Where does my favourite software SAS stand in 2014? Is it still the leader in Analytics ? Where can we find some comparative data which we can analyse and conclude on?

Let’s look at the Magic quadrant for business intelligence and analytics platforms which is published annually by Gartner. Gartner is the world’s leading IT research and advisory company. It delivers technology related insights from CIOs and senior IT leaders in corporations to business leaders in high-tech and telecom enterprises and services firms. All of us use Gartner and its reports to come to conclusions about the various software suites we would like to buy.

Over the last decade the world of business intelligence and analytics has undergone a change. Many organisations have adopted business intelligence and have started to look at data not only for measurement and reporting purposes, but also for prediction, forecasting and optimisation. Because of this growing importance of advanced analytics, organisations have started looking at solutions which can deliver end to end services. That’s why over the last few years we have seen software suits developed which have both BI and predictive analytics capabilities.

Well let’s see what does Gartner say about SAS?

SAS: SAS’s analytics portfolio spans platforms for BI, performance management, data warehousing, in-memory databases, data integration, data quality, decision management, and content and social analytics, with a core strength being advanced analytics. SAS also offers industry- and domain-specific analytic applications built on its product portfolio.

Strengths

• SAS’s analytics portfolio spans platforms for BI, performance management, data warehousing, in-memory databases, data integration, data quality, and content and social analytics. However, unlike most other BI platform vendors, SAS’s core strength is in its advanced analytical techniques, such as data mining, predictive modelling, simulation and optimization, for which it is acknowledged as a Leader in “Magic Quadrant for Advanced Analytics Platforms.” 

• Historically, SAS tools have been used primarily by power users, data scientists and IT-centric BI developers. That this remains the case is shown by SAS customers placing it above any other vendor in the Magic Quadrant survey in terms of support for complex types of analysis, but giving it one of the lowest scores for ease of use. During the past five years, SAS has been investing heavily in revamping its user experience to change this situation and encourage more mainstream user adoption. This aggressive and unmatched strategy is part of the reason for SAS’s favourable Completeness of Vision position. 

• SAS also differentiates itself from most other BI platform vendors by productizing and selling industry- and domain-specific advanced analytic applications that are focused on specific business problems and built using many of its technology platform products. This enables it to sell “value,” rather than components.

• Data access and integration and the ability to support large volumes of data are the main reasons customers choose SAS, according to the survey. In fact, while SAS deployments support a below-average number of users, its data volumes are among the highest in the survey.

Cautions

• SAS customers consider its software among the most difficult to use and most difficult to implement, with ease of use for business users being identified as a limitation on broader deployment by a higher percentage of customers than for any other vendor.

• Although SAS has exploited its core competency in predictive analytics to encapsulate and automate advanced analysis for business-user-oriented guided data discovery in Visual Analytics, it will face competition from data discovery and BI and analytics platform vendors with leading-edge data discovery capabilities, such as Tableau and Tibco.

• Despite SAS’s success as a Leader in the predictive analytics space, the company still faces a challenge to make it onto BI platform shortlists, unless customers already use its other advanced analytic capabilities and require integration and leverage of skills.

• SAS’s reference customers rated functionality used in traditional BI areas (reporting, dashboards, OLAP, interactive visualization and so on) lower than for most of the other BI Leaders.

We can conclude that SAS is still very much a market leader today. However it must be noted that free and open source software like R are fast gaining popularity and for a data analyst to remain marketable they need to master at the least, both SAS and R packages.

If you have found this post interesting and want to know more about the software that Gartner has covered in this report, access the report on their website (http://www.gartner.com/technology/reprints.do?id=1-1QLGACN&ct=140210&st=sb )

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Thanks Kaushik for letting us know that Jigsaw helped you begin a career in Data science. Here is what he said…

“I really enjoyed the Jigsaw course, thanks to the sharp faculty and prompt IT support. The course instilled in me a curiosity towards statistics and I am eager to learn more! I strongly recommend this course to anyone who wants to delve deeper into the world of analytics. Apart from learning, it helped me switch to a data scientist role.”

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Web analytics is a way to understand website traffic and the visitor’s behaviour on the website. Companies and organizations often use web analytics tools to know the number of visitors on the website and the number of visitors on a page. They can monitor the number of unique visitors and how they come to the site. These tools help the companies to know if a website page is working properly or not. It tells them which area of the website are more popular and which area do not get traffic. There are many tools in the market that give easy to understand reports from the data.

