PEGAPCLSA86V2 information source - Lead System Architect (LSA) Pega Architecture Updated: 2023

[RetroBytes] nicely presents the curious history of the SPARC processor architecture. SPARC, short for Scalable Processor Architecture, defined some of the most commercially successful RISC processors during the 1980s and 1990s. SPARC was initially developed by Sun Microsystems, which most of us associate the SPARC but while most computer architectures are controlled by a single company, SPARC was championed by dozens of players.  The history of SPARC is not simply the history of Sun.

A Reduced Instruction Set Computer (RISC) design is based on an Instruction Set Architecture (ISA) that runs a limited number of simpler instructions than a Complex Instruction Set Computer (CISC) based on an ISA that comprises more, and more complex, instructions. With RISC leveraging simpler instructions, it generally requires a longer sequence of those simple instructions to complete the same task as fewer complex instructions in a CISC computer. The trade-off being the simple (more efficient) RISC instructions are usually run faster (at a higher clock rate) and in a highly pipelined fashion. Our overview of the modern ISA battles presents how the days of CISC are essentially over.

IBM may have been the first player exploring RISC processor concepts, however work by two different university groups was more visible and thus arguably more influential. The Stanford group commercialized into MIPS  and Berkeley RISC commercialized into SPARC.

SPARC Versions 7 and 8, the first two versions of SPARC, were 32 bit architectures. Evolution to SPARC Version 9 jumped up to 64 bits but preserved backward compatibility. While having 64 bit registers, legacy 32 bit instructions operated identically as they had in previous versions.  Only a handful of new 64 bit instructions were required and those automatically made use of the upper 32 bits. Other advancements in SPARC Version 9 exploited knowledge from existing code to identify performance improvements. These included cache prefetch, data misalignment handling, and conditional moves to reduce branching. Other major improvements in SPARC Version 9 boosted OS performance.  These included instruction privileges, register privileges, and multiple trap levels.

The SPARC Version 9 improvements were defined by SPARC International, members of which include Sun Microsystems, Fujitsu, Texas Instruments, Cray, Ross, and others.  Sun was a significant part of SPARC International, but they did not go it alone.

Since SPARC Version 9, progress has mostly focused on multiprocessing with Fujitsu still manufacturing SPARC-based mainframes. SPARC has also become open and royalty free and found a footing in embedded computing.  Some have even synthesized SPARC processors onto inexpensive FPGAs.

It is becoming extra important to be aware and think twice before accepting information from a given source on the internet or for example from another person. In today’s time anyone from anywhere can access any data within seconds.

With such ease in accessing information one would think that some core issues of the society like health and wellness, emotional well-being, etc. would and should Excellerate but unfortunately that has not been the case to a large extent. On the contrary, it can be observed that these core issues are on the rise. Onset of health problems from an early age due to misinformation/lacking awareness on food and nutrition is increasing at an alarming rate despite having the web of information that we can access easily today. If one is aware and mindful, then in true sense having this vast sea of information also poses a serious challenge for all of us.

There are many people / organizations that put out information that might be best for their self-interest but might not be good for the overall well-being of the society. These forces often mindlessly have an aim to make profits for their capitalistic agendas by willing to go to any lengths.

To set the points mentioned above in perspective, following are two examples that I have personally observed on a large scale. The lack of awareness on how important the food we consume is seriously alarming. People are putting “junk” in their bodies without even realizing what harm it does to them right away as it enters our system (we might not see the effects of such food right away, but it slowly builds up and ruins the body over time, taking us to the very point when onset of diseases start to happen). Now, a lot of times people are convinced that such food items are not that bad, and why, because information on internet backs that these foods are good for us. We fail to understand that the companies out there are analysing what pleases our taste buds, and then not a single thought is given further. The next steps for them are to directly come up with food items that are chemically processed (in a way that pleases our taste), manufactured, and packaged, and of course companies find ways to back and justify that these are good for us. Right away the chain of profits start coming their ways. At the same time, we continue to consume these without understanding that each time it enters our body, it is basically sowing the seed of diseases that start to come our way in a matter of time.

This leads us to the second example which is now as we start to have diseases because of consumption of such foods, our first response becomes to go seek treatment. From a young age we are prescribed medicines, and sadly we again feel satisfied that as we are having the medicine, we can continue to live the way we are. During this entire time companies / individuals who are selling such food items / medicines continue to earn big cash, but at what cost – cost is our health and fault is also ours, as we let them control us, and believe what they have to say without even a single question.

