June 2023

Author: Pallavi Palande, Associate Software Technical Lead

IBM Operational Decision Manager (ODM) helps businesses automate and optimize their decision-making processes using business rules and analytics. Among its powerful components, the Decision Runner stands out — enabling users to test and execute decision services efficiently.

This blog explores custom data providers in IBM ODM Decision Runner — what they are, why they matter, and how they enhance real-time decision-making capabilities.

Understanding IBM ODM Decision Runner

The Decision Runner is a testing tool within IBM ODM. It allows users to:

• Create and manage test cases
• Simulate real-world decision scenarios
• Analyze test results and improve accuracy

By providing input data and executing decision services based on defined business rules, it helps organizations validate outcomes before production deployment.

What Are Custom Data Providers?

Custom Data Providers in IBM ODM Decision Runner give you the flexibility to connect to external data sources during testing or execution.

While ODM includes built-in providers for basic data types (like strings or numbers), custom data providers let you integrate dynamic and complex datasets from databases, APIs, or web services — ensuring your decisions use real-time, accurate data.

Key Benefits of Custom Data Providers

1. Integration with External Systems

Connect seamlessly with systems like databases, web APIs, or enterprise applications to fetch real-time data. This integration ensures your decision models operate using up-to-date, reliable information.

2. Realistic Testing Scenarios

Simulate real-world conditions by pulling live data into Decision Runner. This leads to more accurate test coverage and better decision validation.

3. Dynamic Data Generation

Custom data providers can dynamically generate test data. This helps test time-sensitive or constantly changing inputs, improving responsiveness and adaptability.

4. Increased Flexibility

You can integrate data from various sources — giving you greater flexibility in how decision logic interacts with business systems.

5. Improved Performance

Optimizing how data is fetched and processed leads to faster test execution and overall performance improvements in your decision services.

How to Implement Custom Data Providers

Here’s a simple, step-by-step approach to creating and integrating custom data providers in IBM ODM Decision Runner:

Step 1: Define the Data Structure

Identify the structure and format of your external data — e.g., a database schema or API response.

Step 2: Implement the Custom Data Provider

Develop a Java class that implements the custom data provider interface offered by IBM ODM. This class should handle data retrieval and map it to decision inputs.

Step 3: Register the Provider

Register your custom provider in IBM ODM Decision Runner so it can be selected when setting up test cases.

Step 4: Configure Test Cases

Assign the custom provider to specific input fields in your test cases. Decision Runner will automatically fetch external data during execution.

Why It Matters

By implementing custom data providers, organizations can:

• Enable smarter, data-driven decisions
• Enhance accuracy and reliability
• Improve test coverage and adaptability
• Achieve faster development cycles

This not only streamlines testing but also ensures your decision models remain agile in today’s AI-driven, data-centric environments.

Conclusion

Custom data providers in IBM ODM Decision Runner empower organizations to bring real-time intelligence into decision-making. By integrating with external systems and leveraging diverse data sources, businesses can ensure high-quality, reliable, and scalable decision automation.

Harness the power of custom data providers to future-proof your decision automation strategy and achieve better business outcomes with IBM ODM.

About the Author

Pallavi Palande brings over 7 years of IT experience, specializing in Business Rules Management Systems (BRMS) and IBM ODM development across finance, insurance, healthcare, and telecom domains. Her expertise includes BPM, ODM installation, DVS testing, decision modeling, and Java Execution Object Models. Pallavi’s deep knowledge in designing decision services and project migration reflects her hands-on experience and technical authority in the BRMS space.

Discover how we helped a client overcome scalability limitations, streamline partner onboarding, and eliminate maintenance issues through our cutting-edge integration expertise.

Our Client

Our client is a prominent consumer packaged goods (CPG) company.

The Challenge – Modernization and Streamlining for Business Excellence

Our client embarked on a comprehensive three-year technology modernization roadmap to enhance process efficiency, improve business performance, and foster better collaboration. The primary goals were to modernize legacy SAP applications, integration processes, and infrastructure to ensure improved agility and real-time communications with vendors.

