driving organizational intelligence

Volume 13 • Number 4 • October/December 2010 www.scip.org 27

Most industry players strive to develop an advanced organizational intelligence capability, since in many cases this capability creates a competitive advantage in the marketplace. Intelligence development success can be achieved by artfully utilizing multiple transition methods combined with strong program management and a clear vision of what exactly this success looks like.

In this article we discuss the steps necessary for the creation of your own Market Intelligence Dossier, which is a structured approach to managing the intelligence effort in any organization. We will also help you locate your firm in the market intelligence maturity curve, and finally we will work together on some of the best ways to move your firm through the maturity curve using tried methods.

THE PLAN-COLLECT-ANALYZE-ADAPT MODELBefore your company embarks on an effort to develop

intelligence capabilities at multiple organizational levels,

By KaSandra Husar and Rom Gayoso, Intel

Figure 1: The PCAA Model

IntelligenceCapability at the

Organizational Level

Implementing

28 www.scip.org Competitive Intelligence

first ensure that the organization’s individual decision-makers involved in this effort are thoroughly trained and educated on the Market Intelligence Process Plan-Collect-Analyze-Adapt (PCAA) Model (see Figure 1).

Adopting the PCAA Model is a wise investment of your resources for many reasons. First, the primary benefits of the Plan phase reside in its ability to assess the task at hand, and to define the scope of the deliverable. The plan also serves as a rallying point for the troops, as its development requires greater engagement and closer collaboration among different stakeholders.

The Collect phase is an important step in the effort since it translates the data discovery process into the tactical execution. It is also an excellent opportunity to reach out and involve other members of the organization in the process. In the Analyze phase you concentrate your effort not only on processing the data, but also on translating the key findings into a way your own organization can understand and apply it.

Finally, on the Adapt phase the effort is focused on feedback that makes the necessary changes to the

deliverables scoped in the Plan phase. In essence, the PCAA Model is one large feedback loop of continuous improvement.

Also determine that the intelligence organization has the business knowledge and the ability to properly integrate that knowledge into marketing intelligence and that they can influence stakeholders to drive decisions that impact the bottom line (Husar & Gayoso 2010). One way to guide decision-makers through the robust development of their requisite knowledge base and industry expertise is by implementing the Market Intelligence Dossier Development Process (see Figure 2).

MARKET INTELLIGENCE DOSSIER DEVELOPMENT PROCESS

The Market Intelligence Dossier Process basically outlines the knowledge areas that decision-makers must have strong acumen in so that they can most effectively impact the business. In reality, as the organization’s eyes and ears are on the external environment, market intelligence practitioners need to have command of the macro forces surrounding the firm, as well as a deep knowledge of the product offerings and an established process to capture and process intelligence.

By following this process one can be assured of obtaining a substantial knowledge base in the desired area of business and industry. In essence it guides the development of the appropriate knowledge required to make optimal business decisions. Once the individuals in an organization have developed these requisite personal market intelligence and business acumen skills, they can then consider moving the organization as an entity to a desired state of market intelligence capability.

STAGES OF ORGANIZATIONAL MATURITY One way to categorize the current status of intelligence

in an organization and map its progression path towards the desired end state is to apply the Organizational Market Intelligence Maturity Stages matrix in Figure 3. The matrix serves two purposes: one is to help you use defined categories to best describe the current stage of market intelligence development in your organization; and the other is to help you draw a plan to help the firm progress from point to point.

implementing intelligence capability at the organizational level

Figure 2: MI Dossier Development Process


Stage 1: Beginning The first stage of organizational market intelligence

maturity is characterized by rudimentary and basic attempts at developing the intelligence processes. Individuals at the top of the organization or key leadership have minimal expectations for the intelligence capability. The organization participates in ad-hoc benchmarking and applies some attention to the external environment for decision-making purposes.

Pockets of stronger capability in business acumen exist, but for the most part the organization is inwardly focused and tends to make business decisions in an information vacuum. Some individuals may make token efforts to build an infrastructure that enables data and information gathering. In this stage, market intelligence creates a slight-to-nonexistent effect on or input to the development of cost strategies.

