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Nancy Williams

The Intelligent Pathway blog is an informal channel for sharing our ideas and experiences in the areas of business intelligence, performance management, analytics, and data warehousing. This blog is intended to provide our business and technical perspectives on new developments and future directions of the fields. Our blog posts reflect a pragmatic bent that balances business and technical perspectives. As always, feel free to contact us or add your perspective if you wish--we're always willing to hear from people who may have had different experiences or who hold different opinions.

About the author >

Nancy serves as Vice President for DecisionPath Consulting. Focusing her work on how business intelligence (BI) and data warehousing (DW) can be leveraged to improve business performance, Nancy is a well-known industry educator, author, and practitioner. Nancy’s experience includes more than 25 years of business management and technical experience. She has been involved in numerous consulting engagements, providing expertise in the areas of BI/DW assessments, BI/DW strategy, portfolio development and roadmaps, BI/DW requirements and data modeling, and BI/DW project and program management. Nancy is a regular speaker and keynote presenter at TDWI industry events,  co-hosts the BI impact channel on the BeyeNETWORK, and is co-author of the highly rated book The Profit Impact of Business Intelligence. She received her MBA from the Darden School at the University of Virginia. 

Editor's Note: Visit Nancy and Steve Williams' BeyeNETWORK Expert Channel for more articles and resources as well as Nancy's blog.

Financial Planning and Analysis (FP&A) is Changing

The evolution of the FP&A role has involved two primary thrusts:


  • expanding the business focus of the role - the FP&A professional is increasingly expected to help line managers and key decision-makers optimize strategic performance, resource utilization, and profitability; and
  • using more powerful tools - business intelligence (BI) and analytics make it possible to enhance the contributions made by FP&A professionals - both in terms of the breadth of contribution and the level of output achievable in a given timeframe.


Arguably, the ability of FP&A professionals to make enhanced contributions is substantially impacted by the quality of tools provided to them.


BI and Analytics – It's All Business Information in the End


Rather than getting hung up on semantic debates, we can define business intelligence as the use of business information (processed data) and business analyses to support business decisions in the context of core business processes that drive profit and performance.[1]   Typical business intelligence (BI) applications - all of which leverage business data - include:


  • Reports:      standard, pre-formatted information for backward-looking analysis of      business trends, events and performance results;
  • Multi-dimensional      analyses: applications that      leverage a common database of trusted business information and that      fully-automate information slicing and dicing for analysis of the      underlying drivers of business events, trends and performance results;
  • Scorecards and      dashboards: convenient forms of multi-dimensional analyses that are      common across an organization, that enable rapid evaluation of business      trends, events, and performance results, and that facilitate use of a      common management framework and vocabulary for measuring, monitoring, and      improving business performance;
  • Advanced analytics: automated applications that      distill historical business information so that past business trends,      events, and results can be summarized and described via well-known and      long-used statistical methods;
  • Predictive      analytics: automated      applications that leverage historical business information, descriptive statistics,      and/or stated business assumptions to predict or simulate future business      outcomes; and
  • Alerts:   automated      process control applications that analyze performance variables, compare results to a standard, and      report variances outside defined performance thresholds.


Ultimately, all of these forms of BI deliver business information for decision-makers to use to understand past performance and its root causes, model various courses of actions, predict future results, and make decisions that are informed by underlying data.   Simply put, BI is about leveraging business information to drive business results.


