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Archive for Agile

Moving Into a Data Guided Culture Means Abandoning the 35mm Camera Mentality

Some of you might have seen our CEO, Debbie King, speaking at ASAE Annual in Detroit this week.  If not you missed an inspiring presentation about building data analytics into your strategic plans and investing in a data guided culture.  Debbie was joined by Frank Krause, Chief Operating Officer of the American Geophysical Union (AGU), who offered brilliant insight and practical advice on making sure that association analytics are informative and actionable, not merely interesting.  At one point the evolution from static reports to data visualizations is compared to advances from film to digital photography.  This 35mm filmis a fitting analogy that makes the benefits hard to deny.
Think back to when you packed a bag for vacation and you took a 35 mm camera and 5 rolls of film.  Wait, that might not be enough, better bring 7.  You had to stop and think because you knew for a fact every roll is 36 shots, maybe 37 if you get the first 2 threads to catch.  Then you get to the end of the trip and you’ve loaded up the last roll and you want to make sure those shots count.  As a result, you realize you could miss a priceless moment.  So you consider the high cost of stopping at a local shop to pick up some more rolls of film, but then you think of the cost to develop them later. You decide to take a chance that you’ll remember to capture these special moments.
Believe it or not, there are a lot of similarities between film and having a traditional static report built for your association’s analysis needs.  In both cases you have the initial investment cost, time waiting on either IT for your report or the drugstore to process the film, and then determining what you’ll do with the output.  You can create a beautiful picture album to share with all your friends or you can toss the pictures in a shoebox.  The report will have actionable information like showing you event registration numbers are down over the past two years, but will it be too late by the time you discover the information?
And this is where we come to the digital era of instant gratification and why it can be advantageous.  First the digital camera, then nearly equal or better power in your smart phone.  Do you think twice before you turn on burst mode and capture 16 pictures in 3 seconds?  Get two or three bad ones, simply delete them and take more.  There is no upfront investment or limit to worry about and you could go so far as to say it is iterative; this is like an agile data analytics initiative.  You are able to build and rebuild a data visualization on the fly.  Sure, you can have instant access to ask and answer new business questions.  You can even test and experiment on the fly – something you’d never try with various lighting and a film camera.  Build and edit data visualization at at your fingertips.
The bottom line is that everyone wants and expects answers quickly. This allows us to maintain our connection to ideas and helps focus our thoughts to ask better questions.  So it’s time to put away your 35 mm camera and shift your focus to a digital data guided culture.

Modelstorming: Data Modeling Meets Brainstorming

Have you ever heard of the term “Modelstorminig”?  It’s where data modeling meetings brainstorming and it is the best way we have found to build scalable analytics solutions in an agile fashion.  In the old days, when  analytics professionals started gathering requirements for a business intelligence initiative, it was easy to fall into the trap of just doing what we were told. The association staff wants field X, Y and Z and they are used to A, B and C reports. We give it to them. Everyone is happy. For a minute. Then, they have new questions. Then we repeat the whole process again.  It’s time-consuming and frustrating for both the business staff and the IT staff. As they say, “times are changing” and that old model simply doesn’t work. Today we seek to answer both the questions the association staff have now and those they will have in the future. We try to answer questions they don’t even know to ask yet. This is what is means to “have a conversation with the data”.  So, how do we do that?

  1. Listen
    1. I know about ETL scripts, star schemas, and building visualizations in Tableau. Those are a few of my strengths. What I am less familiar with is the business processes of a client. What does it take to get a new member? What marketing or product development tasks go into membership? How do most people join? Are they a prospect before they are a member?
    2. The answers to these questions help me understand the business and help me understand what questions they may have that go beyond “How many new members do we have?”
  2. Discover Business Events
    1. We are delivering solutions that the business can actually use and even, I dare say, enjoy using. The association staff should be part of the entire process so that they understand it. This starts at the very beginning.
    2. We ask the association staff to convey their data stories using subjects, verbs and objects to discover the business events and tell data stories. Business events are the measurable details of a business process. We ask “Who does what?” The answer might be: “individual registers”.
  3. Document
    1. This isn’t boring old documentation! Using whiteboards and spreadsheets and a framework, we collaboratively model the events with association staff in a workshop-type setting where everyone is engaged and active. The documentation we start during the requirements/scoping stage is used throughout the business analytics phase.
  4. Uncover Event Details
    1. Using the 7Ws: who, what, when, where, how many, why and how the association staff describes the details of business events. This helps us determine, in a painless way, how we should store our data – the granularity and even the structure, including hierarchies. Then, together, we start to build out an easily recognizable table of these events that everyone – business and IT – can understand. Now, our business event might be: “Individual registered yesterday for an annual event online using a source code.”
    2. Discuss examples. What might a typical registration event look like? What might a registration look like that is not typical (an exception)? What is the range of dates individuals can register? Can an individual register for more than one event?

