Author: Elizabeth Larson

We all Communicate, So What Makes It So Difficult?

Communicating is something we do throughout our lives. Much communication is verbal, some is not.

We use different languages to communicate different needs. Babies have their language, teenagers theirs. We talk both formally and with slang, sometimes using proper grammar, sometimes shortcuts and acronyms. Sometimes we talk without communicating and sometimes we communicate without talking. Given its pervasiveness, it seems that by now we all would have learned how to do it effectively. But as we are all aware, there is an abundance of miscommunication everywhere we look.

Still, communication is a key skill for all business analysts (BAs) and project managers (PMs). It’s not possible for us to be successful without effectively communicating. Here are three tips for effective communications and how to avoid common communications pitfalls.

Pitfall #1 – Same words, different meaning

As BAs and PMs we often encounter what is known as having different mental models. This happens when a stakeholder uses a term or phrase, and we interpret it differently. Or vice a versa. We use the same words, but it means different things to each of us. One important reason is context. Although we each using the same term, our context is different.


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Recently my husband and I went through a home renovation project with an outside remodeling company. We did this all virtually. We looked at selections on Zoom and had Zoom meetings as needed to resolve issues. At one point we got a text from the PM stating that they had encountered an issue relating to a post in the center of the master bath. This issue had been uncovered during the “demo.” I wrote back to ask when the demo took place and why we, as the sponsors, were not at this demo. A series of texts and emails got us nowhere, so we set up a Zoom meeting. We soon realized that to him the demo meant demolition. I told him that to me a demo was a demonstration. Thus, the confusion. That cleared up, we proceeded to discuss the problem. My context was a Scrum demo, a review of the product with the product owner and other business stakeholders. His context was in the building industry, where demolition commonly precedes construction. The same word had entirely different meanings.

Pitfall #2 – Too much emotion or not enough emotion

Another common pitfall is to put either too much or not enough emotion into our communications. We all communicate our emotions to a greater or lesser degree. We do this either verbally or non-verbally. Non-verbal communication accounts for most of the communication taking place. So even if we never say a word, we usually communicate how we’re feeling. And there’s nothing wrong with that. But when our anger or frustration or other negative feelings are triggered and we react too quickly, we risk throwing up communication barriers that will be hard to break down once we calm down. That’s why we know that we should wait a while before sending an angry email or text or making that phone call to blow off steam.

On the other hand, when the situation calls for empathy and kindness and we show none, we also risk putting up communications barriers. When we come across as Sherlock Holmes, Conan Doyle’s famous analytical detective who was often perceived as cold and dispassionate or Mr. Spock, Star Trek’s half human, half alien epitome of cool detachedness. we also throw up barriers. Like everything relating to effective communications, it’s best when we temper our emotional reactions to the situation.

Pitfall #3 Asking the right questions the wrong way (or asking the wrong questions)

“We thought we had the answers-It was the questions we had wrong” from U2, Eleven O’Clock Tick Tock

Speaking of Sherlock Holmes, I see may similarities between effective PMs/BAs and detectives. Both use logic and intuition to synthesize disparate pieces of information and connect the dots. This ability is important –in the case of the detective to catch the bad guy, in the case of the BA to understand and solve business problems. In addition, both are curious. They ask pertinent questions, listen to the responses, and keep digging until satisfied. Sometimes their questioning takes unusual and unexpected turns. This is because neither accepts the answers given them as being the final answer. They probe. Sometimes they go down rabbit holes. But the good ones know when to pursue a line of questioning and when to let it go, when to ask follow-up questions and when to think further about what’s been said.

Asking pertinent questions is one of the most useful skills project professionals have. Good questions not only uncover needs and requirements but also open communications. Likewise, poorly worded questions can end conversations quickly. For example, “what do you like bet and least about this solution?” can open communications. “ Isn’t this the best option?” can shut it down.

Perhaps even more important is the way we ask questions. “Why…” is a great question. It uncovers almost every aspect of our work, including the current and future state processes, the business need for any given initiative, and questions relating to stakeholder commitment, to name just a few. However, we do not want to sound like cranky toddlers, asking “why, why, why?”.

Our tone is also important and can put people at ease or on the defensive. We don’t want to sound like prosecuting attorneys, which can easily shut down communications. We are not, however, always aware of how we come across. Our intention might very well be to put people at ease, but our effect might be very different. And sometimes when communicating across cultures, tone, facial expressions, and other non-verbals can be misinterpreted.