In many cases, the company bases its future strategic objectives or goals on an extension of its historical or current goals. Then, the market intelligence function is a far from acting as a strategic multiplier of corporate performance as Wolfberg (2006) envisioned it to be. Neither can it operate as the analytical base necessary to develop an operational competitive intelligence system as Hou and Chen (2008) thought it should be as the required organizational, inter-personal, and informational networks at this stage would either be non-existent or not developed enough to allow the market intelligence activity to flourish.

Stage 2: ContainmentThe next stage of

organizational maturity is that of containment. In this stage, the firm’s decision-makers are expected to develop optimal decisions with some input from benchmarking or market intelligence in order to develop a more complex understanding of the industry environment. They also apply data and information which contributes to their strategic planning decisions. In addition, these decision-makers occasionally use market

intelligence to focus on opportunities developing in their outside environment.

Some established infrastructure usually exists in the form of a website that decision-makers can access to find information and data. In some cases a personal knowledge infrastructure is in place to help drive organization-wide competencies and best known methods.

At this stage, strategy deliverables require some element of benchmarking and market intelligence to be accepted by upper management. Even though some knowledge is produced through these two activities, it is still not sufficient to enable the firm to develop a stronger market orientation or a competitive advantage as Wang (2010) envisioned it.

Stages 1 and 2 of market intelligence maturity are characteristic of an organization which is in a reactionary state. In most cases this type of organization finds itself reacting to changing environmental factors and operating in a ‘firefighting’ mode because of its lack of strategic insight into changes in the environment existing outside of the company’s four walls.

Stage 3: In Control The next phase of maturity exists when the

organization has developed, implemented, and applied a full intelligence process. In this phase the top leadership levels convey in a clear and articulated tone that strong


Figure 3: Organizational MI Transformation PIT Model


business acumen, which incorporates knowledge of the company’s environment, is expected when developing strategy. Forward looking planning, anticipation of market trends, and an understanding of environmental impact factors (PEST) are beginning to be the “way we do our jobs,” replacing the event driven and reactionary approach.

In most cases an established central group of intelligence experts are heavily involved in managing and reporting results from an ongoing monitoring of external elements that can impact strategy or be an impetus for reassessing its direction. In some cases, this central team of experts serves a dual function, also operating in an enabling role for the organization.

Here the central team is responsible for developing and managing the intelligence information backbone and infrastructure based on people, information, and tools. Included in this infrastructure is the means to apply the more advanced analytical toolsets and predictive scenario planning. This infrastructure is designed, developed, and implemented for the use of the entire organization. However its operations are primarily focused on developing and driving strategy and decisions.

In this stage the company has created a robust business acumen and intelligence training, and employees have access to skill-set up-leveling tools. When developing strategic objectives and targets, market intelligence becomes an integral part of this goal setting. In many cases, corporate leaders decide to achieve this stage for 80% of the individuals and planning efforts in their organization(s). They move only a few select critical groups of decision-makers to the final stage of role modeling, based on a cost benefit analysis of the time and resources required to reach the stage 4 level.

Stage 4: Role ModelThis organizational maturity stage is achieved

when the firm’s strategic decision-making and market intelligence functions are recognized as an industry role model. In this stage, senior management not only expects decision-makers to be knowledgeable about their business, but also to be viewed as the leading experts in their respective industries. In many cases, these individuals publish white papers, speak at industry conferences, hold patents, process copyrights, and author books.

At this stage the organization operates with the implied understanding that including environmental

knowledge of impact factors is an integral part of “how” one goes about making decisions. This knowledge is embedded in the fabric of an individual’s job role and the culture of the organization.

Sufficient human and financial resources are provided to ensure that the right information is available to the right people, and it assists them in making the right decisions. Appropriate data sources, analytical tools, and planning expertise are in place and funded.

The decision-makers themselves have been thoroughly trained in how to utilize advanced analytical tools such as multivariate factor analysis, wargaming, scenario planning, Porters industry analysis, SWOT techniques, and PEST monitoring. In addition, every decision-maker has a continuously updated dossier in their area of responsibility. This state of knowledge is advanced and proactive in nature.

Based on the availability of strong expertise, at this level internal business partners recognize both the market intelligence team and its individuals as the “go to” sources for business and market knowledge. This results in corporate strategy development benefitting from a highly collaborative front end engagement.

In the role model stage the intelligence infrastructure drives value creation throughout the organization. The market intelligence function not only enables strategy, but is also an integral part of the feedback loop, responsible for monitoring and introducing operational corrections much the same way Peterson & Wofford (2007) envisioned it.

