Business Analytics: Questions for Enterprise

Firms today are increasingly seeking competitive advantage through advances in business analytics and decision support systems. According to a white paper published by nGenera (2008) in collaboration with Prof Thomas Davenport:

The next wave of business reengineering is being powered by business analytics, and the potential performance breakthroughs are just as large as they were 15 or so years ago. Many of these breakthroughs will come through the ability to integrate the demand side of the house with the supply side of the house as never before. Even information-rich industries have tended to concentrate on one side or the other. With the power of business analytics, corporations can make and manage the demand-supply connections – a big step closer to the goal of optimizing the performance of the corporation as a whole.

Here are six topical questions (with supporting questions) posed by nGenera for companies seeking to compete analytically:

1. Where should we leverage business analytics?

• What is our distinctive capability? On what basis do we choose to compete? And how clear and definitive are we about that choice?
• What performance levels or innovations in this area would blow away the competition?
• What information, knowledge, and insight would it take to perform that way? What are the biggest unanswered questions and biggest opportunities?
• How would we act upon that information, knowledge, and insights.

2. Why now?

• What are our direct competitors doing or attempting with business analytics? Is anyone in our industry jumping ahead in terms of analytical capability?
• How are analytics changing our competitive landscape? Are we at risk from non-traditional competitors who may use analytics to encroach on our markets?
• What emerging technologies of information integration and analysis should we be exploring more aggressively?
• How fast can we launch a serious business analytics initiative? What’s holding us back?

3. What's the payoff?

• What are our specific performance goals in the area where we choose to compete?
• How well do we measure them? How might better measurement and analysis of today’s performance reveal tomorrow’s opportunities?
• How well aligned are the organization, its management, and its stakeholders with these performance goals?
• What’s our highest ambition? What would it mean in terms of revenue, profit, and market share if we were really to change the basis of competition?

4. What information and technology do we need?

• Is the information we need at hand? Is the data that support our distinctive capability in one repository, with common definitions of key data elements?
• Is this data integrated enough not only to be accessible, but also to be manipulated with analytical tools?
• How completely and accurately does the information measure and represent our distinctive business capability and basis of competition? Is it up-to-date? What are the most glaring gaps and shortfalls?
• Do we have the technologies in place to support business analytics in this area? Or is technology fragmentation holding us back?

5. What kinds of people do we need?

• Do we have a critical mass of analytical professionals on staff? Are we prepared to hire them? Do we need to “rent” this talent in the short term to fill gaps?
• Who can manage analytical professionals? Who has the necessary experience, credibility, and “bridging” skills?
• Will we be ready to train employees to apply the analytical results and operate differently?
• Is the organization at large oriented toward analytical decision-making, or is it wedded to yesterday’s procedures and rules of thumb? How quickly can the organization come up to speed analytically?

6. What roles must senior executives play?

• Are we committed to competing on analytics, starting at the top of the organization? What are the CEO and executive team doing to demonstrate that commitment?
• Is the leader of the analytical function prepared to act upon the results of the analyses? Are the roles and decision rights of other stakeholders, including the CFO and CIO, clear – especially when their roles are novel or overlap?
• Do we have a project leader who can span the worlds of strategy, process performance, and analytics?

Reference: Business Analytics: Six Questions To Ask About Information And Competition (2008), Austin, TX: nGenera Corp.

Business Analytics: Going the Distance

Business analytics stratifies into three levels of inquiry and findings beginning with descriptive, followed by predictive, and finally prescriptive methods as follows:

Descriptive Analytics: A set of technologies and processes that use data to understand and analyze business performance.

1. Standard reporting and dashboards: What happened? How does it compare to our plan? What is happening now?
2. Ad-hoc reporting: How many? How often? Where?
3. Analysis/query/drill-down: What exactly is the problem?

Predictive Analytics: The extensive use of data and mathematical techniques to uncover explanatory and predictive models of business performance representing the inherit relationship between data inputs and outputs/outcomes.

