Agile Defect Management | Turtle Framework

Agile Defect Management | Turtle Framework

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Hi All

It's been a while since we last connected. I am excited to connect with you all after a long time. Thought I write something on Agile Defect Management with so many of our projects using Agile methodologies, it can be useful for keeping things running smoothly as I have come with the Turtle framework. This approach improves process clarity and efficiency, fosters communication among team members, and simplifies the path to process improvement, leading to a more effective and rewarding workflow                    

Agile Defect Management through the Turtle Lens

I was going through one of the Audit processes. I saw Turtle process is being used and thought why can’t it be used in the Agile Project Management process, especially in improving defect management? 

Most of the teams get confused between Defects & Bugs.

We always treat defects like bugs, whereas bugs are not the same as defects.

Bug: If the software is missing something in the ‘Code’, it is a coding error

Effort: It usually takes 1–8 hours to fix a bug

Defect: If software is missing something from the actual requirements

Effort: It usually takes 2hr to 1 Sprint.

Now that you have better understanding between bug and defect. Lets delve into the defect management lens. 

What is a TURTLE framework?

When planning a vacation, meticulous consideration is given to various pivotal elements such as accommodation, travel arrangements, weather conditions, suitable attire, potential attractions, and, of course, budgetary constraints. This comprehensive process mapping is instrumental in ensuring a well-organized and enjoyable holiday experience.

In a parallel vein, the utilization of a Turtle Framework proves invaluable in conducting a meticulous analysis of any given process; in this instance, we will delve into the intricacies of defect management.

There are 6 areas of the Turtle Diagram:

1) Inputs

2) Outputs

3) How

4) With what

5) With whom

6) What results (performance indicators)

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Envision “Defect Management” situated within the Turtle’s core, as we engage in a collaborative exploration with a dedicated team. 

Through this lens, we scrutinise the following dimensions, unravelling the intricacies of the defect management process:

Process Overview:

• Examine the defect management process comprehensively, delineating its stages, intricacies, and key components.

Interconnected Areas:

• Identify and analyze the various facets that interact with the defect management process, both internally and externally. Understand the interdependencies and relationships with other organizational elements.

Resource Requirements:

• Illuminate the support and resource prerequisites essential for the seamless execution of the defect management process. Consider personnel, technology, and any other assets vital for its success.

Procedural Guidelines:

• Define the procedural steps to be followed in defect management. Establish a clear and structured framework that ensures consistency and efficiency in addressing and rectifying defects.

Output Analysis:

• Evaluate the tangible and intangible outputs generated by the defect management process. This includes not only the resolution of defects but also the potential improvements and lessons learned for future enhancements.

By adopting this methodical approach within the Turtle Diagram framework, we gain a holistic understanding of the defect management process. 

This enables us to make informed decisions, allocate resources judiciously, and continually refine our procedures to uphold the highest standards of quality and efficiency.

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How do we know whether this is actually working fine or not, for this we will have some performance indicators.

Here’s the Defect Management process adapted using the 6 areas of a Turtle Diagram which can be used

Target Audience:

• Teams working on quality and defect management improvement.
• Organizations setting guidelines for defect management at the team level.

1. Inputs:

· Defect reports: Internal/external issues related to software, products, or services, encompassing delays, escaped defects, resolution times, and static analysis tool findings.

· Customer feedback: Inputs highlighting specific defects.

· Testing results: Data from testing activities revealing defects.

· Historical defect data: Records of past defects, resolutions, and recurring patterns.

2. Outputs:

· Resolved defects: number of defects logged, Issues that have been fixed, replicated, or corrected.

· Improved products or services: Enhanced quality and reliability through defect resolution.

· Defect prevention plans: Actions to minimize future occurrences, average time to resolution

· Root cause analysis reports: Investigations into the underlying reasons for defects.

· Lessons learned: Knowledge gained to improve defect management practices.

· Increased customer satisfaction.

3. How (Support processes, procedures, methods): 

   Defect tracking system: Tools like Jira or ADO for logging, tracking, and managing defects.

· Defect prioritization: Ranking defects based on severity and impact.

· Acceptance Criteria: Well-defined DoR & DoD by the team

· Root cause analysis: Techniques to identify underlying defect causes.

· Defect resolution: Procedures for fixing, remediating, or correcting defects.

