How to Select Data Technology

Selecting the right data technology is one of the most foundational decisions a data or IT leader can make. Data management systems directly influence how effectively an organisation integrates, governs, cleanses, and leverages data across the business. From master data management and data integration to data quality, cataloging, and governance, the right technology enables trusted, accessible data that powers analytics, operational systems, and strategic decision-making.

The challenge is that the data management landscape spans multiple disciplines and solution categories. From comprehensive data platforms offering end-to-end capabilities to specialised tools for specific data challenges like integration, quality, or governance, the range of options can feel overwhelming. Making the right choice requires clear understanding of your data challenges, realistic assessment of data maturity, and careful evaluation of which solution will deliver value not just for immediate pain points but as a foundation for data-driven transformation.

This guide is designed to help data, IT, and business leaders understand the key factors to consider when selecting data management technology. At Viewpoint Analysis, we act as 'Technology Matchmakers' and run data selection processes to find and select new enterprise data platforms. More information relating to data technology can be found here.

What is Data Management Technology?

Data management technology encompasses software designed to help organisations integrate, govern, cleanse, and organise data to make it trustworthy, accessible, and valuable. Rather than allowing data to remain siloed, inconsistent, or poorly understood, modern data management platforms provide capabilities to unify data, ensure quality, define meaning, and enable confident use across analytics and operational systems.

The technology acts as the foundation for data-driven organisations - ensuring data is an asset rather than a liability. Modern platforms provide capabilities across:

• Data integration and ETL – connecting to disparate data sources, extracting data, transforming it to meet business needs, and loading it into target systems.

• Master data management (MDM) – creating golden records for critical entities like customers, products, suppliers, or assets by reconciling data from multiple sources.

• Data quality management – profiling data to understand issues, cleansing errors, standardising formats, and monitoring quality over time.

• Data cataloging and metadata management – documenting what data exists, where it resides, what it means, and how it relates to other data.

• Data governance – establishing policies, defining ownership, managing access, and ensuring compliance with regulations.

• Data lineage – tracking where data comes from, how it is transformed, and where it is used to enable trust and troubleshooting.

In short, data management technology helps organisations transform data chaos into data clarity, enabling confident decisions, operational efficiency, regulatory compliance, and competitive advantage.

A Data Management Technology Selection Process

Different companies have different approaches to running technology selection - from the standard RFI and RFP process, to more sophisticated and modern processes that put more emphasis on solving a specific business need and inviting the vendor teams to showcase and sell their approach (our preferred approach).

At Viewpoint Analysis, we run Rapid RFIs, Rapid RFPs, and 30-Day Selection Processes - all with the aim of moving quickly and getting on with the real project - delivering a data solution. In all cases, we run the entire process - from requirements gathering, document write-up, vendor sales outreach, hosting calls, helping with scoring....to final decision.

Whichever approach is chosen, there is going to be an important decision to take - what are the key factors that will be the most crucial ones in determining which data management solution to select? This document outlines the top 20 factors that we believe, from our experience of running data management technology selection processes for companies of different sizes and industries, should be weighed up to make the final vendor decision.

20 Critical Factors in Selecting a Data Management Technology Vendor

Here are our top 20 critical factors to assess when selecting new data management technology (in no particular order):

1 - Size and Fit

One of the most common mistakes in data management technology selection is choosing a platform that is misaligned with organisational data maturity, technical capabilities, or business priorities. An enterprise data management suite with sophisticated MDM, complex governance workflows, and advanced data quality rules may offer impressive functionality, but if the organisation is still struggling with basic data integration or lacks dedicated data stewardship resources, much of that capability will remain unused while adding cost and complexity.

Conversely, selecting a lightweight tool without considering growing data complexity can lead to limitations that become apparent as data volumes increase, sources multiply, or regulatory requirements intensify. What works for a department may not scale to enterprise needs.

