Hiring right talent according to the need is the key factor for a company to be successful. “A good fit for the job equals a good fit for the company” is one of the most appropriate quote during hiring a resource.
Big data value chain is mainly divided in three steps. They are data integration, Big data development, and Big data analytics. We need different skilled resources for these three different phases. A person should be hired when his skills meets the needs of the requirement. Let’s look at these steps one by one..
- Data Integration– As we know that in Big Data, data comes from multiple sources. Connecting these data from different sources leveraging big data technology through big data lab, Amazon web services etc. for collecting data and ingesting to the operations is called Data integration. Data coming from different sources have to connect with the appropriate technology. We need ‘Big Data Admins’ for this purpose who will able to make connection between these two, they must know how to use different data integration tools i.e. Sqoop, Flume, etc.
- Big Data Development– Data comes from different sources in structured, semi structured and unstructured form. Those data need to be stored in an organized manner so different development tools can read it for processing. We need big data developers for this purpose who knows the different data processing technologies like Hadoop, Informatica, Teradata, etc. Their work is to make the data to be readable by data processing technologies. They should also know about different database in which data will be stored.
- Big Data Analytics– This stage contains data processing and converting the processed data for the decision support. Data analysts and Data scientists work in this phase for analyzing data to find out hidden pattern in the data and build statistical models. One of the favorite definitions for data analyst is “A data analyst is someone who is better at statistics than any software engineer and better at software engineering than any statistician.” – Josh Wills” This one line defines the characteristics and needs of a data analyst. A data analyst must be good at problem solving. Companies generally prefer engineering, statistics or computer science background people for this role.
To summarize, some of the key skills needed for Big data team are as follows:
- Hadoop– It is one of the famous big data working framework. Big data people must know this framework.
- NoSQL– On the operational side of Big data field distributed storage like HBASE are used. To work on these databases NoSQL should be known to the person.
- Statistical analysis– This is one of the important skills to be in a big data person. They should be familiar with different statistical modelling tools like R/Revolutio, SAS, SPSS, Alteryx, Mahout Libraries, Matlab and there are many more
- Data Visualization – Person should be familiar with different visualization tools like Tablueau, Spotfire, Qlikview, Rapid miner, MS Excel, etc.
- Programming language– Person to know the general purpose programming language like c, java, python, etc.
- Problem Solving– A big data person must be good in problem solving. So, they can find the solutions of different problems during the analysis.
Big data is comparatively a new field with a lot of opportunities. During hiring process, Companies need to pay attention to what they wanted and go for that. Though, it is not advisable to find people having expertise in all big data tools in three phases mentioned here and it is not necessary. But, it is important that people have a bend of mind for learning new tools.
Defining, articulating and representing business problems is a crucial initial step in any Big Data initiative. To deliver quick results from big data, it is good to have powerful and well organized analytic capability. And, if not? Nothing to worry. Reach out to a right big data partner who can deliver quick results – this could be a Proof Of Concept (POC). Once recognize the POC is successful and could generate business value – yes, go ahead to the next level.
Companies are vying different ways of discovering the value of letting customers create their own unique products. Almost all e-commerce giants leverage Big Data to present a personalized set of products to their customers and Amazon is a successful example.
Now, let us look at how small and medium size retailers can explore the driving force – big data, and how Hadoop can help in this journey.
Hadoop is an open source tool for processing big data. It is an open source framework where data can be stored and processed. It is one of the most used and highly ranked platform for big data processing. Hadoop brings many advantages while applying it in processing of big data. Hadoop allows users to handle increasing volumes of data quickly and efficiently. That makes it friendlier with retail sector –ecommerce as well. There are many practical advantages of using Hadoop.
Hadoop having two parts in its core, one is HDFS (Hadoop Distributed File System) for data storing purpose and other is MapReduce for processing data. Whenever any data comes to Hadoop it breaks those data into small “chunks” and then those small-small part of data store in different Hadoop clusters across the server.
Hadoop framework is extensively used for ecommerce data processing that comes from different sources and analysis. Processing data using Hadoop is a cost effective way to find insight.
Shopping experience has been changing from traditional offline way to online marketing. Concept of brand is getting replaced with customer personalization. Now power has been shifted to consumers from shoppers. So, all ecommerce companies try to attract consumers with many plans. There are many application of Hadoop in ecommerce because of its cost effective data processing characteristic. Some application of Hadoop in ecommerce sector are:
Personalized offer – As we discussed above that shopping experience has been changed in recent years and power shifted to consumers from shoppers. So now customers are important. All ecommerce companies want to treat each customer in personal manner. Customers shop with same retailers in different ways. So using Hadoop retailers collect data of same customer from different sources and provide personalized offer for them.
Improve customer service – Online retailers use big data for a good customer service. Using Hadoop they track the customer data whenever customer contact representatives then customer data should be in front of customer care representative, so they won’t need to ask anything from customer and customer will feel special.
Fraud detection – Using Hadoop retailers detect the patterns of fraudulent. Hadoop is the simplest and best method to detect the pattern of fraudulent. Any other method will be cause for high expenses without certainty of correct result.
Dynamic pricing – These days in ecommerce sector competition is too high. So always each organisation needs to be alert about other rival companies that what they are doing and how? For example pricing. As a customer you may find some difference in price of same product on different retailers. So, companies are using Hadoop to find the changing pattern in price of their competitors and be ready for those situations.
These are the few ways through which we can know that using Hadoop in ecommerce business is a cost effective way to get a solution rather than any other way. The use of big data in business make the business more attractive and successful, and Hadoop makes it even more appealing. So ecommerce companies are steadily moving to apply Hadoop to increase returns and reduce effort.
Web Scraping using python – a technique which can be used to extract a large amount of data from websites using some programs or applications and save it to your computer or to a database for further use. It is a technique to automate the process of collecting data from any website instead of collecting data manually.
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.
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.
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.
“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.
The importance of Big Data is increasing in every passing day. Each and every organization wanted to implement the insight of big data to their business. It is not only applicable to large size of organizations but also for small and medium size companies are also expected to leverage big data for their business to propel growth.
‘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.