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Master Data Management in Big Data perspective

Master Data Management (MDM) is a method to define and manage all critical data of an organization to one file i.e. master file to provide a single point of reference. To define and manage those critical data, MDM includes the processes, governance, policies, standards and tools. The benefits of MDM increases by increasing number of department, resources and related data. So, Master data is a subset of Big data and while analysis MDM provide a starting point.  Applying MDM gives many benefits while leveraging Big Data.
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Hadoop for Ecommerce data processing

Retailers always want real time or near real time analysis of huge data sets that change rapidly or have a very short life, for example web shopping cart. We know that Ecommerce companies sit on huge amount of data due to a large number of transaction & inventory. And for that, retailers leverage Hadoop technology for quick and large volume data processing.

Data processing is a process of manipulating the stored data for further use. Stored dump data need to be converted into meaningful and can be used for decision support. So, after processing the data, it can be fit for different purpose as per requirement. After processing, data format may change, means data may be modified and it cannot be the same that it was earlier.

Hadoop is one of the highly used platform for big data processing. Hadoop has established itself as the highly demanded tools in big data sector. Hadoop is used for data storing as well as data processing. For both purpose it is having different part inside it- for data storing HDFS is there and for data processing MapReduce is there. With the help of Hadoop, retailers started shifting their focus on individual marketing by giving customized retail experience.

Hadoop is the widely used framework for big data processing and MapReduce is the most important massive data processing tool for ecommerce data processing. Once Gartner had predicted “Hadoop will be in most advanced analytics products by 2015” and now we can see that their prediction became close to 100 % correct. There are many reports published on Hadoop which convey about the importance of Hadoop in Big data. Some of them are:

A report of Technology Research Organization says that “The data market currently with the fastest growth are Hadoop and NoSQL software and services”.

According to the Big Data Executive survey “Almost 90% organisations which are leveraging big data have embarked on Hadoop related projects and thus Hadoop skills are in huge demand”.

These are some survey reports that convey the importance of Hadoop in ecommerce data processing.

Now we will see that how and why we use Hadoop for data processing. First see the answer of How?

Hadoop is an open source data management technology which having both data storing capacity as well as data processing. Hadoop distributed file system i.e. HDFS is used for data storage and MapReduce is used for data processing. Whenever data come in Hadoop it break all data in small chunks and store it on different clusters across the server. After storing data, MapReduce job runs according to the requirement.

Now we will answer the question of why i.e. Why ecommerce uses Hadoop for data processing?

Using Hadoop, ecommerce companies process data to utilize big data insight to ensure high profitability. Some of the area where they use analysis result that comes after data processing are:

  • Personalized marketing
  • Fraud detection
  • Improved customer service
  • Dynamic pricing

These are the few areas where Hadoop helps ecommerce sector to ensure high value service.

Hadoop having some advantages that make it better from other tools. It is based on distributing computing concept that makes it different from others. Due to its scalability and effectiveness, companies are heavily adopting Hadoop for data processing.

Web Data Mining: Explore immense Automation Potential using Python and R

Today, massive amount of data is uploaded in web-world creating huge new and exhilarating business opportunities to small and medium size companies. However, collecting all of the required data is only one part of the storyline. Mining and converting these data into actionable is where real business value lies. The overall goal of web data mining process is extract information from various web sources and transform it into an understandable structure for further processing. The task of Data mining is to mine or analyse a large quantity of data using automatic, semi-automatic or manual ways.

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Small Business, Small Investment and Big Data

Big data is only for Big business.” “For leveraging big data, large investment is needed.”

These are some of the myths about big data by which we often come across. Big data doesn’t mean that it is only for big business – and it is not. As we know that leveraging big data is very vital for companies for sustainable growth. Leveraging big data having countless benefits in smaller companies too. But unfortunately many small companies are missing these benefits of utilizing big data because many of them believe that leveraging big data is too costly. But the reality is often different. For leveraging big data, you don’t need to invest a huge money.

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How Small and Medium level companies can leverage Big Data?

Big Data’ is the word which appears on everybody’s lips these days. In recent years, there has been a huge hype of ‘Big Data’ which is use to analyse by different companies and vendors to capture meaningful insights from a vast amount of data that can be used to improve business and decision making. When the data is too big, and too diverse to handle in standard database; then it is called big data.

As it is clear from the above that big data is huge collection of data. So, it is impossible to use all that data at a time. You can, however, make use of a small portion of the data that is beneficial for your business.

Unfortunately, many small and medium size companies are missing out on the benefits of utilizing big data because they believe that leveraging big data is too costly and too complex. But the truth is that big data is neither too costly nor too complex. Small and medium size companies can also leverage big data because Big Data solutions have become much more affordable in recent years.

There are many ways by which small and medium level companies can leverage big data. Here are just a few ways small and medium size companies can leverage big data towards their success.

Look for unused data– Small and medium size companies should look for those data which never used in the entire value chain. These data can be their feedback by customers, emails, vendor transaction, and many more. By leveraging these data, we can get some meaning insight that can be used to improve the business – the way it runs and operate.

Look for affordable and effective big data partner– Small and medium level companies can outsource their data to a suitable third party vendor for processing because developing internal capability may not be a wise decision for them at the initial stage. So find an affordable and effective big data partner for your success.

Go for small– If your company is small and medium level; then go for a small start towards big data. After getting some quick positive results from the vendor then only go for big implementation.

Look for the necessity – Small and medium size companies can go for leveraging only those part of data, where analysis is required. Analysing the whole data can be a waste of time and money for them.

Location– Cost is an important factor for any size companies including small and medium size companies while leveraging big data. So, find suitable big data vendors – today there are good small players in the marker out there where you get what you wanted at lesser cost at higher quality and quick turnaround. For eg: leveraging big data is costlier in USA rather than leveraging in India.

Adopt new tools and techniques- For collecting and leveraging big data; use new tools and techniques from the market – essentially freeware.

Every business needs to know the way to success and increased ROI. Leveraging big data is useful for all level of companies. The point is – how you are using and implementing it. According to a survey of Gartner, investment in big data technologies continues to expand every year. They found that 73 % of respondents have invested in big data or have plan to invest in big data in next two years. So, if you are a small or medium size company and thinking that big data is not beneficial for me then for sure you are leaving your boat.

Big data analytics and Competitive growth

In today’s tough competition, big data analytics is playing a vital role in deciding the winner of the market place. Today’s business is mostly centred on the customers, especially retail sector. So, companies prefer to craft personalized marketing strategy according to the changing behaviour of customers. If ‘Technology’ brought big data concept; then ‘Marketing’ used it most for the organization’s growth.

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Big data and Advanced Analytics: Made for each other

Before knowing the relation between big data and advance analytics let us look at both i.e. big data and advance analytics.

Recent research by analyst firm International Data Corp. (IDC) reported that the global amount of digital data will grow from 130 Exabyte’s to 40,000 Exabyte’s by 2020. The old data processing technologies like RDBMS are simply not capable to process data in such a large amount; so, a new trend came “Big Data”. In simple words big data is a collection of huge amount of data coming from different sources.

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7 ways to monetize Big Data

Data monetization means generating more and more profit from Big Data. Today, every business owner is trying to focus on Big Data to get values from it. These values help them out to make business strategies and increase ROI. By the analysis of the data, organizations come to know more about their customers, vendors, and their behaviour, pattern, etc. This information helps them to make their strategy according to the needs and demands of the customer – effectively serve the entire stakeholders better.

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