Assessment of the newanalysisAfter the new predictive analysis being created, it would beassessed to evaluate the quality and potential impact of the new analysis.
For the evaluation of its quality, two main aspects shouldbe assessed. On one hand, does it meet the data mining goal? This is expectedas a purely technical assessment based on the outcome of the modelling tasks. Onthe other hand, results of the new analysis should be evaluated with respect tobusiness success criteria (IBM, 2014). In other words, has the project achievedthe predetermined business objectives? To answer this, two issues areconsidered essential (SPSS, 2005). One is “how to determine the business valuefrom the patterns discovered during the stages?” Another one is “which toolshould be used to visualize the data mining results?” The operation of thebusiness value recognition should be dependent on the interactions between, inthe case, the data scientist, and Frank Magnotti, the business analyst anddecision maker. This is because being fully aware of the purpose of the datamining goal may not be possible for the new data scientist, and understandingthe sophisticated mathematical results could be a challenge to Magnotti as well.In addition, the interactions between the two can be an effective suggestion toensure the data scientist stays on track and does not get lost in technicaldetails.
As the chief executive officer (CEO) of Fluitec Wind, Magnotti had clearideas of the business objectives and the needs of those important prospectivecustomers. The expression of those ideas to the data scientist could enable herto understand what functionalities of the model Fluitec and major clientsvalue. As for the visualisation tool, the choice of visualisation packages suchas pie chart, histograms, scatter plots and so on can be important in properlyinterpreting the drawn patterns. Moreover, an efficient and productive businessdecision often starts with a good interpretation, whereas a poor interpretationmay lead to the omission of useful information. Apart from the above, since”customers are always asking for additional functionality”, the extension offunctionality may be an indicator of the quality of analysis for thosecustomers. However, the costs and benefits of the extension should be evaluatedhere. Besides, testing the analysis model within the real application would besuggested to assess its quality and to check whether the scientist stays ontrack if the resources and time are available (SPSS, 2005).
After the quality assessment, the potential impact of thenew analysis could be further evaluated and for the evaluation, probingquestions could be put forward around the main users of the analysis and othermain stakeholders, including existing and prospective customers, existing andprospective competitors, Fluitec Wind and its shareholders, wind turbineservices industry and wind industry. For all customers, what cost savings theycould expect from the new analysis? Furthermore, how likely are existingcustomers satisfied with the performance of the new analysis and thereforerenew contracts? Will it be possible that potential customers are convinced andpurchase the predictive analytics? As for Fluitec, what profits or otherbenefits, such as reputation, it could expect from the analysis and how likely andto what extent will the analysis strengthen its competitiveness and contributeto its market share extension? The answers to the impact on Fluitec may helpMagnotti figure out how likely the analysis will be a threat to the existingcompetitors or a barrier to entry for potential competitors. As for Fluitec’sshareholders, especially its parent company, it will not be a trouble if shareholdersare satisfied with the analytical results but it will be the case if theanalysis does not meet their expectations because the major technical supportsare gained from its parent company. Therefore, the product’s impact onshareholders’ decisions should be considered. As for the impact on the relevantindustries, the answers to the impacts on the above parties could be aggregatedto give some ideas.Part 2 – DashboardsIn this part, comments will be put on the general design andthe chosen visualisations of the given dashboard that is advertised by acompany as a dashboard that every sales team member needs.
In order forsimplicity, it is assumed that the six key performance indicators (KPIs) shownon the dashboard are suitable for their purpose and therefore no discussionswill be placed upon the KPIs. Taking a view at the design of the dashboard first, it isobvious that, although subtitle is given to each component of the dashboard, noheadline is given to tell viewers that it is designed to show six KPIs for afictitious coffee shop. Besides, although it is good that the charts in thedashboard use a consistent colour scheme, there is no need to use sequentialcolours for defining each attribute in the charts. On one hand, the usage addsno value to dashboard design and on the other hand, it creates a difficulty inidentifying and distinguishing certain attributes in the charts. The chosencolours should be of different intensities to direct attention around thedashboard. Colours used in the dashboard are suggested to be replaced bycolours that complement each other and colours that blur or clash should beavoided. Moreover, the layout should be criticised as well.
The general rulehere is that the key information should be displayed first as the mostimportant view goes on top or top-left (IB9BW0 Lecture notes). However, itseems that the charts of KPIs are simply placed randomly on the dashboard andeach KPI is treated as equally important.In order to present business information effectively, asignificant part of a dashboard is visualisation types chosen.
The chosenvisualisation tool for each KPI in the dashboard would then be discussed. Themain idea here is the visualisation should help users interpret and analysedata clearly and effectively (IB9BW0 Lecture notes).As for the first chart (Annual Sales by Region), the choiceof a pie chart is apparently not a perfect choice. Although such a chart can beeasily scanned and understandable for users and they should be able to easilyidentity the largest slice in the chart, it is found difficult to accurately comparethe sizes of slices. The situation is worse when the similar colours are used. Looking at the second (Annual Sales by Year) and the third(Annual Sales by Product Type) charts, the good and bad points are quitesimilar. The use of colours and 3-D effects are unnecessary because they cannotadd value or extra information to the visualisation but create interpretationdifficulties instead.
However, using bar charts is appropriate in order forquick comparisons. Again, such a clear and compact method can be easilyunderstood. Then, in the fourthpart, a line chart is used for the comparisons of sales of differentcategories. However, compared with bar chart, which is ideal for comparisons,especially the comparison between each category, line chart should work betterfor trends.
As for the fifth chart, the use of gauges should becriticised since gauges take up too much space and underperform on comparisonsof “Actual vs Plan” results of different regions (IB9BW0 Lecture notes). Theonly good point may be that it can be easy to identify, for each region, whichof actual and plan is higher. However, this could be arguable that thecomparisons are much less effective than comparisons using other tools such asbullet graphs since, on one hand, the eye is better at comparing lengths thanangles, and on the other hand, the differences between actual and plan could beeasier to identify using bullet graphs.In the last part, stacked bar chart and line chart arecombined to respectively show the sales and percent growth of differentproducts. Therefore, comparisons of the sales of each product during the twoyears would not be a case.
Besides, it can be seen that distinguishing and rankingthe growth rate of each product are straightforward. However, the comparisonsbetween the sales in each year would be hard. In conclusion, three problems exist in the general design,including headline, colour and layout problems. When giving insights to eachvisualisation of the dashboard, the main issue is the comparisons betweenattributes of interest. However, the reasons for the issue vary.
Among of thesix parts, the second, the third and the final ones are overall satisfying. Thefourth chart may be acceptable but replacing it with bar chart would be a moresatisfactory choice. As for the rest charts, they are likely to be unacceptableby dashboard users and need much greater improvements.