Archive for business intelligence – Page 2

Association Data Visualization: 5 Steps You Need to Know

5 Steps to Visualize Association Data

BI for AssociationsBig Data, business intelligence, analytics…..these terms have been buzz words headlining most association conferences this past year. When DSK presented at the ASAE Tech Conference last December, 80% of respondents to our straw poll noted their #1 question was “How do I get started with BI?” DSK has defined 5 Steps that form a framework for analytics and business intelligence initiatives for associations:

1. Scope your Project
Start small and define success with your stakeholders. Outline what you already know and document your business questions.
2. Collect your Data
Identify your data sources then describe your business rules in plain English. Inventory and integrate your data for future projects as well.
3. Clean your Data
Determine how clean is “clean” for your organization. Correct duplicate, missing, and inconsistent data and create standard processes to prevent future data issues.
4. Analyze your Data
This is the fun part! Create data visualizations and evaluate the results based on your business context. Did you answer your original questions or discover new questions that ‘you didn’t know you didn’t know?’ It’s an iterative process that will evolve based on feedback.
5. Communicate your Results
Describe the results with meaningful stories to start the conversation. Make sure the level of detail is appropriate for the audience and document new questions that surface for future analysis.
This brief video further illustrates how your association can harness the benefits of BI and demonstrates Tableau data visualization software.

Debbie King is the CEO and founder of DSK Solutions, Inc. Debbie holds a Bachelor of Science degree in Decision Science and Management Information Systems and is a graduate of the Entrepreneurial Master’s Program at MIT. She is certified as a Project Management Professional (PMP), Certified ScrumMaster (CSM) and Balanced Scorecard Professional (BSP). She holds six current Microsoft SQL certifications, including MCITP Business Intelligence Developer. Debbie will complete her Master's Degree in Leadership at Georgetown University's McDonough School of Business in March of 2014. She is frequent speaker on the subject of analytics and serves on the ASAE Technology Council.

Debbie King is the CEO and founder of DSK Solutions, Inc.

Gartner Magic Quadrant Positions DSK Partner Tableau as Leading Analytics Software

BI software quadrant

Gartner Magic Quadrant Business Intelligence and Analytics Platforms


Gartner Review: Tableau
Gartner is the world’s leading information technology research and advisory company. It defines the business intelligence (BI) and analytics platform market as a software platform that delivers 15 capabilities across three categories: integration, information delivery and analysis. DSK has partnered with Tableau to bring analytics to the association community.
Data is an asset.  Associations today know that decisions based on data tend to be better decisions and organizations that use data to make decisions tend to be more successful than others. DSK rigorously evaluated BI platforms to provide the optimal performance for the association community and its unique data and organizational goals. DSK can interpret the story the data is telling and show how to harness the opportunities hidden within the extended data environment – including ‘Big Data’.  DSK helps association staff understand their data visually in order to lead and safely navigate the future.
Please contact us to learn how data-guided decisions can impact membership, sales, event attendance and more. info@associationanalytics.com 703-534-9140.
 
Debbie_Profile_2011

Debbie King is the CEO and founder of DSK Solutions, Inc.


 

Balanced Scorecard and Business Intelligence

Sometimes when an organization begins a business intelligence initiative (BI) they are so excited about data visualization and data transparency in the form of dashboards that the first thing they want to do is start measuring everything.  I believe that strategy comes before measures and those organizations that thoughtfully and purposefully align what they are measuring to their strategic plan achieve more meaningful long-term results from their BI initiative.

The Balanced Scorecard is a performance management system designed to align, measure, and communicate how well an organization’s activities are supporting the strategic vision and mission of the organization.
It was originated by Drs. Robert Kaplan (Harvard Business School) and David Norton as a performance measurement framework that added strategic non-financial performance measures to traditional financial metrics to create a more ‘balanced’ view of organizational performance.  Four strategic perspectives are addressed within the Balanced Scorecard framework:
  1. Customer 
  2. Financial 
  3. Internal Processes – commonly includes technology, systems, etc.
  4. Learning and Growth (aka “Organization Capacity”) – commonly includes people, training, etc.
Objectives (goals) are set for each perspective, measures (numbers) that represent things to be measured (such as sales, customers, returns) are identified and can then be transformed into ratios or counts, which serve as Key Performance Indicators (KPIs).  Initiatives (projects) are undertaken in order to “move the needle” in a positive direction on the KPI gage for that measure.
 
Balanced Scorecard dashboards include both leading and lagging indicators.  For example, customer and financial KPIs are traditionally lagging indicators – the numbers indicate what has already happened.  KPIs for the two perspectives of internal processes and learning/growth are leading indicators.  This is because positive results achieved with respect to internal processes and learning/growth initiatives should lead to a positive result in the customer and financial KPIs.
 
Gartner is a leader in the field of information technology research and they organize BI capabilities into three main categories:  analysis, information delivery and integration.  The concept of “scorecards” fits into their BI analysis category.  Gartner recognizes that tying the metrics displayed in a dashboard to an organization’s strategy map ensures that the most important things are being measured, because each measure on a scorecard is tied to the organization’s strategic plan.  Sounds obvious right?  But it’s still relatively rare and that’s a subject for another post.

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.