Recipe for successful Big Data Analytics Career

Big Data Analytics CareerBig data is seeing a tremendous activity these days. But merely investing money in technology is not sufficient for big data success. You need to keep in mind the below mentioned three key ingredients for the recipe of successful big data analytics career:

Desirability: This means identifying the problem which needs to be resolved. It may be a known or unknown problem or both. Businesses should focus on the correct problems while developing their solutions.

Feasibility: It refers to finding the tools and skills resources to tackle the problem. Tools for tackling the problem are of two types:

  • Data Lakes: Data Lakes are storehouses of data. After identifying the problem, collect appropriate data and create data lakes. Data lakes contain data in structured, semi-structured and unstructured forms.
  • Open Source Tools:These tools have a wide range of capabilities including predictive analysis, insights and forecasting, etc. Open source tools assist in codifying and automating operations thus decreasing the need of human intervention and that too at an affordable cost.

Automation of data management is the most important skill resource in big data analytics. It gives freedom to people resources by eliminating low-level manual processes. These freed resources can contribute to other valuable processes. The basic use of automation is to analyze data and find anomalies at a very fast rate.

Viability: This means measuring the problem’s business value for the enterprise. The problem should be viable enough to find its solution.

Now, you have the recipe for successful big data analytics career and you are ready to go!!

 How can Big Data Analytics Certification help your Career?

 iACT Global offers you a course in Big Data Analytics in association with IBM which will further enhance your skills in problem solving abilities. Armed with iACT Global Certification and Training, you can upgrade your career and reach new horizons. Hence don’t wait, just enroll for iACT Global Certification and Training.

Building a smart phone application of your business! Challenges and Opportunities

56413Increasing number of companies are coming out with their mobile applications to reach to customers.  Creating a mobile application for your business is not difficult. What is more difficult is to integrate your operations and backend infrastructure with your application platform.

When a customer orders a product or service from your mobile application, the entire supply chain should get activated with that order. The IT infrastructure plays an important role in communicating information of this order across the supply chain. The operations team gets the order; packs it; transports it and gets it delivered to the address of the customer. The transportation and delivery part are nowadays being increasingly outsourced to third party logistics vendors.

While designing the application of your business, you should try that customer service is improved because of this application. You can do so by minimizing the number of steps that a customer has to go through for completing the purchase transaction. Smartphone applications can be successful in increasing market penetration of your business only if they increase customer convenience in some way.

When a customer places the order of a product through the smartphone application of your business then he/she expects faster delivery of the product. So the operations and supply chain of your business also need to become more agile to support your application channel distribution strategy.

The smartphone application should give customers the convenience to provide their feedback on the quality of product and customer service delivered. A dedicated customer service team should be there to readily address any grievance or issues that customers raise through the application.  This will go a long way in improving customer service and will encourage more customers to download the application of your business and use it for ordering your products or services.

According to data from eMarketer number of smartphone users in the world, by the end of 2014, were 1.75 billion. India has the third largest smartphone market in the world ,with 315 million users, by the end of third quarter of 2015. With the continuing decline in prices of smartphones, the penetration of smartphones in India will increase further. The penetration of smartphones in rural markets of India is increasing very fast. Therefore without a mobile application, a business may lose on huge opportunities of increasing revenue growth and market share.

A holistic smartphone application strategy can play a very important role in improving customer service and satisfaction. Such a holistic strategy is underpinned on integrating your mobile application with your operations and IT infrastructure

7 major trends related to big data and analytics

Big Data The Three - Volume, Velocity And Variety

Tremendous growth in industries across the nation has increased the use of big data for multiple purposes. It is being utilized by both private and public sector. Some major industries where it is used include technology, media, healthcare, retail banking and real estate to name a few.

Big data is generally defined as enormous amount of unstructured, structured and semi-structured data which can be analyzed for generating useful insights.

Let’s go through some major trends associated with big data and its analytics which are gaining popularity:

  • Security of big data

With multiple cases of security breaches related to big data, various companies are now dedicated to secure the data more efficiently. Technologies like attribute related encryption are gaining popularity for securing vital data in an organization. Also, training of staff on regular intervals to ensure data protection is also part of the trend.

  • Deep learning

Relations between the data can be established effectively using deep learning. It is a type of machine learning that works on a set of algorithms. It helps in abstraction of big data and involves neural networking. The use of deep learning for big data analysis is quite promising.

  • Monetization of data

Various companies are focusing on strategies to monetize the data. Companies can earn profit by getting revenues from their own data. Combining data from multiple resources can also no become an important strategy to monetize the data.

  • Data agility

Various companies are now getting more attracted towards data agility. It is the property of extracting the information quickly from big heaps of data. Also, implementing this extracted data for improving the processes also comes under this technique.

  • NoSQL databases

NoSQL databases are also gaining popularity for analysis of big data. Various features like cost effectiveness and ability to manage large scale data makes it more beneficial than relational databases. Also, they do not require predefined schema for inserting the data.

    • Cloud technology
  • There is exponential growth in the use of cloud computing to process and store data. Some major cloud service providers include Microsoft Azure, Google Cloud Platform and Amazon Web Services to name a few. A company must choose the type of cloud which includes public cloud, hybrid cloud or private cloud while going for a cloud computing for big data.

