Celebrating 6 years of blogging

My blog is completing 6 years today. Thank you all for reading my blog for the last six years. As always, your readership is really appreciated.

2 More Hot Trends in Business Intelligence

Trends and Outliers, the TIBCO Spotfire's Business Intelligence Blog, published today the Part Two of the update of the post 7 Hot Trends in Business Intelligence, written by me 6 months ago, entitled 2 More Hot Trends in Business Intelligence. The Part One 7 Hot Trends in Business Intelligence (An Update), was published last week.


Earlier this year, I also published a post on their blog with Business Intelligence Predictions for 2011.

Thank you to the people of Spotfire blog for the invitation to write guest posts. I am truly grateful.

7 Hot Trends in Business Intelligence (An Update)

Trends and Outliers, the TIBCO Spotfire's Business Intelligence Blog, published today a guest blog post written by me, entitled 7 Hot Trends in Business Intelligence (An Update). This post is an update of a post written 6 months ago.

Once again, thank you to the people of Spotfire blog for the opportunity.

Celebrating 3 years of blogging

My blog is completing 3 years today. I started this blog as a means to express my interests in Business Intelligence and related themes.

Thank you for reading my blog for the last three years. Your readership is really appreciated.

Below are the most popular posts (no order) in the last 3 years:

- Culture eats strategy for breakfast
- Several executives trust gut
- Does your company have a BI strategy?
- Strategies for Creating a High-Performance BI Team
- Are You Ready to Reengineer Your Decision Making?
- How to Use Twitter as a "Twool"
- Interview with Balanced Scorecard Co-Creator Dr. Robert Kaplan

All books reviews have always been well accessed, I would like to highlight:

- Profiles in Performance: Business Intelligence Journeys and the Roadmap for Change - Howard Dresner
- Competing on Analytics: The New Science of Winning - Thomas Davenport and Jeanne Harris
- The Balanced Scorecard - Translating Strategy Into Action - Robert Kaplan and David Norton
- The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling - Ralph Kimball and Margy Ross
- Five Key Principles of Corporate Performance Management - Bob Paladino
- Performance Dashboards: Measuring, Monitoring, and Managing Your Business - Wayne Eckerson

As you may have noticed, I'm a huge fan of Dilbert. All Dilbert's comic strip I published have been well accessed too, mainly:
- Dilbert on making decisions

11 Guiding Principles for a Successful Business Intelligence Implementation

Tibco Spotfire has one of the most prolific corporate blog in the BI area. Updated nearly every day, the blog, entitled Trends and Outliers, is a great source of news and information about Business Intelligence (Full disclosure: I already wrote guest posts for the TIBCO Spotfire's Business Intelligence Blog). Last week, they published a great post on Successful BI Implementation, where they listed 11 guiding principles for a successful business intelligence implementation from Booz & Co. Below is the list:


1. Drive Change From The Top Down and The Bottom Up
Like any new solution, business intelligence is only effective if people use it. For it to be completely adopted, it needs to be used not only by line managers but also by executives.

2. Create a Comprehensive Definition of Business Intelligence
Business intelligence puts emphasis on measuring performance against goals and establishing accountability for reaching those goals. Make sure you have the supporting processes, systems and change management protocols in place to support this new way of running the business.

3. Use an Agile, Modular Approach
You can achieve more flexible and more effective implementations of business intelligence faster with agile development and by focusing on specific areas that are guided by an integrated, overall strategy.

4. Focus On The Right Metrics
Metrics must be aligned with the company’s strategy and capabilities, including both internal and external inputs, and consisting of both leading and lagging indicators.

5. Keep It Simple
Even though technology might let you drill down 16 levels into the data or slice and dice it 100 different ways, that kind of analysis may be irrelevant and distracting. Be selective by including a few key metrics that are the most important and drill down to the top three or four levels.

6. Build a Unified BI System
Some of the most important insight from a BI system can come from discovering how interdependencies impact outcomes across an organization. Integrate data and data analytics across the organization to allow for custom analytics that can identify root causes of issues.

7. Launch Early
To gain acceptance and support you may need some early wins with a BI project. Start with high-priority areas that have high-quality metrics. Demonstrate the value of a BI solution to help build momentum for the project.

8. Create Detailed System Requirements and Select The Right Partners
Successful BI implementations require a partnership between IT and the lines of business. They also require strong project management skills, systems integration know-how and software tools. Make sure you’ve included and selected the right teams across all these areas.

9. Leverage Existing Infrastructure
Business Intelligence implementations should align with a company’s IT strategy and vision for how IT will support the business. BI should complement existing IT capability which can often be achieved by leveraging current IT infrastructure to provide the back end, and using business intelligence solutions to provide the front end.

