How to drive trusted decisions without changing your current data infrastructure.
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Unleashing the Power of AI with Data Products
Unleashing the Power of AI with Data Products Traditional project-centric data management stifles AI innovation with siloed data, slow workflows, and limited reusability. Enter the era of data products: self-contained modules of data, logic, and infrastructure that...
A Pan-Industry Revolution with DataOS®
A Pan-Industry Revolution with DataOS® Unleash the revolution with Data Products powered by DataOS®. These self-contained data units, bursting with actionable insights, offer unmatched flexibility, agility, and compliance across all sectors. From personalized customer...
The Role of Data Products in Maximizing ROI from AI Initiatives
The Role of Data Products in Maximizing ROI from AI Initiatives This IDC report explores how effective data management, particularly through DataOps and data products, is crucial for harnessing the potential of AI across industries. Highlighting the challenges of data...
A Data as a Product Approach to Data Management
A Data as a Product Approach to Data Management Data is only as good as the insight it produces, so why can’t companies seem to use it? Fragmented tools, poor quality data, and outdated legacy systems prevent true digital transformation. In our overview white paper,...
Data Mesh + Patient360: A Modern Revolution for Healthcare Data
Data Mesh + Patient360: A Modern Revolution for Healthcare DataHealthcare organizations are sitting on a treasure trove of customer data. Operationalizing that data makes it actionable and usable, helping improve services, costs, and patient outcomes. However,...
Unified Data Platform Weaves Distributed, Diverse, and Dynamic Data in Modern Environments
Unified Data Platform Weaves Distributed, Diverse, and Dynamic Data in Modern Environments Implementing customer lifetime value as a mission-critical KPI has many challenges. Companies need consistent, high-quality data and a straightforward way to measure CLV. In the...
A Paradigm Shift in Data Management – Securing Corporate Data
A Paradigm Shift in Data Management – Securing Corporate DataData needs are changing in a transformative way, leaving some companies struggling with reconciling complex data needs. This increased complexity can lead to security loopholes, and some companies have...
A Paradigm Shift in Data Management – Developing Data Pipelines
A Paradigm Shift in Data Management – Developing Data PipelinesCompanies are investing heavily to become data driven only to face challenges in production due to overly rigid architectures. In addition, IT departments are burdened with too many data projects and...
The Evolution of Data 3.0
The Evolution of Data 3.0Over the past decade, traditional data management and data analytics have undergone major transformations.
DataOS – A Paradigm Shift in Data Management
DataOS® – A Paradigm Shift in Data ManagementData is only as good as the insight it produces, so why can’t companies seem to use it? Fragmented tools, poor quality data, and outdated legacy systems prevent true digital transformation. In our overview white paper, we...
A Modern Data Strategy for Enterprises
A Modern Data Strategy for EnterprisesRegardless of industry, size, or product offering, every company has to ask the same question—“Regardless of the amount of data I have, how much of it is actually usable?”
Data Security and Governance in DataOS
Data Security and Governance in DataOS®The Modern Data Company’s DataOS® is a modern Data Fabric that protects your data regardless of where you move, store, or analyze it.
A Modern Data Strategy for Government Organizations
A Modern Data Strategy for Government Organizations Data has no real value unless it is being used. Companies face obstacles such as poor quality data, more maintenance than deployment, and outdated data that prevent them from harnessing actionable insights. Our...
A Paradigm Shift in Data Management – Creating Scalable Analytics
A Paradigm Shift in Data Management – Creating Scalable AnalyticsCompanies undergoing digital transformation must make data available to all stakeholders, not only a select few in IT and C-suite. Obstacles in compliance, security, and governance can prevent succeeding...
A Paradigm Shift in Data Management – Deploying A Data Fabric
A Paradigm Shift in Data Management – Deploying A Data FabricArchitecture and infrastructure teams must harness the power of big data by taking advantage of cutting edge architectures such as data fabrics. Until now, no single solution enabled building such...
A Paradigm Shift in Data Management – Generating Reports
A Paradigm Shift in Data Management – Generating Reports As data needs transform, companies must leverage the power of big data to make decisions and scale where it matters. DataOS offers the only data operating system on the market capable of wrangling all a...
DataOS – The Fastest Path from Data to Decisions
DataOS® – The Fastest Path from Data to DecisionsWe have a proliferation of enterprise data management solutions but many still fail to meet the objectives for those who manage the data. It's time for a paradigm shift.
Data Lakes 101 – Making The Most Of Data Lakes Through Agile Methods
Data Lakes 101 – Making The Most Of Data Lakes Through Agile MethodsAgile methodology is a perfect match for data lakes. Read to find out how companies can use it to increase speed and innovation while ensuring governance and security remain intact.
Data Lakes 101 – Core Value Propositions and Usage
Data Lakes 101 – Core Value Propositions and UsageIn this paper, The Modern Data Company explores the ins and outs of Data Lakes and their core value propositions.
The Data Evolution & Modern Data Management
The Data Evolution & Modern Data ManagementThe world is rapidly shifting to a digital first model for every organization due to world events. The long-known expansion of the size of data has suddenly kicked up its pace.
DataOS – The Modern Data Fabric
DataOS® – The Modern Data FabricBusinesses want to become data-driven, but a majority of non-technology organizations struggle to extract value out of their data. For these companies, data is not a solved problem.