IBM have unveiled “Project DataWorks,” a Watson-powered initiative that is the industry’s first cloud-based data and analytics platform to integrate all types of data and enable AI-powered decision-making.
Project DataWorks is designed to make it simple for business leaders and data professionals to collect, organise, govern and secure data.
Businesses today understand the competitive advantage of gaining insights from data. However, obtaining those insights can be increasingly complex, and most of this work is done by highly skilled data professionals who work in silos with disconnected tools and data services that may be difficult to manage, integrate, and govern. Also, because data is never static, businesses must continually iterate their data models and products—often manually—to benefit from the most relevant, up-to-date insights.
Project DataWorks can help businesses break down these barriers by connecting all data and insights for their users. All data-driven professionals can work together on an integrated, self-service platform, sharing common data sets and models in a trusted manner that helps ensure governance, while rapidly iterating data projects and products. Now, instead of spending time finding and preparing data for analysis, users can focus their efforts on the core mission, uncovering business-changing insights.
Available on Bluemix, IBM’s Cloud platform, Project DataWorks can help to redefine how data professionals collaborate by tapping into a number of key innovations, such as Apache Spark,IBM Watson Analytics, and the IBM Data Science Experience.
It is designed to help organisations:
- Automate the deployment of data assets and products using cognitive-based machine learning and Apache Spark;
- Ingest data faster than any other data platform, from 50 to hundreds of Gbps, and all endpoints: enterprise databases, Internet of Things, weather, and social media;
- Leverage an open ecosystem of more than 20 partners and technologies, such as Confluent,Continuum Analytics, Galvanize, Alation, NumFOCUS, RStudio, Skymind, and more
Additionally, Project DataWorks is underpinned by core cognitive capabilities, such as cognitive-based machine learning. This helps speed up the process from data discovery to model deployment and helps users uncover new insights that were previously hidden to them.
“We are at an inflexion point with data and analytics,” said Bob Picciano, senior vice president, IBM Analytics. “We know that clients spend up to 80 percent of their time on data preparation, no matter the task, even when they are preparing to take advantage of today’s advanced AI and machine learning approaches. Project DataWorks helps transform this challenge by tapping into cognitive capabilities to integrate all data sources on one common platform, enabling individuals to get the data ready for insight and action, faster than ever before.”
The new platform was designed with the same proven approach used by The Weather Company, an IBM Business, to help users gain insights that impact everyday decision-making for both businesses and consumers. This includes a flexible data architecture, rapid ingestion of multiple data sources, and internet-scale data and analytics.
IBM will also enable business partners to certify their offerings within Project DataWorks, providing customers with greater choice to use the latest open source technologies and third-party offerings.
Building a Blueprint for a Data-Driven Culture
IBM today is also announcing the DataFirst Method to help clients derive the full benefits of these innovations. The IBM DataFirst Method is a methodology that enables organisations to assess the skills and roadmap needed to transform into a cognitive business that is driven by insight and gains the most value from data.
With the amount of data that is produced doubling every two years, enterprises are struggling with how to continually increase the value they get from it. They need a clear roadmap that shows them how to progress in their use of data. Using the IBM DataFirst Method, IBM’s more than 2,000 global practitioners can utilise proven practices and methods to help clients transform their processes for data discovery, handling and analytics.