Decision Management, Possibly The Last Frontier In BI

Just read an excellent article on the subject by Tom Davenport. We at Forrester Research indeed see the same trend, where more advanced enterprises are starting to venture into combining reporting and analytics with decision management.  In my point of view, this breaks down into at least two categories:

  • Automated (machine) vs. non automated (human) decisions, and
  • Decisions that involve structured (rules and workflows) and unstructured (collaboration) processes
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Social Business Intelligence: The Knowledge Management Connection

Business intelligence (BI) has always had a “pipeline” orientation—in other words, a primary focus on the one-way flow of data, information, and insights from “sources” (e.g, your customer relationship management systems, enterprise data warehouses, and subject-area data marts) to “consumers” (e.g., you). 

But we all know that this pipeline orientation—also known as “simplex” information transfer—doesn’t describe the predominant flow of mission-critical intelligence in our lives. Quite often, the most important insights are those that issue from other people’s heads, not from our companies’ data marts. Many real-world intelligence flows are full-duplex, many-to-many, and person-to-person in orientation. This fundamental truth will continue to drive the spread of “social” architectures in core BI and advanced analytics.

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Why I Don't Want To Research BI Market Size

I know, I know, this is what analysts do. But I personally would never want to get involved in doing a BI market size – it’s open game for serious critique. Here are some of the reasons, but the main one is a good old “garbage in garbage out.” I am not aware of any BI market size study that took into account the following questions:

  • What portion of the DBMS market (DW, DBMS OLAP) do you attribute to BI?
  • What portion of the BPM market (BAM, process dashboards, etc.) do you attribute to BI?
  • What portion of the ERP market (with built-in BI apps, such as Lawson, Infor, etc.) do you attribute to BI?
  • What portion of the portal market (SharePoint is the best example) do you attribute to BI?
  • What portion of the search market (Endeca, Google Analytics, etc.) do you attribute to BI?
  • What is the market size of custom developed BI applications?
  • What is the market size of self built BI apps using Excel, Access, etc?
  • On the other side, what is the % of licenses sold that are shelfware and should not be counted?

Plus many more unknowns. But, if someone indeed did do such a rough estimate, my bet is that the actual BI market size is probably 3x to 4x larger than any current estimate.

Use A Four-Step Approach To Select The Right BI Services Provider

BI projects are never short, and, alas, many of them don't end since a fast-paced business environment often introduces new requirements, enhancements, and updates before you're even done with your first implementation. Therefore, we typically recommend doing sufficient due diligence upfront when selecting a BI services provider — as you may be stuck with them for a long time. We recommend the following key steps in your selection process:

  1. Map BI project requirements to potential providers. Firms should use Forrester's "BI Services Provider Short-Listing Tool" to create a shortlist of potential providers. With the tool you can input details about your geographic scope, technology needs, and the type of third-party support you need (i.e., consulting versus implementation versus hosting/outsourcing).  The tool then outputs a list of potential providers that meet the criteria. For each potential fit, the tool also generates a provider profile summary that offers key details around practice size, characteristics, and areas of expertise.
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Join Me For A BI Strategy Workshop In Cambridge, Mass., On October 19, 2010

Business intelligence (BI) continues to be front and center on the agendas of businesses of all sizes and in all industries and geographies. Ever-increasing data volumes, complexity of global operations, and demanding regulatory reporting requirements are just some of the reasons. But also, more and more businesses realize that BI is not just a tool but rather a key corporate asset that they can use to survive, compete, and succeed in an otherwise increasingly commoditized global economy.

However, we consistently find that many BI initiatives fail and even more are less than successful. Well, maybe we can help. Even if just a little bit. Come to our interactive one-day BI Strategy Workshop to learn the fundamentals and best practices for building effective and efficient BI platforms and applications. The Workshop will also include hands-on exercises with tangible deliverables that you can take back to your teams to help you jump-start or adjust the course of your BI initiatives.

Why attend? Because hundreds of organizations have already benefited from reading Forrester research and working with Forrester analysts on the topics covered in this Workshop. I plan to present Forrester’s most recent research on:

  • Why are BI initiatives at the top of everyone's agenda, while many of them still fail?
  • What are some of the best practices necessary to achieve successful BI implementations?
  • What are some of the next-generation BI technologies and trends that you can't overlook, such as Agile BI and self-service BI?
  • How do you assess your BI maturity so that you can get a solid starting point on the way to your BI vision and target BI state?
  • How do you assess whether your organization has a solid BI strategy?
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Findings From Forrester Wave: Customer Service Analytics Empower The Predictive Process

Bill Band and I have just published the latest Forrester Wave on customer relationship management (CRM) customer service solutions. We included 19 vendors in this in-depth market study and evaluated their solutions against 196 criteria. As you can well imagine, it took time and a fine-toothed analytical comb to compile the research and double-check the facts before we scored these sophisticated product suites. 

One of the key findings from this Forrester Wave is that a growing range of CRM vendors have incorporated deep analytics features into their customer service capabilities. Most provide embedded, out-of-the-box business intelligence (BI) features such as reporting, query, online analytical processing, dashboarding, scorecarding, and key performance indicators prebuilt to support their customer service applications. That’s no surprise, because these core BI features enable enterprises everywhere to keep track of how well they’re providing customer service across diverse CRM interaction channels and to identify opportunities to improve satisfaction, retention,  upsell, agent productivity, and other key metrics. 

