Kisaco Leadership Chart on Engineering Application Lifecycle Management Solutions 2020-21 | Kisaco Research

About the Author

Author:

Michael Azoff

Chief Analyst
Kisaco Research

With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst. 

Eitan Michael Azoff, PhD, MSc, BEng.

HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.

In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks when KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 3 backpropagation was invented and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting and wrote a book on the topic for publisher John Wiley & Sons in 1994.

Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.

My analyst coverage areas at KR Analysis

My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.

Our analysis showed that the market naturally fell into three areas of hot activity:

▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.

▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.

▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).

We produced four Kisaco Leadership Charts out of this research.

We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.

Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.

  • Motivation

    The application lifecycle management (ALM) solution market has evolved considerably since it first emerged as a distinct software suite of capabilities some two decades ago. The topic addresses the need to manage the software development lifecycle, and for non-trivial projects or product development that needs to scale across dimensions of technology complexity on one hand and numbers of developers on the other – up to multiple teams spread geographically with hundreds and possibly thousands of developers. ALM solutions provide the transparency into the development effort and help manage work across the whole lifecycle with an integrated set of tools that allow traceability of work items, from requirements to test cases and deployed code. Increasingly ALM is becoming indispensable in safety-critical and highly regulated environments, helping provide the verification, validation, and auditing necessary for compliance.

    As digital transformation drives more software into advanced engineered products the need for ALM has grown in the engineering industries. Other highly regulated industries such as finance and life sciences also need ALM to govern their software development in order to manage risk. As a consequence, the use of ALM in these sectors has grown, and with it the special demands of engineering manufacturing and high regulation that does not exist in the other main market for ALM, enterprise IT.

    This report focuses on ALM for advanced engineered product development and for satisfying the regulatory demands of software development in safety-critical environments, comparing side by side five top ALM vendor solutions, and presenting our findings in the Kisaco Leadership Chart (KLC).

  • Contents

    Kisaco Research View.......................................................................................2

    Motivatio..........................................................................................................2

    Key findings......................................................................................................2

    Engineering ALM: technology and market landscapes....................................3 

    The impact of agile and free open source tools on ALM.................................3

    The components of an engineering ALM solution...........................................4

    The next phase for ALM ..................................................................................5

    Cloud native computing ..................................................................................5

    Machine learning..............................................................................................5

    Solution analysis: vendor comparisons.............................................................6

    KLC on Engineering ALM Solutions 2020-21...................................................6

    Engineering ALM solution vendor comparisons .............................................6

    KLC results for engineering ALM solutions .....................................................6

    Vendor analysis.................................................................................................9

    IBM, Kisaco evaluation: Leader.........................................................................9

    Kisaco Assessment...........................................................................................11

    Inflectra, Kisaco evaluation: Contender...........................................................12

    Kisaco Assessment...........................................................................................13

    Intland Software, Kisaco evaluation: Leader....................................................14

    Kisaco Assessment...........................................................................................17

    Jama Software, Kisaco evaluation: Emerging Player ......................................17

    Kisaco Assessment...........................................................................................20

    Siemens Polarion, Kisaco evaluation: Leader...................................................20

    Kisaco Assessment...........................................................................................23

    Appendix..........................................................................................................24

    Vendor solution selection ................................................................................24

    Inclusion criteria ...............................................................................................24

    Exclusion criteria...............................................................................................25

    Methodology ...................................................................................................25

    Definition of the KLC........................................................................................25

    Kisaco Research ratings....................................................................................25

    Acknowledgements .........................................................................................25

    Author ..............................................................................................................26

    Kisaco Research Analysis Network....................................................................26

    Copyright notice and disclaimer ......................................................................26

  • List of Figures

    Figure 1: The ALM space targeting engineering and regulated industries

    Figure 2: Heat map analysis of participating vendor solution features

    Figure 3: KLC on engineering ALM solutions 2020-21

    Figure 4: KLC on engineering ALM solutions 2020-21: ranking of vendors

    Figure 5: IBM ELM combines engineering V-model disciplines with agile iterations

    Figure 6: The Inflectra Suite

    Figure 7: codeBeamer ALM coverage

    Figure 8: Jama Connect product development platform

    Figure 9: Jama Connect for medical device development

    Figure 10: Siemens Xcelerator portfolio of software and digital services

    Figure 11: Siemens Polarion ALM

  • About the Author

    Author:

    Michael Azoff

    Chief Analyst
    Kisaco Research

    With over 17 years analyst experience, most recently at Ovum/ Informa, Michael Azoff joined Kisaco Research, the company behind the AI Hardware and Edge AI Summit series, in 2020 as Chief Analyst. 

    Eitan Michael Azoff, PhD, MSc, BEng.

    HQ’d in Kisaco Research’s London office, Michael's current focus is launching Kisaco Research vendor product comparison reports with the new Kisaco Leadership Chart (KLC) analyst chart. The first KLC is also the first analyst chart in the AI chip industry, with 16 vendors having participated in the research.

    In his career Michael worked at Rutherford Appleton Laboratory building simulators for electron and hole transport in semiconductors for UK national and European community research projects and published papers in learned journals. He then turned to building neural networks when KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 3 backpropagation was invented and created a startup selling his Prognostica Microsoft Excel add-in for time series forecasting and wrote a book on the topic for publisher John Wiley & Sons in 1994.

    Since 2003 Michael has worked as an IT industry analyst covering software engineering topics, from agile and DevOps, to application lifecycle management and cloud native computing. He started covering machine learning when deep learning emerged as the most recent wave of interest in AI and left his position as Distinguished Analyst at Ovum/Informa to join Kisaco Research and help build an analyst capability within the company.

    My analyst coverage areas at KR Analysis

    My first research project at KR was to create the first analyst comparison chart for AI chips. We invited AI chip producers to participate and were fortunate to have 16 vendors participate from across the globe: USA, UK, France, and China, and a mix of established players (Nvidia, Imagination, Intel, and Xilinx, to startups.

    Our analysis showed that the market naturally fell into three areas of hot activity:

    ▪ Data centers and high-performance computing environments (HPC): here large boxes are installed and the aim is to achieve maximum performance for training and inferencing AI systems. The buyers are cloud hyperscalars, national research labs and agencies, and some large enterprises with big investments in AI.

    ▪ Small edge: the opposite end of the spectrum, building the smallest useful chip possible to sell as cheap as possible and embed in edge devices. AI is inferencing here.

    ▪ Automotive: an active industry in AI but highly regulated creating hurdles and technology adoption cadences that can be challenging for suppliers. AI is mainly inferencing here (for systems installed in vehicles).

    We produced four Kisaco Leadership Charts out of this research.

    We are also researching the machine learning (ML) software tools space, and our first report here is ML Lifecycle Solutions. The biggest challenge for enterprises is taking the research AI systems developed by their data scientist and deploying these into production at scale. Using a host of open source tools to achieve this is possible but time consuming to build and maintain, as well as prone to breakdown. This is why the ML lifecycle solution space exists.

    Finally, in our first batch of KR Analysis reports we produced the KLC on engineering application lifecycle management (ALM) solutions. While ALM has been in existence as a distinct practice since KR Analysis and Michael Azoff introduction © Kisaco Research. All rights reserved. Unauthorized reproduction prohibited. 4 around 2003, it continues to evolve. We found the engineering and highly regulated industries relying on engineering and compliance oriented ALM to help manage risk and complexity.

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