Assessment of the new

After the new predictive analysis being created, it would be
assessed to evaluate the quality and potential impact of the new analysis.

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For the evaluation of its quality, two main aspects should
be assessed. On one hand, does it meet the data mining goal? This is expected
as a purely technical assessment based on the outcome of the modelling tasks. On
the other hand, results of the new analysis should be evaluated with respect to
business success criteria (IBM, 2014). In other words, has the project achieved
the predetermined business objectives? To answer this, two issues are
considered essential (SPSS, 2005). One is “how to determine the business value
from the patterns discovered during the stages?” Another one is “which tool
should be used to visualize the data mining results?” The operation of the
business value recognition should be dependent on the interactions between, in
the case, the data scientist, and Frank Magnotti, the business analyst and
decision maker. This is because being fully aware of the purpose of the data
mining goal may not be possible for the new data scientist, and understanding
the sophisticated mathematical results could be a challenge to Magnotti as well.
In addition, the interactions between the two can be an effective suggestion to
ensure the data scientist stays on track and does not get lost in technical
details. As the chief executive officer (CEO) of Fluitec Wind, Magnotti had clear
ideas of the business objectives and the needs of those important prospective
customers. The expression of those ideas to the data scientist could enable her
to understand what functionalities of the model Fluitec and major clients
value. As for the visualisation tool, the choice of visualisation packages such
as pie chart, histograms, scatter plots and so on can be important in properly
interpreting the drawn patterns. Moreover, an efficient and productive business
decision often starts with a good interpretation, whereas a poor interpretation
may lead to the omission of useful information. Apart from the above, since
“customers are always asking for additional functionality”, the extension of
functionality may be an indicator of the quality of analysis for those
customers. However, the costs and benefits of the extension should be evaluated
here. Besides, testing the analysis model within the real application would be
suggested to assess its quality and to check whether the scientist stays on
track if the resources and time are available (SPSS, 2005).

After the quality assessment, the potential impact of the
new analysis could be further evaluated and for the evaluation, probing
questions could be put forward around the main users of the analysis and other
main stakeholders, including existing and prospective customers, existing and
prospective competitors, Fluitec Wind and its shareholders, wind turbine
services industry and wind industry. For all customers, what cost savings they
could expect from the new analysis? Furthermore, how likely are existing
customers satisfied with the performance of the new analysis and therefore
renew contracts? Will it be possible that potential customers are convinced and
purchase the predictive analytics? As for Fluitec, what profits or other
benefits, such as reputation, it could expect from the analysis and how likely and
to what extent will the analysis strengthen its competitiveness and contribute
to its market share extension? The answers to the impact on Fluitec may help
Magnotti figure out how likely the analysis will be a threat to the existing
competitors or a barrier to entry for potential competitors. As for Fluitec’s
shareholders, especially its parent company, it will not be a trouble if shareholders
are satisfied with the analytical results but it will be the case if the
analysis does not meet their expectations because the major technical supports
are gained from its parent company. Therefore, the product’s impact on
shareholders’ decisions should be considered. As for the impact on the relevant
industries, the answers to the impacts on the above parties could be aggregated
to give some ideas.

Part 2 – Dashboards

In this part, comments will be put on the general design and
the chosen visualisations of the given dashboard that is advertised by a
company as a dashboard that every sales team member needs. In order for
simplicity, it is assumed that the six key performance indicators (KPIs) shown
on the dashboard are suitable for their purpose and therefore no discussions
will be placed upon the KPIs.

Taking a view at the design of the dashboard first, it is
obvious that, although subtitle is given to each component of the dashboard, no
headline is given to tell viewers that it is designed to show six KPIs for a
fictitious coffee shop. Besides, although it is good that the charts in the
dashboard use a consistent colour scheme, there is no need to use sequential
colours for defining each attribute in the charts. On one hand, the usage adds
no value to dashboard design and on the other hand, it creates a difficulty in
identifying and distinguishing certain attributes in the charts. The chosen
colours should be of different intensities to direct attention around the
dashboard. Colours used in the dashboard are suggested to be replaced by
colours that complement each other and colours that blur or clash should be
avoided. Moreover, the layout should be criticised as well. The general rule
here is that the key information should be displayed first as the most
important view goes on top or top-left (IB9BW0 Lecture notes). However, it
seems that the charts of KPIs are simply placed randomly on the dashboard and
each KPI is treated as equally important.

In order to present business information effectively, a
significant part of a dashboard is visualisation types chosen. The chosen
visualisation tool for each KPI in the dashboard would then be discussed. The
main idea here is the visualisation should help users interpret and analyse
data clearly and effectively (IB9BW0 Lecture notes).

As for the first chart (Annual Sales by Region), the choice
of a pie chart is apparently not a perfect choice. Although such a chart can be
easily scanned and understandable for users and they should be able to easily
identity the largest slice in the chart, it is found difficult to accurately compare
the 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 quite
similar. The use of colours and 3-D effects are unnecessary because they cannot
add value or extra information to the visualisation but create interpretation
difficulties instead. However, using bar charts is appropriate in order for
quick comparisons. Again, such a clear and compact method can be easily

 Then, in the fourth
part, a line chart is used for the comparisons of sales of different
categories. However, compared with bar chart, which is ideal for comparisons,
especially the comparison between each category, line chart should work better
for trends.

As for the fifth chart, the use of gauges should be
criticised since gauges take up too much space and underperform on comparisons
of “Actual vs Plan” results of different regions (IB9BW0 Lecture notes). The
only good point may be that it can be easy to identify, for each region, which
of actual and plan is higher. However, this could be arguable that the
comparisons are much less effective than comparisons using other tools such as
bullet graphs since, on one hand, the eye is better at comparing lengths than
angles, and on the other hand, the differences between actual and plan could be
easier to identify using bullet graphs.

In the last part, stacked bar chart and line chart are
combined to respectively show the sales and percent growth of different
products. Therefore, comparisons of the sales of each product during the two
years would not be a case. Besides, it can be seen that distinguishing and ranking
the growth rate of each product are straightforward. However, the comparisons
between 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 each
visualisation of the dashboard, the main issue is the comparisons between
attributes of interest. However, the reasons for the issue vary. Among of the
six parts, the second, the third and the final ones are overall satisfying. The
fourth chart may be acceptable but replacing it with bar chart would be a more
satisfactory choice. As for the rest charts, they are likely to be unacceptable
by dashboard users and need much greater improvements.

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