Finally, many BAs and PMs ask the wrong questions, often in the form of leading questions. Leading questions sound like questions, but they’re really solutions. Questions like “have you ever thought about…” or “Isn’t this solution the best choice …” sound like we’re engaging our stakeholders, but in reality, we’ve just cut off communications. We’ve presented what we think rather than asking what our stakeholders think. After we’ve asked all our questions, we do want to present our recommendations. But not until we’ve asked our questions and done our analysis.

These three pitfalls represent just a few of the many that get in the way of effective communications. However, understanding the context, displaying the right emotions for the situation, and asking the right questions is a great start.

AI and the Digital BA—What’ It All About? Part 3

This is the last of a three-part article written with answers to some of the most frequently-asked questions I get about artificial intelligence (AI).

In Part 1, I addressed some common terms and issues related to AI as it is used in a business context. In part 2, I focused on the various roles that BAs play on AI efforts. In this article I will discuss various subjects like the need for AI translators, the importance of AI governance, and the digital PM. As with Parts 1 and 2, I will use a Q/A format.

Why is the role of AI translator so important?

Recently there have been numerous articles in journals like Forbes and Harvard Business Review (HBR) about the need for an AI translator role, someone who acts as a go-between between the organization’s data scientist and strategic decision-makers. These articles don’t mention the BA specifically, but their descriptions are consistent and describe a role that BAs have routinely played—that of ensuring that business stakeholders and technical staff understand each other. I think the AI translator is a perfect role for any experienced BA. Data scientists need to understand the strategic direction of the organization, the business need for the initiative, and the related business rules that will be required on many of the AI systems. Business stakeholders need to understand the impacts of their decisions.

In the early days of AI, it was not uncommon for data scientists to guess at the business rules and make AI-related decisions themselves. This did not go well, as documented in Computer World.[i] The next phase was to have data scientists get input directly from the business. This, too, did not go well. So some organizations have introduced an intermediary role—the AI translator. They understand that they need to have someone who understands the importance of business input and who can also speak comfortably with the data scientists—a translator role. That’s where the BA comes in. We’ve always been translators. Translating the requirements into designs and back to ensure stakeholders get the functionality they ask for and really need. Yes, this is a perfect role for the BA and one that can greatly contribute to successful AI projects.

How much governance is needed on AI initiatives?

Many of the challenges on AI initiatives are no different from those on other projects. In a survey published in Information Magazine in July 2019, respondents included these factors as the major challenges:[ii]

  • 50% – Lack of leadership buy-in
  • 49% – Lack of metrics, especially surrounding data (bad data, ownership, etc.)
  • 37% Internal conflict
  • 31% Time required to implement (takes longer than expected)
  • 29% Unexpected costs

What do these factors have to do with governance? Each one directly relates.

  • Executive buy-in. Among other things, no executive buy-in makes it almost impossible to reach consensus on the need for and nature of governance itself.
  • Data metrics. Governance guides such metrics as how accurate historical data needs to be.
  • Internal conflict. Governance establishes guiding principles around conflict, how it will be resolved, and by whom.
  • Time and cost overruns. Project governance will help such things as keeping projects on track, how and when to communicate when they’re not, and even what “longer than expected” means, so forth.

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The article goes on to suggest that in order have successful AI initiatives, organizations need to hire data stewards to manage and coordinate the organization’s data. The data steward would be a steward in the real sense of that word: someone to manage, administer, and generally take care the data. In order to manage and administer, this role needs to help the organization determine what that governance will work and then to be responsible for its governance. Sounds like a BA!

In a podcast, cited in Harvard Business Review (HBR) in August 2019, De Kai and Joanna Bryson join Azeem Azhar to discuss the importance of governance on AI initiatives.[iii] They define governance as coordinating resources involving both internal AI modules and humans. They suggest that there needs to be an independent, oversight group with the authority to apply agreed-upon governance, and I think the seasoned BA is in a perfect position to facilitate this group.

Is there such a thing as a digital PM and if so, how does that role differ from a digital BA?

Digital BAs are similar to all BAs in that they do BA tasks, use BA techniques, and need the same BA competencies (see Part 1). Likewise, digital PMs do PM tasks, use PM techniques, and need PM competencies. They work with the sponsor to charter AI projects and help organizations implement them. Although not yet a common role or title, having someone with experience managing AI projects can be valuable to organizations. Again, they’ll still do their tasks and use their techniques appropriate to PM work, but being a PM on an AI project and coordinating all the resources entailed on such an initiative will most certainly require a healthy working knowledge of AI.

