Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

Training IS a Best Practice – Not Just a Component

Staying abreast of best practices isn’t enough.  You also need to know how to implement the fundamentals.  

A report by AIIM/IBM on business process management (BPM) took a look at issues experienced by organizations implementing BPM.  Top of the list: underestimating the process and organizational issues (45%); lack of staff knowledge and training (41%), excessive scope creep (29%).  And, this in not confined to BPM.  The take-away, what you don’t know will hurt you.

Many organizations may consider training a high priority.  However, management and employees rarely have the time or understand how to get the proper training.  Simply reading trade books and articles or attending event seminars and college courses is not enough.  Management and employees need hands-on and micro-seminars that focus on building the analytic skills and innovation techniques that bridge the gap between high level ideas and take it to practice.  High-level content needs to be tailored to the company’s issues and environment.

In the three issues listed above, training is core to the pain felt in underestimating process and organization issues, and scope creep.  Pain is felt because most learning is being done on the job after business requirements and requests come in.  Instead, project leaders and subject matter experts need to educate themselves on process, tools and the implementation scenarios within their environment to focus on the right inquiries during the business analysis phase prior to project launch.  

Training is also necessary for successful implementation.   In many cases, training is an afterthought of the project and inadequate.   Change in processes or the implementation of new tools can be difficult for end-users and can take 6 month of more for people to be proficient and effective.  Comprehensive training along with appropriate documentation and reference tools will make the transition faster and smoother helping you realize improvement and ROI sooner.

How to Leverage Training

Analysis Proficiency:  People should understand scenario based analysis and how to use tools, data, and inquiry techniques to align best practices to internal business environments.  Examine processes and pain-points with possible next steps accounting for various methods that fit your business, goals, and capabilities.

Right-sourcing:  Training resources need to be tailored to closing gaps between generic concepts and internal practice.  Micro-seminars and peer-reviews focusing on specifics within like organizations with similar problems will help develop and refine road-maps.

Future-casting:  Once abreast of new practices and solutions that align to your business, start training.  You’ll most likely have an idea of business requirements and hurdles that lead you to finding value in a concept.  Get smart before you begin the project.

Hire an Expert:  If you are truly re-engineering, have an expert(s) on the project to represent the business and technical aspects to be the go-to for in project training.  They can help with scenarios, alignment, analysis, and solution decisions that streamline a project and position for success.

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Business Intelligence: Decisions, Decisions

Business Intelligence is all about supporting business decision.”

How many times have you heard that?  It’s become the standard mantra.  It is so ubiquitous that I don’t think anyone questions anymore the validity of the statement.  It just is.  However, this is probably the hardest part to facilitate when building out you business intelligence practice.  Facilitating decisions is what makes BI stragetic.

Just what is the business decision? What does a business decision look like?

Elements of a Business Decision:

  • Purpose:  drive a business outcome – ex: revenue, shareholder value, profitability, market share
  • Position:  leads a company, division, department
  • Point in Time:  transition along a process or environment

A typical approach during the business analysis phase for BI is to at business decisions across a business process and where questions are asked to change behavior in that process.  Although, the difficulty with this level of granularity is that it is too deep.  These transition points are tactical.  Intelligence across this process and at these decision points is important, but you don’t get the strategic value of BI at this level.  You need to look at the outcome of the process and provide a platform that supports the decision of what to do next.  This is the unstated question.

Let’s take an example.  Sales management will always want a perspective on the pipeline and forecast.  This shows them how they are meeting their numbers quarter to quarter.  However, outside of conversion and volume, there are business decisions that sales managers need to make.  Should they adjust their territories to capture new opportunity or shore up existing business?  Are there changes needed in commissions to incent sales people along certain products and services to improve profitability or revenue?   BI can lead sales management with insights that will guide them to optimize their processes and management rather than just data.

Purpose:  market share, revenue, profit
Position:  sales
Point in Time:  aligned to quarterly pipeline and forecast

To align BI to the business decision it is important to include executives in the discussion.  Get beyond the reports they want to see and ask the question about how they manage their business.  Walk through scenarios of what they ask as changes in the market or the business arise and how information can help them make a decision.  The better able you are to see how they manage their business, the more valuable the BI practice will be to supporting the business.