Therefore, a general rule of thumb could be to not believe and follow things that we see / read or are told blindly and mindlessly, while a reasonable approach could be to take a step back and not rush to come up with a decision right away. It is quite important to question new information, and then if anything makes sense to us, we can try to adopt it, and then further evaluate if it makes sense or not. It is crucial that we become very selective on what we are to incorporate in our lives.

As we see society might be having the set norms the way it does, it is important to not forget that we all as individuals are unique and different. Today, we are lucky that we have access to such data and information at no cost, but as we know with great power comes great responsibility. The responsibility at our end therefore becomes to identify and ensure that source of information has a genuine intent on improving and helping the society in becoming a better place to live, and direct us to push ourselves to live our lives to the fullest potential by becoming the best version of ourselves.

We all have the ability to question information, experiment with the information on ourselves, apply / adopt it if it makes sense, and then further evaluate if it is something to be incorporated in our lives on a sustained basis based on the experience we might get as we experiment with it!

END OF ARTICLE

What is information architecture?

Information Architecture [IA]
noun

1. The structural design of shared information environments.
2. The art and science of organizing and labeling web sites, intranets, online communities and software to support usability and findability.
3. An emerging community of practice focused on bringing principles of design and architecture to the digital landscape. (Adapted from EuroIA and the Information Architecture Institute.)
4. The analysis and design of the data stored by information systems, concentrating on entities, their attributes and their interrelationships; the modeling of data for an individual database; and the corporate data models an enterprise uses to coordinate the definition of data in several distinct databases (in information system design).
5. Some have suggested IA is analogous with "taxonomy." A contrary view is that the activities involved in the creation of a taxonomy are a subset of the activities involved in developing IA (since developing IA typically also involves articulating the objectives of the information, and understanding the intended audience). (Adapted from Wikipedia.)

IA — The key to a great web redesign effort

Unlike a physical building such as a house, where the architecture is directly visible and tangible, IA is more ethereal, and therefore sometimes hard to explain. (Raise your hands, those who understood all of the above definitions on first reading!)

When applied to a website, IA is more noticeable by its absence than its presence. For this reason, some people will tend to define IA based upon what is often lacking when the IA is not strong. I can't tell you how many of the RFPs for consultants made this mistake by equating IA with a site map, a menuing and navigation system, or some form of search optimization. This is a common, but false, equivalence.

Good IA is the blueprint and the foundation upon which the rest of the site is built. It is essential for the site map, navigation system, and search feature, as well as, for communicating a strategic message. Simply building a site where each of these are addressed independently, however, will not ensure (and will seldom produce) a strong IA. Solid IA not only provides a framework for developing these features, but allows for the evolution of the site—enabling the site to weather the inevitable changes to message, content, design and navigational structure— thus providing long-term sustainability for the site.

Without a good IA, we risk deploying an aesthetically appealing, technically sophisticated site with great content which will not grow with us. After such a great investment of time and resources in this redesign effort, it would be tragic, a few years from now, to be left wondering why it has devolved to a state (similar to its current condition) which seems unsustainable—where content cannot be found, strategic message is lost, there is no clear context for the placement of new information, and old and stale content are prevalent. Under such circumstances, the administration who funded the project, not to mention those of us who are devoting untold hours in its implementation, will be hesitant to embark upon such an undertaking again!

To go back to the physical analogy of a house, over time we'll often rearrange the furniture, paint the walls, replace carpet with hard-wood floors, and much more. None of this changes the architecture of the house. We might do some heavier re-facing to change the curb-appeal, or update the kitchen from "country" to a shiny stainless steel look. Does this cost money? Sure. Does it take time, talent, and expertise? Of course! But it doesn't change anything about the underlying structure of the house—which walls are load-bearing, or how deep the foundation was laid. And it doesn't take as much effort or money as replacing the foundation, or changing the load-bearing characteristics of the house on top of cosmetic updates.

As it applies to a website, we can swap out the content, design and navigation system. We can even modify our strategic message. This will take time, effort, expertise, and even money, to do well. But with a strong IA underneath, these can all be done much more easily, without having to lay a whole new foundation. Lacking strong IA, changes that ought to be the equivalent of laying new carpet become more akin to adding another bedroom, or possibly leveling the old house and building a new one in its place—after all, that carpet was getting really dated looking!

