Week 5 & 6 - Information Dashboard Design

There are various types of dashboards for different purposes - Strategic, Analytical and Operational purposes.

Other than the guides in the lecture slides to display an effective dashboard to top management, end users or administrators. Rich Freeman from Microsoft stated a few of the best practices in developing an effective dashboard as follow:

1. Track only the most important metrics
- Determine which metrics to track
- Should align with company's strategic as defined by top management
- Keep dashboard simple, which focus on critical measures

2. Use the right tools
- Need a database (E.g. Microsoft SQL Server)
- A portal application (E.g. Microsoft Office SharePoint Server)

3. Keep source data accurate and up to date
- Encourage sales team to update financial, enterprise resource management (ERM), and customer relationship management (CRM) systems at least weekly
- Use dashboard and inspire action that will improve sales and marketing effectiveness

For more details of the above points, you guys can read it more at here.

These series of video clips will provide some tips in creating an effective dashboard.
1. YouTube - Dashboard Reports # 1 - Users
2. YouTube - Dashboard Reports #2 - Mediums
3. YouTube - Dashboard Reports #3 - Colours
4. YouTube - Dashboard Reports #4- Layouts
5. YouTube - Dashboard Reports #5- Length

For this week lecture, it also taught us about using visual perception to highlight those important attributes, using the appropriate display media to present the information and ways of organizing sets of information such as using tables, spatial maps and small multiples. Next will be testing out the usability.

Week 4 - Data Warehouse and OLAP

Data Warehouse
According to VISUAL-BASIC.NET (2001), a data warehouse is a collection of data marts representing historical data from different operations in the company.

BENEFITS
VISUAL-BASIC.NET (2001) mentioned few of the benefits of creating a data warehouse as follows:
1. Data warehouses are designed to perform well with aggregate queries running on large amounts of data

2. The structure of data warehouses is easier for end users to navigate, understand and query against unlike the relational databases primarily designed to handle lots of transactions

3. Data warehouses enable queries that cut across different segments of a company's operation. E.g. production data could be compared against inventory data even if they were originally stored in different databases with different structures

4. Queries that would be complex in very normalized databases could be easier to build and maintain in data warehouses, decreasing the workload on transaction systems

5. Data warehousing is an efficient way to manage and report on data that is from a variety of sources, non uniform and scattered throughout a company

6. Data warehousing is an efficient way to manage demand for lots of information from lots of users

7. Data warehousing provides the capability to analyze large amounts of historical data for nuggets of wisdom that can provide an organization with competitive advantage

This article provides other terms related to data warehouse by VISUAL-BASIC.NET.

Need for Data Warehouse?
Despite the benefits that a data warehouse give, not all organizations will need to create one. Gary Holmes (2001) mentioned that organizations may want to ask themselves some of the questions such as: Is building a data analysis system an appropriate use of scarce resources in a soft economy? Does your organization have the experience to do it successfully? These questions will be answered differently by each organization.

He continued that if one could increase revenue, cut costs, or improve your market position by using data analysis capabilities, an investigation is certainly justified.

This article will shows more about implementing a successful data warehouse by Gary Holmes.

To add on, this article (Four Ways to Build a Data Warehouse) shows the 4 approaches in building a data warehouse. It also mentioned about the benefits and downside of each of the approaches.

Online Analytical Processing (OLAP)
Found this image from MicroStrategy website about OLAP. I thought it was pretty clear and easy to understand.

2 different OLAP analysis

I have learnt from this week lecture about types of dimension (Parent-child, Type 1, Type 2 and Type 3), designing of a data warehouse by choosing the right schema, steps in designing the data warehouse and different types of OLAP.

Week 3 - Developing Dashboard

This week lecture has taught us about developing a dashboard. Before developing a dashboard, one will need to understand the business process, this is to enable us to understand and communicate processes to management, staff and users.

Before that, I've research what is a dashboard. A dashboard has many definitions, but it all meant more or less the same thing. According to Stephen Few (2006), a dashboard is a visual display of the most information needed to achieve one or more objectives which fits entirely on a single computer screen so it can be monitor at a glance.

The definition provided by Stephew Few has mentioned what is a dashboard and the purpose of a dashboard.

To develop a good dashboard, one will need to know the business process. And to better visualise the whole business process, a process map can be used to show a clearer view of the complexity of the processes.

So, how can we create a good process map?

According to Charles McKeever, there are 7 steps:

1. Begin with a single word that describes the process to be mapped

2. Create branches for any supporting topics of the processes off your topic

3. Continue to identify branches off the first level of branches until every branch is expanded to its smallest parts

4. Review each branch to include any gaps as you analyze the entire process map

5. Include notes to detail each part within the branches

6. Move any parts that may not have been placed in the right order

7. Regularly review the document to make certain the map still matches your business processes

It will be best to use a software to map out the process for a long-term success.

This blog elaborates about mapping a good process map by Charles Mckeever. This link here shows the meaning of the symbols and shapes for the process map.

These 2 links should give us a rough guide on creating a process map.

