• Getting Big Value Out of Big Data

    In this webinar, we'll explore key findings and lay the groundwork for overcoming big data challenges-with real-world experiences and proven best practices.

  • 16 MDM Technology and Implementation Best Practices

    Evaluating a master data management (MDM) technology or implementing one can be a daunting exercise. The road to MDM is littered with both success and failure. While many companies have been successful in their MDM journey in every phase of their project, a few have shelved the product after a disastrous initial implementation. What separates success from failure? What technical capabilities separate the best-in-class from mediocre MDM? How to plan around the implementation minefields so that your project doesn’t blow up?

  • Adaptive Biotechnologies: A data-centric startup

    Adaptive Biotechnologies is a thriving biotechnology startup, headquartered in Seattle, and playing a major role in the immunotherapy approach to curing cancer. Adaptive Biotechnologies has been growing in leaps and bounds, both organically and through acquisitions - and amassing vast amounts of data in the process.

  • How Kelly Services Uses MDM & iPaaS for Data Estate Modernization

    At 70 years, Kelly Services is a global workforce solution company serving 91% of Fortune 100 companies with more than 1 million workers in the talent supply chain. In 2017, Kelly Services began modernizing its data platform with a cloud-first strategy to quickly connect the right talent with the right customers. Critical to the success of Kelly Services is a multidomain strategy for MDM, starting with the customer domain.

  • Banking POV: Framework for Building a Modern Data Foundation

    Leading financial services companies are embracing sweeping digital transformation strategies, including next best experience, personalization, omni-channel consistency, and compliance with customer-centric regulations such as FACTA, KYC, GDPR and CCPA. The common denominator underpinning all of those initiatives is a solid data foundation and a Customer 360. Most financial institutions have tons of client information residing across many systems and silos, but customer profiles tend to be less than 20% complete and lack insights from unstructured data, such as web chats, phone notes, and emails. Getting to 100% doesn’t happen with conventional matching capabilities or from raw data in a data lake. To take full advantage of all data, it requires modern capabilities including Machine Learning, Natural Language Processing, graph visualization and more.