· Defect prevention: Strategies to reduce future defect occurrences.

· Defect reporting: Processes for communicating defect status and updates.

4. With what (Materials and equipment used):

· Defect tracking tool

· Testing tools: Resources for identifying defects during testing phases.

· Communication and collaboration tools: Slack, Team, etc., for sharing information and coordinating efforts.

· Data analysis tools: Software for analyzing defect trends and patterns.

5. With whom (Competence, skill, training):

· Defect analysts: Individuals responsible for analyzing and triaging defects.

· Developers or engineers: Teams responsible for fixing or remediating defects.

· Testers: Individuals responsible for identifying defects during testing.

· Project managers: Individuals overseeing defect management within projects.

· Quality assurance professionals: Experts in quality management and defect prevention.

6. What results — (Performance indicators):

· Defect resolution rate: Percentage of defects resolved within defined timeframes.

· Defect escape rate: Percentage of defects reaching customers.

· Defect severity: Distribution of defects by severity level.

· Defect density: Number of defects per unit of code, product, or time.

· Customer satisfaction: Metrics related to customer satisfaction with quality.

· Cost of quality: financial impact of defects, including prevention, detection, and resolution costs.

· Time to resolution: Average time taken to resolve defects.

· Rework rate: Percentage of work needing to be redone due to defects.

OUTCOME:

· Reduced number of production bugs

· improved quality, increased customer satisfaction

· Improved project outcomes

Sharing two defect matrixes for scrum teams to go through to understand defects

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Web Services vs API for Rookies

                                             Web Services vs API for Rookies

Hi Guys, I am so sorry for writing back after a long gap! I was occupied with so many things and I got sailed away from writing. I am back now...

I have got deluge of emails and requests,so I am writing again. I have observed that many of the HR professionals and people with zero knowledge on technical area are getting into Workday and a lot of freshers are showing interest to get into Workday jobs. 
Most of them are  in two minds and come with queries whether they have to write programs or mandatory to have technical experience on programming side ?  

I suggest that to have knowledge is always good as you can understand the flow and the functionality easily.
You will tend to  hear about web services in WORKDAY STUDIO, so I am writing the difference between web services and API for better understanding. 

As you know that functional area doesn't require much knowledge on the technical aspects but when it comes to the integration and Workday Studio, I feel its better to have an idea on the API and Web services ( SOAP, REST)

I have already covered XML area in my blog. 

I am writing this for peers who doesn't have any technical background. I hope this workday blog helps them. 


                                               Web Service vs API :
WEB SERVICE:
A Web service is designed to have an interface that is depicted in a machine-processable format usually specified in Web Service Description Language (WSDL). Typically, “HTTP” is the most commonly used protocol for communication. Web service also uses SOAP, REST, and XML-RPC as a means of communication. 

It's a service available over the web. In a simple way to understand, here goes an example. 
Lets take the restaurant scenario: A french customer visits an Indian restaurant and wants to order some traditional Indian food. The Hotel staff should be in a position to understand the french language to take the order and serve the customer their signature Indian dish. 
At this point, the waiter who understands the language will be taking the order from the customer and translates the same to the kitchen department to cook.
In this scenario, the 'waiter' acts as a WEB SERVICE and customer and kitchen as TWO DIFFERENT LANGUAGE applications. 

A Web service is merely an API wrapped in HTTP. An API doesn’t always need to be web based. An API consists of a complete set of rules and specifications for a software program to follow in order to facilitate interaction. A Web service might not contain a complete set of specifications and sometimes might not be able to perform all the tasks that may be possible from a complete API.
The APIs can be exposed in a number of ways which include: COM objects, DLL and .H files in C/C++ programming language, JAR files or RMI in Java, XML over HTTP, JSON over HTTP, etc. The method used by Web service to expose the API is strictly through a network.