IA—Grasping the intangible

Let's consider two case studies to try to get a handle on what IA is—or, at least, how we can observe its presence or absence.

Let's look at the positive example first. Here's a search for "red wine" on Amazon.com.

Notice that a list of categories is provided which the items that meet our search criteria can be sorted. Whether I'm looking for a book or song about red wine, or a camera (electronics) that is wine red in color, the search is actually useful. Even if what I want is not on the first page of results, I can get there quickly by selecting a category.

What is not apparent, because it is made to look easy and transparent, is that the ability to provide information (in this case items for purchase) in context by category is only possible because the information has this context by category already associated with it. To generate context on-the-fly by some form of data analysis is both slow and inaccurate.

Additional information about our content (whether the content is items for purchase, news stories, or multimedia) is called meta-data. There are various systems for managing metadata, making use of taxonomies, ontologies, or the like... At this point the details are unimportant (that's why we have a consultant, and why the consultant will help us in our CMS selection process). The big deal is to realize that there is a system and process that manages this metadata!

IA—A diversity of examples

Sometimes a lesson can be reinforced by comparing experiences of interacting with differently designed sites—some more transparent and simple than others. In these examples, let's look for a "cordless drill" across a number of sites:

You can click through all of the above if you like—but I'll summarize the results as we discuss them, so don't feel compelled to look at them all. The interesting thing to note here is the diversity of results. Given our previous example, we aren't surprised by the results on Amazon.com—and we find Sears.com and Lowes.com a little different in terminology but similar in usefulness.

Google is interesting, in that it is a generic web search utility. Nevertheless, it provides results that are surprisingly good in two ways. First, the results are sorted by relevance—a "secret formula" Google has for figuring out what you wanted from the terms, and what pages are most likely relevant to what you wanted. This is automated metadata analysis at its best; and Google pays top-dollar for programmers to keep their "secret formula" ahead of the competition. (There are instances where Google does less-well at figuring out the relevant sites, but in this case, the results are not bad.)

The second surprisingly good element to these Google search results is the ability to focus Google to one type of result. Google presents additional context against which it can make this query. If you click the "more" link, you find additional contexts! From here I built the link for Google Products, which again provides reasonably "relevant" results in a generally useful fashion. We don't have the breakdown by "category" or "department" as we do within a single vendor's site. But we have to deliver point for the fact that this data is being pulled, all but dynamically, from the web itself!

There are two possible reasons why the retailers above make use of strong IA to help users find what they desire. First, they are retailers—they want you to find what you are looking for so you will buy it! Black and Decker, on the other hand, is not a reseller. So there is less direct financial incentive to increase the usefulness of the search function. The second reason may be a lack of strong IA underneath the system. The failure of the search system to perform well does not necessarily mean that the IA and metadata needed for this do not exist within their product database. It is possible that the search system was simply thrown together quickly without focus on making use of the IA underpinnings. If this is the case, the effort required to code new search capabilities should be attainable, as using existing metadata is not nearly as involved as generating the data from scratch. If, however, the metadata is not accessible (or non-existent), and the IA under the hood is limited, then deriving greater performance from the search feature means one of the following:

• manually redirecting common search terms to useful landing pages (a never-ending maintenance nightmare)
• paying top-dollar to get a Google-class programmer to develop a "secret formula" (used to derive relevance and context from the pages themselves)
• or rebuilding from the ground up with a new IA (one that provides metadata supporting keywords, categories, relevance and context)

In limited defense of Black and Decker, one might argue that customers more often come to their corporate site to browse products by category, or to find information about a particular product. If we search the site for a product number, we get fewer results, with one of them generally pointing us to the correct product page. This is not to say that the retailers do not offer this feature on top of their search capabilities.

IA—the "so what" of the web redesign

What do we learn from all of this? That some sites have better search tools than others? Why don't we just buy a Google appliance and be done with it? Google certainly has great programmers, and can pull some semblance of order from the chaos of the web, can't they do that for a single site? What if I like Yahoo! results better? Can't Web 2.0 just let us aggregate all of our data (or all of the search engines)?

Let's go all the way back to the purpose of the site we're on right now:

re.web

What are the goals of the re.web project? We see terms like "navigation scheme," "search feature," "content model" and "information architecture." But the unwritten goal, as we read between the lines, is to establish something that is very unlike the chaos we have now, and something which won't devolve into the chaos we have now. What we want is a web presence that can handle the incorporation of a fresh message, fresh content, fresh media—a site which can even handle the evolution of the web itself!