Stage 5: Thought LeadersThe final stage of organizational maturity can be

characterized as that of a role model but with added impact across industry lines which can completely change the way the company conducts business. In this case, the decision-makers are thought leaders. They push the envelope by developing new ways to do business and driving the industry with innovative strategies at the leading edge of thought and development (and in many cases across industry lines). For example, when a market intelligence professional in Delta Company develops a new and innovative type of analytical tool or method that is not only useful in Delta Company’s markets but also across unrelated industry lines, this would be characteristic of the game-changing stage.

An organization operating at this level considers its market intelligence capability a competitive advantage with



direct impact on the company’s success in the marketplace. It would be viewed by others as best-in-class in the practice of market intelligence. This company’s employees would be actively recruited to impart cutting edge knowledge by participating in global consortiums and cross-industry forums such as the Corporate Executive Board (CEB), Strategic and Competitive Intelligence Professionals (SCIP) and more.

At this stage, senior managers utilize the market intelligence functions to sustain strategic competitive advantage (Vanoy & Salam 2010). Their individual experts work to continuously create decisions that ensure sustainable superior corporate performance (Tang & Liao 2010).

Stages 3 through 5 of the market intelligence maturity curve are characteristic of an organization which is in a proactive state. In this state an organization is operating a strong early warning system to identify potential threats and opportunities on the horizon. Its managers develop or adjust the company’s strategy so as to intercept these potential blips on the radar.

LEADING AN ORGANIZATION THROUGH THE MARKET INTELLIGENCE MATURITY STAGES

It is a substantial challenge to lead an organization through the market intelligence maturity stages and transition it from ‘Beginning’ to ‘Thought Leader.’ One of the methods to drive this type of fundamental intelligence change is to apply the organizational market intelligence transformation People-Information-Tools (PIT) Model. (see Figure 4)

This transformation method provides the basis on which to develop the backbone infrastructure for the three main pillars of organizational knowledge: people, information, and tools. In tandem, this approach is also applicable to the industry methodology of Transition Change Management (TCM) which helps transition individuals through the changes in expectation and performance that will lead to their creating the desired results.

The need to focus on people as change agents is not new (see Perme 1999). But over time the focus on people has evolved to include an understanding of how those empowered employees actually became a source of creative disruption (Prewitt 2001). Currently Transition Change Management is viewed as a more holistic process (Prosci 2010). It encompasses not only managers and senior leaders in the organization, but also includes a focus on individual employee capabilities as well as on teams.

Much of the focus on enabling people can be understood by analyzing the Awareness Desire Knowledge Ability Reinforcement (ADKAR) model (Hiatt, 2006). This model basically organizes a combination of awareness, desire, knowledge, ability, and reinforcement to produce a structured way to bring about change. In essence, a central intelligence team should drive the organization through the phases of maturity utilizing both the PIT and the TCM methodologies in tandem.

PEOPLE INFRASTRUCTUREThis pillar of organizational structure refers to the

actual individuals who must be dedicated to harnessing the knowledge of the entire company. They ensure that valuable and accurate knowledge is developed, retained, and shared among the various parts of the organization. By identifying a market intelligence lead role for each segment


Figure 4: Organizational MI Maturity Stages


of the business, these individuals ensure development and operation of the proper TCM, communications, leadership engagements, sub-org intelligence build, and infrastructure maintenance.

People infrastructure also refers to building networks for strategy development and intelligence. These can exist within multiple partner organizations and across a larger division or corporation. Whenever effective intelligence networks are required, team members must become masters in the art of influencing (Husar & Gayoso 2010). The goal of an effective people infrastructure is providing access to both overt and tacit knowledge and expertise across a large organizational span. This creates reduced search costs and increased transparency, leading to more effective collaboration.

INFORMATION INFRASTRUCTUREThe pillar of information structure rests largely on

ensuring that decision-makers who are responsible for strategy and its execution can quickly find and access the data and information required to make optimal decisions for the bottom line. This infrastructure usually encompasses a robust ‘one-stop-shop’ for the benchmarking and market intelligence needs of these decision-makers.