    • Accuracy of data

    In present scenario, various firms are realizing the importance of data accuracy. More accurate data helps in detailed segmentation at the consumer level which ultimately helps in making more wise decisions.

    The dimension of big data is constantly expanding and various technologies related to it will keep on increasing to help the organizations generate maximum benefit out of it.

    Big Data Analytics for solving transportation and logistics problems!

    A recent report by consulting firm McKinsey & Company says that Big Data aConnected_Car_Brings_Intelligence_to_Transportationnalytics will be used more in future for resolving some of the problems in transportation that are currently being faced.  Big Data is the term used to denote analysis of large amount of data. This analysis can result in generation of important information and insights. This information can then be used for making better decisions.

    The McKinsey report explains that big data analysis is being used for reducing road congestion in Israel.  On a major highway in Tel Aviv – the capital of Israel- data from toll tax receipts is constantly analyzed to identify peak hours of traffic. In turn the toll taxes are raised during peak hours in order to control traffic in these hours and discourage those from using the highway that do not have a need for it. Toll taxes are reduced during off-peak hours in order to shift more of traffic to these hours.

    Brazil is using big data analytics for reducing air traffic congestion.  The country uses data from Global Positioning System (GPS) for optimizing air traffic, says the McKinsey report.

    Other companies and institutions are likely to use more of big data analysis in order to solve transport related problems. Similarly private companies too will use more of big data analytics for solving issues related to logistics. For instance, a company can control logistics costs by analyzing data on transportation rates paid in the past and identifying periods when these rates are low. Then it can control some of its logistics costs by shifting more of its transportation needs to these periods.

    The McKinsey report estimates that annual savings of $400 billion could be made globally if big data analysis is used for optimizing the usage of public infrastructure such as roads. . The use of big data analysis in the area of transportation and logistics is just one instance of how companies and institutions are using data analytical tools for optimizing their performance.

    As companies and institutions use more of Big Data analytics for solving problems in the areas of transportation and logistics, the ability to operate big data analytical software will be an important skill for job candidates.  It is the availability of large number of business intelligence software that has made analysis of large amount of data in real time possible.

    How do Search Engines Work?

    seo Search engines perform several activities in order to deliver search results.Crawling – is the process of fetching all the web pages linked to a web site. This task is performed by a software, called a crawler or a spider (or Googlebot, as is the case with Google).

     

    Indexing – is the process of creating index for all the fetched web pages and keeping them into a giant database from where it can later be retrieved. Essentially, the process of indexing is identifying the words and expressions that best describe the page and assigning the page to particular keywords.

     

    Processing – When a search request comes, the search engine processes it . i.e. it compares the search string in the search request with the indexed pages in the database.

     

    Calculating Relevancy – Since it is likely that more than one pages contains the search string, so the search engine starts calculating the relevancy of each of the pages in its index to the search string.

     

    Retrieving Results – The last step in search engines’ activities is retrieving the best matched results. Basically, it is nothing more than simply displaying them in the browser.

     

    Search engines such as Google and Yahoo! often update their relevancy algorithm dozens of times per month. When you see changes in your rankings it is due to an algorithmic shift or something else outside of your control.

     

    Although the basic principle of operation of all search engines is the same, the minor differences between their relevancy algorithm lead to major changes in results relevancy.

    What is Software Testing?

    software-testingSoftware testing is a process of executing a program or application with the intent of finding the software bugs.

     

     

    It can also be stated as the process of validating and verifying that a software program or application or product:

     

     

    Meets the business and technical requirements that guided it’s design and development Works as expected Can be implemented with the same characteristic.

     

     

    Let’s break the definition of Software testing into the following parts:

     

     

    1)  Process:  Testing is a process rather than a single activity.

     

     

    2)  All Life Cycle Activities: Testing is a process that’s take place throughout the Software Development Life Cycle (SDLC).The process of designing tests early in the life cycle can help to prevent defects from being introduced in the code. Sometimes it’s referred as “verifying the test basis via the test design”.The test basis includes documents such as the requirements and design specifications.

     

     

    3)  Static Testing:  It can test and find defects without executing code. Static Testing is done during verification process. This testing includes reviewing of the documents (including source code) and static analysis. This is useful and cost effective way of testing.  For example: reviewing, walkthrough, inspection, etc.

     

     

    4)  Dynamic Testing:  In dynamic testing the software code is executed to demonstrate the result of running tests. It’s done during validation process. For example: unit testing, integration testing, system testing, etc.

     

     

    5)  Planning:  We need to plan as what we want to do. We control the test activities, we report on testing progress and the status of the software under test.

     

     

    6)  Preparation:  We need to choose what testing we will do, by selecting test conditions and designing test cases.

     

     

    7)  Evaluation:  During evaluation we must check the results and evaluate the software under test and the completion criteria, which helps us to decide whether we have finished testing and whether the software product has passed the tests.

     

     

    8)  Software products and related work products:  Along with the testing of code the testing of requirement and design specifications and also the related documents like operation, user and training material is equally important.