How Tableau Helps CIOs See DATA Differently

With Tableau’s IPO this past Friday (trading under the ticker symbol “DATA”) and IDC’s title as “the world’s fastest growing Business Intelligence Company” back in 2012, Tableau is making big strides as a top data visualization tool. With more than 100,000 users, Tableau is being used in revolutionary ways to solve diverse problems from maximizing customer loyalty for a clothing brand to minimizing patient wait time in hospitals.

BI Analytics tool

Tableau is a BI analytics solution that lets you probe, question, interact and understand data through visualization. Tableau allows you to instantly filter, zoom, compare, sort and group your data to tell a story. From GI mapping to heat maps, Tableau offers many visual formats to help discover data-driven answers to business questions.

What distinguishes Tableau from other tools is how seemingly user friendly it is. Data analytics is now becoming accessible to the masses with Tableau’s easy drag-and-drop interface.  Analysts who were previously limited to interpreting reports are now empowered to build their own unique views of the data. IT developers who used to create these reports now have more time to work on proactive technology initiatives. You can share Tableau reports to a viewer, group of viewers or even the public. Tableau is additionally accessible in any web browser or on tablets.

CIO’s are looking to Tableau as a top Business Intelligence analytics tool for their businesses, recognizing even its structural power to increase efficiency amongst analysts and enable IT to delve into other projects. The accessibility and usability further brings Tableau to the forefront of CIO’s interests.

Hilary Perry is a business analyst with Bodhtree who focusses on emerging Big Data technologies.  Bodhtree empowers enterprises to navigate Big Data challenges and opportunities with its GPS solution portfolio: Growth, Productivity and Security.  To explore what your business can achieve with Big Data, contact Bodhtree at business@bodhtree.com.

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PIG and Big Data – Processing Massive Data Volumes at High Speed

For most organizations, availability of data is not the challenge.  Rather, it’s handling, analyzing, and reporting on that data in a way that can be translated into effective decision-making.

PIG is an open source project intended to support ad-hoc analysis of very large data volumes. It allows us to process data collected from a myriad of sources such as relational databases, traditional data warehouses, unstructured internet data, machine-generated log data, and free-form text.

How does it process?

PIG is used to build complex jobs behind the scenes to spread the load across many servers and process massive quantities of data in an endlessly scalable parallel environment.

Unlike traditional BI tools that are used to report on structured data, PIG is a high level data flow language which creates step-by-step procedures on raw data to derive valuable insights. It offers major advantages in efficiency and flexibility to access different kinds of data.

What does PIG do?

PIG opens up the power of Map Reduce to the non-java community. The complexity of writing java programs can be avoided by creating simple procedural language abstraction over Map Reduce to expose a more Structured Query Language (SQL)-like interface for big data applications.

PIG provides common data processing operations for web search platforms like web log processing. PIG Latin is a language that follows a specific format in which data is read from the file system, a number operations are performed on the data (transforming it in one or more ways), and then the resulting relation is written back to the file system.

PIG scripts can use functions that you define for things such as parsing input data or formatting output data and even operators. UDFs (user defined functions) are written in the Java language and permit PIG to support custom processing. UDFs are the way to extend PIG into your particular application domain.

PIG allows rapid prototyping of algorithms for processing petabytes of data. It effectively addresses data analysis challenges such as traffic log analysis and user consumption patterns to find things like best-selling products.

Common Use Cases:

Mostly used for data pipelining which includes bringing in data feed, data cleansing, and data enhancements through transformations. A common example would be log files.

PIG is used for iterative data processing to allow time sensitive updates to a dataset. A common example is “Bulletin”, which involves constant inflow of small pieces of new data to replace the older feeds every few minutes.

Sailaja Bhagavatula specializes in SAP Business Objects and Hadoop for Bodhtree, a business analytics services company focused on helping customers get maximum value from their data.  Bodhtree not only implements the tools to enable processing and analysis of massive volumes of data, we also help business to ensure the questions being asked target key factors for long term growth.

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How BI for mobile devices on top of Big Data can transform your employees, customers, and enterprise



BI is going Mobile – in a ‘Big’ way.   Business decisions are not always made in the corner office or cubicle.  They are made on retail floors, in delivery trucks, on an ambulance, or at the laboratory.

Mobile BI delivers intelligence to where ever your team makes the critical decisions.   By delivering the right information to the right people at the right time, mobile BI accelerates response to real-time information and enables a more agile enterprise.

Big data

Big data is exponentially enhancing intelligence quotient of BI by better leveraging all data inside and outside an enterprise; this intelligence can empower business decisions in terms of both timing and reasoning

Combination of Mobile BI and Big Data

Now the combination of Mobile BI and Big Data further reduces the gap between data generation and business decision.

In a recent survey conducted by SAP, 70% of CIOs envisioned killer Big Data apps useful to their enterprises, but interestingly most of them chose not to reveal the idea as it would reduce the competitive advantage.

What types of mobile analytics on Big Data apps could these CIO’s consider so critical to a market advantage?  Possibilities include geospatial intelligence, behavioral intelligence, enhanced customer interaction, etc.

Sample Use case

For U.S. Xpress Inc., a trucking company based in Chattanooga, Tenn., the driver to move to Big Data analytics and real-time BI reporting was a desire to get more out of the large volumes of sensor data being collected from the company’s trucks. U.S. Xpress was looking to use the data to enable its fleet managers to “answer very specific, detailed questions” about trucking operations.

Phani K Reddy is a Big Data Architect with Bodhtree, a leader in Data Analytics, Business Intelligence, and Big Data services. Bodhtree provides Hadoop implementation and maintenance services as an end-to-end service to solve specific business challenges.

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