10. Establish a Centralized Governance Structure
A business intelligence implementation can touch every area of an enterprise. It requires cooperation and shared ownership from the business and IT, new data management protocols, strong project management and ongoing analysis of the metrics used. For maximum success, you should create an overall governance structure led by the business and supported by IT.

11. Proactively Manage Change
Introducing business intelligence requires extensive change management. When evaluating performance against metrics, there should be clear accountability, consequences for not meeting goals and incentives for exceeding them. As with any initiative that requires change management, a successful BI implementation requires senior leadership support, training and communication throughout the enterprise.

Traits of Next-Generation BI

Dave Kellogg writes an excellent blog where he covers next-generation database management, search, and content management technologies along with commentary on Silicon Valley, venture capital, and the business of software. Dave's blog is worth reading. This month, he wrote a great post, entitled Traits of Next-Generation BI (Business Intelligence), where he commented on his thoughts of the future of BI. Below are the Dave's Traits of next-generation BI:

In memory, columnar, and compressed. Most solutions rely on the fact that the source data for most problems can now fit in memory, typically using a columnar and compressed format. Some solutions are even able to perform work on the data without first decompressing it.

Fast. The dream of BI — particularly for interactive analysis tools – has always been “speed of thought” analysis. Thanks to the above point and thanks to additional performance optimizations (e.g., to expoit CPU cache locality), this dream is becoming a reality.

Directly connected. Next-generation BI tools generally connect directly to the underlying source databases (and/or the Internet) to capture data. This means they must also have basic data integration capabilities both so they properly align data from different systems and dynamically refresh it.

Schema-free. In order to accomodate semi-structured information and to be able integrate information from different sytems, next-generation BI does not require the up-front definition of a schema. Instead, relationships among data (e.g., hierarchy) are discovered dynamically.

Beautiful. While this is best exemplified by Tableau (where visualization is the principal focus) next-generation BI tools generally provide beautiful visualizations that are more powerful than the basic report and bar chart. (Note that I named a name here because I consider Tableau mid-stage, not early-stage.)

Mobile. Next-generation BI tools typically assume a brower-based client and often the need to create device-specific clients (e.g., a native iPad app) to supplement it. Some companies focus exclusively on mobile BI.

Neutral. Next-generation BI tools exploit the fact that a multi-billion dollar vacuum was created in the market when the BI leaders were consolidated and became units of IBM (e.g., Cognos) or SAP (e.g., BusinessObjects).

About the future of data warehouse, he wrote: if you can fit your entire data set in memory and dynamically calculate the answer to any question at high speed, then why do you need a data warehouse full of precalculated aggregates again? I thus see a “middle squeeze” on the data warehouse market in the future.

- For most applications of normal size and analytic complexity, people will use next-generation BI on top of raw data sources, unless they have very messy data or a need for extensive history.

- For large applications (i.e., big data) and/or high analytic complexity, people will use advanced analytic platforms (e.g, Aster Data). This, of course, begs the question whether anyone is working on BI tools that exploit and optimize the new, high-end analytic engines and the answer to that question is happily “yes” as well.

Business Intelligence Predictions for 2011

Trends and Outliers, the TIBCO Spotfire's Business Intelligence Blog, published today a guest blog post written by me, with the Business Intelligence Predictions for 2011.

The post was also published on the site Smart Data Collective. Thanks to the staff of Spotfire blog for the opportunity.

7 Hot Trends in Business Intelligence

I was invited to write a guest blog post for the TIBCO Spotfire's Business Intelligence Blog. TIBCO Spotfire is a leader in the data visualization space. Today they published the post, entitled 7 Hot Trends in Business Intelligence. Thank you to the people of Spotfire blog for the invitation.

Videoblog on strategy, organizational behavior and performance management

Frank Buytendijk writes a nice blog on performance management at Oracle's corporate blog. I enjoy reading his posts, he knows deeply about the subject and writes clearly and concisely. Recently, he started a videoblog on strategy, organizational behavior and performance management. At the moment, he already published 4 videos: Best Practices, Return on IT, Book Review 1 and Dealing with Dilemmas.

That is a good initiative by Frank. Watch and enjoy!

Below is the first videoblog: Best Practices



Frank Buytendijk wrote a very good book called Performance Leadership. He is writing a new book entitled Dealing with Dilemmas: Where Business Analytics Fall Short (The book is on pre-order at Amazon.com. He commented about the book in the videoblog Dealing with Dilemmas).

