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posted Jun 16, 2013, 5:14 AM by Leroy Dyer   [ updated Apr 21, 2016, 5:16 PM ]

The creation and deployment of dashboards and scorecards is also a key factor in presenting business intelligence data. Operational / Strategic often referred to Executive and Analytical are the main types of dashboards created. Each dashboard type has a level of detail or summarization.

These dashboards are often used to monitor data and traffic and real-time information. Operational dashboards have added considerations such as refresh rate, as the refresh interval can affect performance of the dashboard.  

These dashboards often contain key performance indicators tracking costs, sales and performance over time. Annual, quarterly and monthly this level of summarization is generally all that is required for such dashboards.


These dashboards also offer additional drill down functionality, enabling deeper insight into the data being analysed


When designing dashboards it is important to select the right type of dashboard, as adding functionality where it is not needed clutters the dashboard. Anything selected to be placed in a dashboard must have relevance to the insight being displayed. Selecting the correct chart is also important. Many charts can be created for various types of data presenting and selecting the KPI or metrics required is often important for the insights being shown or projected. Tableau software now has 23 different chart types for dashboard design. This is bringing the tableau software to the current market leader in dashboard technologies. Oracle and Microsoft are also leaders in the development of business Intelligence dashboards. Microsoft Excel has been used to also create pivot charts which have also been widely used. A useful dashboard should be tailored to the employee or users’ needs and requirements. By using caption and headings to identify charts is also an important factor as concerns with placing too many charts on a dashboard often comes with the added sacrifice of “explanation”.


I believe yes; dashboards enable the necessary business stakeholders easily identify information; with traditional database queries and spread sheet analysis often information is not easily identified, and easily missed. Insight are difficult to determine and relationships not always apparent. Using software such as Tableau OLAP cubes can be visualized more effectively. Dashboards can contain maps, charts, tabulated data, and word clouds and other visualizations; the human mind reacts well to such stimulus. The deployment of the dashboards is relatively simple, the functionality and metrics on a dashboard give the company an insight into the data being presented. These interactive charts have enabled business and data modelling to become a fore runner in the big data industry and also in the management and analysing of sales and customer data collected from online transactional data collection. This makes them invaluable to data driven business models.

Dashboards are being commonly used in many forms from gaming to flight statuses. Poker sites use dashboards to present gamers with statistics about games and present winning statistics even offering comparisons with other player’s statistics. Whereas websites offering flight-stats offer present flight data and seat statuses. Such dashboards could be classed as consumer business intelligence. These types of “consumer” dashboards are used to present data to web users or gamers seamlessly over different forms of platforms from gaming consoles to mobile applications. The consumer often is unaware that they are using a dashboard or scorecard. Consumers are being trained in the use of business intelligence dashboards via such portals. It would seem that business intelligence and presenting data has become common place.

With the increasing development in analytical modelling software, companies are now employing advanced visual modelling algorithms and techniques. By visualizing data, companies can visualize their data in various unseen dimensions. Customers can be represented graphically, even geographically or even as clusters which can be segmented by using advanced regression techniques such as , separating customers by Recency, Frequency and Monetary. This gives us the ability to determine valuable customers. The valuable customer segment can then be identified. Their similarities examined, this can then be used to determine business strategies and valued customer insight. The ability to display 3d or interrogate views is of a paramount importance in the increase of these advance business techniques and algorithms. Visual modelling has now become a major driving force in the development of customer driven businesses and has impacted many companies sales and allowed them to achieve an increase in specific target marketing strategies.

Insight driven graphical representation of business data and operational data have already provided businesses with valuable customer insight, market information and predictive analysis. The ability to monitor market changes visually has provided growth opportunities to companies proving its value in the business marketplace. Amazon have used transactional and web mined data to identify customer buying strategies, such data is used in developing customer insights and targeting mechanisms. This data can also be made available in shared data ware housing knowledge bases ready for advanced analytics and visual modelling techniques.