The Print When Expression

The Print When Expression

The print when expression in Jasper Studio or Jasper iReport is very useful in so many requirements and it is defined as the name itself implies
i.e Print /Show a String, Column and so on Based on a condition passed.
Now here are some requirements on how/when we can implement The Print when expression.

Requiremnt: Show a particular column when a parameter is selected
Here we made a parameter Called ShowLocation with values either ‘Y’ or ‘N’/blank space
now when ‘Y’ is selected then a Location column should show and when ‘N/blank space then it doesn’t show.

2 ways i did approach this:

1. I made a 2 Table Components one with the Location Column added and the other without the Location Column added.
one on top the other.
so when my parameter calls a ‘Y’ value it picks the Table with the Location Column and vice versa.

This approach is good but can be time and performance consuming

the 2nd approach was:

2. Only one Table Component was used, Opened the Table Component and selected the Column i want to restrict/show based on the parameter passed, in my scenario or requirement it is the Location column as
shown in the image below:


now i would select the cell button

Cellin the properties Tab and then Select the
Print When Expression.

Now here in the print when expression box i would write :
$P{showlocation}.equals(“Y”) as shown above


and you are good to go for a preview.
Now Let’s have to put a N or blank space in the parameter showlocation we get the following output
and now let’s pass ‘Y’ in the showlocation parameter

So Now with this condition it will show the column when the parameter is checked in with a ‘Y’ Value.
and voila the output:


Sohail Izebhijie.

What is analytics?

What is analytics?

Global Data Analytics is the fastest growing sector in the word in terms of almost all types of industries be it in the field of finance, E-commerce, retail, sports, telecom or health. In today’s competitive world where everyone is so busy and impatient and wants wealth and success in no time, data analytics serves as boon.

Well, the way to take your business to the heights of success somewhere goes through analysis of company’s past records in every aspect. E.g In education institutes the admission cell always go through the past results of the candidate to judge his performance in the future. Similarly in retail store the selling count of product is useful in predicting the market growth relative to the particular product. So, no matter whatever is the industry we are analysing data in one or other way to increase the business and growth in market.

Discussing more technically we can categorise the analytics broadly in three categories:-

A) Descriptive Analysis: As the name suggests it has something to do with the description , the history like what has happened in the past. It deals with data records in files or store. The data is summarised, categorised, filtered and cleaned as per its application and targeted to the particular group of users.

B) Predictive Analysis: In this we use the statistics and calculations over old data to predict the future. Often the judgement given after observing the regular trend and pattern proves to be correct in 95% cases. You can actually play the role of astrologers, predicting future by being keen observant of data patterns and behaviours extracted from lakhs of records stored in data basis on the basis of reports generated after descriptive analysis.

By optimising and simulating the first two types of analysis the power to prescribe or suggest remedies comes in hand of Data Analysts. Now, not only prediction about what can happen but what is going to happen in different cases and what can be done as preventive measures is also known beforehand with the help of analytics. Risk analysis and risk management can be derived from same.
Market strategies and rise and fall of various industries are just the part and parcel of proper analysation of the available data.

Nisha Sahu

Business Intelligence Vs Business Analytics



What’s the difference between Business Analytics and Business Intelligence? The correct answer is: everybody has an opinion, but nobody knows.

For example, when SAP says “business analytics” instead of “business intelligence”, it’s intended to indicate that business analytics is an umbrella term including data warehousing, business intelligence, enterprise information management, enterprise performance management, analytic applications, and governance, risk, and compliance.

But other vendors (such as SAS) use “business analytics” to indicate some level of vertical/horizontal domain knowledge tied with statistical or predictive analytics.

At the end of the day, there are two things worth differentiating:

  1. The first is the business aspect of BI — the need to get the most value out of information. This need hasn’t really changed in over fifty years (although the increasing complexity of the world economy means it’s ever harder to deliver). And the majority of real issues that stop us from getting value out of information (information culture, politics, lack of analytic competence, etc.) haven’t changed in decades either.
  2. The second is the IT aspect of BI — what technology is used to help provide the business need. This obviously does change over time —sometimes radically.


Quick Comparison


We know that Analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of data to gain insight and drive business planning. Analytics consists of two major areas: Business Intelligence and Business Analytics.

What is often overlooked is how the two differ based on the questions they answer:

  1. Business Intelligence : traditionally focuses on using a consistent set of metrics to measure past performance and guide business planning. Business Intelligence consists of querying, reporting, OLAP (online analytical processing), and can answer questions including “what happened,” “how many,” and “how often.”
  2. Business Analytics : goes beyond Business Intelligence by using sophisticated modelling techniques to predict future events or discover patterns which cannot be detected otherwise. Advanced Analytics can answer questions including “why is this happening,” “what if these trends continue,” “what will happen next” (prediction), “what is the best that can happen” (optimization).


The Evolution of Business Intelligence vs Business Analytics

In the past, BI has been used to talk about the people, processes and applications used to access and extrapolate meaning from data, for the sake of improving decisions and understanding the effectiveness of targeted decisions. But this is where BI as a baseline failed; something that runs entirely off of static, historic data severely limits a user’s ability to make predictive decisions and forecast for the future market. When an emergent situation arises on a Friday afternoon, the user doesn’t greatly benefit from looking at metrics collected prior to the introduction of that situation.

The rapid growth and demand for BA comes from this failing, and is in a way the evolved form of BI solutions. In a business world whose speed is ever-increasing, the user needs to be able to interact with information at the speed of business, not looking back over his or her shoulder at what happened in the past. BI setups alone do not support the occurrence of users asking and answering questions in the face of marketplace events as they happen. A company that is data-driven sees their data as a resource, and uses it to hedge out competition. The more current the data the user has, the better jump he or she has on the competitor, who may or may not have become a threat in a time so recent that traditional BI data reporting wouldn’t even take them into consideration.

Many companies are commonly implementing advanced analytics on top of their data warehouses, to bridge the gap between BI and current day needs. Perhaps this is the origin of the confusion between terms, as organizations pick and choose from different combinations of services and have no real understanding of what to call these mashups.

Equally relevant is the fact that more and more people are being asked to interpret data in roles that are not strictly analytical. Product managers, marketers and researchers are moving towards data as a way to formulate strategies, and traditional BI platforms make it difficult to push data into real-time situations and what-if scenarios.  With the importance of data-driven decisions increasingly becoming a realization for less tech-savvy branches of company teams, the need for more user-friendly and faster producing platforms also grows. Moreover, delivering the data that supports these decisions to a broader company team demands a more visual form of modeling tool, to improve understanding across all departments. Charts and graphs showing BA findings are quicker and more impacting than written out statistics and excel sheets full of data.

Data interpretation and the manipulation method of choice change as the market demands. While having a set of established methods is important to the effectiveness of a company’s strategy, it’s understanding the need for flexibility in the face of these changes that can be a company’s most valuable asset.

To summarize,

WHAT is happening to your business = Business Intelligence (For Visibility)
WHY it is happening, WHAT WILL likely happen in future = Business Analytics (For Investigation, Prediction & Prescription).