The Anti-Creativity Checklist

Michael Ianni-Palarchio has a nice blog where he shares his thoughts on technology. I watched in his blog, an interesting video called The Anti-Creativity Checklist, originally published by Youngme Moon, in the section blogs of the Harvard Business Review site. She is Professor of Business Administration at the Harvard Business School, where she focuses on marketing and strategy innovation.

Youngme wrote: If you had to come up with a checklist for your organization that was guaranteed to stifle imagination, innovation, and out-of-box thinking...a checklist designed specifically for people who want nothing to do with disruptive change...what would it look like?

Michael commented: Of course, this is really telling you what NOT TO DO if you want to have innovation become a part of your organization.

In my opinion, this thought-provoking checklist has almost everything what you need if you don't want creativity, innovation and disruptive change in your organization. It is worth to watch.

My Anti-Creativity Checklist from Youngme Moon on Vimeo.

Two years of blog

Exactly two years ago, I was publishing my first post in this blog. After almost 300 posts, the blog celebrates its second anniversary. When I created this blog, my original idea was to comment about books (the URL of the blog will not let me lie), mainly books reviews, but I also started to write about everything related with Business Intelligence and Performance Management, like news, articles, posts, events, new concepts, tools and products. I also started to write about others subjects intrinsically related, as Business Strategy, Management, Leadership and Innovation.


With that, although I keep reading several good books, I haven't done many book reviews in the blog. I try to mention books when I write about an article, post, or event (webinar, interview, lecture, presentation, etc) from an author. From now on I plan to do more book reviews, my main motivation to create this blog two years ago.


Many thanks to everyone who supported me!

The Unified Performance, Risk, and Compliance Process Model

Recently, Nenshad Bardoliwalla wrote a nice series of 4 posts, entitled The Unified Performance, Risk, and Compliance Process Model, published in his blog and also in the Enterprise Irregulars website. The series of posts were excerpted from his excellent book Driven to Perform: Risk-Aware Performance Management From Strategy Through Execution, written with Stephanie Buscemi and Denise Broady, where they describe how they unified performance, risk, and compliance into a coherent strategic management process framework.


He wrote one post for each phase of the classic performance management lifecycle: Part I - Strategize and Prioritize, Part II - Plan and Execute, Part III - Monitor and Analyze, and Part IV - Model and Optimize. The posts are well detailed and illustrated graphically, and provide prescriptive guidance in how to put all the pieces together in their model. Below is a summary of his posts:


Part I - Strategize and Prioritize:

Understand the Corporate and Departmental Contexts

Review the corporate strategic goals, strategic plans, initiatives, and metrics. Contextualize them to the implications they have for the departments and use this context to drive the PM lifecycle.

Develop and Set the Strategy

First, review the environment. To get a holistic picture of risk, understand where you currently stand and assess the internal environment and properly define and prioritize the most important risks with the greatest impact and likelihood of occurrence (risk type, impact, probability, timeframe, and mitigation strategy/costs).

Next, get a holistic picture of the full set of compliance initiatives you will intersect with, such as SOX, OSHA, data privacy laws, and global trade regulations.

The next step is to set the mission, values, and vision:
- Define mission (the fundamental purpose of the entity, especially what it provides to customers and clients).
- Define core values (the attitude, behavior, and character of the organization).
- Define the vision. A vision is a concise statement that defines the 3- to 5-year goals of the organization.

Next, set the goals. Define a strategy and set business objectives using risks as a key variable for deciding which strategies to pursue.

Assign KPIs to Goals and Set the Right Targets

Define KPIs and targets that translate strategy into performance expectations.

Perform Additional Risk Analysis and Set KRIs

Now look again at risks to see what could keep you from meeting your goals.

Set a response strategy for the risk (treat, tolerate, transfer, or terminate).

Define KRIs and risk thresholds and tolerances for those risks.

Perform Additional Compliance Analysis

Define your compliance requirements. Define policies, procedures, and controls that must be in place to ensure that you can meet the compliance requirements.

Work on the Strategic Action Plan and Initiatives

The strategic initiatives help define the exact methodology (the roadmap) for achieving the various goals. The results of this planning may require revisiting the strategy.

First, develop the roadmap (sequence of actions) for achieving performance, risk, and compliance expectations.

Next, define critical success and failure factors for all initiatives. Every project or investment must, in addition to defining the critical factors for its success, also define its critical “failure factors,” that is, those circumstances under which the project or investment is no longer likely to be successful.

Finally, develop different risk-adjusted scenarios with contingency plans should risks to achieving plans materialize.

Cascade Accountability

Cascade accountability of KPIs, KRIs, and controls throughout the organization and ultimately into individual MBOs for alignment.

