An Executive Intelligence protocol: (part I)
Caroline Dawe and Sue Wright
Executive intelligence (EI) is a collective noun used to describe the various concepts of intelligence important to the functions of management. These are Management Intelligence, Business Intelligence, Financial Intelligence and Operational Intelligence. In addition, it is often expanded to include Corporate Performance Management, Decision Support, Business Analytics and others of a similar vain.
Executive Intelligence is a discreet function of the management process. It is supported by technologies based on Geographical information Systems (GIS) or mapping technology and OLAP (On-line Analytical Processing) technology specifically designed to support EI solutions and overcome the limitations of Relational and basic spreadsheet technologies.
Executive Intelligence should not be confused with Business Analysis, Market Research, Information Management, Information Systems or Information Technology, all of which are valuable but are very different functions.
Executive Intelligence can be described as a comprehensive understanding of the circumstances surrounding a specific management environment. It provides the best and most appropriate representation of the facts and their derivatives, perceptions, and their interpretations both independently and in combination. The result is the best understanding of what is happening and the reason why. In addition, an understanding of how to improve the situation or to achieve a given objective is also attained. It is not about accuracy or completeness, it is about making the best decisions possible in the given circumstances. Finally, but most importantly, EI needs to be driven by intelligence users (Department Heads / Organisation Executives), rather than Information specialists, data providers or analysts. Executive intelligence is not obtaining all possible information from which to select answers but understanding and answering the right questions.
“It is not about accuracy or completeness, it is about making the best decisions possible in the given circumstances.”
Learning from history
It is generally recognised that many management developments owe their origins to periods in history that required accelerated development within the various functions of management science. In most cases these periods in history would be more accurately described as periods of world conflict or war. In such times, improvements in efficiency, production numbers, and manufacturing lead times were not just beneficial but could mean the difference between life and death. Such periods are in fact responsible for the fundamental principles behind many management techniques used today. Production Engineering, Industrial Engineering, Organisations &, Methods, Operations Research and Intelligence Services, all have their origins in such times. However, with regards to Intelligence Services they also learnt a particularly important lesson that even today most organisations fail to recognise, which is that having accurate intelligence based on meaningful information is only an asset if it is distributed to those who can use it.
We are all aware of the Central Intelligence Agency (CIA), as is common with American organisations, they seem happy to provide a schematic representation of what they call the intelligence cycle. If we consider this cycle in combination with relevant aspects of best management practice, then it is possible to construct a best practice protocol for Executive Intelligence.
Figure 1: The Executive Intelligence Cycle (Based on the CIA’s Intelligence Cycle)
shows the three major components of the Executive Intelligence Cycle.
The Management Cycle: 1 Management
(Management and Leadership of the EI Function)
The EI function is critical to an organisation with regards to the proactive provision of accurate, valid and up to date data and hence information for management to: –
• Decide on overall strategy
• Take the correct strategic and operational decisions at the appropriate times
• Drive short term tactics and ongoing performance
This puts the EI function at the heart of a competitive, growing and forward looking organisation that wants to remain at the top! There will inevitably be a trade off between cost and accuracy or depth of data, but the EI Function will consider the options and present the best intelligence to senior management for their consideration and action.
“This puts the EI function at the heart of a competitive, growing and forward looking organisation that wants to remain at the top!”
External intelligence about global economics, the marketplace and future projections are key to decisions about how the organisation grows and develops and could involve vast amounts of relevant data and expense. In some sectors, such as pharmaceuticals, comprehensive purchasing and supply data are available, but in other sectors all these data will have to be collected internally. However, in both cases, creativity and inspiration could be vital in spotting future trends that would allow an organisation to steal a march on its competitors and forge ahead.
The management of intelligence is therefore concerned with the timely and accurate provision of appropriate and valid intelligence to the organisation’s management team, at proportionate cost to deliver value for money and competitive advantage.
2 Capture and Discovery
(Information purchase, capture and discovery relative to organisational requirements)
The start point for collecting any intelligence data is a request from organisational management regarding a required outcome. This ideally should be provided in the form of a user Intelligence Requirement Specification (IRS). An example of this can be provided on request.
The IRS sets out what intelligence is required by management and for what specific purpose and represents the thoughts of both management and the EI function. If the user’s requirements are well thought through, the EI role is simply to establish whether the intelligence can be purchased or otherwise acquired. However, nothing is ever this simple and there will be inevitable compromises to be discussed on what is available, within what timescale and at what cost. Several iterations of the IRS may be required to refine this down to a specification which is clear and unequivocal. However, this is not a bureaucratic exercise but a meeting of minds of those who need it and those who would provide it.
It may well be that the information can be purchased, but will contain much more information that is not relevant to the user. In this case, the intelligence will need to be rationalised to exclude the unwanted Intel.
(The development and implementation of automatic processing and reporting)
Information and data that are purchased are often in a format that cannot be used or understood straight away, because they are either meaningless as displayed, too specialist, or just plain unintelligible. The EI function’s first job is therefore to make them readable and useable. Again, if the IRS is good, then this is relatively straight forward, but very often there is a need to compromise on both the key data and the way they aredisplayed. Management and EI will then work together until they have got the right data in the right format.
