Acavio: A diagramming tool with data analytics power (Part 1)

This is a new project that I started. The essence of the idea is to have a modern-looking diagramming tool that comes with the data analytics capabilities. Here are the reasons why such tool can be useful:

  • A portable diagramming tool – a modern-looking windows application that is portable and lightweight. You can just copy the file and use on any computer. This is especially handy when comes to cases where the computer is prepared by the organizers such as in conferences, talks, and many kinds of presentations.
  • A free tool – many universities and schools in developing countries do not have the funding for proprietary software like Microsoft Visio. This portable tool can be distributed by the school or coordinator to the users in the institutions without an internet connection. The portable nature allows the tool to be run on most of the computers, with no administrative right required.
  • A data organizing tool – data can be stored and organized using shapes in a diagram. It supports both well-structured and non-structured information. You have to flexibility to store your data in different forms, including text, rich-formatted text, number, date, time, duration, image, percentage, and color.
  • A visual way to analyze data – the power of your data stored in the shapes is unleashed by the analytics features. This is the ultimate goal of this project. If each shape in your diagram is representing a key person in the crime investigation, you can now ask the tool to find out “a [person] who stays in [Queensland] for at least [5 years] and have a connection with [Mr. Governor X].

Here are some screenshots from the internal release of the tool.


Industry-standard interface: minimal learning curve


Inserting data into ANY shape. Default info fields.



One of the unique features in Acavio is that it enable data to be stored within each of the shapes. What kind of data? it’s all up to you. Acavio supports most commonly use data types such as text, memo, numbers, date, time, duration, color, percentage, image, and attachment to be saved as data specific to a particular shape. In other words, the Acavio puts the power of a graph-based database on your hands, without the needs for dealing with the technical complexity.

Work it Your Way: Flexibility to attach any data types to your shape


Have it Your Way: Define your data structure 



I’m currently working on this project as a casual work. While our experience at our university is showing that there is a need for this kind of diagramming tools among academicians, I would also wish to find out is there any other academicians or users who are interested in the features introduced in this post. Feel free to comment on this post to let us know.

Spoiler: Part 2: How you can use the data stored in Acavio?



Acavio: A diagramming tool with data analytics power (Part 1)

ForResearch Project 1: Visualization Designer

Want to create attractive and interactive visualizations but finding Tableau can’t let you save your visualization as private and the selections of databases that you can connect to are limited unless you pay?

We create a powerful alternative to commercial visualization tools which like no other free visualization application. This visualization designer that we created put the power of interactive visual analytic to everyone, without the hurdle of needing to know technical skills. To make it practically useful for you, the visualization designer, which now named as Xsionable Dashboard allows you to connect to most of the database servers out there. You can also feed your Excel or CSV file directly into the tool.

ForResearch Project 1: Visualization Designer

What is Analytics in the BIG DATA era

In this era of Big Data, massive volume of data is produced at an incredible rate and in various formats. The data volume of most modern businesses is expanding by 30% to 50% annually. An modern institution nowadays handle more than 60 terabytes of data annually, which is about 1,000 times more than a decade ago (Beath, Irma, Ross, & Short, 2012). Exponential growth of data from social media, sensory devices, mobile applications, and personalized information has flood the institutions with myriad amount of data. This data that is highly complex, heterogeneous, uncertain, and dynamic requires institutions to have new breed of analytics tools to reap the benefits of Big Data.

Analytics is a multi-dimensional discipline that arises to meet the needs of greater analytical capability. Analytics comprises a set of data-driven processes, technologies, and concepts which pertinent to gaining insight from data. The outcome of Analytics is actionable insights that can inform decision and devise action (Stubbs, 2011). Figure 1 illustrates analytics from a process perspective which includes three major phases, namely manage, integrate, and analyze data.

What really matters in the end is not how much data can be collected, it is about what users can do with the data that counts. An analysis gap exists given that the capacity to collect and store data has been grows rapidly, but the capability to analyze these data increases at much lower pace. This study focuses on the “analyze” phase (orange-colored oval) where the end-users interact with Analytics applications to glean actionable insights from the data. These applications can be categorized into a) visual analytics, and b) advanced analytics, according to their approach to data analysis. They serve different purposes, deal with different data types, and require different levels of skill to operate. Table 1 provides a comparison view of the three categories of analytics applications.

Visual Analytics Advanced Analytics
Purpose § To gain deeper, qualitative insight of the data § To extract hidden knowledge from massive data
Targeted Problem § Operational

§ Tactical

§ Operational

§ Tactical

§ Strategic

Input Data Structure § Structured to Semi-Structured Data § Structured to Non-structured Data
Input Data Volume § Low to Medium § High to Enormous
Main Approach § Human-driven Sensemaking and Reasoning § Machine-driven computations and modeling
Techniques § Interactive visualization

§ Supporting human cognition

§ Statistical and mathematical modeling

§ Artificial intelligence and machine learning

Analysis Result § Descriptive

§ Reflective

§ Descriptive

§ Predictive

§ Prescriptive

User Friendliness § High (Easy) § Low (Difficult)
Processing Capability § Low § High
Common User § End Users

§ Power Users

§ Power Users

§ Data Analysts / Data Scientist

§ IT Specialists

Example of Applications § SAS Visual Analytics

§ SAP Visual Intelligence

§ IBM Cognos Insight

§ Tableau (Stanford University)

§ IBM SPSS Modeler

§ SAS Enterprise Miner

§ RapidMiner


§ R Project

Note that there is a tradeoff between the user-friendliness and the processing capability of the Analytics applications. The applications in the advanced analytics category such as data mining and statistical analysis are known for their powerful processing capability to create knowledge from massive and complex data (Fayyad, Piatetsky-Shapiro, & Smyth, 1996). However, It is widely agreed that they are too complex for wide-spread use because highly specialized knowledge and skills are required to operate them (Nemati, Earle, Arekapudi, & Mamani, 2010). Studies shows that less than 17% of institutions have been actively using advanced analytics for supporting their operations. Whilst visual analytics applications are relatively easier to use, their processing capability is limited to human-driven analysis. Visual Analytics relies on manual knowledge extraction, generally more time consuming, and highly depends on the user’s domain knowledge to derive valuable knowledge.

