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How retailers can leverage Big Data?

Big data is not only for technical things. It is having a wide application in different industries. Retail sector’s major growth is dependent on the big data analysis. According to a survey by Gartner — a technology research firm, 64% of the organizations are investing or planning to invest in big data technology.


How to train existing employees on Big Data?

It’s very challenging to talk about Big Data without taking about the skills required for big data. At present companies are suffering from Big Data skill gap. According to a CompTIA survey of 500 U.S. business and IT executives, 50 percent of firms that are moving forward on the way to leverage big data, and 71 percent of firms that just started leveraging big data, feel that their staff are not so proficient in data management and analysis skills. Due to the lack of knowledge about big data processing, employees couldn’t give their full effort and it may cause for the project failure. So, the training of existing employees on Big Data should be an integrated part of big data processing.

Now, if you are a caretaker of a company you may have a question that “How”? How to train your employees on big data?

Let us try to find answer for these questions and to make your current team more big data ready.

  • If you are a company having a base of developers in java, python or Ruby then you may further proceed towards their training on big data because these are some of the language useful for big data processing. So, overall you first need to train your employee on these languages i.e. java, python, ruby, etc.
  • You also need to train them on database development side with NoSQL Big Data systems like HBASE, MongoDB, Cassandra etc. You may choose database developer with good SQL knowledge to train on database part.
  • For the installation or setup big data lab you may need to train some people as Linux admin so that they make you big data lab ready for Hadoop or AWS. For this you may choose people with Linux knowledge.
  • After all these theoretical training you may need to go for some practical work on big data processing. For this use Hortonworks or Cloudera services to give practical training to your employees.
  • There are many free and paid online courses available for big data training. Can start with free course and can move to paid ones, if anything specific required. You may provide these materials to your employee where they will get online study material as well as practical works on big data processing.
  • For the analyst work, first you need to find employees with good analytical and reasoning skills. Train them to use different big data analysing tools.

With the help of these ways you may train your existing employees on big data and make them ready to start. The McKinsey Global Institute estimates that by 2018, there will be a shortage of 1.7 million workers with big data skills in the U.S. alone—140,000 to 190,000 workers with deep technical and analytical expertise and 1.5 million managers and analysts with the skills to work with big data outputs. It is a truth that big data processing and most of its components are new but you can easily cross train your employees on big data if you really want to get in this field. You just need to take existing developers, analyst and admins and cross train them.

Challenges in setting up a Big Data lab?

Anything and everything we do in this connected digital world – be it online shopping, Facebook liking, responding to social events, adding new friends in social media, blogging, tweeting, sharing product review, etc. leave a trace of data about us as consumers. Extracting the huge volume of data, identify business value from the data and use them for decision making is all about the intent of Big data analytics.


Leverage big data to improve customer experience

It is a fact that customers who are happy with your products or services are much more likely to come back and buy from you again and again. To be in competition every organisation need a wide range of satisfied customers. And, for growth, a business needs to be able to retain, satisfy and engage their high value customers effectively. So in another word it is an era of connected customers. Companies invest a big amount on big data strategy to collect, store, organize and analyse the information about their customers to make personalised marketing strategy to make each one feel special. Big data helps companies to find their customer’s need and expectation from various customer touch points – data.

According to a survey of Driving Performance While Managing Risk, KPMG showed that 41% companies will use big data analytics to improve customer experience in next three years.

Companies cannot depend only on the traditional way for keeping their customer happy. They know that there is a need to leverage big data in their business. Let’s see some area where you can leverage big data to improve customer experience.

Customized marketing – In the era of customized marketing, companies try to reach every possible customer in personalized way. It helps both companies as well as customers. Customers feel special when they receive a personalized service from the service provider and companies get a new customer.

Personalised service – It is very difficult to analyse every customer purchase history transaction data to get information about their behavior, interest, and preferences. Big data makes it easy. With the help of these information companies can prepare their sales strategy to push customers to the point of purchase.