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How to Measure the Business Impact of Data Quality

So, you want to invest in data quality but you need to prove ROI before you get the resources. Intuitively you know that data quality is impacting your business. How to measure that to make a case is the test.

Many businesses focus on data elements that are easy to see and understand like company and contact information.  However, as obvious as some of these elements may be, they don’t always lead to the highest bang for the buck.  Data elements have priority levels within processes depending on the desired business outcome.  In addition, data elements have dependencies outside of how the information comes into the system.  You need to take this into account as you conduct your business analysis and map your data across your business processes.

During business analysis it pays to establish a foundation that validates recommendations and shows ROI through case studies.  You can do this through data analysis and pilot programs.  Data analysis can be applied through meta data segmentation within processes where you look at the existing state of the data.  You can also improve portions of the data and perform the segmentation and analysis.

These steps will prepare your case but will also help establish dashboards to allocate resources for future projects.

1) Identify the processes you think are most impacted by poor data quality . The processes should be tied into key business functions. For instance, in marketing you may want to look at lead qualification and management. Processes that are well defined and have a tangible link to businesses objectives work best as they are most likely mature and revenue has been tied to them.
2) Pinpoint smoking guns in the processes.  There are bound to be several points in a process that are key indicators of success where data quality has negatively impacted the outcome.  Your business analysis will or should show this clearly.  These smoking guns should be called out clearly in the processes.  What you should determine is which data elements are impacting the most and can be easily focused on or addressed.
3) Select data quality issues that you can segment the process into influence tracks.  This step is critical to measurement.  You need to dissect the process to create scenarios of what good vs. bad looks like in process outcomes.  In the lead management process suggested earlier, it could be the point where you would qualify a lead to move into the sales pipeline.  
4) Measure performance success with good quality vs. poor quality data.  At this stage you should be able to run an analysis that shows the difference in process outcomes and performance when you run scenarios between good quality data and poor quality data.  

The real benefit is that at this stage you’ve provided the dashboard to measure improvements to the business.  Rather than wait until the data quality projects are completed, this provides the foundation for predicting where you will get the most impact from your investments. Instead of focusing solely on metrics that measure the completeness, accuracy, and uniqueness of records, you can focus on how these metrics within processes influence business outcomes.  Now you have a case for linking data quality with ROI.

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Cool Data Visualization – What is That?

Want to help your organization optimize operations, extract market opportunity, see what customers think?  Provide a visual representation in a single slide that tells your senior executives what is happening and what to do.  That is, if they can understand it in a milli-second.

There are a lot of really great algorithms that are creating interesting visual presentations of behavior, influence, and connections across people and topics.  The problem is you might as well be looking at fractals for all the business insight you gain.  It may provide perspective for the resident math geek, but for the average business executive it is just modern art that needs further interpretation.

I remember in college when I first started programming mathematical equations to model data and played with fractals.  It was exciting, creative, and helped me to link data with a tangible result versus a simplified equation or answer.  I used to put fractals up on my website like works of art.  I even included a link to input random numbers for others to create their own.  It was so cool!  Was it practical?

Don’t get me wrong, I love data visualization obviously.  It can simplify very complex analysis to gain insight faster.  What I’m struggling with is needing the ability to connect data visualization with executive intuitiveness.  Heat maps, network graphs, and the variety of data maps I see being generated today are a far cry from what I would bring into a budget meeting let alone show to board members.  More time is spent explaining what executives are looking at than having conversations about business objectives and investments.  It is also not just executives.  Business managers and directors need to understand the business as well.  Pretty is nice, but value is better.

The other aspect that of today’s data visualization leaps is that it disassociates the business from the information.  If only a small group of geeky mathematicians and programmers understand the data, it creates a mysticism that can lead to distrust of information.  If people don’t understand it, they don’t learn, and they don’t improve.

What I hope we can do as really smart statisticians, data analysts, and programmers is make the connection between information and visualization so that it further democratizes insight and empowers our business rather than mystify.

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