We can't predict the future. We can't ensure that every scenario will be covered. But we know from experience that building a web presence in an unmanaged, organic fashion, without any underlying framework, doesn't work! The re.web project is our opportunity to build something which will work, and which will provide the infrastructure for sustainability.

One of the reasons mStoner was selected as our consultant is because the re.web RFP committee found evidence of a strong commitment to IA in the mStoner proposal, as well as, in the backgrounds of those assigned to our project. We believe they "get it"—and they'll leave us with a sustainable architecture for the future.

Pegasystems Inc., the low-code platform provider empowering the world’s leading enterprises to Build for Change, is introducing Pega GenAI—a set of 20 new generative AI-powered boosters to be integrated across Pega Infinity ‘23, the latest version of Pega’s product suite.

“We’ve added a staggering number of powerful generative AI-powered boosters across Pega Infinity to help organizations quickly leverage the power of generative AI to work faster and more efficiently,” said Kerim Akgonul, chief product officer, Pega. “Our clients will have the flexibility and security to power our generative AI features and build their own using their large language models of choice. The Pega GenAI boosters released in Pega Infinity ‘23 are just the start—we plan to add new ones on a regular basis as we continue to evaluate new ways to responsibly and securely leverage generative AI and as new models come to market.”

Pega GenAI will provide organizations with the architecture and integrated use cases to drive value from generative AI now and into the future, according to the company.

Because the responses from generative AI are mapped directly into Pega’s model-driven architecture, low-code developers can easily configure and change these suggested starting points to rapidly deliver a completed application.

Pega GenAI boosters like these will be infused throughout Pega Infinity, allowing users to accelerate their low-code application development, enhance customer service, and Excellerate customer engagement.

A new API abstraction layer, called Connect Generative AI, will allow organizations to get immediate value from generative AI with a plug-and-play architecture that allows for low-code development of AI prompts.

Rather than directly calling OpenAI, or other APIs directly from UIs or workflow steps, Pega provides an API abstraction layer so developers can easily swap out large language models running on both public and private clouds and build reusable generative AI components that can be leveraged across applications.

Connect Generative AI will be able to automatically replace personally identifiable information (PII) data with placeholders in generative AI prompts, helping organizations enforce their data protection policies and advancing secure use of public and private models.

Generative AI powered boosters in Pega Infinity ’23 facilitate rapid development of innovative new capabilities and deliver low-code developers the power to infuse generative AI functionality into decision-making and workflow automation.

As large language models, cloud services, and data privacy needs continue to evolve, this “AI choice” architecture allows Pega and its clients to continuously innovate new secure solutions. Pega will initially offer connectors to OpenAI’s API and Microsoft Azure’s OpenAI APIs and will be supplemented by additional downloadable connectors to other providers on Pega Marketplace.

According to the vendor, Pega’s approach to generative AI allows organizations to confidently deploy their AI models of choice in a responsible and governed way while minimizing risk. It incorporates auditing, rules-based governance, and workflow-managed human approval to advance safety, security, and reliability.

Pega will allow for all AI-generated text to be reviewed, edited, and approved by authorized staff to mitigate the risk of inaccurate or biased text from being exposed to customers.

Pega GenAI builds on Pega’s decades of experience in applying computer intelligence, including rules and data-driven AI, in responsible and effective ways.

It complements the already powerful AI-powered decisioning engine in Pega’s low-code platform, which brings together decision management, predictive and adaptive analytics, natural language processing, voice recognition, business rules, and a robust set of MLOps and testing capabilities for monitoring and governing AI models, according to the company.

Full details on additional Pega Infinity ‘23 features will be showcased at PegaWorld iNspire, the annual Pega user conference, from June 11-13 at the MGM Grand in Las Vegas.

For more information about this news, visit www.pega.com.

Description

My buy rating for Pegasystems (NASDAQ:PEGA) remains unchanged following the release of 1Q23 results, as the stock price has yet to reflect the intrinsic value I attribute to the company. In particular, PEGA's ACV growth has been strong continuously, and it is now growing above its own ACV growth guide. The success of PEGA's sales team, which has done an excellent job of penetrating the company's current base of large customers, is a big reason for the company's consistent success, in my opinion. The primary growth indicator for subsequent quarters (backlog) also increased, reaching 14% growth, a full 1000bps above 4Q22 growth. In my opinion, this is a testament to the success of the move to Pega Cloud, which should remain a driving force behind future expansion. Revenue and profitability came in below consensus, which may explain why PEGA stock price did not react as strongly as I had hoped. However, in my opinion, the miss in earnings was purely optical and not structural. Based on my analysis, I believe that the revenue mix shift to Pega Cloud mainly contributed to the poor term-license revenue performance. Once the transition is done, I anticipate a surge in activity similar to Pega Cloud's explosive expansion. In sum, I am heartened by the efficient operation, and I think the current valuation represents a good opportunity for long-term investors who are willing to be patient.

