is super-difficult and time-consuming. Semi-structured. Semi-structured data contains a mixture of both structured and unstructured data. Data Integration.

Big Data 80

Big Data Software Unstructured Data Data Integration 80