• Existing landscape involved hundreds of vendors and relied on thousands of point-to-point interfaces using EDI (Electronic Data Interchange).
• Complex network led to errors, cost inefficiencies, sluggish performance, and potential issues with invoicing, production, and shipments.
• Specific challenges included scalability limitations, lack of reuse, extended partner onboarding, inflexibility, poor lifecycle management, and maintenance and support issues.

Action – Translating Vision into State-of-the-Art Architecture

The project employed an architecture-agile approach, establishing a reliable and scalable framework for future interface transformations. Prolifics’ relied on its expertise in implementing cutting-edge Canonical Design patterns to oversee design, implementation and testing phases.

Hani Alhaddad, Prolifics’ Director of Client Success, states:

“Canonical Design promotes seamless integration by establishing a standard and consistent representation of Data. It simplifies the integration process and interoperability leading to more efficient and scalable integration solutions.”

• Prolifics gained a deep understanding of the client’s modernization vision and identified pain points through comprehensive landscape analysis.
• A new architecture was proposed to streamline interface management and simplify the environment, leveraging reusable components, decoupled integration, and an API-first strategy.
• Customization capabilities were implemented to meet specific customer requirements without the need for separate code versions.
• Non-EDI interfaces, including app-to-app connections for communication between SAP and the payroll system, as well as internal application integration, will also be analyzed.

The successful translation of our client’s vision into a tangible framework positioned Prolifics as a trusted partner, leading to an expanded scope of work and an additional project for detailed design of EDI conversions and API interfaces.

Technology

What began as a project to modernize legacy SAP PI EDI-based integration quickly transformed into a landmark endeavor within our client’s project portfolio. The mission is to modernize their entire integration landscape using a component of SAP Business Transformation Platform (BTP) called CPI (Cloud Platform Integration), a cloud-based integration platform that allows organizations to connect different systems and applications. It supports both cloud-to-cloud and cloud-to-on-premises integrations, making it easier for organizations to connect and synchronize data between different applications and platforms.

More about Prolifics Integration Modernization

Integrations that connect, scale and flex; in months.

As your Integration Modernization partner, Prolifics offers comprehensive coverage of the full systems integration lifecycle, empowering your organization with enhanced efficiency, agility, and innovation. Our services span implementation, modernization, and management, backed by a proven methodology and a range of reusable IP accelerators.

About Prolifics

Prolifics is a digital engineering and consulting firm helping clients navigate and accelerate their digital transformation journeys. We deliver relevant outcomes using our systematic approach to rapid, enterprise-grade continuous innovation. We treat our digital deliverables like a customized product – using agile practices to deliver immediate and ongoing increases in value.

We provide consulting, engineering and managed services for all our practice areas – Data & AI, Integration & Applications, Business Automation, DevXOps, Test Automation, and Cybersecurity – at any point our clients need them.

Email: solutions@prolifics.com or visit us at prolifics.com

By Gregory Hodgkinson | Chief Technology Officer , Prolifics

A new AI platform has taken center stage. At IBM’s Think conference, watsonx was introduced as the “next-generation AI and data platform to scale and accelerate AI.”

Is it just hype, or does watsonx genuinely have the “X factor”, that special quality that sets it apart? Let’s dive deeper into what makes this platform stand out.

What Is watsonx?

IBM positions watsonx as a unified platform designed to support AI initiatives end-to-end. As Arvind Krishna explains:

“Clients will have access to a toolset, technology, infrastructure, and consulting expertise to build their own, or fine-tune and adapt available AI models, on their data and deploy them at scale in a more trustworthy and open environment.”

watsonx isn’t a single product; it’s a trio of complementary capabilities:

1. watsonx.ai – the “lead performer”
2. watsonx.data – the foundational data engine
3. watsonx.governance – the steward of trust and compliance

The Components of watsonx

watsonx.ai

The platform’s “frontman,” designed for training, tuning, testing, and deploying both machine learning and generative AI models.

