Intersting comparison of how Election-2014 was fought on Twitter!

PollAnalytics for Elections - Criminal Cases, Education and Assets

This post is the outcome of the data analysis that we did of wining candidates focusing on criminal cases against each along with their educational qualification and assets they have.

The findings, for now, are solely of the 2009 Lok Sabha elections, but in our future posts we will share a full time line of 3-4 elections, if comprehensive data is available.

We start off with a bar-chart that is a combination of all three points-of-interest, i.e. Criminal Cases, Education and Assets. It gives a very fair picture as to the type of candidates who were given tickets to contest in the election.

The graph above is a visualization of winning candidates (Lok Sabha 2009) that have most criminal cases based on their asset class and education. Two major pointers that may be observed here are:

• Candidates who are graduates and belong to the “High” asset class, have the max number of criminal cases against them
• Candidates belonging to the “Low” asset class, and having completed their education only till 10^th standard, have the max number of cases against them. The same goes with “Medium” asset class candidates

Winning candidates who are 10^th and 12^th pass along with graduates together constitute 58% of all the criminal cases lodged. Thus, "educated" candidates have the most criminal cases against them.

The High Asset class group "contributes" the most to the criminal cases lodged. Evidently more money means more power which in turn helps the candidate to secure a ticket and eventually the seat to political power.

The above chart tells about the conversion rate of candidates i.e. what percentage of candidates in which education group was able to win the election. Far ahead from the rest, 22% of all graduate professionals that contested in the elections won in their respective constituencies.

Furthermore using data particularly of Karnataka and Andhra Pradesh, a predictive model was applied on it. Based on attributes like Education, Criminal cases and Assets, a total of 42 instances out of 45 were predicted correctly, as to whether that candidate will Win or Lose.

Are Higher Education Institutions Really Leveraging Data with Business Intelligence to Generate Value

Introduction

Education has always been an important cornerstone of our social structure and economy. Higher education institutions are a vital component of the educational landscape. They need to keep changing to cater to the demands of the students as well be efficient with their delivery. But have they been able to leverage data to their potential especially in emerging countries such as India?

With our experience we can conservatively say the shift has been happening with some institutions ahead and others still grappling with the operational level issues. Traditionally education institutions have been collecting data for operational purposes or academic purposes but then is it the end of the value we can derive from this treasure trove of data? The answer obviously is no. Business Intelligence can not only show the reality through indicators but also convey why a certain event may be happening, predict it and ultimately prescribe in case required. On a systemic view, Business Intelligence helps decision makers to not just see but act effectively.  

Value Propositions

For our particular case, we will be showing high level example usage of business intelligence and its impacts.  Talking about the Indian context, a report by Times Higher education in the year 2013 revealed that despite producing world’s brightest students and academics, none of the universities featured in the top-200. There were various metrics such as faculty research, employer reputation, academic reputation, faculty-student ratio etc. which the universities needed to track and take action. So how can Business Intelligence help us?

  

                                    

                                      

Let us look pictorially at an education firm in terms of areas where business intelligence can help out. The classification below is just representative of the education domain and is in no way exhaustive.

            Figure 1: Representative Classification diagram of a typical higher education firm

The leaves of the classification diagram give an idea of the area of application of business intelligence for a typical higher education firm. From tracking Staff performance to understanding the performance of the students there are opportunities waiting to be tapped at each of the leaves.

We will take one example each showing how both reporting and analytics can help firms understand the academic as well as administrative aspect of education.

Example1: Reporting Lead Conversion from Leads generated

For a private education firm Student Leads are one of the most important aspects to delve into. BI

Reporting can help you to keep track of your Lead and show the nature of the leads generated and their conversion to actual students.

Student Conversion as a Key Performance Indicator can help to understand campaign effectiveness, marketing staff performance, geographical split of the potential students etc. This then helps you to decide where to have campaigns; where to work on your campaigns and how to deal with interested leads and convert them into full time students.

Reporting can cover each and every aspect of a higher education institution with Key performance Indicators (KPIs). But again putting the “Key” in KPIs is very important for reports to make sense. It could be a combination of previously used indicators, industry standards and descriptive analytics.

Let us now turn our focus purely to analytics. 

Example 2: Predicting Student Performance

Analytics can play a very important role in understanding education from both academic as well as administrative angle. Almost all the leaves in figure 1 can be understand fully with the help of analytics. But as an example let us take student performance.

We need to first understand Students through descriptive analytics right through their life cycle in a campus. We can then use this analysis to feed in to our predictive models to predict the performance of the students before it actually happens!

There are various algorithms which can be employed to achieve this. Once we predict the performance the next steps would be to employ prescriptive analytics to decrease failure rates of the student. This in turn starts a loop which can easily be shown below:

Figure 2: Loops showing how predictive model can affect student learning and firm revenues

The above loop R2 shows how student performance prediction is leading to effective targeted student interaction which leads to decreased failure rates which ultimately ends up increasing student grades and also increasing revenues. Increased revenues can then be used again for increasing investment on better predictive Models or infrastructure or faculty which keeps both the loop R1 and R2 going. So we end up creating a loop which helps the firm to keep growing with time.

If we take another look at the figure, we find that it takes time even with increased faculty standard and infrastructure to have effective targeted student interaction in loop R1. This is represented by two parallel lines cutting across the arrow towards effective targeted student interaction. But predictive models can pin point on individual students with their performance and help the faculty and administration to take right steps to help these students. The effectiveness of the steps taken can then be easily be tracked by reporting the right performance indicators.

Steps Ahead

These are just one of the many places where Business Intelligence reporting and analytics can help an education institution to not only understand students but help earn more revenues which in turn helps them to grow effectively. With the data driving decisions in almost all the domains why should not educational institutions aspire to be more efficient, effective, follow benchmarks set by the best institutions and ultimately set benchmarks.

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Magic Quadrant for Business Intelligence and Analytics Platforms

The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. Because of the growing importance of advanced analytics for descriptive, prescriptive and predictive modeling, forecasting, simulation and optimization (see "Extend Your Portfolio of Analytics Capabilities") in the BI and information management applications and infrastructure that companies are building — often with different buyers driving purchasing and different vendors offering solutions — this year Gartner has also published a Magic Quadrant exclusively on predictive and prescriptive analytics platforms (see Note 1). Vendors offering both sets of capabilities are featured in both Magic Quadrants.