Here are some of the popular tools (not in any order) that can be used to gain insights from websites.

Google Analytics: It is one of the most popular tools in the market. Whether someone is an expert or a newbie in the area of web analytics, everyone has heard about it. It is free and easy to understand. It gives detail insights about the visitors, traffic sources, goals, content and e-commerce.

Crazy Egg: It tells you about where visitors are moving their mouse on the page and where they click in the form of a heat map. It helps to analyse the areas which are catching the most attention.

Clicky: It provides real time information about website traffic. It is also simple to use and gives all the information clearly. It is free and has analytics features comparable to Google analytics. It also has a mobile version.

Kissmetrics: It has all the other features of  visitor’s tracking, and it also allow you to see the change in behaviour over time, pattern identification and most typical behaviours. The tool gives you deep customer’s insights and gives important insights to improve conversion rate.

Woopra: This can be a great tool especially for e-commerce companies as it has a feature of online chat with the visitors. Another interesting feature is it gives you information where the website has been used. In addition to this it gives information about visitor’s location, what pages they are on now, where they’ve been on your site and their Web browser and other statistics.

Mint: It is also a great tool. It gives information about visitors, like where they are coming from, what pages they are viewing etc. In addition to this it lets you make adjustments to customize with free add-ons.

Chartbeat: The good thing about this tool is, it keeps constant track on the visitors of the website and gives snap shots of what visitors are doing on the website in real time. It basically tells you everything about your current visitor.

PiWik: It is downloadable open source. It gives real time web traffic reports and can track many websites. It has a customizable interface.

Web Trends: It is one of the first web analytics companies to enter into the market. It gives all the information about website visitors and is available in multiple additions for different requirements.

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In Business, this dependent variable can also be called the predictor or the factor of interest for eg., sales of a product, pricing, performance, risk etc. Independent variables are also called explanatory variables as they can explain the factors that influence the dependent variable along with the degree of the impact which can be calculated using “parameter estimates” or “coefficients”. These coefficients are tested for statistical significance by building confidence intervals around them so that the model that we are building is statistically robust and based on objective data. The elasticity based on the coefficient can tell us the extent to which a certain factor explains the dependent. Further, a negative coefficient can be interpreted to have a negative or an inverse relation with the dependent variable and positive coefficient can be said to have a positive influence. The key factor in any statistical models is the right understanding of the domain and its business application.

Linear Regression is a very powerful statistical technique and can be used to generate insights on consumer behaviour, understanding business and factors influencing profitability. Linear regressions can be used in business to evaluate trends and make estimates or forecasts. For example, if a company’s sales have increased steadily every month for the past few years, by conducting a linear analysis on the sales data with monthly sales, the company could forecast sales in future months.

Linear regression can also be used to analyze the marketing effectiveness, pricing and promotions on sales of a product. For instance, if company XYZ, wants to know if the funds that they have invested in marketing a particular brand has given them substantial return on investment, they can use linear regression. The beauty of linear regression is that it enables us to capture the isolated impacts of each of the marketing campaigns along with controlling the factors that could influence the sales. In real life scenarios there are multiple advertising campaigns that run during the same time period. Supposing two campaigns are run on TV and Radio in parallel, a linear regression can capture the isolated as well as the combined impact of running this ads together.

Linear Regression can be also used to assess risk in financial services or insurance domain. For example, a car insurance company might conduct a linear regression to come up with a suggested premium table using predicted claims to Insured Declared Value ratio. The risk can be assessed based on the attributes of the car, driver information or demographics. The results of such an analysis might guide important business decisions.

In the credit card industry, a financial company maybe interested in minimizing the risk portfolio and wants to understand the top five factors that cause a customer to default. Based on the results the company could implement specific EMI options so as to minimize default among risky customers.

While Linear regression has limited applicability in business situations because it can work only when the dependent variable is of continuous nature, it still is a very well known technique in the situations it can be used. It assumes a linear relation between the independent and dependent variables. It must be noted that sometimes transformations can also be applied to non linear relationships to make them applicable in a linear regression model.

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The post Popular Applications of Linear Regression for Businesses appeared first on Jigsaw Academy.

