According to Allstacks, R&D cost capitalization is something that is mandated by accounting standards in the US GAAP (Generally Accepted Accounting Principles).

Allstacks explained that the process of tracking R&D software development costs can be manual and tedious, and it hopes this new offering will help streamline the process. 

The Software Capitalization software also enables splitting up credit among multiple engineers when more than one person is working on a particular project. 

“Teams can easily produce records of capitalizable software work, automating a traditionally time-consuming and manual process, and improving the accuracy of the data collected,” said Hersh Tapadia, co-founder and CEO of Allstacks. “Allstacks customers can now seamlessly account for their engineering investment, understand the cost of capitalizable efforts, and generate defensible reports in audit-ready format that adhere to all applicable compliance standards.”

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Flow metrics can enable companies to determine if all of that orchestration is actually running through the process, and if the company is being efficient in its processes. Ultimately, it can help the company determine if it is realizing value and better understand how to get that value into the hands of customers sooner.

“It’s great that we have these ideas, but how do we actually close the loop and take it down to the end,” said Hersh Tapadia, co-founder and CEO at value stream intelligence company Allstacks. The flow metrics, he said, “are closing the loop. It’s realizing the dream, as we might say.”

This article was first published on VSM Times. Read the full article there.

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“Allstacks’ SOC 2 certification underscores our commitment to customer security and data integrity. We know that any risk to data is incredibly uncomfortable for our customers. It is a point of pride for Allstacks to have no exceptions to our data security,” said Jeremy Freeman, Co-founder & CTO of Allstacks. “We want leaders to focus on improving their team without extra chores, and worrying about vendor data security is one chore we’re happy to eliminate.”

Service Organization Controls (SOC), as defined by the American Institute of Certified Public Accountants (AICPA), is the name of a suite of reports produced during an audit. It is intended for use by organizations that provide information systems as a service to issue validated reports of internal controls over those systems. SOC 2 certification defines criteria for managing customer data based on five “trust service principles:” security, availability, processing integrity, confidentiality, and privacy. A SOC 2 Type 2 certification provides details on the operational effectiveness of a vendor’s system.

The Allstacks platform provides predictive forecasting and risk management to software organizations. This one-of-a-kind product is based on a groundbreaking forecasting algorithm, using benchmark data to help companies predict what will happen during a software development project and when the work will be completed. The platform’s SOC 2 certification assures Allstacks customers that their data is secure. Allstacks has also received SOC 1 certification, which attests to the security of financial information on the platform. Allstacks has proudly passed these independent reviews and is confident that its customers can use the platform to improve software development outcomes without having to worry about data security.

About Allstacks

Allstacks is a predictive forecasting and risk management platform that improves software development outcomes. By running machine learning and AI models across the data from the entire software development lifecycle, Allstacks identifies at-risk initiatives and then provides solutions to get them back on track. For more information about Allstacks, visit https://www.allstacks.com/.

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SDT: We’ve been covering the software development industry for 20 years here at SD Times, and one of the biggest hurdles we always heard about was the disconnect between IT and what business wanted, because they were using different tools, didn’t speak the same language, and couldn’t have insights into the other side as to what’s needed. So value stream intelligence seems to be the long-awaited bridge to get the two sides to come together. But yet, this still seems to be kind of a challenge for a lot of people. Why is it so difficult?

ENNIS: I love this question. And you know, so I’ll give a brief little historical context that I think will be a good way to describe this. And it kind of comes down to why Allstacks was founded in the first place. Our CEO and co-founder was working in the health care field, senior engineering role, and had a seat at the table with leadership. You’re discussing the roadmap, strategic priorities, what’s important to the business, and sales and marketing will come with these beautiful slide decks, metrics, numbers, graphs, and show tangible data to back up questions like, ‘How are we delivering on X project? Where can we improve productivity efficiency here? And how are we delivering that value?’ It was hard for the engineering side to answer those questions. And it shouldn’t be. So that was kind of antithesis, and the reason that Allstacks was created – to understand that disconnect between the engineering side of the house and the go-to-market teams or the business side, and to create a way to bring data to the table and give engineering a voice in that boardroom.

