It is that dreaded time of year again where we have to report via the performance management system (PMS) on our individual performance and the value we bring to the organization. I say dreaded, because we all know that in reality the goals and objectives were set some time ago in the past, maybe a year ago, and a lot has happened since that time. The person you report to may have changed, you were redirected to other tasks, and so on. Everything seemed possible at the time of the objective setting, but now the reality hits that you were or may have been far too optimistic about your own capability. The self-assessment is difficult as you are not sure whether your manager has the same view as you. You believe you met the objective, but does their expectation meet your actual delivery? If a good performance relates to more money, the pressure and stress builds.
So whilst I was preparing for my Orlando Business Architecture Forum presentation I started to think about how business architecture teams measure and manage their performance. One of my next reports for Forrester’s business architecture playbook addresses BA performance. It was also a hot topic for the EA Council members in Orlando. I had a number of 1-on-1’s with clients who particularly asked about BA metrics and performance — in particular, “What do other business architecture teams do?”
I started listing the questions that, when answered by clients, would lead to a very valuable report for all BA leaders:
Do you measure your BA’s performance? Clients often advise me that they have fairly mature BA practices. However, very few can articulate how they measure their performance, and often comment that the business asks them to demonstrate how BA adds value. So, it would be useful to understand whether BA leaders measure their team’s performance and why they do or don’t.
In Forrester’s EA Practice Playbook, we describe high-performance enterprise architecture programs as “business-focused, strategic, and pragmatic.” They are business-focused so that the direction and guidance EA provides has clear business relevance and value. They are strategic because the greatest value EA brings is to help its business to achieve its business strategies. They are pragmatic because, well, the path to strategy is never straight, and EA teams who aren’t agile in their approach get pushed aside.
National Grid, facing the enormous changes to the utility industry, developed an enterprisewide business capability model and made that the center of their joint business-IS planning. The result? All the way up to the C-level, EA is being recognized as a strategic change agent.
Scottish Widows Investment Partnership “reinvented” their EA program, centered on a business capability model developed over four weeks, and used to organize and link all the EA portfolios. They now have business managers as well as EA using their architecture planning tool.
Outside of BPM, one of my other passions is mentoring college students through the process of launching new startups. I enjoy helping students tighten up their business ideas and seeing them build business plans that can attract the funding they need to stand up and implement their ventures.
Recently, after reviewing and providing feedback on a student’s business plan, the student responded, “I can launch my business without a business plan; all this planning seems like a waste of time.” At first, I thought he was joking. However, I could read by the look on his face that he was serious. I am sure you can imagine the conversation that followed.
The next day when I reflected on the conversation, I had a moment of satori. I could see that startups share the same risk/reward profile as business process management initiatives. Just like startups, BPM initiatives promise huge returns to investors and stakeholders. Additionally, just like startups, BPM initiatives are fraught with risks such as inadequate funding, low adoption, and difficulty attracting skilled resources.
My conversation with the student about the importance of business planning seemed to parallel conversations I often have with enterprise architects and business architects launching or retooling their BPM initiatives. Most tend to overestimate the BPM’s potential rewards and downplay — or do not fully understand — the risks involved with launching a BPM initiative. However, for the most successful BPM initiatives, I have found that their leaders tend to have a “lean startup” mentality.
What does it mean to have a “lean startup” mentality?
There was lots of feedback on the last blog (“Risk Data, Risky Business?”) that clearly indicates the divide between definitions in trust and quality. It is a great jumping off point for the next hot topic, data governance for big data.
The comment I hear most from clients, particularly when discussing big data, is, “Data governance inhibits agility.” Why be hindered by committees and bureaucracy when you want freedom to experiment and discover?
Current thinking: Data governance is freedom from risk.The stakes are high when it comes to data-intensive projects, and having the right alignment between IT and the business is crucial. Data governance has been the gold standard to establish the right roles, responsibilities, processes, and procedures to deliver trusted secure data. Success has been achieved through legislative means by enacting policies and procedures that reduce risk to the business from bad data and bad data management project implementation. Data governance was meant to keep bad things from happening.
Today’s data governance approach is important and certainly has a place in the new world of big data. When data enters the inner sanctum of an organization, management needs to be rigorous.
Yet, the challenge is that legislative data governance by nature is focused on risk avoidance. Often this model is still IT led. This holds progress back as the business may be at the table, but it isn’t bought in. This is evidenced by committee and project management style data governance programs focused on ownership, scope, and timelines. All this management and process takes time and stifles experimentation and growth.
Today’s organizations must manage the explosive growth of all types of information while addressing greater-than-ever business demand for insights into customer needs and the business environment. Meanwhile, the significant regulatory and compliance risk associated with information security has increased the urgency for tightly controlled information management capabilities. These requirements are hard to meet, with scant best practices available to tame the complexity that firms encounter when trying to manage their information architecture. Enterprise architects must define the organizational capabilities they need to develop and evolve their information resources — as well as the technology to exploit them. You can only achieve all this with a coherent information strategy that defines and prioritizes your needs and focuses resources on high-impact goals.