Reference: INFORMATION DASHBOARD DESIGN: The Effective Communication of Data by Stephen Few; O'Reilly Media, Inc. (2006)

Week 2 - Business Performance Management

Business Performance Management (BPM) is an umbrella term covering the business methodologies, metrics, processes and systems used to drive the overall performance of an enterprise.

I have always been wondering what is the link between BI and BPM.

According to Colin White from Wiki, she describes a link between business intelligence and business performance management, "The biggest growth area in operational BI analysis is in the area of BPM. Operational BPM applications not only analyze the performance, but also compare the measured performance against business goals and alert business users when actual performance is out of line with business goals."

There are different types of methodologies that can be used to implement BPM such as:
1. Six Sigma Strategy
2. Balanced Scorecard (BSC)
3. Activity-Based Costing (ABC)
4. Total Quality Management
5. Economic Value Add
6. Integrated Strategic Management
7. Theory of Constraints

Taken from: Wikipedia.

Of the 7 strategies mentioned above, BSC is the most commonly used. Besides BSC, I am quite interested to know more information about Six Sigma.

Six Sigma
Six Sigma philosophy is the application of science and data, rather than politics and hierarchy, as the driver of change. Just as the requirement for observable, measurable data drives scientific debate about, for example, the efficacy of a new drug, Six Sigma practitioners (often known in the Six Sigma lexicon as "black belts") insist that decisions that affect the business' performance, processes, or strategies be based on empirical data, analyzed in a scrupulous manner and tested for veracity in the real world (Rick Freedman, 2009).

By applying a simple performance improvement model called Define-Measure-Analyze-Improve-Control (DMAIC), Six Sigma practitioners assist organizations in achieving the highest level of perfection possible in the business environments in which they operate. DMAIC is simply a refinement of the well-known scientific method of inquiry optimized for the business environment (Rick Freedman, 2009).

This article written by Rick Freedman also mentioned about Six Sigma's history and its evolution.

DMAIC is the sub-methodology of Six Sigma. Other than DMAIC, it also got another sub-methodology - DMADV aka DFSS (Design for Six Sigma).

According to Wikipedia, some of the management tools and methods used by Six Sigma includes 5 Whys, business process mapping, et cetera.

Organisations can choose the methodology based on what is the most suitable and best for them. Different methodologies have its own sets of process, but all methodologies serves the same purpose - to optimise the overall business performance.

Temasek Polytechnic is also offering a 1 day course of understanding Six Sigma at a price of S$220 for organizations. This link shows more details about the course.

As for other methodologies, it can be easily found in Google.

Week 1 - Introduction to BI -Business Analytics and Visualisation

Business Intelligence (BI) is about connecting data to effective action by drawing reliable conclusions about current conditions and future events (Phyllis Chong, 2009).

Information is important for the management so that they are able to make the right decision with the right information. Hence, coming out with a better decision making to support the business operations.

Quoted from David Stodder, 2009:
"While it has always been tough to measure the exact impact of BI and data warehousing on overall business performance, it is certain that in many organizations, implementation of BI and data warehouse tools and technologies has forever changed how users access, analyze, report and share data."

The trend of BI is getting obvious. It has become increasingly important as BI translates data into information for organisations (Diann Daniel, 2007).

According to Diann Daniel (2007), he stated 5 trends of BI.
Trend 1 - There's so much data, but too little insight.
Data is there, but it's trapped in different silos and its accuracy cannot be trusted. For example, Finance department and Marketing department could define gross margin differently.

Trend 2 - Market Consolidation Means Fewer Choices for Business Intelligence Users.
Many BI companies are being bought by bigger organisation such as Oracle and SAP. The merging of the companies' technologies may not fit into client's current architecture, competing technical stacks may be an issue. Hence, market consolidation makes it easier to get BI, but users may be left with fewer choices.

Trend 3 - Business Intelligence Expands from the Board Room to the Front Lines.
BI will be available for employees in all levels, even in the front lines - Operation BI. It will integrate data and process dashboards, and event-driven systems that initiate a business process based on certain data conditions (Boris Evelson). For example, alerting a call center worker to offer a particular promotion or to potential credit card fraud.

Trend 4 - The Convergence of Structured and Unstructured Data Will Create Better Business Intelligence.
E-mail, memos, voicemail messages and other sources of unstructured data are rich sources of information, and companies and developers are responding by looking for ways to blend structured and unstructured data for better decision making. For example, retailers could add comments and complaints from e-mail and call centers into a BI application to enhance their market segmentation analysis, says Evelson.

Trend 5 - Applications Will Provide New Views of Business Intelligence Data.
BI applications is moving beyond the pie charts and bar charts into more visual depictions of data and trends. Alternative ways of displaying complex data—to increase interaction and usefulness—is an area that will continue growing in the coming years, say Evelson and Hagerty.

More detailed information can be found in this article.

In conclusion, BI has become a core decision for organisation whether or not to implement it. It make use of the data and translates them into useful information that will helps business to predict future outcomes and make the correct decision. The decision made will add values to the business.