Part II - Plan and Execute

The planning and execution gets into the details of planning the strategic initiatives both from a financial and operational standpoint.

Align Corporate Budget to Departmental Budget and Link Corporate and Departmental Initiatives

The budgeting process takes each of the outcomes or actions from the planning process and aligns revenues and expenses against them. Decisions regarding investment priorities and resource allocations define how the company will operate and set the bar for measuring performance.

To create risk-adjusted budgets, incorporate the range of possible revenues and costs of each action into the budget at the appropriate organizational level. Align risk adjusted budgets with contingency plans should risk events occur, or if risks exceed the acceptable threshold to achieving budgets.

Align Departmental Budget to Departmental Operational Plans

The operational planning process links the financial budget to specific operational factors. Plan out each step of each initiative. Consider what risks you have in each area of the operational plan. If the risk materializes, you would want a contingency plan in place that showed the performance and risk implications if we moved the budget from one initiative to another.

Forecast Performance and Risks

Create rolling, risk-adjusted forecasts of the budget (revenues and costs) and operational plan (including number, capacity, and cost of resources necessary to achieve plan) so that you can see trends over a rolling time horizon for those risks whose probability, consequence, and resiliency over time.

Execute Plans

This step is essential but obvious; put the plan into action. Be prepared to execute on the type of risk associated with the plan once the threshold or tolerance is exceeded.

Part III - Monitor and Analyze

In the monitor and analyze phase of the risk-adjusted PM lifecycle, you monitor to understand what is happening in the business, analyze to understand why it is happening, and for those things not on track, adjust to improve the situation relative to your goals.

Monitor

The presentation of information to be monitored is crucial in order to facilitate decision-making. Risk monitoring is aligned directly to KRIs across the source systems that provide transactional data for the KRI. Dashboards linked with risks should help identify and manage key risks versus overall risks that are being prioritized based on exposure through quantitative/qualitative assessment

Monitor performance. You can evaluate the KPIs you’ve set to identify progress made toward achievement of objectives and trends.
Monitor initiatives. You can also evaluate which initiatives are failing or behind schedule.
Monitor risk. You can then evaluate important key risk indicators to identify:
. What and where are our top risks?
. What are the changes to the risk levels for key activities and opportunities?
. Are risks being assessed in accordance with company policy or according to industry best practices?
. Are our mitigation strategies effective in reducing the likelihood or impact of a risk?
Monitor internal controls. Report key control deficiencies, approvals, verifications, and reconciliations to mitigate risk.
Monitor any incidents and losses. What incidents or losses have occurred? If risks or losses have occurred, or external events are affecting the department, document this information, even if you haven’t been tracking it in the system yet.

Analyze

Analysis is a key step in which you not only look at where you are, but what is happening (or what has happened) and why.

Analyze performance. For KPIs, perform analysis to understand why they are increasing or decreasing.
Analyze initiatives. To evaluate initiatives, perform analysis on the initiative to understand why it is succeeding or failing.
Analyze risk. For KRIs, perform analysis to understand why they are increasing or decreasing.
Analyze controls. When analyzing internal controls, you perform analysis on their effectiveness.
Analyze root causes of incidents or losses. If incidents or losses occur, perform analysis on the root causes and trends.

Adjust

After monitoring to know what has happened and analyzing to understand why it happened, for those things not going according to plan, it is time to set the business back on course by taking what you’ve learned and using that information to adjust the settings across the enterprise.

Adjust performance. If you see KPIs trending in the wrong direction, once you have analyzed the root causes, it should be clear what actions to take to set things back on course.
Adjust initiatives. For initiatives that are not going as planned, it becomes essential to rapidly take remedial action or cancel them.
Adjust risk. For KRIs trending in the wrong direction, once you have analyzed the root causes, it should be clear what actions to take to set things back on course, often by putting the appropriate mitigating controls in place to stabilize them.
Adjust controls. For controls violations, adjustment takes the form of remediation and certification.
Adjust after incidents or losses. For incidents and losses, the correct adjustments typically involve reexamining if we are tracking the right risks and have put the appropriate controls in place to mitigate them.

Part IV - Model and Optimize

In the model and optimize, we strive to assess the drivers of performance and risk at a deep level to understand the various alternatives we can pursue with the goal of making the best decision given a certain set of constraints.

Model

Modeling falls into three categories.

Revenue, Cost, and Profitability Modeling. Modeling the costs, revenue, and profitability implications of performance management, risk management, and compliance management activities and their drivers can be achieved at a very detailed level using activity-based costing and associated methodologies.