BI for FP&A


The role of the FP&A professional encompasses such activities as planning, forecasting, budgeting, controlling, variance analysis, scenario analysis, communicating goals and results, and decision support.  These activities are tools that support the broader FP&A role, which is increasingly focused on reduction of resource waste in business processes and creation of business value through effective resource utilization.  What this translates to is that FP&A professionals need to provide relevant information to the executives and managers who make the decisions that have the highest impact on business results.  This is consistent with the traditional role of management accountants, whose efficacy has been hampered to a meaningful degree by the lack of fully-automated access to high quality business information and the lack of advanced tools for analyzing such information.  While spreadsheets have been a huge advance and will continue to be a mainstay tool in the FP&A toolkit, modern BI and analytics represent the next generation of FP&A tools.  Here are a two high-level examples of how BI can augment and accelerate the FP&A contribution to business results:


  • Common, standardized business information for planning, forecasting, budgeting, modeling, and scenario analysis. Done well, BI is based on an underlying data warehouse and/or data mart, and thus it delivers common multi-dimensional views of business trends, events, and performance.  Planning, forecasting, and budgeting almost always start by looking at what has happened in the past to derive assumptions about the future.  BI eliminates much of the arduous data discovery work needed to develop and justify those assumptions. The same underlying data is also an input to models and scenario analyses, which basically predict and evaluate what might happen in the future under various assumed conditions.
  • Standard scorecards and dashboards for variance analysis, performance control, and communicating strategic and operational results.  Companies today often spend considerable manual effort to generate monthly scorecards and dashboards by extracting bits of information a piece at a time from standard reports or report data files, dropping the information into spreadsheets, and then copying the spreadsheets into presentation decks for upper management.  One study revealed than many companies invest over $100,000 per year in labor costs to produce such manual scorecards and dashboards.  BI automates such work, and it provides a robust platform for drilling down to root causes of variances.


There is much more to be explored when it comes to BI and analytics for FP&A.  In coming articles we will drill down into particulars, using case studies to illustrate the challenges FP&A professionals have described and their planned uses of BI.

[1] Williams, S. and Williams, N. The Profit Impact of Business Intelligence, Morgan Kauffman 2007

[1] Williams, S. and Williams, N. The Profit Impact of Business Intelligence, Morgan Kauffman 2007

Posted September 28, 2014 3:33 PM
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Bill Inmon recently shared a conversation with a consultant who has been involved in many big data proofs of concept.  The consultant reported that 80% to 90% of the big data projects had stalled out after the proof of concept.  A key reason cited was that no clear cut business value had been identified at the start of the Big Data project.  In other words, there was no business strategy for big data.  As a business intelligence and data warehousing consultant to Fortune 500 companies for over a decade, I've seen several cycles where the "next big thing" prompts unrealistic enthusiasm about "silver bullet" data strategies that will purportedly provide a competitive advantage.  When cooler heads eventually prevail, companies do the necessary strategy work to align data and business processes in a way that creates business value.  Here are three key tasks that contribute to a solid business strategy for big data:

1.       Reach a common understanding of how the term "big data" is being used.  There is no accepted definition of what constitutes "big data."   Once approach talks about data "volume, variety, and velocity" - the implication being that big data can be differentiated from regular data by those three factors.  Another approach talks about structured data from standard enterprise IT applications like ERP versus unstructured data from mobile devices, web logs, and other newer sources of data.  BI strategies for leveraging structured data have been in place for over a decade now. For example, see "The Profit Impact of Business Intelligence."  Strategies for leveraging unstructured data are emerging - and in many cases it looks like big data is a "solution" looking for a problem to solve.

2.       Develop a big data value proposition up front.  Let's assume that by "big data" we mean "unstructured data."  The key question that needs to be answered amounts to this: how can our company convert the massive amounts of social media "content" into something that either increases our revenue, reduces our costs, or both.  While the technical methods for making the conversion of social media content into useful content are somewhat different, the method for linking big data to business value are the same as for any business intelligence (BI) strategy.  Our BI Pathway Method is one such approach, and it can be used to make the business case for big data in the same way it has been successfully used on numerous BI strategy engagements with large, complex companies in a range of industries.

3.       Assess organizational readiness for business process change driven by big data.  Once a link between big data and one or more business processes has been established, companies need to assess the breadth and depth of business process change that will need to happen in order to leverage big data and achieve an ROI. 

