Over the past 13 years, my colleagues and I at DecisionPath have had the privilege of helping major companies in many industries formulate their business intelligence (BI) strategies.  While each company and industry is different, there are common components of an effective BI strategy.  Some are specific to BI and others are applicable for any business process improvement initiative.  And make no mistake - BI initiatives must be business process improvement initiatives if they are to create business value.  For more on using BI to create business value, see our book The Profit Impact of Business Intelligence.  In this blog, we introduce five key components of business intelligence strategy.  Over the next few months, we will continue on the BI strategy theme by providing concise excerpts from our forthcoming book, provisionally titled Business Intelligence Strategy: Lessons from Industry Leaders.

Five Key Components of BI Strategy:

1. Business Intelligence Uses.  A BI strategy must clearly articulate how advanced analytics, scorecards, dashboards, alerts, predictive analytics, big data, and multi-dimensional analysis (OLAP) will be used within specific business processes to create a quantifiable return-on-investment.
2. 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.
3. Business Intelligence Roadmap.  A BI strategy must include a pragmatic roadmap that effectively sequences BI technical projects, IT infrastructure projects (if needed), data governance/quality projects, and business process improvement projects.  The business intelligence roadmap should also include a performance measurement component that: (a) measures cost, technical, and schedule performance; and (b) measures improvements to business performance after each BI application is implemented by the business.
4. Business Process Improvement.  A BI strategy must address needed improvements to company business process analysis and improvement skills.  Many industry-leading companies are good at executing their current business processes and business models.  Fewer such companies are actually good at changing and improving their business processes, and they need training in core business process analysis, design, and improvement skills and methods.
5. Business Intelligence and Organizational Change.  A BI strategy must address organizational and cultural change challenges in order to anticipate and overcome the barriers to becoming a "data driven" company.  Make no mistake, this is a serious challenge because many senior professionals are used to operating without good information and sophisticated analyses, and thus decision-making is more of an intuitive, personality-driven process in many successful companies.  Teaching these accomplished people to incorporate business intelligence into their decision processes is essential.

Including these five key components in your business intelligence strategy establishes a solid foundation for BI success.  In the coming months, we'll talk about BI strategy for various industries and business functions.

Today's executives and managers are swamped with ideas about improving business results.  These range from improving so-called "soft skills" like leadership and communications, to methods like Strategy Mapping and Six Sigma, to software tools for customer relationship management or supply chain planning and execution. The panoply of approaches is promoted by an army of capable academics, consultants, researchers, and information technology vendors - and executives and managers are buffeted by claims, many of them difficult to evaluate. 

Within this environment, analytics is yet another entrant, and companies are right to be cautious about jumping on the bandwagon.  On the other hand, the power of analytics to improve business performance is undeniable – and well worth embracing as part of a career development strategy.  Just as companies can out-perform rivals by leveraging analytics, professionals with the ability to marry intuition, business experience, and modern analytics will have a positive impact on business performance.  Accordingly, it makes sense to understand analytics and how to go about developing appropriate skills.

More and more, analytics in all forms is becoming part of the fabric of successful companies.  At the same time, it is new enough that there are opportunities for upwardly mobile professionals to differentiate themselves by learning how to use these powerful tools.  As the tools become more user-friendly, there is no reason for business users to think of analytics as being beyond their ability.  And as younger generations of managers rise through the ranks, it will even become commonplace for senior executives to be hands on users of analytical tools.  This is not to say that the typical business person needs to become a big-data quant jock.  Rather, as analytics become more commonplace, professionals who can quickly marshal the facts and develop solid analyses will have a significant impact on company performance, and that generally bodes well career-wise.

For more on how enbracing analytics can enhance your career, please see my recent article in MWORLD, the flagship publication of the American Management Association.

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

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.

One of the more important trends in business intelligence over the past few years has been a need for speed: an ever-increasing volume of data means that existing BI systems must be able to process information faster and faster. On the business user side, an increased demand for BI puts pressure on BI teams to develop new applications faster, without sacrificing quality.

While there are a number of factors that go into streamlining and speeding up your BI program, one of the most important is having the right tools for the job.  In other words, maximizing you development speed requires having the best tools.   Almost everyone is using dedicated ETL and reporting software,  but have you thought about the peripheral tools that would speed your productivity?

In my previous post,   I discussed how slow-running systems can cripple your development efforts. In some of the feedback I received, folks wanted to know the right tools for their BI program. This post describes some of the problems that sticking to the status quo, or not having the right tools have on your speed of execution. It also identifies tools every BI/DW program should have.

Here are some important tools that no BI program should be without:

SQL execution environment

The bad old way:   Plain old database tools.  Sqlplus.   Isql.  Green screen tools.

Tools every program should have:   TOAD, SQL Server Management Studio, or similar

What you get:   Debugging and development speed

Query Tool

The bad old way: Write SQL queries for everything.

Tools every program should have:   BI tools are faster and deliver more useful results.    Everyone on the team should have them.

What you get:   Faster analysis and design.  Cross training on tools.

System Monitoring

The bad old way: Security and performance concerns limit access.   Only the DBA can see inside the database,  only the sysadm can see inside the server.

Tools every program should have:  Developers have access to what is happening in the database and on the servers.   Especially important are query plans, disk reads, and status of long running queries.

What you get:   Faster testing.  Less time wasted.  Is this query going to run 2 minutes or 10 hours?   Better wait to find out.

Data Modeling

The bad old way: Type table DDL statements in a text editor.

Tools every program should have:   Use a data modeling tool like Erwin or Power Designer

What you get:   Less time fixing table mistakes and inconsistencies.  Higher quality table design.

Change Management

The bad old way: Keep copies in file folders.

Tools every program should have:   Use a change control tool.  Many are free.

What you get:   Faster development, fewer errors.

Desktop Sharing

The bad old way: Phone calls and emailed files.

Tools every program should have:   Desktop sharing tools like WebEx, GoToMeeting, or Live Meeting.

What you get:   Faster development, fewer errors.

Investing in tools will increase the speed that you can delivery new functionality. Our clients that have made tool investments report not just improvements in speed, but increased ability to meet deadlines, shorter backlogs, and happier developers.

Tom Victory, Principal Consultant

2012©, DecisionPath Consulting