Skip navigation

Our Customer Success & Support teams are always working on providing our customers with tips and tricks that will help our customers with the Pentaho platform.


DevOps is a set of practices centered around communication, collaboration and integration between software development and IT operations teams and automating the processes between them. The main objective is to provide guidance on creating an automated environment where iteratively building, testing, and releasing a software solution can be faster and more reliable.


Our continuous integration (CI) and DevOps series was started to fill a need for increasingly complex information as you learn more. The main objective with this series is to provide guidance on creating this automated environment in which building, testing, and releasing a Pentaho Data Integration (PDI) solution can be faster, more reliable, and result in a high-quality solution that meets customer expectations at a functional and operational level. The Introduction to PDI and DevOps webinar will serve as the “prequel” to more complex concepts.


Our intended audience is Pentaho administrators and developers, as well as IT professionals who help plan software development.


Join us on May 7 2019 - 9am Pacific for our first webinar of our PDI + DevOps webinar series. Click here to register now! If you miss the webinar, you can always watch it on demand afterwards.

Guest Contributor: Matt Aslett, Research Vice President, Data, Analytics & Artificial Intelligence, 451 Research


451 research logo sm.JPGAslett_Matt head shot.jpg

Most companies are increasing their investment in data-processing, analytics and machine-learning software with a desire to become more data-driven. Data – and the rapid processing of data – is a key driver in enabling companies to grasp the opportunities presented by digital transformation to deliver improved operational efficiency and competitive advantage.


We have moved from the transactional era, through the interaction era to the engagement era, in which enterprises have recognized that they must store, process and analyze as much, if not all, data that is available to them in order to survive and thrive in the digital economy. This includes data produced by the myriad of sensors, embedded computers, industrial controllers and connected devices such as vehicles, wearable computing devices, robots and drones that make up the emerging Internet of Things (IoT).


Data from 451 Research’s Voice of the Enterprise: Internet of Things indicates that analytics is seen as the most critical technology for success in IoT projects, but also that the largest impediment to IoT projects is technology deployment and integration challenges, followed by security concerns, and a lack of a compelling business case or uncertain ROI.


For IoT projects the primary use-cases are optimizing operations (for preventative maintenance and reduced downtime, for example) followed by reduce risk (such as security and compliance); the development of new, or enhancement of existing, products or services; and enhanced customer targeting for increased sales.


In all of these cases, while data from IoT devices is extremely valuable, and has been stored and processed alone for many years, the greater value comes from blending that IoT data with enterprise data sources. Combining IoT data with data from existing enterprise applications makes the link with customer behavior data, employee behavior data, marketing/advertising data and sales data, for example, to provide a more complete picture and ensure the IoT data is seen in the context of the business goal.


Data from 451 Research’s Voice of the Enterprise Data and Analytics indicates that the complexity involved in integrating and managing data actually grows, the more data-driven a company is. The results show that while the most data-driven companies enjoy benefits such as increased focus on competitive advantage, they are also faced with more data integration and preparation overheads.


Data from each of these sources is likely to be delivered in different formats, meaning that it needs to be blended, transformed and cleansed before it can be used to generate business insights. Data will also be delivered via different mechanisms. Although most will likely be delivered from traditional enterprise applications in batch form, increasingly it will be generated at the edge by Internet of Things devices and delivered via stream processing, for IoT analytics.


Additionally, in attempting to become more data-driven, many organizations are investing in machine-learning tools and developing ML-driven applications. The success of these projects depends on the ability of the organization to operationalize experimental data-science projects through training and testing to model deployment and management.


Much attention is paid to the outputs of these data-processing pipelines – including visualizations and machine-learning models used to drive business decision-making. However, intelligent and automated data-processing pipelines that are able to rapidly integrate data from multiple sources, including enterprise applications and IoT devices, should not be overlooked as a foundation for delivering successful IoT projects that deliver improved operational efficiency and new revenue streams.


To learn more about how to combine IoT and business data to deliver business value, click on one of the links below:


Visit the Hitachi Vantara website.


Download the Business Impact Brief from 451 Research entitled "Agile Data Management as the Basis for the Data-Driven Enterprise.”