The information infrastructure may incorporate streaming data which is centrally managed, and updated analysis on areas that impact multiple parts of the business (such as fuel cost expectations or economic predictions). This reduces the replication of high-level analysis across multiple areas of the business, a common issue within decentralized organizations. Optimally, each part of the organization should collaborate with as many other divisions as possible to draw the maximum benefit from enterprise-wide licensing or open data sources.

In many cases, the information pillar can be the most difficult element to create. Although sufficient systems and software capability may already be available, the human element in sharing information may not be. If individuals or the corporate culture have not developed a ‘mature organizational’ perspective, issues concerning data hoarding can arise. In addition, project managers within the market intelligence team must understand that both data security requirements for information at the individual level and database integrity are essential elements of culture transformation success.

TOOLS INFRASTRUCTUREThe final pillar, tools infrastructure, is the most

straightforward one. It includes all analytical tools, methodologies, constructs, systems, and training required to ensure that those individuals who drive business strategy can not only locate but also utilize these available resources. This ensures making the right decisions at the right time in the most effective and efficient way possible. These tools can include a regression analysis engine, comprehensive secondary research engines, primary research and benchmarking templates, real-time feeds of market indicators and analysis, access to external consortiums, secondary research firms, and more.

Equally important is providing robust training in these tools. This ensures that experts not only know how to utilize the existing resources, but can also find and process the data and information. The goal is to bring them to the point where they can combine the located data and information with their own market expertise to produce key intelligence. This intelligence can then be applied to decision-making and strategy development. One appropriate methodology is the market Dossier Development Process (see Figure 2), which governs the information-to-action process.

The business environment has become so complex that it is even difficult for very experienced decision-makers to process all the available information in an effective way (Gayoso & Husar 2008). Fortunately the recent confluence of data analysis and computer science has generated one of the most powerful tools in recent times, data visualization.

Data visualization was originally developed for applications such as creating 3D models of cities and geo-visualization (Hotta & Hagiwara 2009) and as tools to process climate data (Ladstadner & Steiner 2010), with yet other practical applications to come. The ability to detect and exploit pattern structures in large data sets (Kuznetsov 2009) resulted in applying data visualization and mapping displays as a creative way to present complex information and help individuals process vast amounts of information in record time (Chen & Ebert 2009).

In fact Luboschik & Schumann (2007) postulated that data visualization tools can not only deal with massive data sets, but also aid in the comprehension of very complex relationships. In addition Sackett & Williams (2006) found specific applications for data visualization in the manufacturing environment. A clear example of data visualization tools can be found within Egwuekwe’s (2010)



work, which is an illustrated map of the unemployment levels in the United States from January 2007 to August 2010.

As the old adage says, a picture is worth a thousand words.

SUMMARYIn this article we discussed how market intelligence

is important only to the extent that it is applied and contributes to the decision-making process. In reality even the most impactful intelligence becomes irrelevant if it is not acted upon and used to determine strategic direction based on environmental factors.

Market intelligence practitioners can add value to an organization as they adopt a structured approach to advancing a large organization through the phases of the market intelligence maturity curve to create competitive advantage. The maturity curve charts a complex but critical path to developing a strong early warning system that identifies potential threats and opportunities on the horizon. It also provides one recognized way to transition a group of executives from reactionary mode to that of a proactive mentality, and empower them to make optimal decisions that impact the bottom line.

REFERENCES Chen, Min; Ebert, David (2009). “Data, information and

knowledge in visualization.” IEEE Computer Graphics and Applications, January, p12-19.

www.cs.swan.ac.uk/~cschenm/ftp/Chen2009CGA.pdf Egwuekwe, Latoya (2011). The decline: geography of the

recession. January 12 updated. www.latoyaegwuekwe.com/geographyofarecession.html Gayoso, Rom; Husar, KaSandra (2008). Buy-side market

intelligence practice: reflections form high tech manufacturing. Competitive Intelligence Magazine v11n2, March-April p26-32.

www.scip.org/Publications/CIMArticleDetail.cfm?ItemNumber=3146

Hiatt, Jeffrey (2006). ADKAR: A model for change in business, government and our community – how to implement successful change in our personal lives. Prosci Research.

www.amazon.com/ADKAR-Government-Community-Successful-Professional/dp/1930885504

Hotta, Hajime; Hagiwara, Maafumi (2009). “On line geo visualization with fast kernel density estimator.” Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 1. p622-625.