DAHLGREN: We just find that the more complex the set of business units, use cases, teams processes are, the more value there is to be captured from putting these principles in place. And, and like Parker was saying earlier, it’s simply turning the lights on. It is amazing how difficult it is for people in these large, complex enterprises to even be able to benchmark their current state. How wrong they often are just about where they think they are, they’re the things that are on fire, they’re often wrong about what is on fire, and then the things that they think are not on fire are actually on fire. It is amazing. It’s really incredible how surprising it can be when these leaders first get a sense for, ‘Oh, this is actually this my state of affairs, this is my composite view of reality.’ That’s an exciting thing.

Read the full conversation on VSM Times.

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Unfortunately, there are a few engineering KPI anti-patterns that reduce the organizations’ ability to understand how their performance relates to business outcomes.

When teams start measuring for the first time, they tend to use those metrics to drive accountability and predictability. But according to Jeremy Freeman, co-founder of value stream intelligence platform provider Allstacks, failing to trace those metrics back into business outcomes is the first of three KPI anti-patterns he discussed in a recent call with SD Times.

The message from executives is that organizations need to be more data-driven, and there are countless metrics that can offer some visibility into the activities and performance of the product development team. “If you’ve not done engineering KPIs before, you might grab the first six or seven that pop up,” Freeman explained. He highlighted metrics like average downtime, churn, and on-time delivery, then warned that engineering leaders often neglect to do the next step: “You don’t actually ever really go back and connect those back to the business value.” And that’s where things get dicey with the business side of the house. Freeman elaborated, “Would the CEO go to the company board and say, ‘we improved our Scrum velocity by 50%?’ Because the board would say, ‘Why do we care? Does that move the needle on our revenue? Does it somehow reduce our costs?’” Engineering leaders often don’t know how to use their data to answer these questions. 

Keep in mind that the engineering team in a lot of organizations is responsible for multiple different outcomes. The team could be responsible for working against a roadmap developed in another part of the business, but also responsible for delivering that software to the user, and ensuring that it’s running and users have a good experience, which impacts the business. “What ends up happening is leaders tend to search for one key metric for engineering performance, and tend to forget that there are multiple outcomes the org is responsible for,” he added. “It’s not just to be the most efficient development organization possible.”

The second anti-pattern Freeman discussed has to do with the term “cargo culting.” He said he has heard the term used to describe people adopting Agile practices for the sake of adopting Agile. “A team will read a bunch of information or hear talks at a conference and say that ‘Agile seems great. And all we’ve got to do is have a standup once a day.’ But they don’t see any of the benefits. They’re pulling in these practices, hoping that they’ll get the benefits, but don’t really understand the connection between actions and outcomes. That ends up actually being a big anti-pattern.” 

In this scenario, engineering KPIs become checkboxes like “What’s our PR cycle time” instead of “Will improving the our PR cycle time improve our organization’s delivery ability?” Without seeking to answer the second question, any measure gets clunky, hindering instead of helping teams achieve efficiency. 

The third anti-pattern? Freeman readily identified “using metrics for evil.” 

The Allstacks CTO explained that as the misuse of data and losing sight of the overarching goal it’s trying to track. “If you are so focused on the metric, it can lead to some really toxic behaviors,” he said. Freeman used the example of an organization’s SLA that requires all bugs to be finished within two weeks. The team might be tempted to put in hasty fixes, or try to skirt the metric by creating a new ticket that resets the clock. “For junior leaders, it can be tempting to tie metrics like that to performance reviews and potentially even compensation or bonuses. If that happens, people are going to get real focused on hitting the metric at the expense of the business objectives and the job that they’re supposed to be doing.”

If people are incentivized to game a metric, Freeman concluded, you’re using metrics incorrectly. “You should be using your KPIs and metrics to point people in the right direction and drive the right behaviors, not punish people for getting off-track.”

Content provided by SD Times and Allstacks

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Because of that, before you can even begin to measure how your team is performing, it’s critically important to understand your organization’s approach to development and how the teams are structured to maximize that effort.

“Finding good metrics, like flow metrics, end up being a balance between … do you treat what developers are doing as a manufacturing process? Or do you treat it more as a creative process?” said Jeremy Freeman, co-founder and CTO at Allstacks, providers of value stream intelligence software.  

Freeman referred back to the “Iron Triangle” approach to software development quality, which states that you can either develop things quickly, cheaply or at high quality, and everything between them is a tradeoff. 

This approach, he said, can also apply to flow metrics. 