 If your company can accomplish these three tasks, the groundwork will have been laid for a pragmatic business strategy for big data.  Traditional business intelligence and data warehousing strategies are well-understood and relatively lower-risk.  Big data strategy is essentially research and development strategy, where a hypothesis about how big data can improve business results has to be established and then tested through a proof of concept.  Without a business-driven hypothesis, investments in big data become even more risky that there already are

Posted September 28, 2013 2:50 PM
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Business Intelligence Strategy - The BI Roadmap for Success


The world of business intelligence and analytics is entering a new hype-driven phase, and it is easy for even the most successful companies to get confused about what BI has to offer and what constitutes success.  As experienced business intelligence consultants, we have seen companies of all sizes and from many industries struggle to create a compelling BI Roadmap that sorts through the hype and focuses the enterprise on a clear definition of success.  We've also seen business leaders be unduly influenced by the siren songs of vendors whose roadmaps are geared to selling and installing products - which alone comes nowhere near any reasonable business-driven view of success.  Further, we've seen proposals from well-known consulting firms and systems integrators whose BI roadmaps fail to put client interests ahead of their own - often pitching technical products and services that leave their clients paying large sums for the most basic of BI capabilities that offer no competitive advantage and little in the way of improved business results.  In this environment, a prudent approach is to create an objective, product-agnostic BI Roadmap that is driven by ROI considerations and defined business process improvement opportunities.  In this blog post, we'll discuss the main elements of a business-driven business intelligence roadmap.


Five Main Criteria for an Effective BI Roadmap


Basically, we use a roadmap to guide a journey from a starting point to a desired destination.  As part of a BI strategy project, companies identify:


  • how BI and business analytics will be used by business people;
  • what business processes will be changed to leverage BI and analytics to generate ROI;
  • what execution gaps exist; and
  • the degree of change required of business units and IT.


Armed with this information, the BI strategy team - or analytics strategy if you prefer - must then determine how to move toward a future state vision for BI use within the company.  The BI Roadmap they put together serves to guide the journey to the future state.  To be successful, the Roadmap must meet these criteria:


  1. It must address the key Workstreams necessary for BI success, which are: (a) strategy, organization, and management; (b) business intelligence and data warehousing technical execution; (c) business process improvement; (d) data governance; and (e) change management;
  2. It must include specific activities to overcome identified execution gaps - including gaps in technical capabilities and gaps in business units' ability to assimilate BI into their business processes and manage change;
  3. It must incorporate specific communication and linking mechanisms between the business units and the BI team so that an effective partnership is formed and so the BI initiative is jointly owned;
  4. It must incorporate all of the technical activities and projects that must be undertaken to ensure that a cost-effective BI infrastructure  is in place - including a data warehouse that supports enterprise uses of BI; and
  5. It must be based on sound program management, project management, process improvement, and change management fundamentals to ensure effective delivery of BI applications that are adopted by the business into business processes that favorably impact revenues, costs, or both.


More broadly, the BI Roadmap cannot be based on wishful thinking - all of the above considerations warrant hard-nosed analysis by the company's best thinkers and experienced BI strategy professionals.  BI is a complicated endeavor that should not be viewed through rose-colored glasses.  A suitable BI Strategy and a pragmatic BI Roadmap can help your company on the road to BI success.

Posted August 17, 2013 3:26 PM
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Business Intelligence Strategy:  Capability-Focused or Customer-Centric?


In the course of our business intelligence strategy work with major companies across a range of industries, we've seen two general types of BI strategies.  One strategy centers on marshaling resources and deploying organizational capabilities.  Once the capabilities are deployed, then work on development of actual BI applications can begin.  We call this approach a "capability-focused" BI strategy.  Another approach focuses on identifying BI-enabled improvements to the business processes that drive business results, and then on rapidly building and deploying BI applications to capitalize on those opportunities.  We call this approach a "customer-centric BI strategy."  The two approaches have different implications for the timing and level of BI investment, and for the magnitude and timing of a return on investment.  Accordingly, companies need to consider which approach works best for their particular business circumstances.  In this blog post, we will describe these two strategies and then offer some factors for companies to consider when choosing their BI course.


