Monday 13 April 2015

Internet of Things, Big Data and Business Intelligence

Internet of Things :

Kevin Ashton in 1999 proposed, the Internet of Things (IoT) which refers to identifiable objects and their virtual representation in an Internet-like structure. IoT are nothing but, The things we encounter in our daily life, Machines & appliances we operate in our office and at our home, buildings we live in and cars, trains and flights we travel. Even we ourselves with wearable technology would become Internet of things.

In simple words, your refrigerator would order milk when it runs out, propose dishes you can cook with combination of items inside. Your air conditioning will change temperature based on the temperature outside and what you are wearing. If you think these are cool, imagine what it can do to Supply Chain, Health Care, Retail, Security and Transportation domains. We are building "Smart Homes"(Developed by Smart Building Institute in Saudi Arabia) which can manage all facilities via smart devices to "Smart Cities" ( Dublin has created sensors throughout city to manage and respond to real time events).

According to Pew Research, nearly 83% of tech experts believe that the use of embedded and wearable devices will be more widespread and will prove to be beneficial by 2025. One of the expected trends of 2015 is the expansion of Internet of Things as companies seek better ways to manage all the data those devices provide.

In 2020, 200 billion things are expected go digital. Many of these machines will be quiet demanding with their response to events. While some of them needs human interaction, many of the responses needed by the machines can/should be automated. This will challenge two important domains of current Technology: Big Data and Business Intelligence.

The way we approach Business intelligence will change and will improve drastically. Managing big data and extracting meaningful information out of it has been one of the biggest challenges for businesses. IoT could make it easier for to collect more relevant information from the depths of the Internet.

In the world of Big data, there is no shortage of data , But we need to know what to do with the data . Distinguish the good data from bad data. How to make sense of it and how to present it for the respective decision makers. Systems should be able to manage the Big Data, the flow of data through Network, Cleanse, Store, Analyse and Predict Trends that are quality information for improving the business operations. Although the Big Data and Business Intelligence look expensive, ROI for Business on Data collection and Business Intelligence is quite tangible. Its the right time for organisations to sort out the Data management issues, define strategies in creating sustainable Data warehouse and Business Intelligence solutions using technologies like SQL server, ETL, Hadoop, R programming etc.

You may wish to have a look at Michael Learns To R : Programming for Statistics and Data Management - Multiple Source Data : Conversation of Consolidation & OLAP - Online Analytical Processing : Resource Guide .Contact us to know more about Data Warehouse and how it can benefit your organisation. Thanks for dropping by and Have a fantastic day!

Srivatsan Aravamudan

Srivatsan Aravamudan

Design and Communication

Thursday 9 April 2015

Michael Learns To R : Programming for Statistics and Data Management

Doesn't matter if you are a Manager thinking about investing in R , or a programmer hoping to learn some skill or the end user. The following are the reasons why you should use R

To begin with R is an elegant and comprehensive statistical and graphical programming language.. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has very powerful graphing functions that the user has to spend time learning, and widely this is one of the reason why many programmers don't invest in R. However R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies like Google.

Why R ?

1. Its free :

Lets state the obvious at first, Its free!. R has no license restrictions. R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. It's also open-source. So anyone can examine the source code to see exactly what it’s doing. Your bug fixing just got lot easier with that.

2. Its the leading Tool:

R is the leading tool for statistics, data analysis, and machine learning. It is more than a statistical package; it’s a programming language, so you can create your own objects, functions, and packages.

3. Compatibility- Cross Platform:

It runs on a variety of platforms including Windows, Unix and MacOS. It is popularly used on GNU/Linux, Macintosh, and Microsoft Windows, running on both 32 and 64 bit processors. ˆ

4. Integrate:

R allows you to integrate with other languages (C/C++, Java, Python) and enables you to interact with many data sources: ODBC-compliant databases (Excel, Access) and other statistical packages (SAS, Stata, SPSS, Minitab).

5. Community:

Perhaps the best of all the R is supported by a community of more than 2 million users and thousands of developers worldwide. Whether you're using R to optimise portfolios, analyse genomic sequences, or to predict component failure times, experts in every domain have made resources, applications and code available for free, online. R has over 4800 packages available from multiple repositories specialising in topics like econometrics, data mining, spatial analysis, and bio-informatics. R has active user groups where questions can be asked and are often quickly responded to, often by the very people who developed the environment

Having said these things, I have to agree there are some challenges in the journey you take with R. Like R has a steep learning curve, Documentation is sometimes patchy and terse, and impenetrable to the non-statistician and The quality of some packages is less than perfect. However R hits the home run with more advantages as one of a kind programming tool for Statics and Data Management. You may wish to have a look at Multiple Source Data : Conversation of Consolidation & OLAP - Online Analytical Processing : Resource Guide .Contact us to know more about Data Warehouse and how it can benefit your organisation. Thanks for dropping by and Have a fantastic day!


Srivatsan Aravamudan

Srivatsan Aravamudan

Design and Communication

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