Intelligent Metrix

Data to Metrics to Insight to Intelligent Decisions

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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Stuck in First Gear

porscheBig investments were made in recent years in IT.  IBM, Oracle/Siebel and SAP lead the market and were successful not only with the multi-national enterprise companies but, also with mid-sized companies.  There are a lot of companies out there that have purchased application and data management/data warehouse solutions only to find themselves using a portion of what it could do.  It’s like driving a Porcshe in first gear.

There are some fundamental reasons for this, outside of the fact that companies may feel it is the fault of their sales execute selling them the wrong bill of goods.  IT will blame the business for not knowing what it wants.  The business will blame IT for not getting it.  Doesn’t really matter, there is plenty of blame to go around.  What matters is that now you have a solution that isn’t giving you the benefits that it really could and should be.

Maybe I’m a bit biased since I’m the data chick.  Well, more than a bit.  Regardless, I think that from a data management perspective, companies are failing.  The maniacal focus on process efficiency has drowned out the fact that process runs on data and feeds data.  This focus has put data in the back seat too long and now when we need it to better understand our customers, our business, and make decisions, it is sorely lacking.  Our data lacks unity, structure, definition, and most of all purpose.  Companies simply cannot leverage their information except at very basic levels.  When things are good, this may be okay.  When things are bad, this is a real problem.

What makes this even more sad, is that companies are looking to spend more money on applications and data infrastructure to ‘fix’ the problem.  The promise of the new model and more sophisticated bells and whistles that will solve anything you throw at it is just marketing.  Until you can understand and control what you already have under your hood, getting something bigger, better, and shinier isn’t going to help anymore than it does now.  So, there was no ROI on existing purchases and there won’t be any ROI on new purchases.

There are two things companies need to do to make the investments in enterprise solutions worthwhile:

  • Clean-up the back-end data management practice so that it is fluid with business process and application usage.
  • Have a clear data management strategy for new applications that is fluid and scalable outside of application databases.

Your company may already be embarking on SOA or MDM projects.  But, have you looked at how these new practices will support applications outside of changing the oil?  Can the data drive process?

Today, applications are bogged down because data is treated as something to put in the trunk and horde.  Until data is thought of as fuel, you’re IT investments will stay in 1st gear and never get to 6th.  Now how fun is that?

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Why Business Intelligence is So Difficult

Reading the buzz on the Jim Davis’s presentation at SAS Global Executive Forum, what it made me realize is that if as an industry we can’t agree on what Business Intelligence is or Business Analytics, how are we supposed to make sense of it in implementation?

business intelligence confusionYou have analytics players, enterprise application vendors, business process consultants, and analysts all trying to sell the ‘hype’ of a better way to analyze your business and makes decisions.    SAS wants to sell their analytic solution that really pioneered data mining in businesses.  Oracle and IBM wants to push dashboard solutions that links to business processes and their enterprise applications.  Gartner that tries to tie together people, process, and technology but is really is focused on what technology to buy.  Then, you have consultants that are trying to help you implement the technology even as they document your processes.  The problem is that it’s all boiling down to the one with the best tool wins.

Enter in the ‘Business’ and now you have a problem.  All they want to know is how they can meet their business objectives.  IT is trying to sell the solution and make them understand the technology, and the business glazes over and can’t figure out what to focus on.  I’ve sat in these discussions where IT tells me, “You tell us what to do, we’ll do it.  Don’t worry about the solution.”  It is open ended.  This leads to IT unable to work towards tangible goals and results.  The business walks away frustrated, projects run from months into years, and original budgets are thrown out the window.  I liken these projects to Boston’s Big Dig.

Neil Raden provided a perfect way to get through the fluff and hype that surrounds analytics and business intelligence. See article From BI to Business Analytics, It’s All Fluff

“I don’t like the term business analytics; it doesn’t tell me anything. Frankly, I think business intelligence as a term is downright laughable, too. What does that mean? Is integrating data intelligence? Is generating reports intelligence? Maybe its informing, but isn’t intelligence something you HAVE not something you do? Does doing what we call BI lead to intelligence, or just some information? A long time ago we called this decision support, and that gets my vote.”

So here’s my take on what steps to take when and how to venture into BI and analytic solutions.


  1. What decisions need to be made?
  2. At what point in our business and business processes are these decisions made?
  3. What information is needed at these points?
  4. How should our applications and data provide this information – triggers or visualization?

See the steps?  It starts with the business decion and ends in the technology.  So, when you begin to review vendors and solutions, make sure you have steps 1,2,3 in mind before you determine how to solve step 4.

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Who I Want at the Business Intelligence Table

Go and look at any guide on implementing Business Intelligence (BI) and invariably executive leadership is touted as a must.  I couldn’t agree more.  In the end, the product of BI is to help them manage the business and make the company successful.  But, the reality is that they look to their department managers and IT teams to work out the details.  Show them the investment and ROI, then get busy on delivering it.

