Hello there! As your trusted technology advisor, I‘m excited to provide this comprehensive playbook to simplify your enterprise data management software evaluation and selection.
With data volumes and sources exploding across most industries, seamlessly integrating, processing and analyzing information is no longer optional – it‘s a competitive necessity to drive revenue and operational efficiency.
However, as many IT executives have experienced, piecing together the right data stack with legacy tools proves complex, costly and risky over time. The chance of failure is dangerously high.
This insider guide cuts through the noise in the data management space. I‘ll equip you with clarity on must-have capabilities, leading solution fitment based on use cases and a framework to choose what‘s optimal for your growth trajectory.
Let‘s get you on the fastest path to enable easy yet controlled access to high quality data – fuel for powering organizational success!
Why Invest in Scalable Data Management Now
Before exploring technology options, let‘s align on key drivers propelling information infrastructure modernization for mid-size to large companies like yours:
1. Exponential Data Growth
- Global data generation expected to grow 61% from 2022 to hit 173 zettabytes by 2025 [Source: IDC]
- Unstructured data like documents, emails, media constitute 80%+ of this growth
- On average, companies use only 37% of their data leaving insights buried [Source: Veritas]
2. Rising Stakeholder Expectations
- 81% of employees demand access to more data sources to perform their roles [Source: Splunk]
- 69% of customers expect real-time, consistent interactions across channels [Source: Accenture]
- 82% of CEOs planning new data driven products, services and business models over the next 3 years [Source: PwC]
3. Limitations of Current Data Environments
- 76% organizations struggle with accessing data trapped in silos [Source: Denodo]
- 90% report infrastructure complexity increases costs and time-to-market [Source: Hanover Research]
- Legacy systems unable handle pace of new data sources like IoT devices, micro-services etc
The above underpins why holistic data management stacks built ground-up with cloud scale, automation and enterprise governance are becoming the gold standard for staying competitive.
Must Have Capabilities in Modern Data Management Software
Let‘s cover 7 key features needed from solutions to enable seamless usage of data at scale:
1. Hybrid and Multi-Cloud Data Integration
- Ingest data from 1000+ sources – apps, warehouses, IoT and more
- Support batch and real-time streams across public, private and hybrid cloud
- Consistent pipelines across environments prevent vendor lock-in
2. Secure and Governed Data Lakehouses
- Consolidate data into flexible, scalable lakehouses
- Apply schema on read instead of early rigid modeling
- Manage via hierarchical policies, user access and data lifecycles
3. Metadata-Driven Automation and Discovery
- Business glossary and catalog mapping structures and meaning
- Understand lineage across systems
- AI to autocomplete mapping and detect data quality issues
4. Advanced Analytics-Ready Transformation
- Intuitive visual interface for transforming data
- Library of validations, joins unions, aggregations and custom logic
- Ensure analysis-ready quality levels
5. Easy Monitoring and Collaboration
- Track data pipelines, workflow status and arising issues through intuitive dashboards
- Annotate and search metadata to foster trust
- Proactively optimize and tune environment
6. Secure Environment with Granular Access Controls
- Authentication, authorization and auditing safeguards
- Dynamic masking and encryption to minimize risk exposure
7. Flexible Scaling and High Availability
- Handle exponential data volumes while maintaining performance
- Built-in fault tolerance, redundancy and failover manage unplanned outages
Now equipped with key considerations, let‘s explore your options.
Comparing 10 Leading Enterprise Data Management Platforms
Let‘s evaluate how cloud-native data management stacks address modern complexities. I‘ll provide an insider‘s snapshot of 10 popular solutions across 3 key dimensions:
Vendor Fit: Overview of provider and platform positioning
Critical Capabilities: Summary of top features
Ideal Persona: Type of teams, use cases they excel in
1. Informatica Intelligent Data Management Cloud
Vendor: Informatica pioneered the extract, transform and load (ETL) market over 25 years ago. Their unified subscription cloud brings AI-driven automation to modern data workloads.