The key is to balance current data challenges with realistic maturity plans. Start by defining immediate priorities. Is the main driver integrating data from acquisitions, creating trusted customer data, improving data quality, or demonstrating regulatory compliance? Then assess which capabilities will become essential as data initiatives mature. By matching requirements to vendor offerings thoughtfully, data leaders can avoid over-investing in unnecessary sophistication while ensuring the platform can grow with organisational needs.

2 - Data Integration and Connectivity

Data management begins with connecting to data wherever it lives - databases, cloud applications, files, APIs, streaming sources, and legacy systems. The platform's ability to connect to diverse sources and handle both batch and real-time integration is fundamental to delivering value.

When evaluating platforms, assess connectivity breadth and depth. Does the platform offer pre-built connectors for your key systems? Can it handle your data volumes and latency requirements? Does it support both structured data (databases, applications) and semi-structured or unstructured data (files, APIs, documents)?

Also consider how integration is managed. Can business-oriented data engineers build integration flows without extensive coding? Are there visual designers and pre-built templates? Can integration logic be tested and validated before deployment? Can the platform handle incremental data loads efficiently rather than full refreshes?

Strong integration capabilities reduce implementation time, lower ongoing maintenance costs, and ensure data management initiatives can scale across the enterprise.

3 - Master Data Management Capabilities

For organisations struggling with inconsistent, duplicate, or conflicting data about core entities (customers, products, suppliers, assets), Master Data Management provides critical capabilities to create and maintain "golden records" - single, authoritative versions of truth.

When evaluating platforms, assess MDM capabilities if this is a priority. Does the system support your critical data domains? Can it match and merge duplicate records intelligently? Does it handle survivorship rules (determining which source provides the most reliable data for each attribute)? Can it manage complex hierarchies like customer organisations or product families?

Also consider the MDM approach. Does the platform support registry-style MDM (just storing references and links), consolidation-style (creating a master repository), or coexistence (managing masters while keeping source systems synchronised)? Different approaches suit different organisational needs and technical architectures.

For organisations where data inconsistency directly impacts customer experience, operational efficiency, or compliance, robust MDM capabilities can be transformational.

4 - Data Quality Management

Poor data quality undermines analytics, drives operational errors, and erodes trust in data-driven initiatives. Data management technology should provide comprehensive tools to profile data, identify issues, cleanse errors, standardise formats, and monitor quality over time.

Assess data quality capabilities thoroughly. Can the platform profile data to understand patterns and issues? Does it provide pre-built quality rules and the ability to define custom rules? Can it automatically correct common errors like address standardisation or duplicate removal? Can it validate data against reference datasets?

Also consider quality monitoring and alerting. Can quality metrics be tracked over time? Can stakeholders be alerted when quality degrades? Is there visibility into which processes or systems are introducing quality issues?

Strong data quality capabilities ensure that data management efforts deliver trusted, reliable data rather than just moving poor-quality data between systems more efficiently.

5 - Data Governance and Stewardship

Data governance - the policies, processes, and roles that ensure data is managed as a valuable asset - is essential for sustained data management success. Technology should support governance by enabling policy definition, workflow for data stewardship, and accountability for data quality and compliance.

When evaluating platforms, assess governance capabilities. Can data ownership and stewardship roles be defined? Are there workflows for data issue resolution and policy exceptions? Can data policies be documented and enforced? Is there visibility into compliance with governance policies?

Also consider how the platform supports data stewards in their daily work. Are issues surfaced clearly? Can stewards prioritise and manage their workload efficiently? Is there collaboration capability for resolving complex data challenges?

Technology alone cannot create effective governance, but the right platform provides the tools and visibility that enable governance to work in practice rather than remaining aspirational.

6 - Data Cataloging and Discoverability

A significant data management challenge is that organisational knowledge about data often resides in individuals' heads rather than being documented systematically. Data catalogs address this by providing a searchable inventory of data assets with business-friendly descriptions, lineage, quality metrics, and usage information.

