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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.

October 2012 Archives

The argument for jumping into the fray with big data goes something like this:

  • there is an increase in the volume, velocity, and variety of data flowing around companies that is driven largely by social media and that is increasingly comprised of unstructured data – tweets, likes, etc.
  • companies that can harvest and mine big data will  out-perform companies that don't.

As with any business or technology innovation, the proponents can always marshall case studies to support the value proposition.  That being said, what is the right big data strategy for your company?

For our purposes here, let's make a distinction advanced by other industry observers and practitioners – that “big data” is mostly about the large volume of unstructured data generated via social media and Internet behavior.  Sure there are large volumes of stuctured data – transactional data and reference data stored in ERP systems and other enterprise applications.  That being said, leveraging sturctured data  has been the focus of business intelligence, analytics, and data warehousing for 15+ years and is well-covered elsewhere.

If big data is unstructured data, it is useless for business purposes unless it can be organized and leveraged to either increase revenues, reduce costs, or both.  Another way to put it is to say that unless big data can be converted to business intelligence and/or analytics, it will be of little business use.  Accordingly, I submit that formulating a big data strategy requires the same approach as formulating a business intelligence/analytics strategy.  We need to answer the following:

  1. What information do we need?
  2. For what kinds of analyses?
  3. To improve which business decisions?
  4. Via what changes to which business processes?

In the world of big data, this translates to a more basic question: how will analyzing unstructured social media data and web site behavior data help improve our business results?  Will it help in marketing, sales, and customer service?  Will it help our supply chain organization?  Will it help with financial management? Will it help with operations management?  There are case studies to support a “yes” answer in some of these cases, but each company needs to consider the questions from their own perspective.

More broadly, there are some other strategic questions to consider.

  1. Are business intelligence and analytics strategically important in your industry and/or to your company?  Generally, the more complex your business, the more important it becomes to leverage business intelligence and analytics to compete effectively, serve your customers well, and optimize costs in relation to revenues. As some business move more and more in the direction of being digital businesses, unstructured big data will become more strategically important.
  2. How experienced and successful is your company at leveraging regular stuctured data for business intelligence and analytics?   My experience with leading companies in financial services, manufacturing, distribution, retailing, and other industries suggest that some companies are not even ready to leverage small data or medium data – or whatever you want to call the gigabytes or terabytes of structured data most companies have.  The are at least 5 Barriers to BI Success that impede companies as the strive to leverage business intelligence and analytics to improve results. If your company isn't good at the basics, it may be a stretch to jump into big data in a big way – and perhaps an R&D pilot would be a useful strategy.
  3. How complex is your business – and how digital?  The more customers, products, channels, and geographic areas your company needs to manage, the more complex your business.  Business intelligence and analytics are great for coping with complexity and optimizing the business.  Big data can be very useful for improving marketing, sales, and customer service in businesses that heavily digital – think Amazon, online banking, and so forth.
  • There is no magic answer for what the optimal big data strategy is.  On the other hand, there are structured methods for getting to a practical answer for your company.  Considering the questions we've posed are a good start toward formulating a big data strategy that is clearly focused on an important business improvement opportunity – one that will yieled a reasonable ROI.

by Steve Williams, President

Copyright 2012  DecisioniPath Consulting

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Posted October 29, 2012 8:35 PM
Permalink | No Comments |

The argument for jumping into the fray with big data goes something like this:

  • there is an increase in the volume, velocity, and variety of data flowing around companies that is driven largely by social media and that is increasingly comprised of unstructured data – tweets, likes, etc.
  • companies that can harvest and mine big data will  out-perform companies that don’t.

As with any business or technology innovation, the proponents can always marshall case studies to support the value proposition.  That being said, what is the right big data strategy for your company?

For our purposes here, let’s make a distinction advanced by other industry observers and practitioners – that “big data” is mostly about the large volume of unstructured data generated via social media and Internet behavior.  Sure there are large volumes of stuctured data – transactional data and reference data stored in ERP systems and other enterprise applications.  That being said, leveraging sturctured data  has been the focus of business intelligence, analytics, and data warehousing for 15+ years and is well-covered elsewhere.

If big data is unstructured data, it is useless for business purposes unless it can be organized and leveraged to either increase revenues, reduce costs, or both.  Another way to put it is to say that unless big data can be converted to business intelligence and/or analytics, it will be of little business use.  Accordingly, I submit that formulating a big data strategy requires the same approach as formulating a business intelligence/analytics strategy.  We need to answer the following:

  1. What information do we need?
  2. For what kinds of analyses?
  3. To improve which business decisions?
  4. Via what changes to which business processes?

In the world of big data, this translates to a more basic question: how will analyzing unstructured social media data and web site behavior data help improve our business results?  Will it help in marketing, sales, and customer service?  Will it help our supply chain organization?  Will it help with financial management? Will it help with operations management?  There are case studies to support a “yes” answer in some of these cases, but each company needs to consider the questions from their own perspective.

More broadly, there are some other strategic questions to consider.

