Posts Tagged ‘Presentation Techniques’

The purpose of analytics is to guide business practices by empowering decision makers with clear and accurate insights into the problem at hand.  So even the best piece of analytics can fall short of this goal if the link between the analyst and the ultimate decision maker is ineffective.  Therefore, analysts should invest time in perfecting the art of presenting their findings, not just the science of reaching them.

A good presentation begins when the project begins, it does not begin only once the results have been calculated.  In order for a piece of analysis to effectively guide decision-making its objectives must be aligned with the project’s objectives from the very start. 

The easiest way to ensure that the analyst is working in the same direction as the decision maker is to employ the story board technique.  Much like a film maker will create a high-level story board to explain how their story will develop from scene to scene; an analyst should draw a high-level story board showing how the story underlying their analysis will develop from slide to slide.  The analysis should proceed only once the decision maker has agreed that the logical flow presented will achieve the desired end goal.  No fancy software is needed; story boarding can be done by hand or in PowerPoint.

One way to keep the flow clear is to use the headings as summaries of the slides message.  For example, instead of using a heading along the lines of ‘Utilisation Figures’ in the second slide above, I used ‘Utilisation is very risk biased’.  The audience immediately knows where I am going with this slide and doesn’t need to work towards this same conclusion as I speak.  This simple trick will also help you to quickly spot inconsistencies in the story flow.

The story board method works because, in many ways, a good piece of analysis is like a film in how it tells a story: like a film, it must tell a story that flows logically from one point to another culminating in a coherent and memorable message and, like a film, it must often find concise visual summaries for complex concepts. 

Using the story board approach from the start helps to put the piece of analysis in context.  By defining the scope it prevents time being invested in non value-adding activities and by confirming a logical thread it ensures a fruitful outcome. 

The analyst should follow a structured process to create a logical and value adding piece of analysis, such as the five point plan below:

(1) the problem must be fully understood;

(2) the analysis must be designed to address each key aspect of the problem;

(3) the analysis must be carried out;

(4) the results should be interpreted in terms of the problem and used to create the final presentation;

(5) actual performance of the solution should be monitored and compared to expectations.

Understanding the problem is the most important step.  Many an analysts feels that their understanding of a particular analytical technique is their key value offering.  However, the results will be sub-optimal at best and value-destroying at worst unless the problem to which that technique is to be applied is well understood.  Understanding a problem requires research into the business problem at hand, the key factors involved, the relationships between them and the relative priority of each.  The analyst should not be happy until each of these are understood and all of the inherent assumptions have been challenged and proven valid. 

When the analyst has a complete understanding of the problem they will be in a position to prioritise each component part.  Once the problem has been understood and its component parts prioritised, the analysis itself can be designed along the logical lines of the story.  Here dummy graphs and tables can be added to the story boards.  Once again, before the next step is taken it is worth verifying that the proposed measures will indeed prove the point covered by each particular story board.

Once the dummy graphs and tables have been inserted the analyst should ask themselves questions like: would a table showing the relative percentage of good and bad accounts with balances over 90% of their limit, when shown together with a table of average utilisations, prove that the current credit limit policy is enabling higher levels of bad debt?  If not, alternative measures should be considered and weighed in the same way. 

It is important to note though that the intention is not to find the one graph that supports your pre-determined beliefs but rather to find a measure that will prove or disprove your key message.  The analyst should make this decision before the numbers are included to prevent this sort of intentional bias.  In the above example the decision is made before we know for sure what patterns will emerge from the data.  If the data later shows no significant difference in average balances and utilisations between each group, the analyst should be willing to accept that perhaps there is less value in the project than first imagined; they should not try to manipulate the results to hide this fact.

I said earlier that a presentation often has to use visual tools to concisely summarise complex concepts.  These visual tools can include hand drawn schematics (useful when drawn live as an interactive tool for explaining concepts but less able to communicate numerical analysis accurately), graphs (less interactive but more accurate when it comes to presenting numerical results) and tables.  When using visual tools it is important to not let the visuals distract from the message you want to communicate.  The wrong scale can, for example, make trends seem to appear where they don’t exist and disappear where they do.  Excess information, unnecessary legends, the wrong choice of graph, etc. can all work to ‘encode’ your message.  It is important that your visual message faithfully reflects the message of the underlying data, just using an easier to interpret medium.

The same logic applies to animations.  I believe that animations in presentations can add great value when used well but in many – if not most – cases they simply distract.  I tend to use animations when I wish either to create a sense of interaction or when the order in which events progress is important – as when discussing a process with multiple steps, each building on its predecessor.

Once the analysis has been designed and approved it must be delivered.  This is where the most focus has been traditionally and it is indeed a vital step.  The value that an analytical approach to problem solving brings to a business is the ability to make decisions based on a true understanding of the underlying business and its component parts.  Unless the analysis is accurate this is not possible and so great care must be taken when selecting and implementing analytical techniques.  However, this step is most valuable when it comes on top of the solid foundation created by each of the prior steps.

The results of the analysis must be substituted into the story board in place of the dummy graphs and tables.  The final touches should be applied to the presentation at this stage, as should any changes in the message necessitated by unexpected new information. 

Once the presentation is complete, it can be delivered to the decision maker in whichever format is most appropriate.  Thought should be given to the question of the delivery channel.  Presentations that are to delivered face-to-face should include fewer and less detailed bullet points, while those that are to be sent to a large, indirect audience  should contain more detailed information.

However, that is not where the process should end.  I started this article by saying that the purpose of analytics is to guide business practices and so until the extent to which business practices have actually been changed – and the impact of those changes – has been understood, the ultimate value of the analysis will not be known.  Any piece of analysis should therefore cater for a period of on-going monitoring where key project metrics can be measured and the actual results compared to expected results.  The nature of each specific piece of analysis will dictate how long this period should be and which metrics should be included.  But, in all cases, the analysis can only be considered successful once it can be shown that the business has made beneficial changes based on it.

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