Jigsaw Academy, a renowned data analytics training company has announced some very interesting combo discount offers for the month of May. People can enroll in a combination of two or three Data Analytics and Big Data courses and avail of big discounts. Read the full article here

Check out for more on our  May Combo offers

Click here to read about the Foundation Course in Analytics

Click here to read about the Data Scientist Certification Course

Click here to read about the Big Data Analytics 

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Analytics is for engineers! Heard that before? Well, believe it or not, that’s just a myth. Let’s meet Clarence Wong, Commerce graduate from St. Xavier’s college Calcutta who successfully made a career in data analytics. He completed the Jigsaw Academy Data Science Certification in 2013 and is currently enrolled in the December 2013 Batch of the Great Lakes’ Post Graduate Program in Business Analytics. We had a really interesting chat with Clarence, where he talked about his journey into analytics, his passion for the subject and his dreams for the future. Below are snippets of our conversation.

Clarence’s journey into analytics…

I began my career with HSBC. In many ways my analytics journey began there, though in a very nascent manner. I worked in the online fraud prevention team and there we used some basic analytics to improve service deliveries, improve productivity and service errors. After about four years I joined Google and that’s where things really took a quantum leap forward. Google really promoted an open source environment and it was here that I got introduced to R and Python and where I began to use analytic techniques like clustering and decision trees.

When I moved to ADP (Human Capital Management Industry) two years later, I again had a good mix of data operations and client services. I became more fluid and began to use a blend of lean and analytic frameworks. That’s when I decided to get a more solid understanding of analytics and learn the theory behind the applications. Ofcourse I did a lot of self learning as well but I though with so much buzz about Analytics and Big Data in the industry, it was imperative that I picked up some in demand analytics skills as well.

On getting trained in analytics…

I looked at several course options like the ones at ATI, IIM and NIIT imperia. I finally enrolled in Jigsaw Academy’s Data Science Certification course because I felt that they were the most application oriented and their curriculum was flexible and had the right content. I read some great student testimonials, also found that they had been featured in Analytics India Magazine’s list of Top 10 analytics courses. I also read a comparison of IIT Mumbai and Jigsaw done by one of their students, that was informative and led me to believe that Jigsaw was a good choice. Quite importantly their courses were also priced very competitively.

Jigsaw’s Data Science Certification course was great and taught me more than enough to make greater inroads in the analytics space and work as an analyst or senior analyst.  However if I wanted to make a complete career shift into analytics, being that it would be a mid career shift, I would have had to start from scratch and I didn’t want that. I wanted to become more business savvy in the analytics context and so decided to enrol in the Great Lakes Post Graduate Programme in Business Analytics. I wanted to learn how to be a better analytics and people manager in the context of a cross functional analytics team and how to better cost and leverage your models to improve profitability.

The training led to a great job offer!

This proved to be a good decision as mid way through the programme, I got and accepted an offer from Value Labs as Project Lead – Analytics. In my new role I will be responsible for setting up the Global Analytics Consultancy for the organization, it’s vision, strategy and coordinating the efforts of the analysts.

Personal Insights

I think all of us use analytics to some extent in our work. We all have an analytics capacity. We use analytics in our daily life, without knowing it. Every time we choose a route to take, or which brand to purchase, we are using our analytic capabilities.  It doesn’t matter what our educational background is, we can all crack analytics. I did it as a commerce graduate.  If you have an interest in data and want to transition to an analytics role, believe in yourself and make it happen.

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In March Business World hosted a Big Data and Analytics Summit in Bangalore. This Big Data Conclave was a great platform for all Big Data enthusiasts to talk about how India can use Big Data across various industries and sectors to improve business practices across the board. It was well attended by big data enthusiasts as well as all business executives who if not already, will soon use analytics and Big Data in their everyday working life.

Jigsaw’s CEO Gaurav Vohra was a panellist at the second session of the summit, where the topic was ‘Are Indian software services companies gearing up for Big Data projects, what are global clients demanding?’ Let’s take a look at a recent article in Business World that covers the summit and shares the great insights of the panellists, on how analytics companies and IT companies approached selling their data business lines.

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The juxtaposition of analytics and gamification offers great insights into understanding user behavior and can produce better business results by delving deeper into the human psyche. This is what Gaurav Vohra explains in his recent article titled ‘Gamification for Better Business Insight’ published in CXO Today.com. Read the full article here

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