1. Data mining: What data is correlated with other data?
2. Pattern recognition and alerts: When should I take action to correct or adjust a process or piece of equipment?
3. Monte-Carlo simulation: What could happen?
4. Forecasting: What if these trends continue?
5. Root cause analysis: Why did something happen?
6. Predictive modeling: What will happen next if?

Prescriptive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.

1. Optimization: How can we achieve the best outcome?
2. Stochastic optimization: How can we achieve the best outcome and address uncertainty in the data to make better decisions?

While descriptive analytics provide a starting point for understanding problems and performance, the more significant purpose and objective of analytics is to achieve predictive and prescriptive findings via higher levels of technique. Make certain that your business analytics strategy is not short-changing decision makers by concluding with descriptive findings alone. Said another way, insist that your business analytics leaders and teams have the training and discipline to go the distance into all forms of advanced analytical methods and techniques as required. The questions posed above under each level of inquiry can provide the interrogative tools for evaluating your firm's current capabilities.

Source: Lustig, I, Dietrich, B, Johnson, C, and Dziekan, C (2010, November-December), The Analytics Journey, Analytics Magazine, 11-18.

Analytics: Hotter Than Ever

Timo Elliott is predicting that 2012 will be the year that analytics takes the lead as a business driver in today's economy:

The real trend this year is not the technology. It’s about helping business people make better decisions, and actually change the way companies do business. Analytics has always been about transforming business, but the recent huge changes in analytic technology have created interesting new opportunities for business innovation....

In particular, companies want better visibility about what’s going on in their market, and increased organizational agility in order to be able to deal with change fast. It’s like driving in the fog without a map – in order to survive, you should invest in better visibility, brakes, and steering to be able to spot and avoid fast-moving objects looming out of the fog.

Analytics provides these capabilities: business intelligence to peer into the road ahead, risk-management to provide fast alerts to new obstacles, and flexible financial planning systems to help swerve around them....

Many companies are going beyond "just" improving their existing analytic capabilities, using analytics in new ways to change the way they do business. Instead of analytics being something that is used to monitor and eventually improve a business process, analytics is becoming a more fundamental part of the business process itself.

Read More

Timo Elliott

Let's face it, analytics are hotter than ever, especially in today's competitive economy.

Source: Elliott, T (2012, February 10), 2012: The Year Analytics Means Business, Business Analytics.

Related Posts

Self-Service Business Intelligence and Analytics Means Just That

The mantra of "self-service" is now reaching a crescendo in the business intelligence and analytics community. Jorgen Heizenberg of CapGemini acknowledges budget constraints in the current economy make self-service business analytics an enterprise imperative. However, he cautions that security monitoring by information technology (IT) departments is still required.

The current state of our economy is also impacting IT budgets. That’s a fact that nobody can deny. At the same time the need for relevant information has increased considerably. Organizations are more and more focusing on their customer and need supporting data. That is another fact. As a result IT is reconsidering its position (back to the core?) whilst the business is waiting for the much needed report or analysis. This need for faster time to information and less IT involvement has given rise to something that is often called Business or Self Service Reporting (SSR). Traditionally BI reports are created by the IT department. SSR allows business users to do this for themselves using end user oriented query and reporting tools.

Read More

Jorgen Heizenberg

From where I sit, the supervisory involvement of IT in the production of business intelligence and analytics is abating, though security monitoring will continue. However, the commoditization of IT means that budget-constraints will limit IT's capacity to manage business analytics projects directly. Moreover, the demand for analytics itself is expanding at a rate that IT cannot now contend with internally given existing or diminishing resources. Self-service business analytics are the future, which means that IT's involvement in producing business intelligence and analytics will flag with time.

Source: Heizenberg, J (2012, January 18), Self Service Reporting Good! Traditional BI Bad?, CapGemini.

Related Posts

Embedded versus Embodied Decision Support

Within the realm of decision support methodologies, two very different paradigms are vying for the attention of enterprise. The first is what I call the embedded approach to decision support. The embedded approach to decision support emphasizes scientific logic and rigor, and is grounded firmly in the traditional disciplines of operations research, systems analysis, decision analysis, and risk analysis[1].