Scenario Modeling. Scenario modeling can be applied to financial and operational modeling and focuses on creating different business scenarios.

Simulation Modeling. More advanced modeling including Monte Carlo simulation supports creating a broad range of scenarios based on multiple iterations of input assumptions and combinations.

Optimize

The goal at this phase of the PM lifecycle is to determine the optimal way to achieve objectives by taking into account the entire context of the problem, including all relevant constraints and assessments (costs, benefits, risk, labor and time).

Wrapping Up

From a process unification perspective, risk and compliance management operating in tandem with performance management will become differentiating capabilities in the management of an organization.

From a technology unification perspective, business intelligence can be conceptualized as the base of the pyramid upon which performance management and governance, risk, and compliance are built, since it provides the basic technology capabilities and infrastructure that serve as a foundation for the higher layers of the pyramid. Connecting governance, risk and compliance capabilities with performance management capabilities through a common business intelligence platform establishes a single, unified, cleansed repository of information and common semantics on top of that information, which is critical to enabling risk-aware performance management business processes. Without this common foundation, it is impossible to obtain any synergies that extend beyond deploying any one of these capabilities in isolation.

When You’re Not Ready for Data Governance

Gwen Thomas recently published a good post in her blog Data Governance Matters, entitled When You’re Not Ready for Data Governance, where she lists some conditions that need to be in place for a Data Governance program to be successful in delivering value and the signs that you're not ready:

1. A driving reason for Data Governance, supported by high-level sponsors.
Is there concensus about what needs to change, and why, and what will happen if it doesn’t? Are high-level stakeholders supporting the changes that will come with governance?

A sign you’re not ready: key stakeholders say they don’t see a need for Data Governance, and there’s no executive mandate to “trump” their opinion.

2. Political will.
The activities of data governance - setting standards, enforcing them, and resolving issues - are only needed because conflicts and disputes exist. What will happen if key data stakeholders insist that THEY don’t have to adhere to rules? What if they reject the rulings of roundtables, councils, or boards? Before you get to that situation, your Data Governance Sponsor should decide how they would react to stakeholders who reject being governed.

A sign you’re not ready: key stakeholders are not getting what they need from data, but they state they would not be willing to vote against a dissenter in a council meeting. Another sign: mid-level participants state they would not be willing to champion a standard unless their business sponsors provided “political cover” for them.

3. A commitment for participation.
Is there a commitment for time and support by those who will be triaging requests for support, setting standards, and resolving issues?

A sign you’re not ready: those whose attendance will be required at rule-making sessions are routinely double-booked or triple-booked, and cannot commit to being in attendance at required sessions.

4. Project Management/Documentation/Communication.
Someone has to “herd the cats,” facilitate sessions, create documents that summarize issues, draft rules and standards, and meet with stakeholders to mine their knowledge and address their concerns. Without this level of support, you’re likely to have a series of non-productive meetings.

A sign you’re not ready: You can’t get a commitment for enough hours from the right resource; instead, you might be assigned resources who can’t write well, aren’t strong faciliators/mediators, don’t understand the politics associated with data decisions, or are not regarded by participants as as politically-neutral, trusted brokers of information.

5. Knowledgeable data analysis.
Someone has to bring knowledge to governance sessions - knowledge about where data is, how it is actually used, undocumented but followed rules, and points of contact within the organization for understanding protocols, policies, and historical decisions.

A sign you’re not ready: If you can only assign a new hire to this role. Why? New resources (rather than seasoned veterans) are rarely successful in this role; even if they understand their discipline very well, they probably are lacking in tribal knowledge or even where to obtain it.

She finishes with: "An important part of configuring a program is being honest with yourself about your organization’s readiness. Just because something is the right thing to do doesn’t mean that RIGHT NOW is the time to do it."

Information Visualization Manifesto

Manuel Lima is an expert on the topic of Information Visualization. He publishes a good site called Visual Complexity. According him, the site intends to be a unified resource space for anyone interested in the visualization of complex networks. He also publishes a blog called VC blog. He wrote in his blog an interesting Information Visualization Manifesto, where he listed 10 directions for any project on Information Visualization:

Form Follows Function
Form doesn’t follow data. Data is incongruent by nature. Form follows a purpose, and in the case of Information Visualization, Form follows Revelation.

Start with a Question
“He who is ashamed of asking is afraid of learning”, says a famous Danish proverb. A great quality to anyone doing work in the realm of Information Visualization is to be curious and inquisitive. Every project should start with a question.