• Supports AutoML workflows to accelerate model building, even for smaller teams.
• Provides foundational models (pre-trained and domain-adaptable) with the flexibility to remix or fine-tune for enterprise use cases.

This helps organizations use generative AI efficiently, without reinventing the wheel.

watsonx.data

The “backbone” of watsonx. Data isn’t an afterthought here, it’s central.

• Built on a lakehouse architecture that integrates storage and querying for both analytics and AI.
• Allows data-in-place operations, reducing duplication and cost.
• Powered by Apache Iceberg, emphasizing openness and broad industry support.

By unifying AI and data infrastructures, watsonx reduces complexity while maximizing scalability.

watsonx.governance

The “guardian” ensures AI is trustworthy and compliant. Its functions include:

• Automating oversight processes to reduce manual risk and cost.
• Providing transparency, bias detection, drift monitoring, and explainability.
• Aligning AI with ethics, regulatory frameworks, and privacy requirements.

By embedding governance into the architecture, watsonx helps organizations build responsible AI from day one.

Why IBM Is Well Placed to Deliver

watsonx builds on IBM’s deep roots in AI and data:

• Historical pedigree: Early AI ambition with Watson (Jeopardy, healthcare).
• Analytics foundation: Investments in Cognos, SPSS, Netezza, and Information on Demand.
• Cloud-native flexibility: Runs on cloud or on-prem via OpenShift.
• Complementary approach: watsonx doesn’t replace Cloud Pak for Data but expands IBM’s portfolio.

With its legacy strengths and modern design, IBM is positioning watsonx as a trustworthy, open, and scalable AI solution.

Does watsonx Really Have the “X Factor”?

Key Demand	How watsonx Delivers
Scale & cost efficiency	Lakehouse + data-in-place reduces duplication and overhead.
Generative AI readiness	Foundational models + remixable workflows.
Governance & trust	Governance built in, not bolted on.
Flexibility & portability	Hybrid, cloud, and on-prem deployment options.
Ecosystem & backing	IBM’s reputation and global client base.

The “X factor” isn’t a gimmick, it’s how watsonx integrates these strengths into one cohesive platform.

Timing Is Key

watsonx arrives as organizations face pressing AI needs:

• Generative AI is now a baseline expectation.
• Responsible AI and governance are non-negotiable.
• Data duplication and siloed systems are unsustainable.

If IBM executes well, watsonx could not only ride the AI wave but shape its future.

What’s Available Now , and What’s Coming

Available today:

• watsonx.ai (model building, tuning, deployment)
• watsonx.data (lakehouse storage and querying)

Coming soon:

• watsonx.governance (compliance, ethics, monitoring)

The full trio is still coming together, but the early releases already showcase its potential.

Final Thoughts & Recommendations

watsonx may not be perfect on day one, but it deserves serious consideration for enterprises seeking scalable, responsible AI.

Recommendations for adoption:

• Start with a pilot project in a domain where data is clean and moderately complex.
• Build governance and fairness early, not after deployment.
• Break down silos by leveraging lakehouse architectures.
• Monitor open technologies like Apache Iceberg to avoid vendor lock-in.

Prolifics Can Help

At Prolifics, we combine deep expertise in data, AI, cloud, and governance to help organizations harness platforms like IBM watsonx effectively. From strategy and architecture to deployment and governance, we ensure your AI initiatives are scalable, responsible, and business-driven.

Ready to explore watsonx for your enterprise? Let’s build your AI roadmap together. Contact Prolifics today. 

Author: Sounder Mannathan | Senior Blockchain Architect, Prolifics

Innovation is not solely reserved for grand ideas; small ideas can often be the seeds of transformative solutions. In this blog, we will embark on a journey, Gartner-style, to explore how small ideas can be nurtured into impactful solutions. By incorporating elements such as idea management, proof of concept, technology selection, vendor lookup, implementation, and evaluation, organizations can navigate the path to success. Let’s dive into the Gartner-inspired process of turning small ideas into remarkable solutions.