Below is a sample matrix which maps a registration event:
These initial discussions are exciting and enlightening and pave the way towards successfully adopting business analytics.
Note: We use much of the framework discussed in the book Agile Data Warehouse Design by Lawrence Corr. It’s a good read!

Using Agile for Association BI Initiatives

In traditional project management, every engagement is defined by the triple constraint:  Budget, Schedule, and Scope.  However, we find that the most valuable and sustainable business intelligence results are achieved using the agile methodology.  By defining the “Scope” iteratively and collaborating closely with your association team, we are able to achieve the highest value from analytics within a given budget and time frame.
Traditional Viewpoint vs Agile Vision of Maximizing Value Given Constraints*

Triple Constraint


What is Agile BI?

Agile BI is a method for uncovering better ways of developing your BI solution.  Rather than emphasizing a rigid plan and extensive documentation, our approach is to generate working, “quick win” deliverables and encourage collaboration with our customers to improve the deliverables and results.   As shown in the figure below, by first focusing on the analysis that offers the best ratio of value and ease of use, the team benefits from consistent learning as we move forward in time.  Inevitably, this leads to more effective development throughout the life of the BI initiative.
Diagram of DSK’s Agile Business Intelligence Methodology
Agile Visual
More importantly, it allows the team to pivot and improve on the process of implementing BI at each unique association.  Our 15 years of experience working with associations has shown us that it is essential to stay flexible yet results-driven when implementing a large technology initiative.  By planning “sprints” or deliverables, rather than trying to execute entire project in one shot, teams are encouraged to look for efficient solutions and make intelligent trade-offs.
In conclusion, we have many years of experience enabling associations to effectively use their data.  However, during the initiation phase we find there is always a high level of uncertainty about what precisely is needed.  By mastering the agile process for implementing business intelligence, we deliver solutions based on the priorities of your organization and on the continuous feedback of the end user!
*Agile diagram courtesy of David Bulkin, LitheSpeed Certified ScrumMaster Workshop

The Value of Data Discovery for Associations

The Magic Quadrant

In February 2013, Gartner Inc. released an important report entitled Magic Quadrant for Business Intelligence and Analytics Platforms which details the current state of the business intelligence (BI) market and evaluates the strengths and weaknesses of several of the top vendors. It’s interesting to note that in this report, Gartner emphasized the emergence of data discovery into the “mainstream business intelligence and analytics architecture”, something we have been highlighting at DSK Solutions for years.
What is data discovery? Associations and nonprofits are sitting on large quantities of data and don’t always realize the value of this powerful asset. The old days of spray and pray are gone. Remember direct mailing blasts? How ineffective! Associations were shooting in the dark and wasting resources that could have been allocated to better serve members. Unfortunately, some associations still rely on this marketing approach, but there is a better way: segmented target marketing based on data.
All of your data – including CRM or AMS (customer data), general ledger and budget (financial data), and Google Analytics (Web data), can be pooled together to illuminate your member strategy. Think of each data source as a small flashlight that reveals a little bit of the path in front of you. When your data sources are pulled together, the path becomes much clearer. When analyzing your data with data discovery, it becomes possible to discover things you did not know before.

Necessary Steps

Clients frequently come to us seeking guidance on how to begin the task of leveraging their data to inform better decision making. Before you can embark on data discovery, you have to do two things:

  • Ask the right questions.  What is meaningful to your organization? What are you trying to find out about your members, prospects, products, services and profit?
  • Clean your data. If your data is filled with duplicates, inaccuracies, inconsistencies and other forms of noise, your analysis will be flawed. Remember: Garbage in, garbage out.  Quality data as an input allows for accurate analysis as an output, which results in the improved ability to make good decisions.

These two steps form the foundation of the data discovery process. Almost always, the answers you derive from your data will lead to more questions. It’s okay to ask why. In fact, you should be asking why! Start by asking questions like these:

  • How dependent is your association on dues revenue?
  • What is the price elasticity of membership (Full Rate v.s. Discounted Rate)?
  • Which members are at risk for not renewing?
  • How far (in miles) will registrants travel to attend a meeting?
  • Which products or services have the highest profit?