Medium: HTTP/Internet
Format:XML/JSON

There are two types of web services -SOAP and REST

SOAP(Simple Object Access Protocol)- A web service that compiles to the SOAP web services specifications is a SOAP web service
Medium: HTTP ( POST)
Format: XML

W3C(world wide web consortium) defines the standards.
There are two SOAP web services specifications
1) Basic 2) Extended
* SOAP
* WSDL
* UDDI  

All the information that happens between service consumer(CLIENT) and service provider(SERVER) is over a common format XML. XML message has a defined structure : SOAP MESSAGE consists of 
* Envelope
* Header
* Body

Here is the SOAP XML structure:-

<?xml version="1.0"?>

<soap:Envelope
xmlns:soap="http://www.w3.org/2003/05/soap-envelope/"
soap:encoding Style="http://www.w3.org/2003/05/soap-encoding">

<soap:Header>
...
</soap:Header>

<soap:Body>
...
  <soap:Fault>
  ...
  </soap:Fault>
</soap:Body>

</soap:Envelope>

Envelope: It is the root element of a SOAP message. This is basic unit of the XML document which contains other units like Header and body. 

Header: A element provide the information about the message itself. Header might include complex types, routing information etc.

Body: It contains actual data of the request that is meant to be sent to the server. 




REST: REpresentational State Transfer----- It is more flexible and less rigid than SOAP

Medium:HTTP (POST, GET,PUT,DELETE,...)
Format: XML/JSON/TEXT

REST is often misunderstood in web service. A web service that communicates/exchange information between 2 applications using REST architecture is called as Restful web service . It is an architectural style . It doesn't have any protocol or strict specifications unlike SOAP and there is no central body controlling.

REST defines a set of principals to be followed while designing a service for communication/data exchange between 2 applications. When these principles are applied while designing web services ( client-server interactions) we get RESTful web service.

The principals of REST Architecture that should follow to become a restful web service 
* Uniform Interface
* stateless 
* cacheable etc... 

Resource: Everything is a resource 
URI: any resource/data is accessed by a URI 
HTTP: make explicit use of HTTP methods

1) Resource: everything is a resource ...

Example- We are creating Human Resource tracking system ( HRTS). We can create this system on any platform and on any language and on any database at the back end. 
To develop HRTS, we should have some modules like employees which will have attributes like 'employee name' and 'employee ID' and then Department module like 'Department name' and 'Department ID' and other modules.
The concept of resource says that we can define any information on any module as resource.
We can define resource as 'employee' 'department'... every module we can name as resource.

2) URI: we can access resource/data through URI ( uniform resource identifier)
If the HRTS software is developed and hosted somewhere and we can access it by using the domain name/ employees to get the employees data
http://hrts.com/employees ( we get all employees data).
If we want employee 26 details then we can access http://hrts.com/employees/26

3) HTTP: let us see how we make use of HTTP methods:
HTTP -  GET, POST, PUT, DELETE are few methods. We can CREATE using POST
READ using GET, UPDATE using PUT and DELETE using DELETE----- CRUD ( SHORT FORM)

using HTTP methods along with URI, we can access/modify any resource or resource information.


                REQUEST                    RESPONSE 

GET- http://hrts.com/employees list of employees
GET- http://hrts.com/employees/26 lists details of employee with ID 26
DELETE- http://hrts.com/employees/26 deletes the details of employee with ID 26
POST- http://hrts.com/employees ID of new employee
+
  Data of new employee

PUT- http://hrts.com/employees/26 modify data of employee 26
+
data to be changed

http:// GET hrts.com/employees/26


Difference between SOAP and REST web service to get the details of the employee having ID 26


                                            SOAP
========================================================================

<?xml version="1.0"?>

<soap:Envelope
xmlns:soap="http://www.w3.org/2003/05/soap-envelope/"
soap:encoding Style="http://www.w3.org/2003/05/soap-encoding">

<soap:Body pb="http://hrts.com/employees">
  <pb:GetEmployee>
<pb:Empid>26</pb:Empid>
</pb:GetEmployee>
</soap:Body>

</soap:Envelope>

=============================================================================


                                REST

http://hrts.com/employees/26

In Rest we will not be writing the complete coding rather we go with simple link 
 ============================================================================


                         COMPONENTS OF WEB SERVICES: 1) WSDL 2) UDDI

WSDL- Web service description language
Service provider publishes an interface for his web services that describes all attributes of the web services. This is XML based interface called WSDL.