Assess cataloging capabilities. Can the platform automatically discover and catalog data assets across systems? Does it capture both technical metadata (tables, columns, data types) and business metadata (definitions, owners, quality, sensitivity)? Can users search for data assets by business terms? Can data be tagged and classified?

Also consider how business users and data consumers interact with the catalog. Is it genuinely accessible to non-technical users? Can they request access to data through the catalog? Can they rate data quality or provide feedback?

Strong cataloging capabilities transform data from a black box into a manageable asset that stakeholders across the organisation can understand, find, and use confidently.

7 - Data Lineage and Impact Analysis

Understanding where data comes from, how it is transformed along the way, and where it is used (data lineage) is essential for troubleshooting issues, assessing change impacts, and building trust in data. Without lineage, tracking down the root cause of a data problem or understanding the downstream impact of a system change becomes time-consuming detective work.

When evaluating platforms, assess lineage capabilities. Does the system automatically capture data lineage across integration flows, transformations, and consumption points? Can lineage be visualised in ways that make sense to both technical and business users? Can it support impact analysis showing what would be affected by changing a source system or data element?

Also consider lineage coverage. Does it span just within the platform, or does it extend to external systems, analytics tools, and reporting? End-to-end lineage that connects source systems to business reports provides the most value.

Strong lineage capabilities reduce troubleshooting time, enable confident change management, and build trust by making data flows transparent and understandable.

8 - Metadata Management

Metadata - the data about data - is essential for understanding, governing, and using data effectively. Comprehensive metadata management captures technical metadata (structure, format, location), business metadata (definitions, ownership, business rules), and operational metadata (data quality, usage patterns, lineage).

Assess the platform's metadata management capabilities. Does it capture metadata automatically from connected systems? Can business metadata be added manually or imported? Is there a metadata repository that unifies information from multiple sources? Can metadata be searched and browsed efficiently?

Also consider metadata versioning and history. Can you see how data definitions have changed over time? Is there an audit trail of metadata changes? This is particularly important for regulated industries where demonstrating compliance requires showing not just current state but historical understanding of data.

Strong metadata management transforms data from cryptic technical assets into well-understood, trusted resources that business stakeholders can confidently use.

9 - Data Security and Compliance

Data management platforms handle sensitive and regulated information - from personal data subject to GDPR and CCPA to financial information subject to SOX or industry-specific regulations. The platform must provide robust security controls and support compliance requirements.

When evaluating platforms, assess security capabilities. Does it provide fine-grained access controls? Can data be masked or tokenised for non-production environments? Are there audit trails of data access and modifications? Does it support encryption of data in transit and at rest?

Also verify the vendor's own security credentials and compliance certifications. Are they certified to relevant standards (ISO 27001, SOC 2)? Do they have expertise in your industry's specific compliance requirements? Can they support data residency requirements if needed?

Strong security and compliance capabilities protect the organisation from breaches and regulatory risks while enabling responsible use of data as a strategic asset.

10 - Scalability and Performance

Data management workloads can be demanding - processing millions of records, handling real-time data streams, or running complex quality rules across large datasets. The platform must deliver acceptable performance today and scale to handle growing data volumes and complexity.

Assess performance and scalability carefully. Can the platform handle your current data volumes within acceptable timeframes? How does performance scale as data grows? Can it support parallel processing to leverage modern computing infrastructure? Does it offer options for performance tuning?

Also consider architectural scalability. Is it cloud-native with ability to scale resources elastically? For on-premise deployments, can capacity be added incrementally? Can the platform distribute workloads across multiple servers?

Poor performance undermines data management initiatives by creating bottlenecks and frustrating users. Ensuring the platform can deliver responsive performance at scale protects the investment.

11 - Cloud vs On-Premise Deployment

Data management platforms can be deployed in the cloud, on-premise, or in hybrid configurations. Each approach has advantages, and the right choice depends on organisational priorities, data location, and IT strategy.