Simple Framework

Idea Management: The first step on this journey is to establish a robust idea management system. Encourage employees from all levels and departments to contribute their small ideas. Implement a centralized platform, like an idea management software or intranet portal, to capture and evaluate these ideas effectively. Foster a culture that values and rewards innovation, embracing Gartner’s principles of inclusivity and open communication. By doing so, you can harness the collective intelligence of your workforce and unlock the potential of small ideas.

Proof of Concept or Technology: To validate the feasibility and potential of small ideas, consider developing a proof of concept (POC) or leveraging existing technologies. A POC involves building a prototype or conducting small-scale experiments to test the viability of the idea. Embrace Gartner’s recommended practices, such as setting clear objectives, defining a controlled scope, and establishing meaningful metrics for evaluation. The POC serves as a crucial steppingstone, helping you identify technical challenges, assess scalability, and gather feedback for further improvements.

Technology Selection: While small ideas may not require groundbreaking technologies, the right technology can amplify their impact. During the technology selection phase, consider Gartner’s research-based insights and recommendations. Evaluate technologies that align with your small idea and organizational goals. Assess factors such as scalability, integration capabilities, security, and long-term viability. Leverage Gartner’s Magic Quadrant reports or vendor evaluations to gain deeper insights and make informed decisions.

Vendor Lookup: In some cases, implementing small ideas may require external expertise or technologies not readily available within your organization. Conduct thorough research and evaluation of vendors who offer relevant solutions. Consider factors such as vendor reputation, expertise, cost-effectiveness, and compatibility with your specific requirements. Gartner’s market research and vendor analysis can serve as invaluable resources during this process, helping you identify potential partners who align with your goals.

Implementation and Evaluation: With the technology and vendor selected, it’s time to implement your small idea and turn it into a tangible solution. Assign dedicated resources to oversee the implementation process, ensuring a seamless integration and addressing any technical challenges that may arise. Once implemented, closely monitor the solution’s performance against predefined metrics and objectives. Gartner’s approach of continuous evaluation and feedback loops can aid in assessing the effectiveness and impact of the solution, facilitating further improvements and optimizations.

Summary

This blog can guide organizations on a transformative journey, turning small ideas into remarkable solutions. By fostering a culture of innovation through idea management, conducting proof of concepts, or leveraging existing technologies, carefully selecting appropriate technologies and vendors, and implementing and evaluating solutions, organizations can unlock the full potential of their small ideas. Remember, innovation is an ongoing process, so continuously iterate, learn, and refine your solutions. If you need assistance in navigating this journey and unlocking the potential of your small ideas, our team is here to help. Contact us today to embark on your Gartner-style innovation journey and transform your small ideas into remarkable solutions.

Contact our Prolifics team at Solutions@Prolifics.com to learn more about our innovation services.

“Remember, greatness can be achieved from even the smallest of ideas. Embrace the journey and let your innovation shine.”

Soundar Mannathan

About the Author: Soundar Mannathan is a senior blockchain architect with Prolifics, where he designs and develops solutions using open source technologies, enterprise products and cloud-based solutions. He has more than 16 years of experience in Cloud technologies, Blockchain/NFT, AI/ML, Java/J2EE, Spring/Spring boot, PL/SQL, Oracle, DB2, Mongo, Angular, React, Node and NPM. He holds an MBA from the University of Dayton and BA in Electronics and Communications Engineering from Anna University.

By Craig Smikle – Security Engineer

A secure, scalable, and intelligent identity and access management (IAM) system is one of the most critical investments for any modern enterprise. One of the biggest questions organizations face is:

Should we deploy IAM on-premises or in the cloud?

Both approaches have strengths and tradeoffs. This article explores their differences, helping you choose the best fit for your organization.

Introduction to Identity and Access Management (IAM)

Identity and Access Management (IAM) defines the policies, technologies, and processes that ensure only the right users and devices access your systems. It’s a cornerstone of enterprise cybersecurity and compliance.

Depending on your goals, IAM can be deployed on-premises, in the cloud, or through a hybrid IAM solution.