Embedded Decision Support Methodologies

The second still emerging approach is what I call the embodied approach to decision support. The embodied approach to decision support traces its roots to the information technology movement and enjoys critical acclaim for its potential for automated performance monitoring, business intelligence, and business analytics.

Embodied Decision Support Methodologies

Note that the content-validity of the embedded approach is widely accepted amongst professional researchers and analysts as a body of knowledge. The literature underlying the embedded approach is vast and rich in empirical evidence supporting the validity and reliability of its methods. This extant literature regarding the embedded approach is synonymous with the disciplines of operations research, systems analysis, decision analysis, and risk analysis.

The content-validity of the still emerging embodied approach remains in question. Researchers and analysts are still debating many of the terms used in the embodied approach, and a broad consensus regarding what exactly business intelligence and business analytics entail is not yet evident. The existing literature supporting the effectiveness of embodied methods is mostly descriptive with scant empirical evidence to support its validity and reliability as a proven decision support methodology.

The significance of the differentiation between embedded and embodied methods lies in the warranties that each provide the decision maker. An impressive quality of the embedded approach is that all the terms and concepts used are clearly defined and widely accepted by professional researchers and analysts thus enabling users to articulate universally their findings and recommendations.

In contrast, the lack of consensus regarding the validity and reliability of embodied methodologies limits the utility of what we know to be business intelligence and business analytics. Indeed, the methodological frameworks for business intelligence and business analytics are still emerging in the form of dashboard reporting systems and other untested visualization methods that some researchers argue can lead to cognitive distortions of the evidence uncovered by such methods.

Future consilience between the practitioners of embedded and embodied methods is far from complete or even certain. More empirical evidence will be needed before the embodied approach can be fully converged or enjoined within the deeper conceptual foundations of embedded methodologies. In the mean time, decision makers are advised to take precautions to ensure that embedded methodologies take the lead in verifying and confirming the findings and recommendations of embodied technologies.

[1] Note that risk analysis is not to be confused with risk management, which is a different function and discipline all together.

Parsing Business Intelligence (BI)

Business intelligence (BI) is now a vital imperative of 21st century enterprise. Yet, a unified view about just what BI is and does for enterprise is only now emerging. Prof Ronald K Klimberg and Prof Virginia Miori (2010) address the challenge by parsing BI as follows:

Organizations, both corporate and academic, have been rushing to the table with their own BI groups and programs. Though these groups share a common title, BI, they do not share a common understanding of all that BI comprises. The establishment of their functions follows from the specific strengths and expertise within all of these organizations. This shared limitation was not based on a lack of inclusiveness, but merely a lack of cohesive vision. Further, consider that industry’s definition of business intelligence is by and large quite different from academia’s definition. More so, within industries and within academia, these definitions also vary. The definition of BI seems to depend heavily upon your particular perspective or training. What then is business intelligence...?

Despite the appearance of BI in both academia and industry, until now the field has lacked a clear definition. Not all aspects of BI will be exploited in every situation, but it is still important to know what the future holds. Within this structure, BI was broken down into three significant areas: business information intelligence (BII), business statistical intelligence (BSI) and business modeling intelligence (BMI). Specialists exist in all of these areas, but the importance of the intersection and unions of these areas needs to be emphasized. True intelligence results from the melding of all of these technologies and tools....

Figure 1 presents a cohesive vision of business intelligence as a melding of technologies, models, techniques and practices. The three circles of the Venn diagram each represent areas of study and application that had previously been considered quite distinct: 1. information systems and technology, 2. statistics, and 3. OR/MS [Operations Research/Management Science]. It serves to encapsulate the broadening definition of BI. With this new vision, we may now characterize BI from each of three viewpoints as: business information intelligence (BII), business statistical intelligence (BSI) and business modeling intelligence (BMI). Each of the viewpoints has particular business aspects, and academically speaking, courses that are independent of the other viewpoints. Conversely, each viewpoint can work together or utilize techniques/skills from one or possibly two of the other disciplines. For example, data mining, which requires a high level of statistical knowledge as well as the availably of necessary data, may require significant IT skills and/or knowledge. Further, if data mining analysis demands a systematic process of analysis, modeling skills may be required.