Visual analytics (VA) application is an emerging breed of Analytics applications which developed to address Big Data problem. Visual analytics applications capitalize on interactive visualization to facilitate human analysts for effective understanding, reasoning, and decision making on the basis of very large and complex data sets (Daniel Keim, 2012). Visual analytics strives to empower non-technical users with considerable level of analytical capability via user-friendly interface (Eckerson, 2009). The self-service analytics enabled by visual analytics extends the scope of analytics beyond scope of dedicated data scientists and business analysts to wider organizational users who have traditionally relied on others for this analytics capability. This idea of pervasive analytics give greater flexibility to users to refine their information seeking strategy on the fly and allow them to react more quickly to their problem and eventually shorten the time to value. This is in conjunction with the ideal situation where the people who need the insight should be the same people who analyze the data given that they possess the domain knowledge which allow them to harness the true value of the information (Mirel, 2004). This is especially important for complex and strategic problem in which human discretion and judgmental heuristic are critical for deriving the solutions. Despite the ideal visions advocated by Visual Analytics, most Visual Analytics applications in existence are nothing more than data visualization tools repackaged under the name of visual analytics as the result of marketing strategy.

Advanced analytics (AA) applications are a group of machine-driven analysis tools which relies mathematics, statistics, and artificial intelligence techniques to automate knowledge discovery (Surma, 2011). Advanced analytics applications can be broken down four types according to their purpose, namely data mining, predictive analytics, simulation, and optimization. With the computational techniques, advanced analytics allows analyses that go beyond human capability. Advanced analytics can handle enormously huge set of complex data, exhaustively and consistently execute the analysis, and provide quantitative and statistically rigor results. Such capability is especially important for Big Data problem that exceeds the human’s attention perception, and cognition ability, even with the computer-aided scaffolding supports. However, the knowledge discovered by advanced analytics are mostly passive knowledge. This knowledge is often highly technical and with little domain context (Cao, 2012). The practical value of such passive knowledge is usually trivial as it tells users very little about how to act upon it.

What is Analytics in the BIG DATA era

Intro to Business Analytics and Actionable Insight (Part I)

A key driver for businesses to invest in business analytics (BA) systems is to be able to better leverage big data (i.e. myriad quantity of complex data) to improve decision quality. Traditional approach in businesses intelligence (BI) system to provide decision makers the access to huge amount of “nice to know” information is no longer an effective strategy. Businesses shift their focus to business analytics systems which aim to provide decision makers with “actionable insight”, that is, timely and relevant information that provide basis for making decisions and taking action which in turn yield positive outcomes for the businesses.

Ideal Business Analytics
Ideal Business Analytics

What’s Business Analytics?

Business analytics enable businesses to extract useful knowledge from data, to understand complex problem situation, and to provide foresight into the impacts of the actions to be taken. Business analytics can be grouped into two main categories

1) visual analytics – the analysis tools that capitalize on interactive data visualization techniques for ad-hoc querying and data exploration. Here are some major vendors who provide user-friendly, powerful solutions:

2) advanced analytics – the analysis tools that use sophisticated quantitative methods includes predictive analysis, data mining, simulation, and optimization.

What’s Missing?

Despite “Actionable Insight” has been used widely, no one can precisely tell what is it, how to achieve it, and what a good actionable insight is. Without truly understand what they going to get out from business analytics, a lot of businesses jump onto the bandwagon and expect actionable insights will be realized automatically.

Recent reports have shown biggest hurdle for business is to determine how to take action based on the results of business analytics. Many organization encountered the complexity on translating analytical results into knowledge and into positive outcomes for the business.

Gaps between Business Analytics and Actionable Insight
Gaps between Business Analytics and Actionable Insight

Therefore, it is vital for businesses to understand what actionable insight is, so they set realistic expectations and be informed in business analytics-related investments. Moreover, to be able to measure the quality of actionable insight is even more important. It allows businesses to compare, benchmark, and evaluate different advanced analytics software before acquiring it.

We are a group of University researchers who currently conduct a survey about industrial experts’ perspective on actionable insight. A logical framework and a measurement model for actionable insight has been created. Now inputs from industrial experts like you will be incorporated to enhance the practicality of the findings.

If you’re interested in the survey, please go to:

We will send out our final reports to all our participants. Kindly indicate that you wish to receive a copy of the final report at the end of the survey. The final report will consist of:

  • What is “actionable insight”. Defined in a structured concept. Allowing you to improve your analysis practices and knowing what capability you’re looking for when acquiring business analytics software.
  • What are the tools used by the industry experts.
  • What are the information sources that industry use in their data-driven decision making.
  • How to measure “actionable insight” in your business
  • How all above can be varied across different industries, sectors, and other demographics.
Intro to Business Analytics and Actionable Insight (Part I)