• On-premises IAM: Managed within your own data centers and IT infrastructure.
• Cloud IAM (IDaaS): Managed by a third-party vendor and delivered via subscription.

Each approach impacts control, cost, customization, and compliance differently. Let’s explore these factors through what we call the “8 Core Cs” of IAM decision-making.

1. Control vs. Constraints

On-premises IAM gives you full control over configuration, data storage, and security policies. You can:

• Choose hardware, vendors, and licensing models.
• Customize integration with legacy systems and workflows.
• Enforce stricter security configurations.

However, cloud IAM (like Okta or IBM Security Verify) comes with certain constraints:

• Limited customization options.
• Shared infrastructure among customers.
• Dependence on provider’s policies and permissions.

In short: On-premises = complete control. Cloud = managed convenience.

2. Customization vs. Consistency

Cloud IAM services prioritize consistency and scalability, offering a standardized user experience across clients. You can personalize branding and feature packages, but deep customization is limited.

On-premises IAM, on the other hand, enables full customization for:

• Complex enterprise workflows.
• Integration with custom-built apps and APIs.
• Specialized compliance and reporting requirements.

Quick takeaway: If your business processes are unique, on-prem IAM offers unmatched flexibility.

3. Compliance and Confidentiality

Regulatory compliance is often the deciding factor in IAM on-premises vs cloud decisions.

• Cloud IAM vendors stay updated on global standards such as GDPR, HIPAA, and PCI DSS. Updates and patches are automatically rolled out.
• However, ultimate responsibility for compliance remains with the organization.

When to choose on-premises:

• Data residency rules require storage within specific regions.
• You handle highly confidential or regulated information (e.g., finance, defense, healthcare).
• You need complete visibility into data handling and access logs.

Featured snippet tip:

Cloud IAM simplifies compliance management, while on-premises IAM ensures maximum data confidentiality.

4. Competency and Competition

For most organizations, IAM isn’t a core business function — it’s a means to secure digital operations.

• Cloud IAM providers (like Okta, Ping Identity, or Microsoft Entra) specialize in this space. They invest heavily in R&D and continuously innovate with AI-driven authentication, adaptive MFA, and risk-based access.
• On-premises systems, while customizable, require in-house expertise, increasing operational overhead.

Pro insight: If your organization lacks dedicated IAM resources, identity-as-a-service (IDaaS) may deliver faster results and stronger protection.

5. Complexity vs. Convenience

IAM implementation is inherently complex — covering authentication, authorization, MFA, passwordless access, and user provisioning.

• Cloud IAM reduces complexity with ready-to-deploy templates and integrations (SAML, OIDC, OAuth).
• On-premises IAM may be simpler for companies with heavy legacy integration or proprietary workflows.

Voice search snippet:

“What’s easier to manage — on-premises or cloud IAM?”
Answer: Cloud IAM offers faster setup and easier scalability, while on-premises IAM provides deeper customization and integration control.

6. Cost and Capital

Cost is one of the top deciding factors.

On-Premises IAM	Cloud IAM (IDaaS)
High upfront capital	Subscription-based OPEX
Requires in-house expertise	Vendor-managed updates
Predictable long-term ownership	Predictable short-term billing
Risk of underutilized investment	Risk of vendor lock-in

Tip: Over a long lifecycle, hybrid IAM may offer the best total cost of ownership (TCO) — combining predictable costs with control over critical assets.

7. Connectivity and Collaboration

The modern workforce is distributed — with hybrid, mobile, and remote users accessing SaaS tools daily.

• Cloud IAM simplifies secure access from any device or location.
• On-premises IAM may struggle to integrate with multiple cloud-based applications without extensive networking investments.

Example:
Cloud IAM solutions often include built-in connectors for Office 365, Salesforce, and ServiceNow, reducing integration overhead.

8. Confidence and Contingency

Reliability and disaster recovery are critical to IAM success.

• Cloud IAM vendors offer built-in redundancy, uptime SLAs, and global data centers.
• On-premises IAM provides direct visibility into logs, events, and recovery protocols — ideal for organizations that require total control over incident response.

