Evolution of Big data

Let’s dive today about the real time examples. The enormous data growth now presented a big challenge for the organizations who wanted to build intelligent systems based on the data and provide near real time superior user experience to their customers. Data was received from the transaction system and overnight was processed to build intelligent reports from it. This kind of approach is a data warehouse concept where it had its own share of challenges whenever unstructured data is stored. Now the traditional database has limits to store large volumes of data that comes in variety. Hence to cover this gap, big data science evolved with the idea of capturing the data and present to the customers in an innovative approach.

What is the role of Cloud computing in Big Data?

Cloud computing- It’s one of the hottest buzzword in IT world. It refers to use of scalable public computer network for data storage and to perform computing tasks. One of the easiest example to explain cloud computing is Dropbox. It allows us to store and access your documents via internet connection.

Ex: AWS (Amazon web services), Google Cloud.

Cloud Architecture

In the cloud architecture there are 3 models developed over time;

    Private cloud – As the word suggests, its intended for one organization and do not share physical resources. Data center is managed internally.

      Public cloud – This one is developed by commercial providers like Amazon, that hides the complex infrastructure and provides various resource services.

 Hybrid cloud – It is mix of both Private and Public cloud that helps in achieving security, elasticity and cheaper load capabilities. It can have huge impact to organizations if they are serviced poorly.

Cloud and Big Data – characteristics

Below is the list of characteristics of cloud computing

·         Sc ability

·         Elasticity

·         Low cost

     Things to watch out in future:

IT companies rely heavily on cloud computing integrated solutions to deliver quality projects that delivers business needs. There are few things to consider when deploying big data on cloud solutions.

·         Check on Data Integrity issues

·         Initial cost

·         Performance issues

·         Data Access security requirements

·         Compliance.

Use case Example for Big-Data

Let’s consider a Retail Industry and how big data rules Retail sector

In the past we know that stores that reacted to the demand where growing vastly. But today due to rapid growth of technologies top retailers rely on Big data to gain a competitive advantage.

Big data helps to focus on:

 Ø   Predicting trends in the market and preparing for future demand

 Ø  Real time analytics helps the company to synchronize prices hourly with demand.

 Ø  Performs deeper analytics on all data to find hidden insights.

Big Data analytics is now being applied at every stage of the retail process – working out what the popular products will be by predicting trends, forecasting where the demand will be for those products, optimizing pricing for a competitive edge, identifying the customers likely to be interested in them and working out the best way to approach them, taking their money and finally working out what to sell them next. So big data is useful in other industries too. Take a look at the below examples

·      Starbucks Earns more customer credit by giving new promotions and deals where customer can access via mobiles.

·         Hotel Chain Uses Big Data to Increase bookings.

·         Financial services score New clients.

Why Big data and Analytics projects fail?

Business analytics projects tend to make big promises. Among them, the projects propose to give executives a better understanding of their current business environment and to help them anticipate future business conditions; to facilitate more predictive and prescriptive decision-making; The success and failure depends upon the execution of the data in a methodical approach. It’s based on too many factors like Infrastructure, right tools based on your business application.

There is some major reason for that. Take a look below:

·         Lack of miscommunication between data analyst and the top management level. Frequent communications may reduce the bridge gap on implementation snags and risk of unfortunate downstream surprises.

·         Next is when the company has ineffective access to clean and reliable that. The data need not be spotless, but it can be cleaner with few data issues. Data maturity is what an organization must look for when implementing big data.

·         When the organization starts to focus on technology rather than business opportunities and when they fail to provide data access to subject related matter.

·         For a project to be successful an organization must ensure that it has uncommitted stakeholders that ultimately drives business results. In case if there are any absence of stakeholders it might lead to poor insights and bad decision making skills.

·         Lacking the right skills is an another woe. For new technologies like hadoop skilled and efficient programmers are require to make a project successful.

      See you soon on my next blog!!!