Another way to look at digital PMs is that they use AI systems and tools to manage AI projects. In an article in Forbes Magazine on July 2019, the author focuses on the use of automated AI systems and tools to help digital PMs manage their projects.[iv] He says, “AI, with its unique ability to monitor patterns, is a capable assistant to PMs.” In addition to helping with the routine admin tasks, AI can provide all kinds of predictive analytics. AI tools can look at hidden complexities and all the moving parts inherent in a complex project or program and predict areas of concern, from project slippage to team members behavior and more.

The digital PM, then, is one who not only takes advantage of AI tools to do a better job of managing projects, but also has enough AI expertise to manage complex AI projects.

Does “digital” have to be related to “AI?”

In the past, the term “digital” was used broadly. It referred to any digital project, like development of a website, digital marketing, or developing the organization’s presence on social media. Nowadays the term is generally used to refer to “AI,” which encompasses all things related to machine learning, predictive analytics, and data mining. More recently the terms “AIs” and “AI systems” are also commonly used.

I hope you have enjoyed this three-part series. Look for more AI-related content in the future.

 

[i] https://www.computerworld.com/article/2484224/12-predictive-analytics-screw-ups.html, Robert Mitchell, July, 2013

[ii] https://www.information-management.com/opinion/data-governance-in-the-age-of-ai-beyond-the-basics, Data Governance in the Age of AI, Gienna Shaw, Information Magazine, July 19, 2019.

[iii] https://hbr.org/podcast/2019/08/governance-in-the-age-of-ai, Podcast, De Kai and Joanna Bryson

[iv] https://www.forbes.com/sites/cognitiveworld/2019/07/30/ai-in-project-management/#195242a6b4a0,, Forbes, Tom Schmelzer, July 30, 2019

AI and the Digital BA—What’ It All About? Part 2

This is the second of a two-part article written with answers to some of the most frequently-asked questions I get about artificial intelligence (AI).

In part 1 I addressed some common terms and issues relating to AI as it is used in a business rather than technical context. In this article I will focus on the various roles the BA plays to help organizations with their AI initiatives. As with the last article, I will use a Question and Answer format.

Quick Review of Part 1

What is AI?

AI is an umbrella term that encompasses all digital technologies, like machine learning and predictive analytics, which are used to make predictions and recommendations using massive amounts of data. In short, it’s machines doing human tasks that range from simple to complex.

What is a digital business analyst (BA)?

A digital BA is a trusted advisor who helps organizations with their AI strategies. Rather than developing the strategies, they provide their advice about impacts to and value of AI initiatives.

What skills does a digital BA need?

The skills don’t change, but the subject matter is incredibly complex.

How successful are most companies with their AI efforts?

Not very. Most AI initiatives totally miss the mark and result in all kinds of issues, not the least of which is financial. A recent Forbes article details some of the resulting issues.[i]

What is digital fluency?

Digital fluency is defined as “The ability to interpret information, discover meaning, design content, construct knowledge, and communicate ideas in a digitally connected world.” [ii]

Part 2

What is the role of the BA on digital projects?

A digital BA can be involved in many aspects of an AI initiative. Some of the roles that a BA may play include one, several, or all of these:

    • Strategic BA. In this role BAs help organizations determine the value and direction of the AI effort. Some of the specific outputs can include:
      • Business case on the value of the AI initiative
      • Recommendation(s) on the best strategic approach to the AI initiative
      • High-level implementation plan
      • Pitfalls to avoid
      • First look at state of the data to be used
      • High-level governance plan

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  • AI coordinator who implements the AI strategies. In this role the BA coordinates AI initiatives across project and portfolios.
  • BA on a project(s) that is part of the AI initiative. Although this role is similar to any BA role, there are some differences. The BA will need at least working knowledge of, if not expertise in, AI.
  • Business data analyst. In this capacity the BA may
    • Analyze the current data to determine how much is useable, how much needs to be cleansed, and how much needs to be collected
    • Recommend an approach to cleansing the dirty data
    • Help determine the data needed for predictive analysis and other AI functions
    • Interpret statistical analysis resulting from AI functions
    • Be an AI translator to facilitate communications between the data scientist and the business stakeholders.

What’s the difference between a data scientist, data analyst, and BA who works a lot with data?

These 3 roles can be confusing. At first glance we might not recognize differences or understand why the distinctions are important, but they are. I discussed the possible roles of the BA above, so here is a brief description of the other two.