Then start asking “why”.  Remember the idea of the Ishikawa (or fishbone) diagram?  It’s an easy and useful way to begin thinking in terms of cause and effect – you ask “why” 5 times, until you arrive at the root cause of an effect.  Now with interactive data discovery you ask these questions directly by interacting with the data in a visual way!  At DSK we describe it as “having a conversation with your data”.  For example, a certification department of an association wanted to look at their pass/fail ratio for an exam.  Using data discovery, they discovered many more college-aged people were registering and doing poorly than in the past.  In the process of asking “why” the failure rate was increasing, they discovered an opportunity not only to publish a new study guide, but also they located an entire new source of prospective members and created a new membership type to serve the college market.
Data discovery is an iterative process where you ask questions of your data in an interactive way. Drilling down both vertically and horizontally into your data allows you to not only answer the questions you know you have, but shed slight on those unknown-unknowns and enables associations to make better decisions.

 SS 2 Filtered on Type 2

Agile Business Intelligence

Business Intelligence (BI) projects that incorporate key aspects of Agile processes dramatically increase the probability of a successful outcome. 
I wonder why business intelligence (BI) projects have a reputation for being slow, painful and ineffective – and why do they often fail to deliver on the promise to improve data-driven decision-making?  I believe part of the answer is in the approach: the waterfall, linear, command and control model of the traditional System Development Life Cycle (SDLC) that is still pervasive in most technology projects today.  There is a better way!
One of the core principles of Agile is embracing a realistic attitude about the unknown.  It is interesting that at the beginning of a traditional technology project, when the least amount is actually known about an organization and its business rules, environment, variables, players, questions and requirements, that the greatest amount of effort is made to lock in the scope, the cost and the schedule.  It’s understandable that we want to limit risk, but in reality the pressure to protect ourselves can lead to excessive time spent on analysis, which often still results in unclear requirements, leading to mismatched expectations, change orders and cost overruns.  This is a well known phenomenon – at the very point where we have the least amount of information, we are trying to create the most rigid terms.  See the “Cone of Uncertainty” concept. 
I think part of the reason for this paradox stems from an intrinsic lack of trust.  Steven M.R. Covey explains in his book, “The Speed of Trust”, that trust has two components:  character and competence.  In each situation in which you are asked to trust, you must have both.   For example, if your best friend is a CPA, you might trust them as a friend, have complete confidence in their character and trust them to handle your taxes, but you will not trust their competency to perform surgery on a family member.   It’s the same in business.  We might have confidence in a vendor’s base software product, but not trust their ability to understand our needs or implement the solution well.  And trust has to be earned.  Once an organization has trust, the speed at which change can be communicated and accommodated dramatically increases.  And this increase in speed translates into an improved outcome and a reduction in the cost, both of which are a by-product of the clear communication that is possible when trust is present.
What does all this have to do with business intelligence?  I believe BI projects lend themselves to an agile, iterative approach, and this approach requires trust in order to work.  I’m not a big fan of some of the Agile terminology – terms like “product backlog” (doesn’t “backlog” sound negative?) and “sprint” (is it a race?)  But I do fully embrace the concept of working solutions vs. endless analysis, communication and collaboration instead of rigid process enforcement, responding to change vs. “hold your feet to the fire” denials of needed change requests.  In general, it’s the concept of “outcome” vs. “output” that is so inspiring to me about Agile.  I’ve seen examples where a technology project met all of the formal “outputs” specified in the contract, yet sadly failed to deliver the most important thing – the “outcome” that the organization was really trying to achieve.  For example, the CRM implementation that was delivered on time and on budget but that none of the staff would use, or the BI project that resulted in dashboards that measured the wrong things.  These are not examples of successful projects because the true desired outcome was not achieved.
How can Agile concepts be used in BI? 

  1. Identify an initial high profile “win” and complete a small but important aspect of the project to inspire the team, generate enthusiasm, engagement and feedback
  2. Facilitate data discovery : create a hypothesis -> investigate and experiment -> learn -> ask new questions and repeat the process
  3. Value the learning and the teamwork that is intrinsic to the process and which builds trust and speeds the ability to adapt to change

In a future post I’ll debunk some of the common myths that surround the topic of agile processes.

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