Business model advantage

I have briefly touched on PEGA competitive advantage previously, which was its ability to utilize AI to automate processes and is highly adaptable and capable of digitizing most business processes. I think it's important to go into more depth about AI because of the exact surge in interest in it (thanks, ChatGPT). With its model-driven approach, I think PEGA has a leg up on the competition when it comes to the subject of generative AI. At the current stage, PEGA models and software are being informed by generative technology, which simplifies solution implementation and developer support. PEGA is also taking strategic steps to enhance its capabilities in this area, integrating Bedrock from AWS and tools from other leading cloud providers. This integration empowers developers to efficiently build and scale Generative AI applications in the cloud. Of notable importance is PEGA's proactive approach to mitigate the potential impact of increasing automation on its customer base. The company has successfully transitioned from user-based pricing to an outcome-based revenue model, which management anticipates will eventually dominate the company's revenue streams, currently comprising 75% of its business. This shift provides insulation against potential challenges arising from the adoption of AI, ensuring a stable and sustainable revenue stream for PEGA. Overall, I believe the value of this generative AI ability lies in its fundamental capacity to stimulate further demand. This is especially true for PEGA, as its platform allows for complex workflows to be accomplished with little to no programming.

Pega Cloud

My opinion is that the revenue and profit shortfall compared to the consensus is not cause for alarm because it is primarily an optical issue (i.e. P&L does not reflect the underlying change in business accurately). The miss was primarily driven by a 39% decline in term license revenue, which is naturally expected as PEGA is shifting clients to cloud. Mathematically, revenue will decline as cloud recognition happens across a period of time while term licenses are one-off. The same logic applies to profits as well. While some might argue that Pega Cloud did not perform as well as it should have, I would point out that the 19% growth Pega Cloud faced a tough comp last year. It's also worth noting that PEGA has been putting most of its effort into tapping into its existing (albeit only 10% penetrated) customer base. As PEGA pursues new logos in the coming months, I anticipate a growth spurt in the near future.

Macro weakness

Despite my confidence in the company and the stock, I am wary of the macro environment, which shows no signs of improving soon. Fortunately, PEGA appears to be handling this period well, and management noted that it has not seen the macro environment worsening the business in 1Q23. It appears that PEGA's underlying customers are resonating well with the company's transition to the cloud, and most importantly, PEGA's sales teams have executed well to capture this demand. Therefore, I continue to have some hope that PEGA will make it through this period unscathed.

Valuation

PEGA's valuation remains appealing at 2.8x revenue if investors are willing to wait for growth to inflect as the transition to Pega Cloud is completed. Using the FY23 guided revenue figure and my assumption of accelerated growth, PEGA should generate around $1.7 billion in revenue in FY25. Even if we assume that revenue multiples do not inflect, as I believe they will, the upside from the current share price is still very appealing at 28%.

Summary

In conclusion, my buy rating for PEGA remains unchanged based on the exact 1Q23 results. PEGA's strong ACV growth and the increase in backlog and the successful transition to Pega Cloud demonstrate the potential for future growth. While revenue and profitability fell below consensus, I believe this is a temporary optical issue driven by the shift to Pega Cloud. PEGA's model-driven approach and integration of generative AI technology position the company favorably in the market. The transition to an outcome-based revenue model insulates PEGA from potential challenges arising from increasing automation. Despite macroeconomic uncertainties, PEGA has navigated this period well, and its sales teams have executed effectively. Lastly, the current valuation of PEGA presents an appealing opportunity for long-term investors, with potential upside as growth accelerates and the transition to Pega Cloud is completed.

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Lainie Petersen writes about business, real estate and personal finance, drawing on 25 years experience in publishing and education. Petersen's work appears in Money Crashers, Selling to the Masses, and in Walmart News Now, a blog for Walmart suppliers. She holds a master's degree in library science from Dominican University.

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