Interactivity is Key
As defined by Ben Shneiderman, Stuart K. Card and Jock D. Mackinlay, “Information Visualization is the use of computer-supported, interactive, visual representations of abstract data to amplify cognition”. This well-known statement highlights how interactivity is an integral part of the field’s DNA. Any Information Visualization project should not only facilitate understanding but also the analysis of the data, according to specific use cases and defined goals. By employing interactive techniques, users are able to properly investigate and reshape the layout in order to find appropriate answers to their questions. This capability becomes imperative as the degree of complexity of the portrayed system increases. Visualization should be recognized as a discovery tool.

Cite your Source
Information Visualization, as any other means of conveying information, has the power to lie, to omit, and to be deliberately biased. To avoid any misconception you should always cite your source. By doing so you allow people to review the original source and properly validate its authenticity. It will also bring credibility and integrity to your work.

The power of Narrative
Human beings love stories and storytelling is one of the most successful and powerful ways to learn, discover and disseminate information. Your project should be able to convey a message and easily encapsulate a compelling narrative.

Do not glorify Aesthetics
Aesthetics are an important quality to many Information Visualization projects and a critical enticement at first sight, but it should always be seen as a consequence and never its ultimate goal.

Look for Relevancy
Extracting relevancy in a set of data is one of the hardest pursuits for any machine. This is where natural human abilities such as pattern recognition and parallel processing come in hand. Relevancy is also highly dependent on the final user and the context of interaction. If the relevancy ratio is high it can increase the possibility of comprehension, assimilation and decision-making.

Embrace Time
Time is one of the hardest variables to map in any system. It’s also one of the richest. If we consider a social network, we can quickly realize that a snapshot in time would only tell us a bit of information about the community. On the other hand, if time had been properly measured and mapped, it would provide us with a much richer understanding of the changing dynamics of that social group. We should always consider time when our targeted system is affected by its progression.

Aspire for Knowledge
A core ability of Information Visualization is to translate information into knowledge. It’s also to facilitate understanding and aid cognition. Every project should aim at making the system more intelligible and transparent, or find an explicit new insight or pattern within it. It should always provide a polished gem of knowledge.

Avoid gratuitous visualizations
To the growing amounts of publicly available data, Information Visualization needs to respond as a cognitive filter, an empowered lens of insight, and should never add more noise to the flow. Don’t assume any visualization is a positive step forward. In the context of Information Visualization, simply conveying data in a visual form, without shedding light on the portrayed subject, or even worst, making it more complex, can only be considered a failure.

Instrumenting Your Enterprise for Maximum Predictive Power

I read an interesting post in the Forrester's blog, entitled Instrumenting Your Enterprise for Maximum Predictive Power, written by James Kobielus, where he told about why the companies need to be able to predict future scenarios.

He started explaining: "predictive analytics can play a pivotal role in the day-to-day operation of your business. It can help you focus strategy and continually tweak plans based on actual performance and likely future scenarios." He said: "The grand promise of predictive analytics—still largely unrealized in most companies—is that it will become ubiquitous, guiding all decisions, transactions, and applications. For the technology to rise to that challenge, organizations must move toward a comprehensive advanced analytics strategy that integrates data mining, content analytics, and in-database analytics. Already, we’ve sketched out a vision of “Service-Oriented Analytics,” under which you break down silos among data mining and content analytics initiatives and leverage these pooled resources across all business processes."

He defined: "For starters, assess whether your analytics tools support the following capabilities for developing, validating, and deploying predictive models:

- Model multiple business scenarios: You should be able to build complex models of multiple, linked business scenarios across different business, process, and subject-area domains, using such key features as strategy maps, ensemble modeling , and champion-challenger modeling.
- Incorporate multiple information types into models: You should be able to develop models against multiple information types, including unstructured content and real-time event streams, while leveraging state-of-the-art algorithm in sentiment analysis and social network analysis.
- Leverage multiple statistical algorithms and approaches in models: You should be able to develop models using the widest, most sophisticated range of statistical and mathematical algorithms and approaches, including regression, constraint-based optimization, neural networks, genetic algorithms, and support vector machines.
- Apply multiple metrics of model quality and fitness: You should be able to score and validate model quality using multiple metrics and approaches, including quality scores, lift charts, goodness-of-fit charts, comparative model evaluation, and auto best-model selection.
- Employ multiple variable discovery and assessment approaches: You should be able to build and validate models using various approaches for variable discovery, profiling, and selection, including decision trees, feature selection, clustering, association rules, affinity analysis, and outlier analysis."