So, what next?  Who do I want on my BI team?

Rather than looking at this in terms of titles and functions, let’s look at this in terms of areas of expertise and levels of project responsibility.  They reason I do this is because as much as process is the typical focus for the typical IT project, BI is a big picture endeavor.  Process will show where data originated from and how data is defined, but it won’t always be a direct linkage to the business objective.  Example:  Predictive analysis might focus on anticipating customer defection.  The process is a component of the customer experience, but the analysis crosses multiple processes.

Business Side:

  • BI Leader:  Strategic thinkers with deep expertise across department practices.  This is where you get bench strength for the project.  You need these strong generalists that see the big picture of customer relationship, financial management, or operational functions as they pertain to business objectives.  These people have been there and done it in some shape or form.  They may have even moved across departments and shifted across areas of expertise.  They know the type of information necessary to be strategic, improve process with information, and how to focus and prioritize information needs.  Where to look: Manager and director positions close to the executive sponsor that have proposed or driven change.
  • BI User:  Analytic champions that have mastered company information providing a range of analysis.  BI Users will be the real developers in the details of the requirements.  They know the company data better than the people that designed the warehouse to manage it.  They can tell you the limitations they have in providing analysis that is meaningful.  Champions have a wide array of analytic techniques from the highly simple to the more complex.  In fact, they could fulfill analytic requests regardless of the department.  They are the scientists in the company.  Where to look:  They support the successful managers, directors, and executives that use data to influence business decisions and priorities.
  • BI Business Analyst:  Business technologist that has experience across multiple types of implementations.   Technologists have a pretty good understanding of how to optimize and fulfill business needs with technology.  Many times they are considered the liaisons between the business and IT.  However, this is more than the note taking of requirements and passing along to IT, project managing, and then ensuring priorities are met.  The technologists are versed in their businesses.  They have migrated from a business role to technology focused role rather than starting from IT and moving to the business.  In addition, they have a depth of technical knowledge and know how to converse and validate recommendations and solutions from IT.  Where to look:  They have most likely been through a couple of solution implementations or data integrations and have a key role in establishing requirements as a business lead or as the lead project manager and business analyst.

IT Side:

  • BI Leader:  Strategic thinkers with deep expertise in driving business outcomes through solutions.  This is the person that talks about the business and rarely about the technology.  They focus on  the strategic implementation and adoption of technology for competitive advantage.  While experts in solutions and infrastructure, the real focus is on efficiency and effectiveness of the business.   They ask, “What problem is the business facing and how do I help?”    They are attuned to company success.  For BI, they need to have a perspective across enterprise applications and data warehouse management.  Where to look:  These are IT leaders that are close to business executives and are goaled on supporting business outcomes.
  • BI Solution Provider:  Solution champions that perfectly unite applications with their back-end information.  They recognize how data relates to the business process as well as the next step of how people use data in the process and to manage the business.  Solution Providers are strategic in their approach to solutions and differentiate between requirement fulfillment and business enablement.  They do what their title says, they come up with solutions rather than just implement technology.  When building applications, data modeling is not far from their thoughts and data warehouse teams are tightly involved in strategy and design.  This is the person that is in the nuts an bolts of best practices of solutions.  Where to look:  These are the people that you turn to for application and data integrations due to M&A activity or legacy system integration and migration.  They spend as much time with the business as they do with IT.
  • BI Implementers:  IT technologies with deep expertise and range of experience in their tools.  BI Implementers will be many spanning across application development and data warehouse.  While not typically in contact with the business during business analysis and design, they are critical to proper development.  They will have silos of expertise in user interfaces, application infrastructure, data warehousing, data management, systems management, data quality, ETL, and database architecture and modeling.  Depending on the size of the company, these silos of expertise will be supported by one or more people.  They will either perform the technical development themselves, or have a team that is experienced to do so.  Where to look: IT Solution Providers that recognize deep strengths in their teams for a strong implementation bench. 

The Often Forgotten One:

Database modeling is one of those aspects of application development and data warehousing that is often left out.  Modeling is a critical factor of success in BI because of its ability to make analysis very easy or force unnecessary issues in performance and programming work-arounds, particularly within ETL and the user-interface.  I can’t stress enough the value of having and expert modeler on hand.  So, if you don’t have a modeler on staff, consider how to fulfill this vital role at the beginning of your design phase.

Using these profiles as a guide to build your team will set you up to successfully design and implement your BI practice.  In addition, you have the ability to see the how the Business and IT balance each other out in experiences and expertise to allow for a good working relationship.  These profiles should also give you a perspective of what, if any consultant or contractor help you need.  The key is to recognize that building your BI team is not unlike hiring an employee and that the more breadth of expertise and experience that person has, the better off you are in the long run.

