10 tools that enable Chief Data Officers to drive greater value from their data
As Chief Data Officer (CDO), you lead and shape your organization's data strategy. However, given the increasing complexity of data flows, evolving regulations and the growing pressure to generate value from data, your tech stack needs to be comprehensive and robust.
The increasing number of technology tools also brings major challenges around their integration and use. CDOs now need to orchestrate a range of complementary tools to cover the entire data lifecycle, covering areas such as governance, data integration, scaling data sharing, business intelligence (BI) and AI. To help we’ve selected ten key tools to help you build a robust and scalable data environment.
From the implementation of a data product marketplace to the automation of data flows and proactive quality management, these tools meet the main challenges of the CDO: democratizing access to data for everyone in the business, ensuring data is reliable, high-quality and secure, and accelerating innovation through analytics and generative AI.
1. Cloud data management platforms: providing flexible and scalable storage and compute
With the explosion in data volumes, many organizations are adopting scalable cloud solutions that can separate storage from compute power. These include:
- Snowflake: A solution known for its simplified management and pay-as-you-go billing.
- AWS Redshift: A data warehouse integrated with the Amazon Web Services ecosystem.
- Google BigQuery: A serverless, pay-per-query tool, ideal for occasional spikes in activity.
User profile: data experts (such as data engineers, data operations and CIOs)
Use cases and added value
By increasing and reducing computing power and capacity on the fly, cloud data management platforms such as Snowflake optimize costs and performance. For example, an e-commerce site can scale up to absorb spikes in orders on Black Friday, and then lower usage as the wave passes.
2. Data catalogs: inventorying and documenting data assets
Data is now generated and collected from a growing number of sources, including CRM and ERP solutions, flat files, and data lakes. This makes it vital to inventory and centralize knowledge about each data asset: what is its metadata? What is its origin? What business use does it serve? This is precisely the role of data catalogs.
Examples include:
- Precisely: A data catalog tool for integrating, verifying, localizing, and enriching metadata.
- Informatica: An AI-powered data cataloging and metadata management tool
User profile: data experts (such as data analysts and data stewards)
Use cases and added value
Telecoms company TELUS used a data catalog solution to improve trust in its data and to make it easier to find. Thanks to this, it has reduced the time data scientists spend searching for data from 5 hours to just 50 minutes.
3. Data governance solutions: controlling quality, security and compliance
Poorly governed data can quickly become an expensive overhead, increasing risks around compliance and security and preventing data being used with confidence. Data governance solutions support end-to-end quality, security, and compliance (GDPR, CCPA, etc.).
Examples include:
- Informatica Axon: A tool for orchestrating global governance policies.
- BigID: A solution that leverages AI to automatically map and classify sensitive data.
User profile: data experts
Use cases
First Abu Dhabi Bank (FAB) has used data governance to improve the quality of its data and ensure compliance. In 12 months, its score with the Al Etihad Credit Bureau increased from 84% to 99%, it reduced errors by 20% and decreased manual efforts by 25% through automation. This strengthened governance has also accelerated data processing speeds by 30%, maximizing customer satisfaction and trust.
4. BI and analytics tools: generating actionable data insights
BI and analytics tools enable raw data to be transformed into business insights and visualizations, such as dynamic dashboards and reports, providing a clear view of trends and performance against KPIs. However, these are technical tools data meaning they are only used by data experts, not the wider business through self-service.
Examples include:
- Microsoft Power BI: A widely-used tool that integrates with the Microsoft ecosystem (such as Office 365 and Azure).
- Looker (part of Google Cloud): Built on LookML, this tool centralizes business logic to drive consistency of metrics.
User profile: data experts (business analysts)
5. Integration and automation tools (ETL/ELT): ensuring continuous and reliable data flows
To deliver reliable results BI, AI and business applications need to be powered by up-to-date data. This means organizations need to industrialize data extraction and transformation, creating end-to-end data flows through ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) solutions.
Examples include:
- Apache NiFi: An open source tool that offers a visual interface to orchestrate real-time or batch flows.
- Fivetran: This tool provides pre-built connections to solutions such as CRM applications for automatic synchronization.
- Matillion: Delivering in-warehouse transformations through a solution optimized for Snowflake and Redshift.
User profile: data experts (such as data architects, database administrators (DBAs), and data engineers)
Use cases and added value
DocuSign deployed an ETL tool to automate the integration of its data from platforms such as Salesforce and Marketo into Snowflake. This has reduced the time spent managing data pipelines while improving the accuracy of business analytics, making it easier to make better-informed, more confident decisions.