Identify customer problem and solve them– If a company doesn’t know about the pain point of their customers then they couldn’t pay attention to their customers. Companies who are using big data analytics, they know the difficulties facing by the customers and try to improve their customer’s experience. If your company get it to the world of bid data analytics, then for sure you will be out front in a competitive market. Delta Airlines used big data to find the lost baggage of their passengers and came out front in the airlines market.

Improve customer service- Companies are using big data for marketing product development but the companies who are using it to improve customer experience and move one step further. If a customer contacts you, for any enquiry, and if you have enough data in front of you about them then the representative can more quickly and competently solve their issues. They don’t need to ask many question of the customer because they already have this information in front of them. This makes customer feel good and satisfied.

Give more options- One dairy company uses big data to customize their product. Any customer can choose the product according to their interest like fat, etc. They analyse the customers review data and found that different customers having different needs. So they put some option related to the needs of customers for their product. This makes both happy, customer as well as company.

Provide them their own data– Make customers excited about their own data. Here data means analysed data not the huge amount of unstructured data. A food diary platform bodymedia gives not only the information about the calories they have consumed but also about their break down protein and fat. Some more companies use big data to help their customers to find their source of expenditures.

Customer service is a very important part of any business. If you don’t have a well organised and behaved customer service; then it is not possible for you to stand in the competition. Today’s companies not only focus on traditional customer service but also a highly managed and arranged customer service with the use of big data.

How to start with Big Data and explore business opportunities?

What is the start line for Big data? From where can I start? How do I start?

These are the questions which often asked by many before starting to work on Big data. Before starting with Big Data everyone should have the answers of these questions.

Big Data is all about analysing the patterns in variable size of data sets. Variable size refers to the growing size of data sets i.e. to add more data from more sources as the needs grow. We used advanced analytics to find the patterns in any data sets.

Big data analysis is used by large enterprises for their benefits, to know about patterns & behaviour of data coming from different sources. For a beginner or starter, they should start small.

Big data is formed of three “Vs” – volume, velocity and variety with a “C”- complexity. Let’s discuss the points regarding the business opportunities for a big data initiative.

Aim – Before jumping right into solving any big data problem, we should step back and invest time and effort to improve our understanding of the problem i.e. what are we trying to solve. Then move step by step towards the solution.

Step by step approach- The thumb rule to analyse big data problems is approaching it step by step. First split the whole problem in different number of smaller problem and then approach to each and every part.

Collecting the data- The first step towards starting big data is collecting the data that is being produced. Collect more data than necessary. We don’t need to keep these data for the lifetime but we won’t get any idea about the data until we start to collect.

3Vs- Three 3Vs in Big data are Volume, velocity and variety. Volume refers to the size of generated data amount, Velocity refers how fast the data is generated and processed to meet the demands and the last ‘V’ Variety refers to the range of data type and sources. It is a fact that a data analyst must know and aware about the types of data.

Complexity. The management of data can be very complex when different types of data came from different sources in large amount. While analysing the data, data must be linked and correlated so the analyst can find the useful information.

Grouping of data- We need to categorize the data according to their logical information. Ex- Data useful for business purpose should be in one cluster, data useful for improvement of quality should be in another cluster. The data should be well categorized and prioritized.

Volumes- ‘Big Data’, As the name shows that the large amount of data but in starting we shouldn’t assume that data is going to be in petabyte or exabytes. If we leave some fortune 100 customers then others don’t have such a large amount of information. So initially we would have to work on some gigabyte of data.

Future Perspective- Let’s assume that your company doesn’t need big data solution now because your database is able to operate the current amount of data but when you try to find the useful information from these data setup you may not get the same results that you are expecting. So it is useful to use big data initiative.

Impact- Impact of solution on the business and organisation! Try to find out the answer of question “ How these analysis of data impact the business and organisation?”.

Selection of right Technologies- Choose the right technology according to your needs. The most famous technology used in big data is Hadoop, but there are several different tools and technologies for big data problems.

