Welcome!

IBM Cloud Authors: Yeshim Deniz, Elizabeth White, Pat Romanski, Liz McMillan, Stefan Bernbo

Blog Feed Post

BPMS Next Generation: IBPMS

In a previous post I discussed Business Process Management (BPM) evolution as reflected by an Ovum report. 
Recently I read new Gartner BPM Magic Quadrant. The Magic Quadrant is titled IBPMS Magic Quadrant.
If you compare it to previous Gartner BPMS Magic Quadrants (you should not compare them), you will find a totally different picture. 

Only three Leaders in the IBPMS Magic Quadrant, namely: IBM, PegaSystems and Appian. No Challengers and a lot of Visionaries I know (Oracle, Software AG, Tibco, Vitria) and two Visionaries I never heard of (Bosch Software Innovations, Whitestein). 

Previous BPMS Magic Quadrant includes many Leaders, many Challengers as well as many Visionaries.

The pattern of few Leaders, No Challengers and many Visionaries is typical to immature markets.
Gartner explains it as a new generation of BPMS tools.

What differentiate IBPMS from BPMS?
IBPMS tools try to address a new Use Case: Intelligent  Business Operations(IBO). IBO is required for better and faster decisions in a dynamic ever changing enterprise. 

The implication of the IBO Scenario is convergence (or at list tighter binding) of BPM with other paradigms and technologies. The word Intelligent included in the IBO acronym implies that Business Intelligence (BI) is one of the related technologies. SOA which was already coupled with BPM is more connected to BPM in the context of IBPMS. Complex Event Processing, Service Orchestration, ESBs and Registries are SOA and EDA concepts and technologies, which are closely related to IBPMS implementation.


MY Take
  • I already read and wrote about IBO. Few months ago I wrote a new Business Intelligence Kit to be included in the next version of MethodA. I met this concept while drilling down BI. Gartner's Magic Quadrant starting point was BPMS.    
  • Gartner's view of a new generation of BPMS is different from Ovum's gradual evolution approach. However, you can find the same Vendors as Leaders (Gartner) or Shortlist (Ovum): Appian, PegaSystems and IBM. Ovum's Shortlist also  includes Oracle. Probably PegaSystems's, Appian's and IBM's products are currently better products than other products. 
  • Product selection is specific to an Enterprise. Enterprises differ in Size, Use Cases, Technological Infrastructure, Applications Technologies etc. Sometimes a Niche product or Visionary product and not a leading product is the best fit for an Enterprise.
  • Expect changes in an Immature markets like IBPMS. Today Leaders will not necessarily be tomorrow (2013 or 2014) leaders. 
  • Not all BPMS products are created equal. Gartner divides them to two major categories: Pure BPMS Products (e.g. Appian, PegaSystems and Bosch Software Innovations) and Infrastructure Products including BPMS (e.g. IBM, Oracle, Software AG and Tibco). 
  • IBPMS and Case Management There are similarities between IBPMS and Case Management but they address different Use Cases. Case Management and IBPMS converge between BPM and other concepts and technologies. However, Case Management is for Knowledge Workers and IBPMS is usually for Managers tasks (sometimes it is also for Knowledge Workers). Some of the technologies included in Case Management such as Knowledge Management and Enterprise Content Management are not integral part of IBPMS.                                  



Read the original blog entry...

More Stories By Avi Rosenthal

Ari has over 30 years of experience in IT across a wide variety of technology platforms, including application development, technology selection, application and infrastructure strategies, system design, middleware and transaction management technologies and security.

Positions held include CTO for one of the largest software houses in Israel as well as the CTO position for one of the largest ministries of the Israeli government.

IoT & Smart Cities Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...