Capability-Focused BI Strategy


A capability-focused BI strategy is based on the philosophy that the best way to succeed with BI is to first establish BI-related building blocks within the company and then leverage those building blocks to deploy BI within the business.  Companies that adopt this approach tend to:


  1. 1.       Think in terms of "standing up" a BI organization (a.k.a.  BI Competency Center or BI Center of Excellence);
  2. 2.       Identify a high-level view of key data subjects - generally Customer, Product and/or Service, Channel, Organization, Financial Performance, and Operational Performance;
  3. 3.       Build an enterprise data warehouse based on consolidating - but not necessarily integrating - data about those key data subjects;
  4. 4.       Invest up front in comprehensive, enterprise data governance disciplines and tools to improve data quality;
  5. 5.       Invest up front in master data management technology;
  6. 6.       Architect and procure data warehousing and BI technology up front that is suitable for an end state vision that may be three to five years off;
  7. 7.       Create directories and metadata that enables business people to find data of interest and create the analyses they want to create; and
  8. 8.       Deploy canned and/or lightly customized reports that come with the BI tool or tools they've chosen.


The basic argument for this approach is that successful enterprise BI requires addressing these building blocks, and once they are in place smart creative people within the organization will figure out cool ways to leverage the data warehouse for various business purposes.  An analogous business strategy would be to build a manufacturing plant, buy flexible tooling that can make various products, hire people to work in the plant – and only then do the marketing work needed to figure out what products to make and who to sell them to.


Customer-Centric BI Strategy


A Customer-Centric BI Strategy is business-driven, whereby specific business opportunities for leveraging BI are identified up front, and these BI opportunities (BIOs) are the central focus of an enterprise BI strategy.  While the various BI-related capabilities described earlier need to be considered, the BI program is optimized around rapid-fire delivery of BI applications and integrating them into targeted business processes to create an ROI based on a very specific pre-defined investment hypothesis.  Companies that adopt this approach tend to:


  1. 1.       Identify ways that BI can be used in business processes that increase revenues, reduce costs, or both;
  2. 2.       Prioritize their BI opportunities for execution;
  3. 3.       Organize their people around BI development projects;
  4. 4.       Incrementally develop an enterprise data warehouse based around data integration for specific BI applications, as opposed to trying to consolidate all data about all subjects a priori;
  5. 5.       Invest in enablers such as data governance and master data management on a scale  that is needed to support the roadmap for rolling out BI applications, with the investment growing over time; and
  6. 6.       Focus on architected BI applications to serve known business purposes within specific business processes, versus just deploying data without having in mind well-specified BI applications for that data.


The basic argument for this approach is that:


  • ·         creating business value from raw data depends almost entirely on deployment of BI applications within core processes that make a different in economic and operational results; and
  • ·         creating and deploying BI applications does not depend on having full-scale versions of all the basic building blocks - those can be deployed in measured increments as BI applications are rolled out over time.


Customer-Centric BI Strategy embraces the perspective: "why spend heavily up front and hope for good results when one can spend less up front and grow the BI program over time?"  By focusing on getting the business people what they need quickly, BI can become a self-funding investment as results are proven.  This approach substantially reduces the risk associated with large-scale "build it and they will come" approaches – which have in many cases been associated with failure and huge consulting bills.


Three Factors to Consider when Choosing


1.  Competitive Urgency.   Are your competitors gaining BI-based competitive advantages and/or is there an economically-relevant motivation for your company to invest heavily ahead of your competition?  If so, a Capability-Focused BI Strategy can be argued for.  On the other hand, a Customer-Centric BI Strategy may be more focused and less risky.  It can be seen as a shotgun versus a rifle approach.