Let’s take the easy one first—the data scientist. Not that the role is easy, it’s just easier to explain why this one is different from the other two. The data scientist is the most technical and needs the most expertise. About three-fourths have master’s degrees in mathematics and statistical analysis. Over half have Ph.Ds.

Data scientists create the predictive models. They determine what the machines need to do in order to meet the business objectives. They decide which algorithms are best given the objective of the AI initiative so that the machines can be trained to learn. Having said that, unless there is good governance and substantial input from business stakeholders and decision-makers, those algorithms have the potential to be created with built-in biases. Likewise, they may not be the best ones to solve the business problem.

The data analyst. This is really a subset of the BA role. I described some of the high-level functions above. On AI projects it’s necessary to focus on the data because it’s so integral to the success of the effort. Machines learn based on historical data. Issues like dirty and redundant data, as well as ownership of the data aren’t easy and require a strong facilitator and influencer to resolve. This data analyst role is such an important role that IIBA has created a new certification—the certification in business data analysis (CBDA).

What are some of the business and technical pitfalls that the digital BA should be aware of?

Here are some of the big ones:

Strategic

  • Beginning with AI as a solution without a defined problem
  • No real AI strategy
  • Unrealistic expectations of what AI can do for the organization

Data and technology

  • Dirty data
  • Business processes don’t support the technology
  • Weak security

Organizational and communications pitfalls

  • Siloed and cumbersome business architectures
  • Inflexible organizational structures
  • The data scientists create the business rules
  • The data scientists talk directly to the business and the business does not understand
  • Confusing roles on AI projects
  • Built-in biases in the algorithms

In Part 3 of this article, we will explore other aspects of how BAs can help organizations get the most value from their AI initiatives. Some of the topics we will cover include the need for governance on AI efforts, the recognition of the importance of the AI translator role, the digital PM, and more. 

[i] https://www.forbes.com/sites/insights-kpmg/2019/12/10/data-governance-is-risk-number-one/?utm_source=TWITTER&utm_medium=social&utm_content=2937780067&utm_campaign=sprinklrForbesMainTwitter#90dd59b91c81

[ii] https://www.slideshare.net/RobinAshford/guiding-learners-toward-digital-fluency

AI and the Digital BA—What’ It All About? Part 1

In this three-part article I’ll answer some of the questions that I am frequently asked about artificial intelligence (AI)

and the role of the BA (business analyst) in helping guide organizations in developing and implementing their AI strategies. In Part 1 I’ll address some common terms and issues relating to AI as it is used in a business context. In Part 2 I’ll focus on various roles BAs can play on AI initiatives and detail some of the more common pitfalls. In part 3 I’ll discuss various topics including the need for governance on AI efforts, the digital PM, and the AI translator.

What is AI?

These days the term “AI” is being used as an umbrella term that encompasses all digital technologies, such as machine learning, predictive analytics, RPA (robotics process automation), etc. In today’s common usage we think of AI at its most fundamental level—any time machines act like humans, that’s an aspect of AI.

Machine learning, another common term, is a kind of AI. When machines use predictive models and massive amounts of historical data, they learn, make predictions, and provide insights. As new data comes in, they keep learning and improving and are able to make better predictions and provide better insights

It seems like most organizations are jumping on the AI bandwagon. Why is AI so important?

During the dot-com boom in the late 90s I asked the same question of a presenter talking about ecommerce. She explained it to me by saying, “everyone’s looking for that next get rich quick scheme. A hundred years ago it was the gold rush. Today it’s ecommerce.” While I would never say that AI is a get rich quick scheme, there is a nugget of truth (pardon the gold rush reference). But I would phrase it differently. Organizations realize that they need to adapt to their environment in order to survive. Survival of the fittest if you will. And today’s environment requires at least some element of AI.

How successful are most companies with their AI efforts?

In a recent survey by Harvard Business Review[i] 72% of organizations said they were not getting the value out of their AI projects that they were expecting. The article stated that 40% of the problems were caused by an ill-defined problem and/or product. In other words, what’s typically lacking is something that BAs do so well—define the business problem to be solved, recommend solutions, and then define the requirements of the solution. Neglecting these things can wreak havoc on any project, as these statistics point out. According to the same article another 40% of the issues are due to bad data, another of the BA’s many bailiwicks. Only 20% of the problems are due to the algorithms themselves, but that’s where many companies put 80% of their resources.