6. Artificial intelligence and machine learning solutions: optimizing processes and predicting future events
AI and machine learning (ML) tools go beyond simple analysis of previous events and can be used to predict the future, automate processes and optimize performance. These platforms accelerate the creation, deployment, and management of advanced AI models.
Examples include:
- Databricks: Created by the founders of Apache Spark, this platform facilitates data science/data engineering collaboration.
- DataRobot: This tool features machine learning automation (AutoML) to quickly compare and deploy different algorithms.
- H2O.ai: A solution that provides a large library of algorithms and integration with R/Python for flexibility.
User profile: data experts (such as data analysts, data scientists)
7. Advanced dashboards and visualizations: deepening data analysis
While traditional BI tools already offer dashboards, some solutions go further by delivering immersive visualizations, sometimes enriched with real-time collaboration features.
Examples include:
- Sisense: A solution that provides the ability to embed analytics applications in other portals.
- Qlik Sense: This analysis engine is designed to quickly detect unexpected correlations.
- Domo: A tool that continuously updates data, enabling real-time management and collaboration.
User profile: data experts (such as data analysts, data scientists)
Use cases and added value
Children’s Hospital Pittsburgh (UPMC) analyzes data from its Cerner system in real-time through an advanced dashboard. This solution helps monitor millions of data points, optimize care processes, and improve the quality of patient services by reducing inefficiencies.
8. Data contract management and SLA tools: defining and controlling data usage
As data is shared between different departments and partners, organizations need to define, monitor, and enforce how it can be used through data contracts and service level agreements (SLAs).
Examples include:
- Ataccama: A platform that covers the definition, application and governance of data contracts.
- Informatica Data Privacy Management: This tool defines privacy levels, enables data masking, and creates SLAs on data quality.
User profile: data experts (such as data owners, data governance managers and IT managers)
Use cases and added value
Raiffeisen Bank has adopted a data contract management solution to strengthen supervision of its master data and to adapt to regulatory requirements. This solution has improved data quality, optimized internal processes, and ensured enhanced compliance with current financial regulations.
9. Data security and compliance tools: maintaining privacy and safeguarding information
When information is shared it is vital that organizations protect sensitive and personal data, such as bank details or medical information, and that they comply with applicable laws and regulations, such as the GDPR and CCPA. This requires solutions that provide proactive monitoring of security and detect and highlight potential risks.
Examples include:
- Varonis: A tool to provide behavioral analysis of data access, generating automated alerts around abnormal activity.
- BigID: A solution that delivers advanced mapping of sensitive data and its automatic classification.
- OneTrust DataGovernance: A tool that coordinates consents and masking, enforcing and supporting privacy management.
User profile: data experts (such as data owners and data protection officers (DPOs))
Use cases and added value
A financial services company uses Varonis to monitor access to customer records. If an unusual amount of data is copied or downloaded, the tool immediately sends an alert. This responsiveness protects the company’s reputation and avoids heavy fines for potential data leaks and breaches.
10. Data product marketplace: democratizing and enhancing data use
The Data Product Marketplace is an essential part of making data accessible to the entire business in the form of easily-consumable data products. It facilitates the discovery, use and monitoring of data products and other data assets, through an intuitive, self-service experience, while establishing a clear governance framework.
Data product marketplaces include AI-powered capabilities for advanced search, visualization creation, or drag-and-drop interfaces, simplifying management and maximizing productivity. Importantly, it provides data that can be acted on by both humans and AI, through machine-readable data to train Large Language Models (LLMs) and provide AI agents with structured, reliable and actionable data.
Examples include:
- Opendatasoft: A flexible, feature-rich marketplace solution that is focused on scaling the sharing of data and data products to increase value, efficiency and innovation.
User profile: data experts (all roles) and business users
Use cases and added value
Schneider Electric uses Opendatasoft to share energy data and tools with its partners. This is underpinning the creation of new services, such as for predictive maintenance and more detailed analysis of energy consumption, as well as deepening engagement. Schneider Electric’s data product marketplace is bringing together its ecosystem to increase innovation around its data assets and strengthen its position in the energy market.