  1. Are business intelligence and analytics strategically important in your industry and/or to your company?  Generally, the more complex your business, the more important it becomes to leverage business intelligence and analytics to compete effectively, serve your customers well, and optimize costs in relation to revenues. As some business move more and more in the direction of being digital businesses, unstructured big data will become more strategically important.
  2. How experienced and successful is your company at leveraging regular stuctured data for business intelligence and analytics?   My experience with leading companies in financial services, manufacturing, distribution, retailing, and other industries suggest that some companies are not even ready to leverage small data or medium data – or whatever you want to call the gigabytes or terabytes of structured data most companies have.  The are at least 5 Barriers to BI Success that impede companies as the strive to leverage business intelligence and analytics to improve results. If your company isn’t good at the basics, it may be a stretch to jump into big data in a big way – and perhaps an R&D pilot would be a useful strategy.
  3. How complex is your business – and how digital?  The more customers, products, channels, and geographic areas your company needs to manage, the more complex your business.  Business intelligence and analytics are great for coping with complexity and optimizing the business.  Big data can be very useful for improving marketing, sales, and customer service in businesses that heavily digital – think Amazon, online banking, and so forth.
  • There is no magic answer for what the optimal big data strategy is.  On the other hand, there are structured methods for getting to a practical answer for your company.  Considering the questions we’ve posed are a good start toward formulating a big data strategy that is clearly focused on an important business improvement opportunity – one that will yieled a reasonable ROI.

by Steve Williams, President

Copyright 2012  DecisioniPath Consulting

 


Posted October 29, 2012 8:35 PM
Permalink | No Comments |

I read an interesting book last week called Immunity to Change, by Robert Kegan and Lisa Laskow Lahey.  In it, the authors focus on individual and organizational factors that impede successful change, and they discuss a distinction between technical challenges and adaptive challenges.  A technical challenge may be difficult, but the skills required to meet the challenge are well known.  On the other hand, individuals and organizations often face adaptive challenges - where successful change requires transforming mindsets by advancing to a more sophisticated way of thinking or acting.  Based on my experience as a business intelligence (BI) strategy consultant over the past 12 years, I believe BI success is more of an adaptive challenge than a technical challenge.  I share my reasoning in a moment.

There are certainly technical challenges along the road to successful business intelligence, analytics, and big data initiatives.  These technical challenges come in many forms, e.g. integrating data from disparate sources, meeting the increasing demand to provide real-time or right-time data refreshes, developing business rules and data definitions, managing metadata and master data, and delivering BI and analytics in a form that users find acceptable, to name but a few.  That being said, there are proven methods and tools, and many skilled professionals who know how to use the methods and tools to overcome the technical challenges.

On the other hand, I've seen many companies struggle with business intelligence, analytics, and big data initiatives due to a mindset that limits their openness to change.  Here are two examples from my experience that illustrate some of the adaptive challenges companies face:

  •  a CPG manufacturer is struggling to make progress with its BI initiative because its business community sees BI and analytics as "better reporting."  As a consequence, they have under-invested and failed to engage to learn more about the profit impact of business intelligence.  And while they say they want to leverage scorecards and dashboards, the idea of using them to manage by exception has not been embraced.  Essentially, their mindset is not where it needs to be for BI success, and this is an adaptive challenge.
  • a financial services firm is struggling to make progress with its BI initiative because its IT leaders see BI projects as being the same as applications development projects, and because using shared services is seen as more important than optimizing their approach to meet the requirements for BI success.  The business sponsor is sharp and sees that the conventional IT approach does not suit the speed and flexibility needed in the BI world.   Further, the conventional approach will waste resources to perform tasks and generate documents that add no value from a BI project execution perspective.  Essentially, the IT mindset is not where it needs to be for BI success.

More broadly, companies that seek to fully leverage business intelligence, analytics, and big data generally face the adaptive challenge of changing the culture around the use of information and analysis to inform key decisions and to measure, manage, and improve business results.  A recent report by Harvard Business School Analytic Services (The Evolution of Decision Making) provides further examples.  For example, the report discusses key adaptations companies make in order to fully leverage BI and analytics:

  •  they leverage BI and analytics to enable quicker, more informed decisions;
  • they make the same information and analyses available to the entire company at the same time in order to eliminate arguments about whose data is right;
  • they standardize the processes for making key decisions;
  • they expand their skills to leverage BI and analytics in more robust ways; and
  • they balance instinct and managerial judgment with the use of BI and analytics.

The report is a good read, and it points to the kinds of adaptive challenges companies face in leveraging business intelligence, analytics, and big data.

More broadly, it is important to understand the specific adaptive challenges your company faces, which can be identified via a business-driven, multi-factor BI Readiness Assessment.  An effective and practical  business intelligence strategy must be based on a firm understanding of both the technical and adaptive challenges, and a BI Readiness Assessment is easy to do and cost-effective.  Armed with the results, the BI team can enlist top management to help overcome the adaptive challenges and ensure BI success.

 

 


Posted October 1, 2012 3:57 PM
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