Business analytics (BA), within our framework, is classified as a combination of business statistics intelligence (BSI) and business modeling intelligence (BMI): BA = BSI + BMI. BI is the union of the areas of BA, BI and business information intelligence (BII): BI = BII + BA or more specifically BII + (BSI + BMI). As evidenced in the data-mining example, black and white distinctions between disciplines can quickly become gray.

Figure 1: Business intelligence/business analytics breakdown

Note that modeling is a central practice in BI. Follow the link below to read the entire article.

Read More

Source: Klimberg, R K & Miori, V (2010, October), Back in Business, INFORMS, 37(5).

Related Posts

Barry Devlin: Business unIntelligence

Argues Dr Barry Devlin in his new book, Business unIntelligence: Insight and Innovation beyond Analytics and Big Data (2013):

The big data affair is coming to an end. The romance is over. Business is looking distraught in its silver Porsche, IT disheveled in the red Ferrari. Of course, it wasn’t just the big data. It started long ago when IT couldn’t deliver the data and business looked elsewhere to PCs and spreadsheets. It’s time for business and IT to renew their vows and start working on renewing their marriage of convenience.

When data warehousing was conceived in the 1980s, the goal was simple: understanding business results across multiple application systems. When BI was born in the 1990s, business needs were straightforward: report results speedily and accurately and allow business to explore possible alternatives. IT struggled to adapt. The 2000s brought demands for real-time freedom: the ability to embed BI in operations and vice versa. The current decade has opened the floodgates to other information, shared with partners and sourced on the Web. Divorce seemed imminent, IT outsourced.

But, almost invisibly, beyond the walls of this troubled marriage, a new world has emerged. A biz-tech ecosystem has evolved where business and IT must learn to practice intimate, ongoing symbiosis. Business visions meet technology limitations. IT possibilities clash with business budgets. And still, new opportunities emerge, realized only when business and IT cooperate in their creation—from conception to maturity. The possibilities seem boundless. But the new limits that do exist are beyond traditional capital and labor. The boundaries are imposed by the realities of life on this small blue planet afloat in an inky vacuum, with its limited and increasingly fragile resources and the tenuous ability of its people to survive and thrive in harmony with nature—within and without.

For the corporate world, Business unIntelligence will succeed when it brings insight into business workings, innovation into business advances, and integration into business and IT organizations. But in the broader context, in the real world in which we all must live, our success in the social enterprise that is business can be measured first and foremost in the survival of the cultures and communities of alleged intelligent man, homo sapiens, as well as all the other creatures of this tiny planet, and finally in our willingness to limit our growth and greediness and embrace the good inherent in each of us. It becomes incumbent on each and every one of us to integrate the rational and the intuitive, the individual and the empathic. To take stock of our personal decision making and reimage it in the vision of the world we want to bequeath to our children.

Learn More

We live in a world where information technology (IT) and enterprises (including governments) are landing in unexplored territories, where the rules of technological governance are colliding with the forces of decency across societies in real-time. Indeed, we live in exciting times...

Source: Devlin, B (2013), Business unIntelligence: Insight and Innovation beyond Analytics and Big Data, Perfect Paperback.

Related Posts

The Analytics Journey: Descriptive >> Predictive >> Prescriptive

A key management challenge of our time is to advance business analytics beyond descriptive methods, into higher order predictive and prescriptive techniques. In the video link below, Dr Jai Menon of IBM argues that while business analytics includes descriptive and predictive phases, the final phase of the journey is prescriptive.



Firms that are analytically competitive are not only practiced in descriptive and predictive methods, but are also adept at prescriptive techniques that can actuate responses to what happened (descriptive) or what might happen (predictive) in the future. Follow the link below to a related post that discusses what differentiates decriptive, predictive, and prescriptive analytics from each other.