Summary: how data management tools integrate with a data product marketplace
| Solutions | Benefit | Integration Advantage |
|---|---|---|
| Cloud Data Management Platforms | Offers elasticity and high availability. | Host the data product marketplace in the cloud to absorb peaks in demand and optimize costs. |
| Data Catalogs | Enables the documenting and inventorying of all data assets. | The data catalog feeds the data product marketplace by providing visibility, metadata, and descriptions to users. |
| Data Governance Solutions | Manage data quality, security, and compliance. | By integrating data governance with the data product marketplace, only reliable and compliant data is published. |
| BI and Analytics Tools | Transform raw data into actionable insights. | Integration with a data product marketplace offers direct access to dashboards and centralized governance of data sources. It also increases the visibility of data assets to a wider audience. |
| Integration and Automation Tools (ETL/ELT) | Ensuring continuous and reliable data flows. | Robust ETL/ELT flows continuously feed a data product marketplace, ensuring information is always up-to-date. |
| Artificial Intelligence and Machine Learning Solutions | Predicting the future, optimizing performance, and automating processes. | Models can be offered as data products that can be reused via the data product marketplace. |
| Advanced Dashboards and Visualizations | Go beyond classic business intelligence and deepen data exploration. | Offering dashboards as data products via the data product marketplace makes them easier to discover, access, and share. |
| Data Contract Management and SLA Tools | Defining data use rules and setting SLAs for data quality and reliability. | Publishing data contracts directly in the marketplace guarantees transparency and aids enforcement. |
| Data Security and Compliance Tools | Protecting privacy and security while ensuring regulatory compliance. | Ensure only authorized users can access sensitive data by integrating policies and tools with the data product marketplace. |
| Data Product Marketplace | Democratize access to data for all roles, maximizing its value. | Thanks to its API-based structure, a data product marketplace can be deployed on a stand-alone basis or combined with a data catalog or governance solution. This ensures rapid access to data assets, including data products, and its consumption at scale. |
Taking an integrated approach, centered on the data product marketplace, to accelerate data consumption by the business and create value
Governance, integration, catalogs, data products marketplaces. Each part of the data management stack has its role, but only intelligent orchestration and integration creates real value. In this respect a data product marketplace doesn’t just centralize data, it transforms it into a strategic asset that can be consumed at scale by the entire business, not just data experts.
Integrating a data product marketplace into the heart of your tech stack enables you to:
- Transform data into actionable insights for business users and create value.
- Make data accessible and consumable at scale in an actionable, reliable, and documented form.
- Ensure clear governance and seamless traceability.
- Maximize the impact of your BI, AI, and cloud investments with a single point of entry.
- Create a data technology stack that powers your own AI models.
Technology alone is not enough. To be successful you need to combine them with:
- A shared data culture and adoption by all teams.
- Tailored training to fully exploit their potential.
- Rigorous governance to ensure data quality and compliance.
Opendatasoft supports organizations as they transform to maximize the value of their data. Our solutions help structure, share, and reuse data efficiently. Ready to take your data management to the next level? Learn how a data product marketplace can become the core of delivering data sharing at scale and unlock the true value of your data.
In today’s data-driven world, many organizations struggle to scale data consumption, with their data scattered across multiple systems, making it hard to access, understand, and leverage effectively. How can organizations transform this raw data into value, enabling it to drive decision-making, innovation, and AI adoption? This practical guide helps Chief Data Officers (CDOs) and data leaders to transform their data strategy and meet their goals by implementing self-service data product marketplaces.
Chief Data Officers are central to organizations becoming data-centric, maximizing data sharing to ensure that everyone has immediate access to the information they need. We explore the challenges they face - and how they can be overcome with the right strategy and technology.
Data Marketplace
To meet their need to increase data consumption and maximize value, businesses are increasingly implementing centralized, self-service data product marketplaces. We look at what they are, and how they deliver value for employees, the business, data teams and data leaders.
In today’s data-driven world, many organizations struggle to scale data consumption, with their data scattered across multiple systems, making it hard to access, understand, and leverage effectively. How can organizations transform this raw data into value, enabling it to drive decision-making, innovation, and AI adoption? This practical guide helps Chief Data Officers (CDOs) and data leaders to transform their data strategy and meet their goals by implementing self-service data product marketplaces.
Chief Data Officers are central to organizations becoming data-centric, maximizing data sharing to ensure that everyone has immediate access to the information they need. We explore the challenges they face - and how they can be overcome with the right strategy and technology.
Data Marketplace
To meet their need to increase data consumption and maximize value, businesses are increasingly implementing centralized, self-service data product marketplaces. We look at what they are, and how they deliver value for employees, the business, data teams and data leaders.