Data fabrics aid data discovery. IBM Cloud Pak for Data provides an integrated data governance, data quality and compliance solution. It is built with a data fabric architecture to improve productivity and collaboration, by reducing data complexity. This has led to DataSkill and IBM applying IBM Cloud Pak for Data to a variety of use cases [...]
Every year, IBM releases hundreds of new software versions – providing new capabilities and enhancements to improve the performance, productivity, and security of the Software. In fact, this continuous development is one the core reasons why DataSkill recommends IBM. IBM Software Subscription and Support (S&S) entitles you to access the latest versions and releases [...]
As we mentioned in our previous blog, Forrester recently name IBM as a leader in The Forrester Wave™: Enterprise Data Fabric, Q2 2022. There were several data fabric capabilities that they deemed IBM to be strong in, one of these was deployment options. Deployment options include: IBM Cloud Pak for Data - This open, [...]
IBM is DataSkill’s No.1 choice for data management solutions. DataSkill has been an IBM Business Partner since 1996 and we have chosen IBM because of its expertise and technology. IBM has deep expertise, a strong track record and proven solutions to help organizations achieve their business goals. In the 2021 Gartner Magic Quadrant for Data [...]
At DataSkill, our focus has been on helping businesses make the best decisions. We have been working with organizations to help them become more data driven. Unlocking the insights that the data provides is just one part of the solution, first the data needs to be accessible. DataSkill recognizes the need for trusted data [...]
DataSkill has found that data-driven decision making relies on speed. Competitive advantage is achieved by discovering insights and taking action before your competitors. To meet the demand for data, privacy and security decisions need to be made in real time. Within a continually changing business landscape, the only way to meet this time constraint [...]