CLIENT (service consumer) - SERVER(service provider)

There are two ways a service consumer can get hold of this WSDL document
1) If the service consumer and the service provider already know each other. The service provider can hand over the WSDL to the client and they can use the service.
2) When the service provider and the service consumer doesn't know each other... how can service consumer can get hold of WSDL document?
Ans: A web service provider publishes his web service (through WSDL) on an online directory from where consumers can query and search the web services. This online registry/directory is called UDDI ( Universal Description, Discovery and Integration)- It is an XML based standard for publishing and to find web services. It is like UDDI is a directory where the service provider will put WSDL documents where consumer can query and get hold of their choice WSDL document.

************************************************************************************************************                                              
API ( Application Programming Interface) and Web service serve as a means of communication.
The only difference is that a Web service facilitates interaction between two machines over a network. 
An API acts as an interface between two different applications so that they can communicate with each other.
An API is a method by which the third-party vendors can write programs that interface easily with other programs.
API may use any means of communication to initiate interaction between applications. For example, the system calls are invoked using interrupts by the Linux kernel API.
An API exactly defines the methods for one software program to interact with the other. When this action involves sending data over a network, Web services come into the picture. An API generally involves calling functions from within a software program.
In case of Web applications, the API used is web based. Desktop applications such as spreadsheets and word documents use VBA and COM-based APIs which don’t involve Web service. A server application such as Joomla may use a PHP-based API present within the server which doesn’t require Web service.

An API is nothing but the flexibility between two applications to talk to each other irrespective of the different program.
Ex: Google map API, YouTube API


Summary:
1. All Web services are APIs but all APIs are not Web services.
2. Web services might not perform all the operations that an API would perform.
3. A Web service uses only three styles of use: SOAP, REST and XML-RPC for
communication whereas API may use any style for communication.
4. A Web service always needs a network for its operation whereas an API doesn’t need
a network for its operation.
5. An API facilitates interfacing directly with an application whereas a Web service is a


                                    If you have any queries, do not hesitate to write to me.

XML- LOOK

Hope all of you had rocked 2015!  Last day of the year, many might be getting ready to party hard and few with resolutions to make it big in their careers.  All the best and make the most of it.

                                                                   XML: LOOK

<?xml version="1.0"? >  Declaration

<!.. My first XML document.. > comment

<message status="urgent" type="friendly">

 start tag  ...attribute...

<text>Hello Joy</text>

<subject>How are you</subject>

</message>

The above document contains start tag, attribute, sub-elements,declaration and comments.

Root is the outmost element in the above case <message> 

Elements can contain child elements ( e.g., message contains text and subject)

Attribute-( message status="urgent" type="friendly") 

Name-value pair separated by "=" sign... similar to attributes in the HTML tags.

Attributes of elements are defined in the starting tag not at the end tag.

XML has become useful in three major areas

1) Putting data on the web

2) Storing/Sending system data

3) Exchanging data between applications.

Data described using markup language

Each individual piece of information is "marked up" A marker shows the meaning of the associated data- <name>Joy</name>

        Start tag-content-End tag

A unit of data to which a meaning has been attached is called an "element". An element consists of 'Start tag', 'content','End tag'.

When required an "attribute" can be described in the start tag of an element showing more detailed information to be assigned to the data.

<name id="26">Joy Joshi</name>

            .attribute.

Mark: Tag names are case-sensitive.They come in two flavours 

1) Paired - <title>Joy to the world</title>

2) Empty: A single tag with no closing tag- Elements require no data

Data can be provided through tag attributes

Ex:  <customer number="CUST123" name="Joy"/>

                                                                                 Empty tag

Attribute value are always quoted strings...passing application must convert to other data types.

Can begin with and contain any characters except (Digits, whitespace,punctuation etc.,)

Example using tag attributes:

<customer number="CUST123" 

status="VIP"

account manager="EMP007"

<name>Joy</name>

</customer>

Customer has three attributes

1) Number

2) Status

3) Account Manager

                                                              COMMENTS

XML uses comments like other languages.

Notes to a human being-Not interpreted by parsers.

                                           <!.. comment.. >

Rule for using comments

1) No comments before XML declaration in the first line

2) No comments within other markups

3) Don't use "--" ( two hyphens) within comment text.

4) No nested comments.

WORKDAY: XML+Xpath+XSL

                                                     XML+Xpath+XSL

I am sorry for taking a long break in writing up. I was held with work, so didn’t get the time... as well excuse me for not replying to the ‘flood of emails’ received from you guys asking suggestions and assistance. I will reply soon once I sort out my inbox.