Related Posts:

Business Analytics: Going the Distance

Self-Service Business Intelligence in 2011

The following is an excerpt from an interview between Mark Brunelli of Search Business Analytics and James Kobielus of Forrester Research:

How do you see the world of self-service BI progressing in 2011?

Self-service BI in 2011 will become the only BI approach that the new generation of information workers will ever encounter. Fundamentally, the way it’s going for us is that everybody wants to have the prestige clients on their desktop for BI -- an in-memory client like a Tibco or a PowerPivot or ClickView. So, what we’re going to see are these in-memory clients that support essentially [light] data mining with interactive visualization. [This will allow users] to bring millions and eventually billions of rows into memory and do some really sophisticated analyses. IT very much wants to go this route since IT doesn’t want to have to build cubes any longer. They don’t want to have to build all of the integration logic if the user can be given front-end tools that they can use to build their own visualizations and to pull data from the data warehouse.

I agree with James Kobielus that the widespread deployment and acceptance of self-service (i.e., "in-memory") business intelligence technologies have arrived...

Source: Brunelli, M (2011, January 28), The Top BI Trends and Analytics Technology Predictions for 2011, Search Business Analytics.

High-Level Systems Components and Integration Now the Priority

“High-level” and “low-level” are terms used to describe and classify systems. High-level systems are generally more abstract than low-level systems, which tend to focus on discrete data specificities within the system rather than on how the system produces information as a whole.

Typical Systems Stack for Bespoke Risk Analytics Production

The graphic above depicts a typical business systems stack that includes data warehousing, integration, and analytical components from multiple vendors. Note that the data warehousing and integration systems appear as low-level components, while the analytical systems appear as high-level components. A key objective of this system is to throughput data into the hands of analysts on a self-help basis.

Over the past decade, systems engineers have worked diligently to install the lower-level components of their systems stacks, including the hardware and software associated with data warehousing and rudimentary integration. However, progress on the upper-level components of these systems stacks has typically lagged. As a result, the realization of the business intelligence (BI) vision in firms has generally been limited to simple performance monitoring with only marginal successes in higher-order analytics production.

The emerging shift in priority from low-level to high-level systems components, and from performance monitoring to higher-order analytics production also means shifting certain decision prerogatives away from information technology (IT) departments toward subject matter experts and analysts. The fact is that BI is not only a production process that requires systems, but also a thinking process that requires both ad hoc and post hoc analysis and testing by subject matter experts. The future of BI requires restoration of the decision support function. Moreover, analysts rather than technologists must assume greater responsibility and leadership over the overall BI effort.

While the shifting emphasis from lower to higher-level systems components brings value-adding potential in the form of higher-level analytics production, this shift also introduces risks and responsibilities that firms must consider in order to better align IT investments with expanding BI requirements. High-level systems components and integration are now the priority.

Related Posts:

Performance Monitoring versus Analytics

Business Intelligence for the Masses Comes Alive

Business Intelligence Requires Thinkers

Business Intelligence versus Business Analytics

What’s the difference between Business Analytics and Business Intelligence? The correct answer is: everybody has an opinion, but nobody knows, and you shouldn’t care.

Read More

Risk Management in Review

I recently responded to a question in a public forum regarding the limitations of risk management. The specific question posed was, “What limits our ability to effectively manage risk?” This is an interesting question given the financial crisis still underway. My response follows:

Risk management continues to be a misunderstood discipline. The truth is we do have the analytics to understand and manage most kinds of risk (at least to some extent). The more serious problem confronting our society is our apparent inability to apply that knowledge. I'm reminded of the story of a civil war soldier who was listening to one of his officers read from a newspaper. The story goes that the officer quoted from a story by commenting, "...it says here there were fifty percent casualties at the battle of..." The soldier who was listening to the officer's comment responded by asking, "...wow, is that a lot?"