Security perspective:

• Cloud = trust in vendor reliability.
• On-premises = trust in internal capability.

Comparison Summary

Factor	On-Premises	Cloud
Control	Full customization	Limited configuration
Compliance	Total confidentiality	Automatic updates
Cost	Higher upfront	Predictable subscription
Complexity	More technical setup	Vendor-managed
Connectivity	Local, secure	Global, accessible
Contingency	Direct recovery	Vendor-managed DR

Hybrid IAM: The Best of Both Worlds

Most modern enterprises adopt a hybrid IAM solution — combining on-premises and cloud components for flexibility, compliance, and scalability.

Benefits of Hybrid IAM

• Phased migration from legacy systems.
• Balanced security and convenience.
• Unified governance across multiple environments.
• Cloud-ready identity federation using your existing user directory.

This approach is ideal for organizations modernizing existing IAM systems while maintaining compliance with regional data regulations.

Expert Insight

“In my role as a Security Engineer at Prolifics, I’ve supported organizations through on-premises, cloud, and hybrid IAM deployments. The best solution depends on your existing infrastructure, compliance needs, and business strategy.”

— Craig Smikle, Senior Security/IAM Engineer
(15+ years in IT | Certified expert in Okta, RSA, IBM Security Verify)

Conclusion

There’s no universal winner in the on-premises vs cloud IAM debate. Each model offers unique benefits:

• Cloud IAM: Ideal for scalability, flexibility, and faster deployment.
• On-Premises IAM: Perfect for full control, privacy, and tailored compliance.
• Hybrid IAM: The future of enterprise identity management — blending the best of both worlds.

As identity management continues to evolve, the key is aligning your IAM strategy with your organization’s security posture, user experience, and compliance roadmap.

The metaverse – what comes to mind?

For most of us today, the metaverse means people wearing virtual reality (VR) headsets, representing themselves as an icon or animated figure (avatar) in an online game. Others may think of augmented reality (AR), like generated overlays of information coming onto whatever you’re looking at through your smart phone. Others may know about cryptocurrencies like Bitcoin, or non-fungible tokens (NFTs), which are digital assets with special identification codes and rights that make each one unique in the otherwise easy-to-replicate digital world. What all the above have in common, and what really is the basis of the metaverse, is the concept of immersing yourself from the real world into the digital world, in which you interact and share experiences with others in the digital world in real time. Like the early days of the internet, the potential of metaverse appears unknown but seems unlimited.

How businesses are using the metaverse today

It’s moving from early adapters to the early majority/mainstream stage. Businesses today are using the metaverse for enhancing customer engagement and extending market reach, while also improving efficiency and scalability. Examples going on right now include:

• Setting up virtual storefronts to sell virtual and real-world merchandise
• Placing virtual ads, sponsoring virtual events or creating digitally branded experiences to promote products and services
• Hosting virtual events and charging virtual ticket fees or selling virtual merchandise
• Partnering with game developers to create branded content and promote their products
• Purchasing virtual land in a popular metaverse platform and charging rent or selling it for a profit

As we said in a prior blog post, “Although the metaverse is in its infancy, its potential is undeniable. Radio, television, and the internet all came before with their world-changing effects. The metaverse is next in that line of technological advancements that savvy business organizations will adopt for their own specific goals.”

What’s Next for Your Business? Step into the Future with Metaverse as a Service (Maas)

With Prolifics and our MaaS, you don’t have to do it alone. We handle the technical complexities and provide the necessary tools for you to explore the virtual world. Enhance customer engagement, extend market reach, improve efficiency, and scale your business with our expertise in IoT, digital twin, virtual, and augmented reality.

Post Author – Mike Hester, Senior Data Architect, Prolifics

Businesses are expanding and collecting more data that is rapidly becoming more complex. The data being collected is commonly used for marketing purposes, helping to make the customer user experience better and will ultimately drive business decisions. This data will come from many disparate sources and will need to be stored in a consistent manner that can be used by everyone within the organization.