http://portal.acm.org/citation.cfm?id=1632436 Hou, Kaihu; Chen,Ting (2008). “Study on a model

about competitive intelligence system of enterprise based on data mining under electronic commerce environment.” IEEE Computer Society in Proceedings of the 2008 International Seminar on Business and Information Management, p203-206.

www.computer.org/portal/web/csdl/doi/10.1109/ISBIM.2008.75

Husar, KaSandra, Gayoso, Rom (2010). Influencing for success. Competitive Intelligence Magazine, v13n2, p7-14.

www.scip.org/Publications/CIMArticleDetail.cfm?ItemNumber=11032

Jin-Fu, Wang (2010). “How organizational learning strengthen market orientation: Findings and case study.” IEEE Computer Society in Proceedings of the 2010 International Conference on Challenges in Environmental Science and Computer Engineering, March p307-310.

http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5493288

Kuznetsov, Sergei (2009). “Pattern structures for analyzing complex data.” Rough Sets, Fuzzy Sets, Data Mining and Granular Computing: Lecture Notes in Computer Science, v5908/2009, p33-44.

www.springerlink.com/content/953q330184319l35/ Ladstadner, Florian; Steiner, Andrea (2010). Climate data

using interactive visualization. Journal of Atmospheric & Oceanic Technology, v27n4 p667-679.

http://journals.ametsoc.org/doi/abs/10.1175/2009JTECHA1374.1



Luboschik, Martin; Schumann, Heidrun (2007). “Explode to explain: Illustrative information visualization.” Proceedings of the 11th International Conference on Information Visualization ’07. p301-307.

www.computer.org/portal/web/csdl/doi/10.1109/IV.2007.50

Perme, Cathy (1999).”Agents of change.” CMP Newsletter June v8n1.

http://www.cmperme.com/pdf/cmp9906.pdf Peterson, J. & Wofford, K. (2007). “Early application of

full spectrum analysis as business process.” Portland International Center for Management of Engineering and Technology, August, p1264-1296.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4349300

Priwitt, Edward (2001). “Interview: Clayton Christensen on disruption.” CIO Insider Newsletter, April 1.

http://www.cio.com/article/30099/Interview_Clayton_Christensen_on_Disruption

Prosci (2010). Change Management Series. Loveland, CO: Prosci Research Publishing.

http://www.change-management.com/tutorial-best-of-2010.htm

Sackett, P. & Williams, D. (2006). “A review of data visualization: opportunities in manufacturing sequence management.” International Journal of Computer Integrated Manufacturing v19n7, p689-704.

https://dspace.lib.cranfield.ac.uk/bitstream/1826/1885/4/Review%20of%20data%20visualisation-2006.pdf

Tang, Ying-Chang; Liou, Fen-May (2010). “Does firm performance reveal its own causes? The role of Bayesian inference.” Strategic Management Journal v31n1, p39-57.

http://web2.cc.nctu.edu.tw/~etang/SMJ2010_TangLiou.pdf


Vannoy, Sandra; Salam, A. F. (2010). “Managerial interpretations of the role of information systems in competitive actions and firm performance: A grounded theory investigation.” Information Systems Research v21n3, p496-515.

http://isr.journal.informs.org/cgi/content/abstract/21/3/496

Wolfberg, Adrian (2006). “Full spectrum analysis: A new way of thinking for a new world.” Military Review, July-August.

http://findarticles.com/p/articles/mi_m0PBZ/is_4_86/ai_n26968780/

Rom Gayoso PhD is an economist with Intel Corporation. His expertise is in econometric modeling and he is responsible for developing several of Intel forecasting models. Rom’s materials have been presented at SCIP, World Future Society, Institute of Business Forecasting, Intelligence for Business Strategy, Executive MindXchange, as well as Market Research Summit, and Future Trends Conferences. He currently holds 14 entries in the Intel Patent Database.

KaSandra Husar is the Manager of the Knowledge Intelligence Management Systems unit at Intel. She is responsible for driving the design and development of the supply side Intel Knowledge Management solution. She also spearheaded the intelligence research skill-set up-leveling for supply chain decision makers, specifically focusing on emerging markets implications. Her materials have been presented at SCIP 2005/2007/2008, the Electronics Supply Chain Association, the Center for Advanced Purchasing Studies and the Corporate Executive Board.

driving organizational intelligence

Documents