Documents Library


    • [guide] Data Integration

    The Buyers’ Guide to Data Integration Software

    The Buyers’ Guide is designed to help you select the correct data integration software and vendor for your organization. The guide will walk you through the decision-making process, step by step. You’ll learn what to consider and what questions to ask before committing to a data integration solution.

    • [e-book] Data Migrations

    Data Migration for Humans

    What is a data migration—really? How involved do you need to be in the process? Is it just an IT problem, or does the whole business have to devote attention to it?

    • [e-book] Compliance

    The Guide To AnaCredit

    AnaCredit (The Analytical Credit and Credit Risk Dataset), is a project from the European Central Bank (ECB) to create a shared database containing information on bank loans to companies. Starting from September 2018, data collection for AnaCredit is due to start, and the regulation will come into full force in 2020. The e-book answers 10 key questions, ranging from how data collection will work, to how firms should prepare for the regulation.

    • [guide] Data Migrations

    The Guide to Data Migration Projects

    The Guide to Data Migration Projects breaks down a typical data migration into 13 project stages. It explores best practices and some red flags so that you can take the right approach to delivering your project.

Case Studies

  • Reducing Salesforce Loading Time with Better Data Integration

    A financial services organization was finding their in-house developed data integration solution time-consuming to run, expensive to maintain and highly error prone. Their existing data processing was preventing them from delivering reliable information to their financial advisor clients.

  • Data Warehouse for E-Commerce

    An e-commerce company were struggling to scale their manual data integration operation to keep up with their rapid growth, and were looking for a better way to harness their data to build better business insights.

  • Identifying Revenue Leaks by Unified View On Data

    To enable a global logistics company to have a single, unified view of their data, the company turned to CloverETL for a solution which would leverage and bring together their shipment data.

  • Customer Case Study:
    Customology | Marketing Strategy Meets Data Science

    CloverETL enables Customology to gather and unify marketing and transactional data to deliver customer‑centric campaigns backed by data science. With CloverETL, Customology has shortened the time to market by replacing bespoke application development with a robust and flexible data integration platform.

  • Automated Analysis and Data Mapping for Complicated Migrations

    To successfully deliver a complex data migration within a tight two-month schedule, a multinational software company turned to CloverETL for an automated data mapping framework.

  • Effectively Migrating Legacy Data Into Workday

    Introducing a data ingestion and validation framework based on CloverETL has radically changed how quickly consultants from a Workday Implementation Partner can move past the migration stage of a project, ultimately leading to more valuable time spent properly implementing Workday.

White Papers

    • Best Practices

    Data Modeling and Data Integration

    Data integration is a powerful tool, but can often be beyond the technical level of business users. Data models can be easier for less technical teams to work with, but they only tell part of the story. This white paper, featuring a financial services case study, looks at how these two worlds can be bridged, with a technique that enables business and technical teams to establish a common language.

    • Best Practices

    Architecting Systems for Effective Control of Bad Data

    Bad data is unavoidable, and even small data issues can have enormous impact. This whitepaper examines how businesses can architect systems from the ground up to better manage bad data, and which tools and practices can be utilized for a more effective data validation and correction loop.

    • Best Practices

    Your Guide To Enterprise Data Architecture

    The volume of data is increasing by 40% per year (Source: IDC). In addition, the structure and quality of data differs vastly with a growing number of data sources. More agile ways of working with data are required. This whitepaper discusses the vast options available for managing and storing data using data architectures, and offers use cases for each architecture. Furthermore, the whitepaper explores the benefits, drawbacks and challenges of each data architecture and commonly used practices for building these architectures.

    • Anonymization

    Addressing Data Anonymization Challenges

    With a well-designed data anonymization process, it's possible for businesses to obtain reliable test data that provides the same use case coverage as the original production data—without falling victim to data security, privacy, and licensing issues. This white paper discusses reasons for and best practices of data anonymization and the advantages of a maintainable, customizable approach with CloverETL.

    • Best Practices

    Designing Data Applications The Right Way

    Utilizing the power of a data integration tool, organizations can future-proof towards large data volumes and complexity. This white paper discusses how to make the most of the data integration layer when designing data applications.

    • Best Practices

    Designing Your Data Future

    It's no secret that data assets are increasing exponentially. With this dramatic growth in volume and complexity, the need to move, manipulate, and analyze data is taking center stage. Today's imperative is to design a data workflow optimized for performance, agility, and usability.

    • Human Resources

    Conquering HR system sprawl and manual data entry without replacing your HR systems

    New rapid data integration techniques are available to cost-effectively reduce the intensively manual "human integration".

    • Big Data

    Take a Break from Big Data

    What exactly is the Big Data buzz about? Is it possible to work with big data without "Big Data"?