BAs can help organizations avoid 80% of the pitfalls mentioned in this survey. If organizations involve BAs to help recommend solid AI strategies and implementation plans, these efforts would be more successful.

What is digital fluency?

Digital fluency is defined as “The ability to effectively and ethically interpret information, discover meaning, design content, construct knowledge, and communicate ideas in a digitally connected world.” [ii] There are a few points in this definition that are worth highlighting.

  • Instead of efficiency and effectiveness, the emphasis is on being effective and ethical. This is because ethics is so important in our digital world. The concept of digital trust has risen in priority, so this competency requires not just speaking the digital language, but also understanding the ethical impacts of AI on organizational decisions.
  • Digital fluency requires the ability to do what BAs have always done. We discover meaning by eliciting information in a variety of ways. We design content when we model the future state. We construct knowledge by connecting the dots and putting the disparate pieces of information together. We communicate ideas to a variety of stakeholders in a variety of ways, always translating and interpreting the technical complexity so that stakeholders can understand and make good decisions.
  • Our world is digitally connected, so we need to do what we have always done—considering that it will be done by a broader range of stakeholders anywhere, anytime, on any device.

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What is a digital business analyst (BA)?

I‘m fond of saying that a digital BA is a BA who helps organizations figure out what’s the best approach to take on digital transformation projects. Digital BAs help organizations make the best use of AI. They help organizations recognize and avoid common implementation pitfalls and risks.

A recent study by IIBA in conjunction with UST Global describes the digital BA as someone who guides organizations as they develop their AI strategies. Once that strategy is created, the digital BA “validates, supports, and executes” that strategy. [iii]

In other words, a digital BA is a trusted advisor who helps organizations with their AI strategies. Importantly, they do not create any digital strategies. They provide their advice through expert recommendations.

What skills does a digital BA need?

We still need to do business analysis work, using business analysis techniques. We don’t need a Ph.D. in statistical analysis, since the role of the BA does not create the predictive models. But we will need to talk to the data scientists, the role that does create the models. So digital fluency is important. Facilitation, conflict resolution, business and industry knowledge, ability to influence, ability to analyze, think critically, and solve problems are key competencies as well.

To summarize, the role of the digital BA on AI initiatives is becoming an essential part in organizations’ AI efforts. In this article we have answered common questions related to this important role. Look for answers to other common questions in Parts 2 and 3 in the upcoming months.

 

[i] https://hbr.org/2019/03/the-ai-roles-some-companies-forget-to-fill, HBR March 2019

[ii] https://www.slideshare.net/RobinAshford/guiding-learners-toward-digital-fluency

[iii] IIBA and UST Global get attribution

Five Trends in Business Analysis, Project Management, and Agile

Since 2009 we have enjoyed reflecting on what’s happened the previous year

in the world of projects and making predictions for the upcoming year. Here are some of the recent trend topics we have discussed:
  • The digital transformation
  • Roles that help organizations maximize value
  • Agile successes, challenges, and use beyond software 
  • Scaling agile 
  • BAs and PMs in the gig economy
Here are five industry trends that we have chosen for 2020:

BAs Helping Organizations Create Value-Driven AI Initiatives

Many organizations, around 72% according to Harvard Business Review, are finding that their AI initiatives are not meeting expectations,   There are many reasons for disappointing AI results, among them that many organizations:
  • Chase fascinating new technology with no clear vision of the business value (?) proposition
  • Focus on implementing the technology without considering how the organization will get and use the AI results
  • Mistakenly think that implementing AI projects will be easy
  • Seek to implement AI with antiquated and burdensome processes that do not support these initiatives
  • Ignore the immense cultural change needed to adopt AI technologies
Some organizations have turned to a role that is perfect for BAs and that can help organizations implement their AI initiatives. Although this role may or may not be called a BA, the work is definitely business analysis. This work includes:
  • Developing a business case to ensure there is value in undertaking the AI initiative
  • Reviewing the current state and recommending changes needed to existing processes, infrastructure, existing and needed data, and types of new roles and positions needed.
  • Recommending how to get everyone on board, given the enormity and difficulty with implementing this transformation. 