Key Capabilities:
- Handles batch, streaming and event data workloads
- CLAIRE engine for metadata-driven automation
- Advanced machine learning for smart recommendations
Ideal For: Mid to large enterprises wanting an end-to-end automated cloud data management platform
2. IBM InfoSphere Information Server
Vendor: IBM offers the highly reliable InfoSphere platform for error-free enterprise data integration and quality needs
Key Capabilities:
- Certified to handle ultra high data volumes
- Deep mainframe and custom app integration
- Advanced data lineage tracking
Ideal For: Heavily regulated industries like banking, insurance with complex legacy ecosystem
3. Oracle Enterprise Data Management Cloud
Vendor: Oracle provides a modular cloud data warehouse covering data integration, analytics, AI/ML and more
Key Capabilities:
- High throughput data processing at scale
- Embedded data enrichment functions
- Usage-based subscriptions
Ideal For: Oracle ERP customers seeking bundled analytical capabilities
4. Talend Data Fabric
Vendor: Talend offers a metadata powered hybrid data integration platform for technical and non-technical users alike
Key Capabilities:
- Unified solution for batch, streaming and microservices data
- Drag-and-drop environment for code-free integration
- Enterprise-grade governance with role based access
Ideal For: Flexible multi-cloud data lake environments
5. SAS Data Management
Vendor: SAS Institute focuses on analytics-specific data integration, quality and governance use cases
Key Capabilities:
- Automate data standardization tasks with AI/ML
- Hundreds of out-of-the-box data quality rules
- Monitoring dashboard highlighting anomalies
Ideal For: Analytics teams wanting to operationalize models
6. Amazon Web Services (AWS) Data Exchange
Vendor: AWS Data Exchange provides third party data subscription and integration
Key Capabilities:
- Discover, subscribe and integrate 1000+ external data feeds
- Automate entitlements and access directly through data analytics services
Ideal For: AWS customers that need reliable commercial datasets
7. Snaplogic Elastic Integration Platform
Vendor: SnapLogic provides self-service data integration supporting technical and non-technical users alike
Key Capabilities:
- Unified graphical interface to build and monitor data pipelines end-to-end
- Extensive library of pre-built connectors and templates accelerate development cycles
- Flexible on-premises, cloud and hybrid deployments
Ideal For: Mid-Size enterprises needing simplified rapid development environment
8. Information Builders iWay Data Management Suite
Vendor: iWay suite combines ETL, data quality, governance, replication, virtualization, streaming and APIs in a single platform
Key Capabilities:
- Unified admin covering ETL operational data integration and more
- Handle complex processing across technologies and data structures
- Real time analytics compatibility
Ideal For: Heavily regulated organizations with diverse data integration needs
9. Hitachi Ventant Information Hub
Vendor: Hitachi Vantara applies machine learning to simplify building and managing resilient data pipelines
Key Capabilities:
- Automated pipeline design and optimization recommendations
- Smart data differencing isolates source output changes to minimize compute
- Multi-tiered policies to manage infrastructure
Ideal For: Enterprise BI teams that need to operationalize reliable and reusable data integration at scale
10. Dell Boomi AtomSphere
Vendor: Dell Boomi positions itself as frictionless integration platform as a service designed for ease of use
Key Capabilities:
- Extensive library of pre-built connectors and workflow templates
- Drag-and-drop visually orchestrate integrations
- Usage-based predictable subscription pricing
Ideal For: Rapid application development teams that want cloud data services
This overview of leading options only scratches the surface of what’s possible. Reach out if you need help shortlisting platforms matching your architecture, security policies and future roadmap requirements.
Now let’s switch gears to qualifying solutions further through an evaluation framework.
Comparing Data Management Solutions: Key Evaluation Criteria
I always advise clients to assess options based on parameters below:
Functional Fit
- Integration methods supported: Batch ETL, Streaming, APIs, Replication etc
- Types of data handled: Structured, Semi-structured, Unstructured
- Data transformation library depth: Validation rules, Joins, Aggregations etc
- Inbuilt data quality functions: Parsing, standardization, matching etc
- Analytics ecosystem integration: BI tools, notebooks, warehouses
Ease of Use
- Skills needed: Citizen integrators, Developers, Data Engineers
- Learning curve for interface and admin console
- Ability to monitor environment holistically
Enterprise Grade
- Authentication protocols compatibility: LDAP, AD, SAML and more
- Backup and disaster recovery provisions
- Horizontal and vertical clustering ability
Cloud vs On-Prem
- Public cloud vendor support: AWS, Azure, Google Cloud
- Private cloud support: VMWare, OpenStack, Nutanix
- Docker, Kubernetes orchestration readiness
Vendor Sustainability
- Number of customers and revenue scale
- Gartner Magic Quadrant placement
- Analyst rating across solution criteria
Cost Structure
- Licensing models: Subscription, Perpetual, Consumption based
- Price banding based on tiered capabilities and support
- Environmental requirements like database licenses
Let me know if you need help framing technical requirements or an invitation to our vendor evaluation bootcamp!
Next let‘s walk through program implementation considerations.
Rolling Out Data Management Software
Beyond features, long term success requires planning for people, processes and having the right project governance.
People
- Assign responsibility to a data steward team spanning IT, analytics and business ops
- Train both developers and citizen data integrators on environment
- Whenever possible, start small with pilot projects
Processes
- Institute mechanisms for discovery, issue reporting and feature requests
- Update monitoring runbooks accounting for new dashboards
- Evangelize self-service to prevent one-off data requests overflow
Governance
- Install a DataOps council involving execs from infosec, finance and line of business
- Make platform design choices based on reuse and standardization
- Routinely assess performance vs benchmarks
Migration
- Calculate total cost of ownership for transition
- Cleanse legacy artifact issues early
- Define policies guiding sun-setting old systems
Project Plan
- Detail all deployment architecture components
- Estimate realistic timeline accounting for complexity
- Layer contingency budget, resources for uncertainty
With cross-functional alignment and planning, data infrastructure modernization can fuel tremendous efficiency gains across the enterprise. Reach out if you need help formulating business justification, stakeholder communications or an implementation blueprint.
In closing, let me recap key recommendations as you evaluate options to consolidate enterprise data on future-ready platforms.
Key Takeaways Evaluating Data Management Solutions
- Invest based on scalability needs considering projected growth
- Ensure platform choice aligns with existing infrastructure and security policies
- Calculate total cost of ownership not just license fees
- Look beyond technology to project team composition and change impacts
- Focus on driving usage and adoption through self service access
- Make data quality, governance and observability priorities early on
I hope this insider guide to navigating the complex data management space helps accelerate your modernization initiative! Reach out with any other questions or for architecture strategy ideation.