2.  Business Process Change.  Investments in BI only pay off when BI is used within the core business processes that drive economic and operational results.  Accordingly, a key question is how good is your company at business process improvement and how much change can it undertake at any point in time.  A large up-front investment under a Capability-Focused BI Strategy could result in an enterprise data warehouse that cannot be fully leveraged for years due to competing process improvement initiatives that fully absorb business unit bandwidth for change.


3.  IT Unit Priorities.  Like other business units, IT units are generally managing a portfolio of programs and projects, so launching and executing a major Capability-Focused BI Strategy may create bandwidth issues.  BI success requires different methods and skills than traditional IT projects, and many IT policies are rightly focused on optimizing cost and service level, and on minimizing operational risk for transactional systems.  BI has different critical success factors which drive the need for organizational change within IT, change which may be better realized more gradually via a Customer-Centric BI Strategy.



Closing Thoughts


A Capabilities-Focused BI Strategy tends to require a materially larger up-front investment for business returns that are less defined, less certain, and further in the future than with a Customer-Centric BI Strategy.  This creates a slower payback and a lower ROI within any given investment horizon.  In contrast, a Customer-Centric BI Strategy tends to achieve a more optimal balance in the timing of investment cash flows and economic returns.  All this being said, the choices between strategies are not as cut-and-dried as I've depicted.  Sharp companies will look at each BI-related building block in relation to competitive factors, business capacity for change, and IT capacity for change and then combine the best of both strategies to create their own unique BI roadmap.


Posted June 23, 2013 2:57 PM
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Having worked as business intelligence consultants to companies with revenues ranging from $100 million to $30+ billion, we've helped these companies reduce or mitigate BI execution risks by proactively identifying commonly-found  gaps in execution capabilities.  In our book The Profit Impact of Business Intelligence, we discuss our analytical framework for identifying these gaps.  In this blog post,  we'll provide a brief overview of the framework  and provide a simple risk-reduction checklist you can use at your company.


Business Intelligence Execution


A BI strategy must clearly identify any gaps in the company's ability to execute a multi-year business intelligence initiative, including:


  • gaps in BI and data warehousing technical abilities;
  • gaps in business units' ability to change their business processes to leverage BI; and
  • gaps in the company's ability to align and govern a multi-year BI program.


Business intelligence programs are complex initiatives with multiple workstreams, including:


  • organization and management
  • technical infrastructure development, maintenance, and operations
  • iterative BI development
  • data governance, stewardship, and quality
  • change management
  • business process improvement


Gaps in any of these areas can derail even the most promising BI program.  Accordingly, we recommend a thorough BI Readiness Assessment as a key component of the business intelligence strategy formulation process.  There are different frameworks that companies can use, and there is no industry-standard approach.  At DecisionPath, we use a web-based readiness assessment survey of business and IT people in a client company and in-depth technical interviews with the IT team.


Business Intelligence Risk Reduction Checklist


  1. Identify how top management views the importance of BI to your company, which speaks to the level of commitment to aligning, funding, and governing a multi-year BI program.
  2. Identify business units' willingness and ability to effectively change key business processes to leverage better business information and analyses, which speaks to whether an investment in BI will create business value.
  3. Identify IT's ability to adapt its policies, infrastructure, and methods to meet BI critical success factors, which speaks to whether or not business intelligence projects can be delivered on time, within budget, and with the full intended scope of business information and analytics.
  4. Determine whether there is a well-designed approach to BI program management and to the placement of the BI team within the larger enterprise, which speaks to the ability to execute multiple BI, technology, and process improvement projects in a synchronized and effective manner.
  5. Determine how the data governance approach is aligned with the BI development approach and whether it is appropriately scoped, which speaks to the ability to maintain focus on timely BI development  to create business value, with data governance playing an enabling role that doesn't  slow down BI projects.


While there are many more detailed items to consider as part of risk reduction, we've found that the five items discussed above are very useful to consider during BI strategy formulation.  A great BI strategy paints a clear picture of how BI can be used to create business value, and it is also positive and practical about potential barriers to success.



Posted May 31, 2013 3:40 PM
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