“According to the results of a survey on customer experience (CX) among businesses conducted in the United States in 2021, the main challenge affecting data analysis capability for CX is the lack of reliability and integrity of available data. Data security followed, being chosen by almost 46 percent of the respondents.”

J. G. Navarro, Main challenges affecting data analytics for CX in the U.S. 2021,U.S.: main challenges for data analytics CX 2021 | Statista, accessed on May 9, 2023

To address these challenges many companies are turning to data repositories. A data repository, also known as a data library or data archive, is an entity that will be isolated and is for long-term storage and storing data for analytic and reporting purposes. A data repository is large and is generally made up of many databases.

Some examples of data repositories –

Data warehouses store large amounts of aggregated data and are not always necessarily related.

Data lakes are large repositories which generally store unstructured raw data, with that data being classified and tagged with metadata. Raw or unfiltered data means it has not been filtered or structured and does not have a predetermined use case.

Data marts are a subset of a data repository and are more targeted for a particular type of business need or user. Data marts are more secure since the users can only access what they need and not the entire data repository.

Metadata repositories store data about data and databases. Metadata can generally explain the lineage of the data, where it was sourced and any additional information that may be important.

Benefits of a data repository

There are many advantages to storing large volumes of data in an isolated manner which allows the business to make informed, data driven decisions. Data repositories require large investments of money, resources and time.

• Storing multiple data sources in a single place makes it easier to manage, analyze and report
• Isolation allows for faster and less complex reporting or analysis since the data is clustered
• Workload for administrators is reduced due to isolation and compartmentalization of the data
• Data is preserved and archived

Disadvantages of a data repository

There are several vulnerabilities that exist in data repositories that corporations must manage effectively to mitigate potential risks, including:

• Growing databases and data sets may slow down corporate systems. Ensuring that database systems can scale with data growth is mandatory.
• When systems are isolated a system crash can affect all of the data. This can be mitigated by a solid backup strategy that limits and isolates access.
• In some cases, unauthorized users may be able to access all or large volumes of sensitive data more easily than if it was distributed across several locations.

Data repository vs a data warehouse

A data repository consolidates data sets from various sources and isolates them in order to make them easier to access and mine for business insights, reporting needs, or machine learning. It is a general term, whereas a data warehouse is a specific subtype of a data repository designed for collecting and storing structured data from multiple source systems across an enterprise.

A data warehouse is best suited for providing a broad, historical view of large data sets integrated from multiple sources to drive strategic decisions that affect the entire enterprise. Other types of data repositories are better suited for handling unstructured or complex data formats, analyzing data for different subsets of business operations, and other use cases.

Data repository best practices

1. Selecting the right/correct extract, transform, load (ETL) tools or applications to load the data repository is key to ensuring data quality throughout the data lifecycle.
2. Initially it is best to limit the scope and breadth of a data repository. Storing and maintaining smaller data sets and limiting the number of subject areas is beneficial. This can aid in maintaining quality. Over time growth will happen and more complexity and subject areas should be added.
3. Automating the loading of a data repository should be a priority. Manually running processes to load and maintain a repository will be too difficult as the system grows in both volume and complexity. Automated processes help with the management of schedules for things like source file receipt, ensuring proper data hierarchy, i.e., parent-child relationships, and process recovery.
4. Prioritize flexibility. A data repository should be scaled as new sources and targets are introduced, as well as different types of data, such as unstructured data. Incorporating a design that allows for growth without rework should be the goal of architecting a data repository.

If you’d like more information on data repositories or would like to discuss your data needs, Contact Us.

About the Author:

Mike Hester is a Senior Data Architect at Prolifics with 36 years of experience in information technology, specializing in DSS/Data Warehousing. He has worked in various roles such as project manager, system analyst, technical architect, and developer, delivering information management solutions in industries like government, engineering, and ERP. Mike is familiar with operating systems like Mainframe, UNIX, VMS, and Windows, and has worked with databases including Teradata, Oracle, SQL Server, DB2 UDB, DB2 DPF, and Netezza.