Kobielus adviced: "to instrument your organization for maximum predictive power, you should also tool your advanced analytics to support the following capabilities:

- DW-integrated data preparation: To speed up and standardize the most time-consuming predictive modeling project tasks, you should be able to leverage your existing data warehouse, extract transform load, data quality, and metadata tools to support a full range of data preparation features.
- Deep application and middleware integration: To deliver models deeply into whatever heterogeneous SOA-enabled platform you happen to use, your predictive analytics tool should deploy on and/or integrate with a wide range of enterprise applications, middleware, operating platforms, and hardware substrate.
- Consistent cross-domain model governance: To avoid fostering an unmanageable glut of myriad models, your predictive analytics solution should support a wide range of tools, features, and interfaces to support life-cycle governance of models created in diverse tools.
- Flexible model deployment: To execute modeling functions--such as data preparation, regression, and scoring—on the widest range of data warehouses and other platforms, your tools should support in-database or embedded analytics. And to scale to the max, your predictive analytics tools should deploy models to massively parallel data warehouses, software-as-a-service environments, and cloud computing fabrics. Your advanced analytics tools should also support development of application logic in open frameworks—such as MapReduce and Hadoop—to enable convergence of data mining and content analytics in the cloud.
-Rich interactive visualization: To deliver their precious payload—actionable intelligence—your advanced analytics tools should support interactive visualization of models, data, and results."

James concluded the article with the following statement: "We see a robust, flexible, SOA-enabled data mining tools as the centerpiece of advanced analytics for fully predictive enterprises. The competitive stakes are too great for businesses to take the traditional silo-mired approach when implementing this mission-critical technology."

James Kobielus wrote a good article, the predictive analytics is increasingly becoming important for help the organizations to make better decisions.

The Decision Management Solutions webinar series

Tomorrow, September 23, 10 AM PT, will start the Decision Management Solutions webinar series with an introduction to the 5 core principles of decision management, by James Taylor. This session will outline 5 core principles of decision management in a non-technical fashion. Suitable for those new to Decision Management as well as those looking for ways to describe Decision Management to non-technical colleagues.

Next week, September 30, Eric Siegel, president of Prediction Impact Inc., will present on optimizing business decisions, how best to apply predictive analytics. Harnessing value with predictive analytics depends on some careful choices: What kind of customer behavior you predict and which operational decisions you automate with it. This webinar will guide you in making these choices, and cover a healthy dose of the core technology along the way.

There are 11 webinars in the series and you can use the registration page for the series to register for several at once. In the registration page, you can also see the details on each webinar.

The Decision Management Solutions webinar series is organized by James Taylor through his company, Decision Management Solutions, and it is a great opportunity to learn more on the subject with leading experts as James Taylor, Eric Siegel, Barbra von Halle, Larry Goldberg and many others.

For those interested in Decision Management, James Taylor and Neil Raden wrote an excellent book: Smart Enough Systems: How to Deliver Competitive Advantage by Automating Hidden Decisions.

Why Performance Management and Business Intelligence are Crucial at this Moment

Howard Dresner wrote recently in his blog, a nice post called Why Performance Management and Business Intelligence are Crucial at this Moment, where he told about the importance of use BI/PM to direct the companies to make better decisions during the world crisis.

He said: "the wise organization carefully analyzes the current threat, developing multiple scenarios for the future and creates suitable short and long term plans. This forward-thinking approach allows management to proceed strategically, with an eye to the future, but still grounded in the present." and "EPM and BI are critical during this time. This is true. However, I don’t mean just the technology. I mean the philosophy! This begins with transparency and accountability. Without those as core tenets of an organization, the best technology won’t help. However, under the right conditions, BI and EPM can work wonders – allowing an organization to quickly develop perspective, to assess strengths, weaknesses and capabilities."



Gary Cokins commented: "It baffles me why some organizations hesitate to apply business intelligence (BI) tools and the performance management methodologies that convert BI’s potential into realized results."

Neil Raden commented: "You're of course right that many organizations will take the knee-jerk approach and cut expenses. Despite all of our exhortations, '09 will probably be a weak year for BI/EPM software. I hope I'm wrong." I also hope Neil Raden is wrong, but unfortunately I agree with him, because I also think 2009 will be a weak year for BI/PM software.

I agree with Howard Dresner, PM and BI are critical during this time, and increasingly important the companies to use BI/PM to make better decisions. Mainly because of the crisis, with the companies cutting costs and decreasing budgets, they should think of BI/PM not only as a cost, but a necessary investment to help them to drive their companies to an effectively strategic management.

Indeed, I think there are some ways for companies save money and reduce BI/PM costs without affecting their business objectives. I believe that companies should consolidate existing solutions and projects, or to develop new solutions using their already purchased BI/PM softwares (I am considering companies that have good BI/PM tools). Thus, the companies can save money on software, hardware and training, and don't fall into the trap of buying one more BI tool. However, if the companies really need to adquire tools, Open Source Business Intelligence (OSBI) or Software as a Service (SaaS) can be good choices to be evaluated.