So, this blog is dedicated to stepping outside the comfort zone once again and into the world of chaos. Not only may you not want to persist in your data quality transformations, but you may not want to cleanse the data.
Current thinking: Purge poor data from your environment. Put the word “risk” in the same sentence as data quality and watch the hackles go up on data quality professionals. It is like using salt in your coffee instead of sugar. However, the biggest challenge I see many data quality professionals face is getting lost in all the data due to the fact that they need to remove risk to the business caused by bad data. In the world of big data, clearly you are not going to be able to cleanse all that data. A best practice is to identify critical data elements that have the most impact on the business and focus efforts there. Problem solved.
Not so fast. Even scoping the data quality effort may not be the right way to go. The time and effort it takes as well as the accessibility of the data may not meet business needs to get information quickly. The business has decided to take the risk, focusing on direction rather than precision.
Arguably, mobile is currently the hottest trend driving both business and technology strategies for executives. If you need any additional evidence, just look at all of the enterprise buzz Apple has generated with the iPhone 5 launch. Unfortunately, today’s business and technology leaders continue to respond to the mobile opportunity with the wrong answers. Business leaders respond to mobile with, “Let’s build a really slick mobile app, put it up on iTunes and we’re done!” Technologists respond to mobile with, “We need a strong BYOD policy and to put device management tools in place!” Both of these responses completely overlook the fact that underlying legacy applications and business processes need optimizing for the mobile experience.
We run into examples of this “lipstick on a pig” approach to mobile all the time. In fact, I ran into a perfect example of this recently when I needed to order a pizza for my family after a very hectic Saturday afternoon. When I picked up my mobile phone to call the pizza delivery place, a light bulb went off over my head. Instead of dialing the pizza delivery company and waiting on hold for 15 minutes, why not download its mobile app in two minutes and order my pizza within another two minutes. I figured I could shave off ten minutes of wait time by simply downloading the pizza delivery company’s mobile app.
Enterprise architects I talk with are struggling with the pace of change in their business.
We all know the pace of change in business, and in the technology which shapes and supports our business, is accelerating. Customers are expecting more ethics from companies and also more personalized services but do not want to share private information. Technology is leveling the playing field between established firms and new competitors. The economic, social, and regulatory environment is becoming more complex.
What this means for enterprise architects is that the founding assumptions of EA — a stable, unified business strategy, a structured process for planning through execution, and a compelling rationale for EA’s target states and standards — don’t apply anymore. Some of the comments I hear:
“We’re struggling with getting new business initiatives to follow the road maps we’ve developed.”
“By the time we go through our architecture development method, things have changed and our deliverables aren’t relevant anymore.”
“We are dealing with so many changes which are not synchronized that we are forced to delay some of the most strategic initiatives and associated opportunities.”
The bottom line is that the EA methods available today don’t handle the continuous, pervasive, disruption-driven business change that is increasingly the norm in the digital business era. Our businesses need agility — our methods aren’t agile enough to keep up.
We last spoke about how to reboot our thinking on master data to provide a more flexible and useful structure when working with big data. In the structured data world, having a model to work from provides comfort. However, there is an element of comfort and control that has to be given up with big data, and that is our definition and the underlying premise for data quality.
Current thinking: Persistence of cleansed data.For years data quality efforts have focused on finding and correcting bad data. We used the word “cleansing” to represent the removal of what we didn’t want, exterminating it like it was an infestation of bugs or rats. Knowing what your data is, what it should look like, and how to transform it into submission defined the data quality handbook. Whole practices were stood up to track data quality issues, establish workflows and teams to clean the data, and then reports were produced to show what was done. Accomplishment was the progress and maintenance of the number of duplicates, complete records, last update, conformance to standards, etc. Our reports may also be tied to our personal goals. Now comes big data — how do we cleanse and tame that beast?
Reboot: Disposability of data quality transformation. The answer to the above question is, maybe you don’t. The nature of big data doesn’t allow itself to traditional data quality practices. The volume may be too large for processing. The volatility and velocity of data change too frequently to manage. The variety of data, both in scale and visibility, is ambiguous.
I recently finished reading Moneyball, the Michael Lewis bestseller and slightly above-average Hollywood movie. It struck me how great baseball minds could be so off in their focus on the right metrics to win baseball games. And by now you know the story — paying too much for high batting averages with insufficient focus where it counts —metrics that correlate with scoring runs, like on-base percentage. Not nearly as dramatic — but business is having its own “Moneyball” experience with way too much focus on traditional metrics like productivity and quality and not enough on customer experience and, most importantly, agility.
Agility is the ability to execute change without sacrificing customer experience, quality, and productivity and is “the” struggle for mature enterprises and what makes them most vulnerable to digital disruption. Enterprises routinely cite the incredible length of time to get almost any change made. I’ve worked at large companies and it’s just assumed that things move slowly, bureaucratically, and inefficiently. But why do so many just accept this? For one thing, poor agility undermines the value of other collected BPM metrics. Strong customer experience metrics are useless if you can’t respond to them in a timely manner, and so is enhanced productivity if it only results in producing out-of-date products or services faster.