Cloud deployment offers lower upfront costs, faster implementation, automatic scaling, and reduced infrastructure management. It provides flexibility and ensures access to platform updates without managing upgrades. For organisations embracing cloud data platforms, cloud-native data management often makes sense.

On-premise deployment provides control over data location, integration with existing on-premise systems, and may be preferred by organisations with specific security requirements or substantial existing infrastructure. However, it involves higher upfront investment and ongoing infrastructure management.

Hybrid approaches allow some data management activities in the cloud while keeping sensitive data on-premise. Consider factors such as where your data lives, regulatory requirements, total cost of ownership, and integration complexity when evaluating deployment options.

12 - Self-Service and Business User Accessibility

Historically, data management has been the domain of IT and data specialists. Modern platforms increasingly enable business users to participate in data management activities - documenting definitions, assessing quality, requesting access, or even building simple integration flows.

When evaluating platforms, assess self-service capabilities. Can business users search and request access to data without IT tickets? Can they document business terms and definitions? Can they assess and provide feedback on data quality? For more technically capable users, can they build integration or transformation logic without coding?

Also consider user experience. Are interfaces intuitive for non-technical users? Is there contextual help and guidance? Can common tasks be completed without training?

Democratising data management beyond IT specialists accelerates initiatives, improves data quality through business expertise, and builds broader organisational data literacy and engagement.

13 - AI and Machine Learning Capabilities

AI and machine learning are increasingly embedded in data management platforms, offering capabilities like intelligent data matching, automated metadata discovery, quality anomaly detection, and recommendations for data classification or stewardship actions.

Assess AI capabilities critically. What specific problems does the vendor's AI solve? Can they demonstrate measurable customer outcomes? Is the AI transparent and explainable, or a black box? Does it genuinely augment human capabilities, or is it marketing hype?

For example, AI-powered matching in MDM can significantly improve accuracy and reduce manual effort. Automated metadata discovery can accelerate cataloging. Quality anomaly detection can surface issues proactively. However, not all AI features deliver practical value.

For organisations ready to leverage advanced capabilities, AI can accelerate data management initiatives and improve outcomes. For those building foundations, it may be less important than core functionality and usability.

14 - Integration with Analytics and BI Platforms

Data management exists to enable better analytics, reporting, and data-driven decision-making. The platform should integrate seamlessly with your analytics tools, data warehouses, and BI platforms - both providing clean, trusted data and receiving feedback about data usage and quality.

Assess integration with your analytics stack. Can the platform feed data to your data warehouse or data lake efficiently? Does it integrate with your BI tools? Can it push curated datasets to analytics platforms? Can usage and quality feedback from analytics flow back to inform data management priorities?

Bidirectional integration between data management and analytics ensures data initiatives deliver tangible business value rather than becoming isolated technical exercises.

15 - Data Privacy and Consent Management

With regulations like GDPR and CCPA establishing rights around personal data, organisations need technology support for managing consent, honouring data subject requests, and tracking where personal data resides.

When evaluating platforms, assess privacy capabilities if relevant. Can the platform identify where personal data resides across systems? Does it support managing consent preferences? Can it facilitate data subject access requests (providing copies of personal data) or deletion requests (right to be forgotten)? Does it maintain audit trails for compliance?

For organisations handling significant personal data, integrated privacy capabilities within the data management platform ensure compliance while reducing manual effort and risk.

16 - Vendor Ecosystem and Community

The strength of a data management vendor's ecosystem influences implementation success and long-term value. A robust ecosystem includes implementation partners with data expertise, complementary technology integrations, training resources, and an active user community.

When evaluating vendors, assess ecosystem strength. Are there experienced implementation partners available? Do they have expertise in your industry or data domains? Does the vendor provide certification programmes? Is there an active user community sharing best practices?

Also consider technology partnerships. Does the platform integrate with your existing data infrastructure? Are there connectors for your key systems? A strong ecosystem provides expertise, accelerates implementation, and reduces risk.