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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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Starting Your Business: Data From the Ground Up

data managementIt is easy when starting up a business to think about selling first, marketing and database management later.  Afterall, revenue is the most important thing to focus on.  Though, once you get over the hump and begin to groove, you realize that data is important.  Now you have to sort through it and it feels worse than diving into list of 300 emails in your daily inbox.  Well, if you have a method to deal with your email inbox, create one for managing customer and contact data.

Here are some simple things you can do up front to stay organized and be better prepared when you are ready to look at and manage your customers and the business in depth.

  • Be consistent about how you collect customer data – There are usually several layers to the importance of customer information elements depending on your relationship.  What you want to do is determine the information that is most critical and collect this consistently across all methods.  Keep in mind that what is mandatory to transaction may be different from what you need to follow-up with customers after a purchase.  So, make sure that you take this into account at the point in time you collect the information.  It is harder and more costly to collect after the fact.
  • Save data elements into dedicated fields – The biggest issue I find with new businesses and small businesses when they need to convert to more robust systems is that data elements are merged together into a single Excel cell.  When collecting contact names, break apart the first and last name into separate fields.   Do the same for addresses having fields for street address, city, state, country, and postal code.
  • Determine what platform has the Master data – The second biggest issue when migrating customers to a robust system is the inability to determine which record is the most valid of duplicate entries.  If you are saving contact and company information between your mobile phone, laptop, website, and company server, which will you consider the single source of record?  Once you determine this, make sure you sync your lists to that source.  I recommend you do this weekly at the least and use your primary server.  Then, include the database in a weekly back-up process.
  • Save, Save, Save – You may have caught this recommendation in the previous bullet.  Backing up is critical.  It is mandatory.  I’ve watch small businesses loose business critical information because they didn’t back up or back up often enough.  There are easy services today that make backing up our information simple.  At the very least, invest in a USB storage device and plug into daily when you sit down and get to work.  Before you do anything, back up.  Make it a habit.

Managing your customer and company information does not have to be difficult or cumbersome.  With a little forethought, when you business gets off the ground and you are ready to invest in better platforms and reporting, you will have a great foundation to do so.

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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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Translating Awareness to Consideration Set in B2B

Want to improve lead quality?  Focus on knowing when a customer includes you in their consideration timing

It is one thing to get someone to notice you, it is quite another to get them to think of you when getting ready to make a purchase.  B2B marketing works to tie these aspects of a customer purchase cycle together through a strong call to action.  In the end, the holy grail when targeting the campaign is reaching the customers that are truly at the beginning of the purchase cycle.  The relevancy of a campaign isn’t just that you provide valuable content to someone that is the subject matter expert (SME) in their company, it is that it is relevant when the SME is ready to become engaged.

Right message, right person, RIGHT TIME.  Timing is everything.

Judging when a customer is ready to engage is not as allusive as you might think.  The key is to recognize behavioral aspects within you customer and contact base.  Opportunity segmentation has typically focused on financial transactions due to its availability and consistency.  It is effective when determining customer value and staying on top of purchase cycles.  Although, this fails to account for the “who” that acts with in high opportunity customers as key influencers and decision makers.  In addition, it fails to account for prospects you’ve brought in and engaged.

The other piece of opportunity identification through behavior analysis is recognizing how contacts are interacting with content on your website, responding to campaigns, support inquiries, and, if available, social media venues.  There are a several ways to leverage this type of information from the simple to more sophisticated predictive analysis.  It will depend on your level of ability to identify behavioral aspects of contacts and linking behavior information across various marketing venues.

  1. RFE Analysis (Recency, Frequency, Engagement) – A modified version of RFM (recency, frequency, monetary) which focused orders, replace M with E (Engagement), you can begin to identify behavioral aspects for simple segment selection.  E is the point when sales recognizes the opportunity and includes in a pipeline and confirmation that the customer includes you in the consideration set.  E can also be another type of event that the outcome is a face-to-face meeting, for example trade show attendence or in-person seminar.
  2. Reference/Word-of-Mouth – There are two aspects of this.  The first is that the contact will be a reference or unrequested acts on your behalf to influence others.  However, the other side is that they are actively seeking out other customer perspectives by reading other’s opinions and asking for opions.  Tying together campaign interactions with a transition to reference/word-of-mouth activity can provide insight that they are ready to engage.
  3. Predictive Analysis – The previous two approaches can be easily done through simple segmentation techniques.  Taking them a step further, you can apply predictive analytics to solidify benchmarks and KPIs.  Indexing of contacts’ behavior and mapping that to scorecards identifies pre-engagement contacts and customers.  The values can be dynamically set so that as contacts and customer reach thresholds they move into campaigns that are targeted to move them into sales engagements and support the sales engagement.

Are you tracking the transition from awareness to consideration?  What do you look at?

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