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Social Media Analytics: You Will Still Need Actual Analysts In The Loop

Social media analytics is one of the most exciting new frontiers in business intelligence (BI). As I noted in a recent blog post, it refers to the application of BI tools, such as reporting, dashboarding, visualization, search, event-driven alerting, and text mining, to information that originates as messages streaming from social media such as Twitter and Facebook. 

Forrester sees growing adoption of social media analytics across the entire customer relationship management (CRM) life cycle. This makes perfect sense, because social media are where customers spend more and more time, voice more unvarnished sentiment, and interact with a growing range of trusted commercial enterprises in addition to their friends and families.

Recognizing this trend, enterprise CRM professionals everywhere have incorporated social media into their public relations, product management, marketing, sales, and customer service processes. In addition to establishing their brands’ presence in the leading social media communities, companies have implemented tools to support continuous listening and engagement with customers, prospects, and the world at large through these channels.

Listening and engaging via social media involves much more than BI dashboards to monitor mentions on Twitter and the like. It may also require tight integration with the company’s CRM, enterprise data warehouse (EDW), business process management (BPM), business rules engine (BRE), complex event processing (CEP), predictive analytics and data mining (PA/DM), text analytics (TA), social network analysis (SNA), and other key tools and platforms. We often refer to this cluster of technologies as enablers for “social CRM.”

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Where Do You Draw The Lines Between Business And IT Ownership Of Data And Information?

I get many questions on this subject and it often turns into almost a religious debate. Let's throw some structure into it. Here's a decision-to-raw-data stack.

  1. Decisions
  2. Strategy
  3. Policies
  4. Objectives (e.g., clear understanding of what is driving revenue performance)
  5. Goals (e.g., achieve x% income growth)
  6. Calculated metrics (any combination, variation of the standard metrics or KPIs)
  7. KPIs (e.g., profitability, liquidity, shareholders value)
  8. KPMs (e.g., enterprise value, trailing/forward price/earnings)
  9. Metrics (e.g., fee income growth %, non-fee income growth %)
  10. Dimensions (part of MDM, e.g., customers, customer segments, products, time, region)
  11. Pre-calculated attributes (standard, cross-enterprise metrics, KPIs, and KPMs)
  12. Pre-built aggregates (used to speed up reports and queries)
  13. Analytical data (DW, DM)
  14. Operational data (ERP, CRM, financials, HR)

Obviously, it's never a clear-cut, binary decision, but in my humble opinion:

  • 1-6 should emphasize business ownership
  • 10-14 should emphasize IT ownership
  • 7-9 is where it gets murky, and ownership depends on whether metric/KPI/KPM is: 1) standard and fixed, 2) fluid and changes frequently, 3) different by product, line of business, or region.

What did I miss? Thoughts?

Agile Data Warehousing: Do You Scrum?

When IT professionals speak of “agile development,” they could be referring to any of countless overlapping schools of thought. It’s best to tread lightly and keep an agile mind to find some hybrid or innovative approach that specifically meets your needs.

By most accounts, this term “agile” refers to any approach that straddles the razor’s edge between traditional top-down development and sheer adhocracy. Agile approaches attempt to speed the development process while enabling rapid shifts in development priorities to meet changing user requirements.

Agile approaches involve incremental, iterative, collaborative development among cross-functional teams consisting of IT professionals and business users. Under agile development, self-organizing teams refine requirements and craft modular solution components as they go. Teams hold regular checkpoint status meetings to review work in progress and reprioritize tasking. At every point in an agile program, the team develops useful prototypes that can stand alone as production-grade business technology systems, or can function as building blocks for larger, more complex, multifunctional systems. Documentation plays catchup to the work being performed, rather than constraining it through imposition of fixed, top-down specifications.

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What’s Next On The MDM Horizon?

Many large organizations have finally “seen the light” and are trying to figure out the best way to treat their critical data as the trusted asset it should be. As a result, master data management (MDM) strategies, and the enabling architectures, organizational and governance models, methodologies and technologies that support the delivery of MDM capabilities are…in a word…HOT! But the concept of MDM - and the homegrown or vendor-enabled technologies that attempt to deliver that elusive “single version of truth”, “golden record”, or “360-degree view” - has been around for decades in one form or another (e.g., data warehousing, BI, data quality, EII, CRM, ERP, etc. have all at one time or another promised to deliver that single version of truth in one form or another).

The current market view of MDM has matured significantly over the past 5 years, and today many organizations are on their way to successfully delivering multi-domain/multi-form master data solutions across various physical and federated architectural approaches. But the long-term evolution of the MDM concept is far from over. There remains a tremendous gap in what limited business value most MDM efforts deliver today compared to what all MDM and data management evangelists feel MDM is capable of delivering in terms of business optimization, risk mitigation, and competitive differentiation.

What will the next evolution of the MDM concept look like in the next 3, 5 and 10 years? Will the next breakthrough be one that’s focused on technology enablement? How about information architecture? Data governance and stewardship? Alignment with other enterprise IT and business strategies?

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