In WORKDAY integration plays an important role for the successful run of the projects.

WORKDAY always interacts in XML format.

Most of the cloud applications talk in XML and WORKDAY generates automatically XML file.

We use XML with the assistance of web services; they are the building blocks for construction of applications.

                                        Extensive Mark up Language-XML

XML is a data language based on text markup (special markers (tags) in the text that identify its parts).

Markup Language: Set of rules for marking up a text. A markup language defines what tags you may use how and where to use them.

HTML is a markup language-The key difference between HTML & XML is…

HTML is designed for display and uses ‘CSS’ for manipulating display.

HTML data is fixed and mostly about presentation.

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XML: It is designed for data and uses ‘XSLT’ for manipulating data & display for multiple output types.

·      Basic XML provides a ‘grammar’ for markup.

·      Elements must conform to certain rules making XML a meta-markup language.

·      Human and computer friendly format.

·      Excellent for long term data storage and data reusability.

·      Handles the data in a tree structure having one and only one root element.

XML gives an excellent service for handling data with a complex structure.

·       Data managed using RDB tables has a regular data structure.

·     In today’s fast changing world, most of the data that exists is not structured and at times cannot be managed using tables like… Complex Structure and Atypical data.

There are many applications & tools in the market competing to streamline the data and one such format that can handle data without an extensive manipulation is ‘XML’.

XML allows for the description of data in a text format as it uses the text data, whereas, data delivered back & forth without OS obligation.

XML data can be stored for long periods without the fear of thinking unusable, whereas, data created through specific application becomes useless or even impossible to access if the application is unusable.

Using XSLT, an XML document can be transformed into a document of a different structure or format (HTML,CSV,etc)  ONE SOURCE-MULTI USE solution.

XSLT style sheet is designed to use single XML document for various purposes (the web or mobile phones)

                                                                                                            

WORKDAY BIG DATA ANALYTICS – MAKING A SMARTER WORLD

I am so overwhelmed by the response for the WD blog; it encourages me to come up with more topics. Thanks one and all for your comments and email communications.

A couple of days’ back, I was chatting with my friend as he wanted to shift to a new challenging role. After working for several years on SAP platform for  one of the Big five companies, he was not satisfied with the kind of work he was doing. I suggested him to go for BIG DATA. He was perplexed by my suggestion and wanted to take some time to think about it. I was taken back this time as he asked me to tell in detail about the BIG DATA. He couldn’t get the information from his peers as well as from his seniors who are having decades of technical and management experience.

I was so pumped to tell him; I love sharing knowledge THAT I KNOW.  He was happy to hear me as usual, then I thought why not I write about 'Big data' to encourage my friends who are looking for a career change and for the ones who want to adopt the latest technology that is in demand.

I hope it will help the young talent to understand the difference between Analytics and Big Data so that they can be on the right  career track. HERE YOU GO…

In Workday, BIG DATA ANALYTICS plays a major role for the companies to streamline their data.

I have come across a nice quote to start with -----------------------------------------------“In God we trust, all others must bring data” – W. Edwards Demming, Manufacturing Guru and Statistician

Analytics, to put simply, is helping businesses with data. Data can be any information about the system or surroundings.

If a person doesn’t feel well, he/ she might experience a fever or any other anomaly in the body, which indicates that there’s something wrong. Similarly...this data about the system or surroundings of a business is shouting if something is wrong. Analytics is all about identifying and capturing this shot and transforming it into a corrective action.

Let’s take an example, how can analytics help in running a factory? The main components of a factory are machinery, human capital and real estate infrastructure. It will be a great relief to the management instead of routine checks if we can predict when a machinery will develop a slag or when can we expect the workers to go on strike or leave their jobs. It’s too hard to imagine to be worked out, but there is a solution for this …

Yes, analytics can help in doing all this. Machines are made up of a number of components. When a machine is not working properly, some components may have been damaged. If we can place sensors in appropriate regions of a machine and track their output, using historical data, we can very well predict when a damage is about to happen to the machine in real time based on a live feed from these sensors. A recent study by one of the top consulting firms elaborates on how advanced analytics can improve manufacturing to reduce process flaws, save time and money. And the applicability of analytics is practically limitless. From helping run businesses better to saving lives in the field of healthcare, analytics are everywhere. If it’s not anywhere, it will be in the next few years as more and more firms and industries are understanding the importance of analytics and implementing the same.