The point is that having the analytics to describe risk, and having the knowledge and training to understand the analytics are two different things. My impression is that many (if not most) business leaders are poorly trained at understanding risk analytics beyond what might be described as layman's terms. What is most needed today is for our business leaders to become more knowledgeable of how to understand and use risk analytics in an effective and meaningful manner. The days of making guesses based on institutions are long gone, especially when those decisions can result in losses of billions of dollars, as well as suffering amongst the ranks of employees and other stakeholders who are ultimately victimized by those decisions.

My advice to business leaders at all levels is to make risk analysis a centerpiece of their training in graduate school. If you puzzle over terms such as variance, standard deviation, stochastic, optimization, and so forth, then it may be time to schedule some training in these skills as part of your lifetime learning plan.

The Compleat Business Intelligence (BI) Analyst

According to Gert H N Laursen and Jesper Thorlund (2010), the business intelligence analyst is "a bridge builder between the company and its technical environment" (p. 134).

[Business intelligence] analysts need to master three professional competencies to be successful: business, method, and data. We can add to this certain key personal competetencies: the ability to listen and to convince. These are necessary if a task is to be understood, discussed with all involved parties, and delivered in a such a way that it makes a difference to business processes and thereby becomes potentially value-adding.... All in all, it sounds as if we need a superman. And that might not be far off, considering the fact that this is the analytical age. (Laursen & Thorlund, p. 101)

Global enterprise is looking for more than a few good people who can fill this standing order for expertise.

Source: Laursen & Thorlund (2010), Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, Hoboken, NJ: John Wiley & Sons.

Related Posts

Advanced Analytics Not Information Technology

I recently fielded a forum question about the cost-creation versus value-adding capabilities of information technology (IT) and advanced (i.e., bespoke) analytics in enterprise. Here is how I responded:

Regarding the linkages between information technology (IT), advanced analytics, and value, I would gently suggest that IT is a cost center, and advanced analytics are the value-adding proposition. In other words, don't go to the IT department if you are seeking to activate value-adding analytics (though I will concede that IT does have an effective role in business intelligence [BI] production, which is very different from advanced analytics in my view).

Unfortunately, IT solution providers know full well that advanced analytics is what creates value, and so IT firms will typically "bundle" various analytic offerings with a proposed IT solution in an effort to bamboozle the client into believing that scarce IT dollars can buy both transaction management and advanced analytical services together in one "big" IT installation deal. Buyers of IT solutions should therefore beware.

What is needed today is for IT managers to yield the analytics space to subject matter experts with analytical solutions that stand separate from the data warehousing infrastructure, while seeking to reduce costs in IT by exploiting the economies of scale that IT solutions typically contribute to the cost analysis.

Again, IT is a cost center, while advanced analytics (separate from BI) are the value-adding activity.

Knime for Business Intelligence

For business analysts and firms seeking to expand their skills and capabilities from localized analytics into the broader realm of business intelligence processes and solutions, check out Knime. According to Knime's website:

Knime (Konstanz Information Miner) is a user-friendly and comprehensive open-source data integration, processing, analysis, and exploration platform. From day one, Knime has been developed using rigorous software engineering practices and is used by professionals in both industry and academia in over 60 countries.

Knime delivers robust features that encompass the full spectrum of business intelligence production requirements, including tools for: a) integrating multi-source data via open database connectivity (ODBC) and real-time processes; b) diverse analytic tools for data mining such as clustering, decision trees, rule induction, neural networks, association rules, scoring, meta-analysis, and more; and c) state-of-the-art presentation tools that easily integrate with existing ad-hoc reporting, automated dashboard, and systems actuation platforms. Knime is also actively supported by third-party extensions that integate Knime with R (Project R), Excel (Microsoft), and other widely-used integration, analytics, and presentation platforms.

Knime is the missing application that analysts have long-sought to enable self-service production of business intelligence. Anyone seeking to understand and manage the entire business intelligence production process will find Knime to be didactically useful.

Follow the link below to learn more.