Organizations can optimize more toward speed and predictability, or they can optimize toward data science and problem-solving. “These types of tradeoffs actually permeate all of your business decisions as technology leaders,” he said. “Do you focus on fixing quality? Or do you focus on fixing or shipping new features? And the flow metrics that are now a core component of the SAFe Framework end up having their own sorts of these ‘Flow Triangles.’ There’s your velocity, cycle time and team load. You always want to have really high-velocity routines. And that is intimately linked to how long it takes you to do things, and how many things are being worked on at once?”

Many high-functioning organizations have different teams working at different speeds, using different processes and tools, so coordinating that work is critical. “Thinking about flow metrics as a way to help make sure teams are working together is really important,” Freeman said. “If you imagine a team working on delivering a sprint goal, then you take a step back and think about how the collection of teams is working against shipping a major feature. You have to think about how fast things are getting delivered, and how that impacts your ship time. Are the levers you have to play with as a leader right? So these metrics are really helpful, and flow is really apt.”

Freeman recommends that organizations first figure out where their problems are, with the development team and all stakeholders. Then you can start measuring some coarse things around outcomes, and as you start identifying potential solutions, then you can get tighter and tighter with what you’re measuring. 

He noted that in talking to development teams, it seems like their biggest bottleneck is getting pull requests across the line. “There’s a high cycle time, no one will review my pull request, and that’s preventing us from actually shifting work,” he said. “In the pull request example,” he said, “maybe we’ll go from measuring your request cycle time to measuring how long it takes to get your first review, to know how long it takes you to actually complete any review cycle. And as you build those metrics up, you’ll actually get better information and start to pinpoint and solve problems.” 

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“We are at an inflection point where software team leaders need more advanced analytics to measure engineering team productivity and outcomes,” said Hersh Tapadia, CEO, Allstacks. “Allstacks meets this need and has been recognized as a leader by Forrester, GigaOm, G2, and others. This investment will help raise awareness and demonstrate that the company’s predictive forecasting platform is a game changer for any software team.”

Allstacks’ value stream intelligence gives engineering teams a strategic voice in the boardroom. The company’s predictive data platform improves engineering efficiency and effectiveness. It adds further value across a company by keeping engineering and cross-functional leaders informed of changing software development timelines and dependencies.

“Allstacks’ vision and exceptional software engineering expertise made our investment decision very easy,” said Firas Raouf, General Partner, Companyon Ventures. “We recognize that Allstacks is a product leader in the emerging field of value stream intelligence which we believe is an important next step in the world of software development.”

“Atlassian Ventures is excited to invest in Allstacks because it enables engineering teams to leverage data across workflows and systems to efficiently build products,” said Matt Sonefeldt, Head of Atlassian Ventures. “Allstacks is at the forefront of the emerging value stream intelligence market, helping companies transform how they develop. We are excited to support Allstacks as it creates mission-critical value for Atlassian’s 200,000+ cloud customers.”

The post Allstacks Announces $12.3M in Series A Funding appeared first on SD Times.

With this, software teams can become more transparent and predictable by de-risking their development and defining their milestones that map to customer value. As a result of this benchmark data, software development becomes better, more efficient, and more transparent within development teams and company stakeholders.

This benchmark data is the first release of a strategy to put the engineering team in a better position to take a seat in the boardroom to help drive predictable customer value and business outcomes.

IBM to add new NLP enhancements to Watson Discovery

IBM today announced additional natural language processing (NLP) enhancements planned for IBM Watson Discovery. These updates are designed to assist business users in industries such as financial services, insurance, and legal services in providing better customer care and accelerating business processes by uncovering insights and synthesizing information from complex documents.

These new planned features for Watson Discovery were created to make it easier for users to quickly customize the underlying NLP models on the unique language of their business. In addition, these updates come as a part of a pipeline of developments stemming from IBM Research.

Workato raises $200 million in Series E

Workato today announced $200 million Series E funding led by Battery Ventures, with equal participation from Insight Partners, Altimeter Capital, and Tiger Global. In addition, Geodesic Capital and Redpoint Ventures also participated in this financing round. With this, Workato is now valued at $5.7 billion.

These additional funds come at a time of rapid growth for Workato and the automation industry as a whole. This influx of financing will go towards expanding Workato’s EMEA customer success team and senior leadership, holding the first conference in Europe, setting up a customer advisory board in Europe, and expanding its ecosystem of partners. 