Once this has been done, the EI function can look at how to automate the process, from receiving the information purchased, to producing data in a format that means the analysis required can be done quickly and efficiently. In effect this process strips out what is not needed and puts the resulting information into a format that can be used in the next stage. This is to analyse it to reveal the essential intelligence that is required by management for decisions and management actions. This will involve various software applications and platforms, which should be completely transparent to the user. It should also offer the most appropriate and intuitive view of the information thereby naturally portraying the essential intelligence available in the data.
A large amount of the work involved in developing and providing intelligence through OLAP and GIS technology is the preparation of the data into an integrated and compatible format.
(The skilled and comprehensive analysis of information to provide intelligence as defined by the IRS)
The analysis stage should be contained within the EI function, despite the fact that many managers will want to do their own. The EI function must ensure that all the important parameters are included in the specification (IRS), so that the data can be comprehensively analysed then presented to management appropriately. If the analysis is based on common data and a consistent methodology, then management can be confident that everyone has been measured in the same way, results can be usefully compared and actions taken with confidence.
“…management can be confident that everyone has been measured in the same way, results can be usefully compared and actions taken with confidence.”
In automating the analysis both managers and the intelligence function need to spend sufficient time working through all the possibilities of the data, so that for example, getting to the root cause of underperformance can be done quickly. The analysis tool therefore needs to be flexible and easy to use. If the data defined sales by sales representative and by product, then an analysis of one may lead to an analysis by the other. E.g. if sales were down, it would be necessary to establish which sales people were under performing as well as which products sales were below target. If it were thought that a major competitor was leading a big push in a specific geography, then analysing sales in that geography would also be required to understand any possible impact.
The regular analysis required by management must be implemented consistently, but management will also require the ability to do broader or deeper analysis if results are not as expected (Exceptions Analysis). For example, if one sales person suddenly starts under-performing, an analysis of that person’s results compared to the average, or their prior year’s performance may reveal some pointers. Alternatively if there is a need to reposition the sales force’s geographical boundaries, the ability to project sales if this was to happen is essential when attempting to decide on future targets.
(The most appropriate presentation means, style and media)
Once analysis has been done, there is a need to easily ‘see’ the result. This could be a table of numbers or a graph to display progress over time, or a breakdown of products per group, for example. Most managers use exception reporting to identify where to concentrate their time, so if all is well, then the need to look at the detail is less important. If something has gone off the rails, then the ability to dig deeper into the detail in crucial. Trying to find the root cause of consistent underperformance can require extensive analysis, whereas trying to find out what caused an unexpected blip can be easier to locate. If an automated process can include parameters to define the ‘exception’ as well as the ‘rule’, then this will save managers precious time in identifying where their attention is required.
The higher up an organisation the more ‘big picture’ display of data is required. Summaries of performance should emphasise whether on track or not and further detail should then quickly get down to the real problem. Where trends are emerging then high level summaries should show data over time so that patterns can be seen and trends identified as they start to emerge, rather than be a retrospective feature.
The display of data can be achieved using many media, and managers vary in their ability to take in data from all sources in equal measure. Intelligence functions need to be aware of this and be able to display data in a variety of forms to help different managers get the most from it. The principle to adopt should be to get the main message to jump out at the reader or presenter, rather than having to dig for or uncover it.
“The principle to adopt should be to get the main message to jump out at the reader or presenter, rather than having to dig for or uncover it.”
Often intelligence is derived from very large amounts of data, from which it is difficult to gain specific intelligence. By using OLAP technology it is possible to build a series of views that enables the targeting of specific areas of data relevant to a particular question or area of interest, and plot this data graphically, enabling quick analysis of the data. It is also possible to “drill down” in any specific area to analyse any identified area of concern. Also GIS technology allows the plotting of this data geographically showing at a glance the areas worst affected or underperforming. In this way it is possible to get very different, albeit supporting, intelligence from the same data by use of different technology. It is important that this data is presented in a way that a decision maker can quickly utilise it themselves, as intelligence analysts can get too involved in the technology, rather than using it to identify and answer specific questions and issues.
(The right person at the right time)
Intelligence is only powerful when the correct people have access to it. Most managers will complain that data is always too late, which emphasises the need for managers and intelligence systems to be able to build in forecasts and predictions. One of the most important decisions in a monthly or weekly reporting cycle is when to shut off results and when to display them, i.e. how long it takes to turn a stream of data into a snapshot of performance. As systems get smarter and more complex this can improve greatly, but there is still a trade off to be made between accuracy and timing.
The IRS needs to flag up the needs of different managers at different times. The more operational the need, the more accurate and short term the data or Intel needs to be. The more strategic and longer term the need, the more the data and Intel will need to cover the bigger picture and therefore the degree of accuracy may be less important. However, many ‘trends’ have been started by inaccuracies and misinterpretation of data!
Part 2 of this article can be viewed here
About the authors:
Sue holds an MBA from the London Business School, and has a B.Ed. Hons. from Cambridge. She worked with the Centre for High Performance Development (CHPD) for many years and is currently an independent management and leadership trainer and is a member of the JEIG. Sue may be contacted at email@example.com.
Caroline holds an MBA from Exeter University and has a BSc (Hons) in Applied Statistics. She is currently Interim Director of Acute and Specialist Commissioning at NHS Bournemouth and Poole and has a special interest in exacting how good intelligence can save lives and money within an ever changing NHS. She is also a member and Chair of the JEIG.
Can you see your data trends as they emerge?