 Analytics has always been around us. We make a lot of day-to-day decisions based on data floating around us and now this is growing as a professional field of study. Analytics will definitely stay and grow!

BIG DATA ANALYTICS

Definition: According to the information technology research and advisory firm Gartner –“Big data is high-volume, high-velocity and high-variety information assets that demand cost-effective, innovative forms of information processing for enhanced insight and decision making.”

Big Data in today’s world

Software and Information Technology has made it possible to generate extremely large amount of data which is generated in real time, typically these data sets are of sizes which cannot be processed using traditional database management systems or other data processing software. In the language of big data sizes, we do not deal with gigabytes rather the data size ranges from hundreds of petabytes to Exabytes (1 million terabytes) of data. Now the biggest questions are –how can an organization make effective use of this data to predict upcoming scenarios and trends and how can it use this raw data to optimize its processes in order to increase efficiency, reduce costs and approach unexplored avenues?

Mining Terabytes or Petabytes of raw, unprocessed data is a very complex task and then drawing out meaningful, relevant insights is an even more complex task, the skills needed to mine this data is what matters in today’s fast changing world.

Big Data technologies:

Hadoop an open source framework from Apache is the leading one used globally. This framework, which is a set of tools allows processing of large sets of data by breaking into clusters. Unlike in traditional computing the processing capability using Hadoop can be scaled up from single servers to thousands of machines. The two main components of Hadoop Distributed file System (HDFS) and Hadoop MapReduce. HDFS is a distributed file system designed to run on a fault tolerant, low cost hardware known as commodity hardware. MapReduce, which is based on the Google’s search technology, distributes large data sets across multiple servers which individually do their processing of partly allocated data sets. LinkedIn is one of the companies that use Hadoop to give real time personalized recommendations.

Analysts believe that 90% of the total data is created in the last few years and this pace is accelerating. It will not be a surprise when companies start to deal with Yotta bytes of data in the coming years. Businesses already have realized the importance of Big Data and started investing substantial amount of their capital into it. Big Data is the future, it has been a trending technology recently and soon we may see many advocates for it in the coming years.

Major components of Big Data

According to industry experts Big data can be categorized into four main components when applying Big Data in real life business operations –, known as the “Four V’s”:

The 4 V’s of Big Data

Volume

When we talk of big data it is implied that we are dealing with enormous volumes of data, this data is typically generated by business processes, automated machines and social networks so the volume of data to be analyzed is massive in size. If we take social networking sites as an example where posts, twitter messages, photos, video clips, etc. Are being generated and shared every second, then we deal with data of the order of Zettabytes or Exabytes. In a research it has been found that all the data generated in the world between the beginning of time and 2014 is equal to the data, which is being generated in a single day after 2015

Thus, data sets are becoming more and more complex and too large to store therefore analyzing data using traditional database technology is often not possible.

Variety

Variety in Big Data jargon refers to the multiple different sources from where data is generated and types of data which can be structured or unstructured. Earlier the majority of data was structured data that stored in relational databases, e.g. Financial data, sales data.

But as technologies have evolved in recent years at a breakthrough pace, almost 80% of data is unstructured in the form of emails, photos, audios, videos, PDFs and social media updates.

With proper implementation of big data technology,we can now make efficient utilization of unstructured data and bring it together with structured data.

Velocity

Advanced technologies have increased the rate at which new data are generated, this data is not localized to any particular region rather it moves around the whole world at a tremendous speed, this flow of data is massive in volumes and continuous.

E.g. Social media post or videos on YouTube, which goes viral in seconds.

Big Data offers the capability to analyze this real time data and allows businesses and industries to make strategic decisions in real time.

Veracity

The data being generated is often raw and unstructured so Veracity refers to the trustworthiness of data, the information which is gathered from various networking sites, business processes or automated machines differs vastly in each instance it becomes difficult to control the quality and accuracy of data. So it is important to differentiate which data are to be mined to draw meaningful insights for analyzing a problem.

We are drowning in data. The volume of data being generated every second is enormous and the rate is increasing exponentially. We have come a long way into a digitized world where every human action is associated with data generation. Social media websites such as twitter and Facebook daily generate data in zeta bytes. In future, it is widely believed that biggest data is not created by humans,but by the ‘Internet of things’. Internet of things is a scenario in which objects equipped with unique identifiers and they automatically communicate by transferring data without human intervention.