What Might Go Wrong in Business Intelligence in 2009?

Timo Elliott wrote an interesting post in his blog, about What Might Go Wrong in Business Intelligence in 2009?, where he lists what he thinks what might go wrong. Below is a summary of his list:

1 - People will try to do without BI -- and fail
BI is important, but rarely urgent. In dire economic conditions, some organizations will be too busy trying to survive to think about doing analysis. But action without analysis is called guessing, and is unlikely to help.

2 - People will revert to hand-coding and excel macros -- and waste a lot of money
Corporate cutbacks, "thou shalt not buy anything" policies, and new levels of sign-off will encourage some people to attempt to do analysis without extra software investment: hand-coded data extraction in SQL, data manipulation using Excel macros, etc.

3 - Organizations will implement standards -- but omit to change organizational structures
Keen to reduce costs, organizations will standardize their BI environments -- but will balk at the perceived cost of implementing a dedicated central BI organization. The result will be lower procurement costs, but without a BI competency center there will still be silo BI projects.

4 - Business units will find it easier than ever to implement their own solutions -- to the detriment of the company as a whole
Chafing against corporate BI standards that they didn't chose, business units will find it easier to ever to implement their own "shadow" BI systems.


The answer? Step one: BI organization
What can organizations do about this? Now more than ever is the time to implement BI shared services or a BI competency center. The first goal of the team should be to prioritize BI projects and consolidate existing projects and solutions, eliminating waste and increasing information flow.

I understand his concerns, and I agree when he said "Now more than ever is the time to implement BI shared services or a BI competency center." The companies need to organize their BI projects,and create a BICC is important to integrate and consolidade business and analytical intelligence process, and also ensure that BI knowledge (BI values, concepts, and technology) is shared throughout the organization.

Letter to Santa Claus

Rick Sherman has a different and fun way to comment what he thinks should change next year in BI area: he writes a letter to Santa Claus.

He starts the article commenting: "I've written letters to Santa before requesting gifts that data management and data warehousing managers would appreciate. With the current economic climate, many business intelligence (BI) managers, are putting together their wish lists for Santa. How many of these things are on your list?"

He writes like a little boy asking a gift: "Dear Santa, I think you'll agree that I deserve more than coal in my stocking. I've been a good BI director all year (actually for years) by working with business people, my development staff and my peers on our BI projects."

And he asks: "Could you use your holiday magic to help out with this wish list? All I want for Christmas is:" (This is a summary of his list, he comments about each item in his article)

- Business groups to commit more time (not capital budget) to our data governance efforts.
- IT management that will accept some new and more cost-effective approaches to BI.
- An adequate training budget to enable my staff to increase their skills, knowledge and, ultimately, productivity.
- More disclosure on partnerships and payments between vendors.
- Software pricing that does not inhibit pervasive use throughout our companies.
- Help us rationally deal with using many BI tools in our company.

I agree with his list and I hope Santa Claus meets his wishes.

He published the article in Search Data Management and you can read about the previous letters in his blog.

The Global Recession and its Effect on Work Ethics

Today, Michael Krigsman published in his ZDNet's blog , a post called IT ethics and the recession, where he talks about a survey entitled The Global Recession and its Effect on Work Ethics. This survey was conducted by Cyber-Ark Software, a Security firm, and interviewed 600 workers on Wall Street, New York, Docklands, London and Amsterdam, Holland.

He said: "With a major recession in full-swing, someone had to come up with a survey covering the ethics of office workers in three countries. The punch line: a large percentage of folks surveyed would steal confidential company data in the event of layoff rumors. The results are fairly ugly, painting a negative picture of ethics in the workplace."

About the question: How far would you be prepared to go to keep your job?, more than a third of workers across the sample confirmed that they would be prepared to work 80 hours a week to keep their jobs, 25% would be prepared to take a salary cut, and 15 % of Americans chose "Blackmail my boss about his/her indiscretions at the office party".

Michael Krigsman's considerations about the survey are:
- Employers should not underestimate the level of stress the recession causes workers. Treat your folks with respect and dignity and they’re more likely to behave decently back toward you.
- Once workers learn they may be targeted for downsizing, their ethics may erode. Employers should be aware of this and enhance security accordingly.
- A small number of workers are just plain dumb. Threats of blackmail? You’ve gotta be kidding.

You can download the survey in the Cyber-Ark Software's website (registration required).