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Product Owner Role Even More Challenging for BAs

An emerging trend is a recognition of the challenges faced when the business analyst (BA) plays the dual role of BA and product owner (PO) on Scrum teams. Here are some of those challenges: 
  • The PO role makes product decisions and sets backlog priorities. When there is no PO, a BA is often assigned as a “proxy” or “surrogate” PO. In this role they make decisions on product features and priorities. However, many BAs are finding that their proxy decisions are often overturned by the sponsor or other stakeholders causing rework and delays. 
  • The Product Owner (PO) role is accountable for quick and continuous delivery of value. The BA role is accountable for requirements. That is, for getting high-level user stories down to the details where they can be estimated and developed. This inherent conflict of getting it done quickly vs. getting it done right makes it difficult to play both roles at the same time. 
  • Some organizations understand the need for full-time POs on scrum teams. Because business stakeholders are unavailable, they assign the role to a BA. When the BA is accountable for the product, rather than being a trusted advisor, they end up “owning” the product and are often blamed if wrong decisions have been made.
We’re hearing more and more BAs speak out about these challenges and about the need for both POs and BAs on Agile projects. 

Putting a Little BA in Everyone’s Toolkit

Years ago, we watched project management evolve beyond the skillset expected of an individual with the title Project Manager (PM) into a core competency. It became expected not only of PMs, but of many middle managers even if they weren’t managing projects. 
In today’s product-focused organizations, we are seeing business analysis (BA) evolve in the same way. It seems that everyone is recognizing the value of enhancing their core skills with BA competencies. In today’s change-driven environments, where questions about what customers want and need are always top of mind, BA is used everywhere. So, it’s no surprise that we are increasingly seeing team members seek to augment their primary skillset with BA skills. 
Organizations are recognizing the benefits. They understand, however, that relying on just one or two individuals with the required BA skills is a recipe for gridlock. In addition to the obvious benefit of alleviating bottlenecks, developing fundamental BA skills in all team members also adds depth to their core skills. And we have recently observed workshop and conference participants, even those that do development work, become evangelists for adding a little BA to their toolkit. It does not get lost on them how some BA savvy is going to make them more effective when working with customers, product owners, and other team members. 

Digital Fluency and the Rise of the AI Translator

There are many ways for BAs to help organizations transform to the digital world and take advantage of AI and other digital technologies. Most of these ways require the BA to be a trusted advisor to the organization and help guide it in the right direction. However, to be a trusted advisor, BAs need to know what they’re talking about. They need to understand this complex world themselves. They need to be digitally fluent. 
Many organizations recognize that they need someone with this skillset to be successful. They are becoming aware of the importance of having someone who can translate the technical complexity of the AI world into business language. Someone who can help them articulate the results they want to achieve with their AI initiative. 
That’s why the title of AI Translator is receiving so much buzz. It’s a perfect role for the BA to fulfill, so look for more and more organization to use BAs in this translator role. 

Everyone But the BA Doing DevOps, But That Will Change

We’ve been writing about DevOps for several years, but it seems to us that its acceptance is just beginning to catch on. One of the main reasons that adoption has been slow is because many organizations don’t know what to make of it. They know DevOps supports continuous delivery, but continuous delivery is hard to define. 
Because DevOps means different things to different organizations, its implementation has been haphazard, and frequently does not include BAs. When we ask why, we often hear comments like, “Oh that’s just for Operations.” Or “Sure our organization has implemented DevOps, but it has nothing to do with us BAs.” Organizations understand that having continuous delivery of features does no good if implementing those features upsets the stability of the production environment. Which is what Dev Ops is all about. 
We think that organizations will soon recognize that BAs (and PMs) understand and are well-equipped to implement tools, foster collaborations, and facilitate cultural change, all of which are needed to support continuous delivery. So look for more and more organizations to include BAs in their DevOps adoption. 

By Elizabeth Larson, Andrea Brockmeier, Richard Larson

Andrea Brockmeier, PMP, CSM, PMI-ACP and PBA, is the Director of Project Management at Watermark Learning/PM Academy. She has 20+ years of experience in project management and related practice and training. She writes and teaches courses in project management, business analysis, and influencing skills.
Richard Larson, PMP, CBAP, PMI-PBA, Founder and former President of Watermark Learning, Richard Larson is a successful entrepreneur with over 35 years of experience in business analysis, project management, training, and consulting. He has presented workshops and seminars on BA and PM topics to over 10,000 participants on five different continents. Rich is a frequent speaker at Business Analysis and Project Management national conferences and IIBA® and PMI® chapters around the world. He has contributed to the BA Body of Knowledge version 2.0 and 3.0, was a lead author for the Needs Assessment chapter of the PMI publication Business Analysis for Practitioners: A Practice Guide, and was an author of the PM Body of Knowledge, 4th edition. He and his wife Elizabeth Larson have co-authored five books on business analysis.