How will different industries use Big Data?

Big Data is expected to bring a lot of benefits to the consumers in the form of improved services, more user-friendly systems and thereby resulting in a more transparent dealing of services.

Big Data can affect all industries starting from Banking, Healthcare, Consumer goods, Information Technology and also the Government services. Gartner predicts that BIG data development will drive up IT spending to $232 billion by the end of 2016. All this is possible only when all organizations and the governments are able to fully start using Big Data and reap its benefits.

With the rise in social networking websites, consumers have found a platform to connect with each other and this helps the organizations capture a lot of data with the help of data analysts. In a nutshell, there is data everywhere and hence it becomes highly important to make use of it for the benefit of the organization and the overall economy.

Big Data in Marketing

Big data is going to shape the products in the new age market. With the customer insight gathered and consumer behavior studied,it is much easier for marketers come up with customized products. The companies who are succeeding are not the ones having the most data, but the ones who are using the data the most.

In the future product development would be hugely backed by Big Data results and it would be surprising if this becomes an important competitive strategy for companies.

Big data in Retail

In the near future, for the conventional Brick and Mortar shops, big data can be a reviving force through video surveillance and conversational analysis

Big data is all poised to be detrimental factor in Airport, Rail, Energy and Social Services through its services.

In a nutshell, we are moving to a concept of SMART CITY and Big data with its other technologies like Cloud Computing, Multi Sensors and Intelligent are going to herald the Smart City Concept.

Big Data in Agriculture

John Deere the US agricultural, equipment manufacturing company has led the way in heralding the big data advent in agriculture.

Big data are used by farmers to figure out the choices of the crop, when and where to plough, and where will the best return come from. Sensors in Tractors, Historical data on soil conditions, weather prediction and crop features.

Big Data is promising a future in Agriculture wherein lands would be highly productive and farmers well profited. Big data strives for a self-sufficient mode.

Recording of data takes place at the most of the simplest of the tasks like the purchase of groceries to the most complex form of utility( e.g. Smart phones).

This count is growing each day and every second and hence categorized as Big Data. Currently these large units of data inventory are growing beyond the ability to be managed and analyzed with traditional data processing tools. The initial idea was to control the size of this information to free up the memory of the computers for a new set of growing data. This led to the introduction of new technologies like Google’s MapReduce and open-source Yahoo’s Hadoop and more. Data collected is used by many internet companies to gain significant competitive advantage in the market. As time passes by, companies have to change their business models to survive and hence this massive change can be categorized as data-driven technological revolution of which we are all a part of.

                         WORKDAY BIG DATA ANALYTICS:

 It is very helpful to make smart decisions for manager, employees and executives with the valuable information that they get.

With workday BIG DATA ANALYTICS we can integrate, analyze and visualize data from workday and non-workday data to identify the root causes, detect patterns and trends and predict accurately and all this can be done by a single application.

WORKDAY BIG DATA ANALYTICS enables organizations’ to answer business questions such as, how we are measuring performance accurately.

Ex: We can augment workday performance data with sales data from CRM systems to determine the average sales performance rating.

To get a clear picture whether worker performance matches the rating, we can drill down to get a closer look at the matrix within the performance rating. This gives the information you need to know to make smart investments in your talent. By using existing capabilities within workday,such as custom fields. We can view worker data coming from data sources outside of workday to give an inside view of low-level details used in reports. Workday BIG DATA ANALYTICS makes it simple to match together from workday and non-workday data source with built in data connectors to common gate resources. Data analysts can easily connect to any data source of any type at any volume with easy to use interface on data analysts with tools to quickly and easily exploring combined data from multiple sources with hundreds of built in functions. Data analysts can perform simple joins and filters with more advance analysis, such as ‘ Forecasting’.

BIG DATA has built in pre-defined templates such as market compensation analysis, retention risk and impact analysis, Global payroll cost analysis and more. Workday BIG DATA analytics are unified with the rest of the workday. It requires no separate infrastructure and no separate security. The best part is reports generated by workday BIG DATA ANALYTICS are treated the same with any other reports/reporting in the workday so that we can quickly gain insight and take necessary actions.