17 - Implementation Complexity and Time to Value

Data management implementations vary dramatically in complexity. Some platforms can deliver quick wins - cataloging data assets or establishing basic quality rules - within weeks. Others require months of data profiling, integration development, and governance process definition before delivering value.

When evaluating vendors, understand typical implementation approaches and timelines. What phases are involved? What data preparation is required? How much organisational change management is needed? What quick wins can be achieved early to build momentum?

Also assess whether the vendor provides structured methodologies, templates, and best practices that accelerate implementation. Proven approaches reduce risk and enable faster time to value.

18 - Ease of Use and Learning Curve

Data management platforms are used by diverse roles - from data engineers building integration flows to business analysts defining quality rules to executives accessing data catalogs. The platform should be accessible to these varied users without requiring extensive technical expertise or training.

Test usability with representative users during evaluation. Can a data engineer build an integration flow intuitively? Can a business user define a data quality rule? Can a data steward prioritise and resolve issues efficiently? Is the learning curve reasonable?

Also consider ongoing support. Are there in-platform help resources, tutorials, and documentation? Does the vendor provide training programmes? Ease of use accelerates adoption, reduces training costs, and ensures the platform becomes embedded in daily work rather than being seen as a burden.

19 - Total Cost of Ownership

Data management technology costs include software licences, infrastructure (for on-premise or self-managed cloud deployments), implementation services, integration development, training, and ongoing support. Understanding total cost of ownership over several years is essential for informed decision-making.

When evaluating options, model costs over a multi-year period. How does licensing scale as data volumes or users increase? What are infrastructure requirements and costs? What implementation and integration costs are realistic? What ongoing support and maintenance costs should be expected?

Also consider the cost of current approaches. What does it cost to operate with poor data quality, manual data processes, or compliance risks? Often the cost of not investing in data management - in operational inefficiency, errors, and risk - exceeds the technology investment.

20 - Trust and Partnership

After evaluating functionality, integration, usability, cost, and all other factors, the final question is: which vendor do you trust?

Trust manifests in how vendors engage during selection. Do they demonstrate deep understanding of data management challenges? Are they transparent about capabilities and limitations? Do they provide thoughtful guidance rather than just selling features?

Data management implementations require close collaboration, change management, and often cultural shifts in how organisations value and manage data. A vendor that aligns with your culture, communicates clearly, and shows genuine commitment to your success creates the foundation for effective partnership.

In most data management selections, when all critical criteria are met, gut instinct about the vendor relationship is rarely wrong. Trust in both the technology and the people behind it provides the best foundation for successful, sustained data management transformation.

Conclusion

Selecting data management technology is a strategic decision that will shape how your organisation integrates, governs, and leverages data for years to come. This guide has outlined the key factors to consider, from integration and quality capabilities through to governance, cataloging, security, and trust.

The data management landscape is complex, but a structured approach to selection makes the path clearer. By focusing on your most critical data challenges, involving the right stakeholders, testing platforms against real-world requirements, and balancing cost with long-term value, you can move forward with confidence.

The most successful data management implementations deliver more than software - they transform how organisations view and use data as a strategic asset. With the right choice, your data management platform will become the foundation for trusted analytics, operational efficiency, regulatory compliance, and data-driven competitive advantage.

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If you are soon to start looking at the data market, why not let Viewpoint Analysis help with your process? We have a range of services for every step of your journey:

• Not sure whether to move away from your current platform? Our 'Stick or Switch' review will help your team to decide whether you can improve what you have, or if the grass is really greener on the other side of the fence.

• Is your current provider ok, but you know they can improve and you'd at least like to try to see if that's possible? Our IT Service Improvement approach could be perfect and sort the situation out.

• If you are set on changing your data management software, and want to look at the market options to get ideas, take a look at our Innovation Series and Matchmaker Services.

• And finally - our Rapid Selection Services help you run the full (and quick) technology selection process.