In addition, this investment will also be used globally to further accelerate the organization’s product roadmap and leadership in enterprise automation, as well as continuing to drive the integration-led automation movement.

Dynatrace releases security gates for assessing software releases

Today, Dynatrace announced that its platform now includes security gates, allowing DevSecOps teams to automatically assess each new software release to ensure that only secure code is able to move through the delivery pipeline. These security gates are powered by the platform’s automatic and intelligent observability and help teams know that code is secure as it progresses from preproduction to production.  

With this update, users can now leverage Dynatrace’s automation and intelligence, along with the observability data that is already flowing through the platform, to automatically detect, access, and manage vulnerabilities in real time.

For additional information on these security enhancements, visit here. 

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But what if you want to not merely play catch-up with what the metrics are telling you, but instead want to mitigate against how things are going to go?

That, according to Adam Dahlgren, VP of product development at Allstacks, is where value stream intelligence comes in. He calls VSI the evolution of value stream management.

Organizations (mostly) all believe they’re doing great work. But from a business level view, many are still flying blind.  He explains: “There’s all these different value streams, for marketing, sales, customer success, product developers, and the like. But they’re all hinged on each other. Let’s say one of these value streams is developers building a feature, and that feature is driving later, and later. Well, the product marketing stream is relying on and reacting to the change in the developer stream. And all they can do right now is just react when I say, ‘Oh, it’s way crap, I gotta do something else.’ I’m scrambling, always playing catch-up. If instead, we can give the developers and the product marketers in this example the ability to communicate with each other on an ongoing basis on how things are changing, now we’ve restored power to each of the orgs to say, well, based on what’s transpiring, how do you want to change your behavior? Now you’ve connected those values. So this developer value stream that maybe is running late, has a cascading impact on the marketing value stream. … And that’s like the intelligence layer. It’s not just what has transpired, it’s given what’s transpired, what are we going to do next?”

Dahlgren said the widely accepted DORA metrics provide organizations with snapshots of how they’re doing. Those metrics — deployment frequency (DF), mean lead time for changes (MLT), mean time to recover (MTTR) and change failure rate (CFR) — are considered the gold standard for organizations seeking the value that can be derived from moving quickly to add features and remove bugs from their software.  But, Dahlgren pointed out, these metrics show you how you’re doing, but not where you’re going. And, they’re limited in scope to the product, and don’t give the wide-angle view businesses need. The DORA metrics, he said, “were a great starting point. But for the real business alignment, and core value capture for these big companies, those DORA metrics are tiny snippets of what’s really possible. Together, they are helpful, but but you can be nailing all four of your DORA metrics and building all the wrong stuff and not actually know what’s going on.”

He gave an abstract example of a city dealing with traffic problems. “You’re looking at timings at traffic lights and some engineer somewhere has decided that the yellow light needs to be three seconds on all these different traffic lights, but there’s one yellow light that’s four seconds. Okay, if that’s out of the boundary, should we change it to three seconds? Did that accomplish anything? It got it into spec. But is there still traffic?” 

“But if you look at it another way, like, at this intersection, there’s a significant amount of traffic,” he continued. “We could fix the traffic at that one intersection. Well, it turns out that, you know, the left turn lane is actually just part of the left lane. And we need to actually create a dedicated left turn lane, that’ll solve the problem. That’s management by exception. That’s the intelligence to say, there’s something out of bounds here. It’s an unknown unknown. It’s not something you knew to monitor, but it’s something you need to direct your attention to.” 

Value stream intelligence, Dahlgren said, can show you where you’ve been historically successful, and the ways you deviate from those successful pathways that then cause delays. “So when these things manifest again, whether you’re monitoring them or not, we’re going to bring them to your attention. And all the value stream management stuff is your supporting documentation.”

This is important when things go out of bounds in a way we’re not familiar with, he continued. So, in looking at related data, and now seeing some things in combination, you can determine what the next steps should be. “Turns out the four-second yellow light is beneficial, because it gives people one extra second to sneak in that extra left turn, which then smooths out the traffic flow. And some smart engineer had just realized that and set it up. When we brought it back into spec [of three seconds], we didn’t realize that that would actually have these cascading impacts [of slowing traffic]. That’s what we’re bringing to bear when you leverage this kind of intelligence.”

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