Knime

Related Posts

Analytical People

A white paper released by nGenera (2008) in collaboration with Prof Thomas Davenport identifies three levels of analytical people to consider when hiring:

1. Analytical professionals. Most successful analytical competitors have a core cadre of people who design and conduct experiments and tests, define and refine analytical algorithms, and perform data mining and statistical analyses on key data. In most cases, these individuals have advanced degrees – often Ph.D.s – in such analytical fields as statistics, operations research, logistics, economics, or econometrics.

2. Analytical semi-professionals. They can do substantial amounts of modeling and analysis using spreadsheets or visual analysis tools, but are unlikely to develop sophisticated new algorithms or models. These individuals might be, for example, quantitatively-oriented MBAs with deep knowledge and experience in the business process or function that’s the analytical focus of the enterprise.

3. Analytical amateurs. The employees who do the day-to-day work of the business also need to understand something of the analytical basis for operations and decisions. For example, if a lodging chain employs sophisticated analytics for revenue management, those who quote room prices to customers need to understand, at least to some degree, how prices are derived, and when they can be overridden.

Reference: Business Analytics: Six Questions To Ask About Information And Competition (2008), Austin, TX: nGenera Corp.

Tame versus Wicked Business Intelligence Problems

I recently commented on a post in a business intelligence forum, and I wanted to share that comment here for others to consider. The forum was discussing the challenges of implementing business intelligence and analytics:

One of the concerns I have with automating business intelligence is that many problems that I am confronted with from my clients are simply not "tame" (in other words, problems we understand, and in which ample data is available). Solving "tame" problems is much different than solving "wicked" problems (in which we have little understanding of the variables or conceptual framework, and in which little pertinent or valid data is available). Clearly, "tame" problems should be automated as much as possible. However, "wicked" problems are often the "writ large" of business intelligence requirements. Not all problems can or should be automated as the first step in solving the problem at hand. Also, automated business intelligence must be transparent enough that validation and reliability testing can be performed manually. Business intelligence is a vital growth area for enterprise in the 21st century, but we must still take care to do the job right in accordance with best research practices.

Good Data Does Not Guarantee Good Decisions

According to Shvetank Shah, Andrew Horne, and Jaime Capellá in Harvard Business Review (2012):

Analytic skills are concentrated in too few employees. When a new form of analytics enters the workplace, companies typically start by hiring experts versed in using it, reasoning that the skills will trickle down to all. But too many companies are stuck in the “expert” phase. They have a handful of highly skilled analytics professionals but have not begun to train everyone else to make use of their analytics methodology.

Read More

Let's face it, the world needs more fully qualified data analysts in all fields. Those entering college should not hesitate to undertake degrees in business-related analytical disciplines, including actuarial science, finance, accounting, statistics, and risk analysis. Go for it!

Source: Shah, S; Horne, A; & Capellá, J (2012, April), Good Data Won't Guarantee Good Decisions, Harvard Business Review.

Related Posts

Three Obstacles to Predictive Analytics

SimaFore cites three key obstacles that firms confront along the path to predictive analytics:

1. Lack of data maturity
2. Inadequate technology
3. Lack of executive support

Read More

Multi-Monte Carlo Function in ModelRisk 4 Software (Vose)

Companies that are still not competing on analytics are behind the power curve. If any of the three conditions above are constraining predictive analytics at your firm, consider remedies.

Source: SimaFore (2012, February 2), 3 Reasons Why Predictive Analytics May NOT Benefit Your Business.

PS: ModelRisk 4 software is a product of Vose Software BVBA, Ghent, Belgium. Follow the link below to learn more:

ModelRisk 4

Performance Monitoring versus Analytics

Find below a useful table (click image to enlarge) detailing the fundamental differences between dashboards for performance monitoring and analytical applications. Most business intelligence (BI) vendors blur their offerings between both categories of BI, often leaving buyers dissatisfied with the solution following installation. The ongoing tensions between information technology (IT) managers and business analysts exacerbate this confusion as IT managers tend to select dashboard monitoring solutions while business analysts often prefer stand-alone analytical applications. The differences between performance monitoring and analytics are instructive.

[click image to enlarge]

Source: Dashboard for Monitoring Performance vs Analytic Application (2010, April 14), On Target.