Data Management Services in Big Data era: A different perspective


Data management is a process of developing and managing the whole life cycle of data generated on different platforms of an enterprise. The official definition provided by DAMA (the Data Management Association) of Data Management is “Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data life cycle needs of an enterprise”.

Big Data is a defining term of growing volume, variety, velocity, and value of data. Organizations leverage big data to find hidden insights and apply them to serve their customers better which eventually decides business growth. Big Data is now seen as the core of enterprise growth strategies.

Data become more powerful when it is integrated. The integration of data, coming from different sources converts data into big data. The rapid growth of big data brings big challenges as well as immense opportunity. To grab these opportunities and converting those challenges in benefits, Data management is very necessary. In other words, for using big data effectively proper data management is compulsory. Data management is often seems to be a fundamental block for big data analytics. The analysis result and building models mostly dependent on the data quality and data management plays a crucial role.

These days business frequently in contact with their customers through different medium like social networking, emails, messages, calls, etc. and because of this, the era of big data came into picture. Flood of data coming from web surfing, mobile devices, sensors and internal process is full of valuable information. All data generated through these mediums are valuable for business provided exploring it decision support in right way. These data are capable to change the way of doing any business. So, managing these data is really important for the growth of any business.

There are many challenges in management of big data as well. It is challenging many long-held assumptions about the way data is organized, managed, ingested, and digested. Managing big data is not always possible using traditional data management techniques used in relational database domain. In big data processing, data management includes different function and process of data including data storing, backup and recovery, processing and many more. All these operations make data complex and tough to handle in proper manner. The size of data is also too big. So, due to these reasons traditional data management methods fall short to handle big data.

Big data comes in Exabyte and Zettabyte and traditional data management is not capable to handle those data. And the backup process makes these data double and triple of its size. For handling big data, different tools and technologies came into industry and it is evolving free and paid ones. Hadoop, Hive, Pig, Hbase, NoSQL are a few of them and there are many more. These tools and technologies are capable to handle big data in proper manner.

For a healthy analysis of big data,proper data management is needed. Quality of data can offer you powerful business insights that can be game changing to proper your business.

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.

A recent survey from Gartner Inc. found that 73 % of respondent have invested or plan to invest in big data in next two years. It is 9% more than the last year record. While the number of business that said they have no plans for big data investment also decreased from 31 % to 24 %.

A SAS report published with the title “Big Data: Harnessing a Game-Changing Asset” showed that 73 % of people surveyed said that collection of data increased “somewhat” or “significantly” over the previous year. So It doesn’t matter that you are a small or big company, just go for big data.

Now, let us discuss that how small companies can leverage big data by small investment.

Use ‘unused’ data– If you are a small organization and you think that you don’t have enough data to analyse; then no need to worry, try to find unused data in your organization. This data can be the system information, review mails, sensor data, vendor data, customer transaction data, PDF files, etc. By analysing these data, you will get valuable insight for your organisation.

Vendor selection- You can take a wise decision on the topic that how to analyse big data, means by developing internal capability or outsourcing to a right big data vendor. Look at your need and then take a decision. If outsourcing big data to a vendor having low cost than developing internal capability; then go for a right vendor. If you want to focus on the core of your business; then also go for a suitable vendor.

Always start small- Always start with small. Don’t go for a large investment. First look for POC (Proof Of Concept) and then invest in whole project. It will minimise your risk.

Tools and techniques– For big data processing, a large number of tools and technology is available. Try to find the appropriate and economic technology. For ex- To store big data, you don’t need to invest on the hardware you may go for Amazon Web Services. Here you will have to pay according to the usage – as-a-service or pay and use.

Vendor location selection– Find big data partner from those part of world where cost is less and availability of high skilled resources are more. Distance between you and your big data partner doesn’t matter today.

 In essence, if you don’t have enough high value customers your business will fail. The same applies if you spend too much money in big data for acquiring those customers or optimize the business value chain. As technology advances, big data is becoming an essential part for small businesses. To be in market competition, it is paramount for all type and size of business to invest in big data.

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