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.
In retail industry, a large amount of data is generated in every moment. They use different tools for analysing these data. There is great benefits of applying big data in retail sectors. The decision taken on the basis of big data analytics gives benefits to both the store and the customer. According to research by Monetate, retailers have big plans in store for big data. But the question is How? How retailers can leverage big data? Which way it will give benefits to their business.
Let’s discuss these benefits in which big data will helpful to retailers.
By determining customers demand– Today’s retail sector is totally dependent on customer’s choice. They need to arrange their business according to the customers need. With the help of big data, retailers can have better understanding of the customers’ needs i.e. which product customers want the most. This information helps them to serve right product at right time.
Matching goods– Most of time customers having the idea about what they want according to their needs but due to unavailability of enough option they were unable to find perfect match. So big data analysis provides you a close choice of customers’ preference so you can include it to your options.
Perfect calculation of offers– As a retailer the most perfect part to execute the sail is to put best offers to the customers at the perfect time i.e. when they are needy of those items. This makes both happy retailers and customers. You can use big data analysis for the information about the needs of customers.
Fraud detection– Big data also help retailers to find fraudulent. By using different techniques of analysing data you can detect the patterns of fraudulent. Keeping secure and safe your business should be highest in priority and big data will help you for this reason.
Analysing sensor data for organised service– By analysing the data of sensor you may get the exact information about the number of customers and their time of shopping. This analysis can help you to organise your store in well-mannered way.
Smart marketing– Today’s getting success in business not only dependent on marketing but more on smart marketing methods. Smart marketing refers to that marketing in which marketing will be customized according to the needs of individual customers – what they call personalized marketing. If a customer adds any product to their virtual cart and due to some reason they remove it from their cart, then you can try to give them some customized offer on that product to covert the sale to dollar.
If we look any leading retail players in the market today, all of them leverage big data and analytics for their decision support. Meaning, there is no other way to withstand in this competitive retail market instead leverage big data for your retail business – and it can help you to stand ahead of your competitors.
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.
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.
Everyone agrees that big data means big insight and big decision support. No one denies that today. Challenge is processing big data and that too, there is no one single way or solution in processing this data. Usually, big data is processed in big data lab. Big data lab is a dedicated development environment for experimentation IT infrastructure with presence of big data tools, algorithms, technologies and approaches to process big data and analytics.
If you are going to set up a big data lab, then it is good to understand about some of the potential challenges ahead. And, some of the quick questions you ask yourself..
- Why we do set up a big data lab?
- How much amount of data do we have?
- What all different data formats are we planning to process?
- Which type of storage system we need?
- Do we have right set of people for setting up a big data lab and analytics?
- Can we bear the cost for all these?
If you have answer to all the above questions – yes, well and good. But still you will face some more challenges while setting up a big data lab. Those are:
Cost is often more than expected – Cost will be the biggest issue while setting up a big data lab. You will need huge amount for the all setups. If you want to build a storage for storing the data then you will need expensive hardware for this purpose which must be capable enough to manage large amount of data. If you plan to go for Amazon Web Services, then also you will have to pay for this – per minute, per application, etc.. Different tools and technology will be needed for the different operation during processing of data. You must hire experts like data architect, designer, data analyst, business analyst and many more to handle and operate different operations. In all these things you need to invest huge amount.
Storage Capacity– Working on big data is quite often a big challenge. You need to have new and flexible tools for processing big data because of its variable size and complexity. We can break big data into three Vs i.e. volume, velocity and variety. While setting up the big data lab, we need to consider all these points. Volume of data means the amount of data. We need to set up big data lab for these variable amount of data to be store easily. Velocity refers to the rate of coming data. Variety of data refer to the different types of data. The big data setup should be able to hold the data of these quality.
Data ingestion and Process– The processing of data needs appropriate technology for data integration and data development. We can’t handle all the data processing with same tools and same people. Each time we may need different tools and techniques to analyse the data and so, different skill set as well. We need to be aware of available tools and upcoming technologies. So, it’s a big challenge to cover all these tools and technology part while setting up a big data lab.
Analytics tools and Expertise – The next step in big data processing is analysis. Analyst always need to do a deep dive analysis to improve the knowledge of our customer’s requirement and to make business strategy. We need data analyst for this purpose. Their work is to analyse the data and find out the valuable patterns in data. If you have tools i.e. right analytic tools (SAS, R, Matlab, Excel, , Tableau, etc.) f in your lab – then it would be easy for the data analyst to analyse those data. It is a challenge, if you are a start-up or medium size team, to manage these tools, license cost and right people for the appropriate work in your big data lab.
Visualization tools and experts– The analysis report, final outcome analytics, needs to be presented in a dashboard, or report or visualize them like chart, graph, etc. so that it can be easily interpreted by the client. Visualization expert work on this step and convert the mathematical value into simple visual form. You should have good visualization expert and visualizing tools to perform this task.
Ready for facing all these challenges? Yes! Then go for it, otherwise think again.
Though there could be many challenges in setting up a big data lab, it is always important to remember that the right data can help you make intelligent and smart decisions. Big Data can be a source for this great insight.
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.
We wanted to make our world a better place to live – always. It is big data – one among, that is changing the way the world is today – business runs and social delivery. Effectively, it helps to improve social well-being.
Reports state that everyday 2.5 quintillions of data is being created which contains information of millions of people. Governments gather an increasing amount of data day by day and often question is – are we converting these into better governance and capacity building? Do we leverage these data for improved social delivery?
Today, there is increasing pressure on governments to convert big data into actionable information and for social delivery. We are in a world where every social organization can improve the ability to leverage big data and data science for increased social delivery.
Every aspects of our life is affected by big data – in one or other manner, and some of them really make a remarkable difference. Let’s discuss a few of them – how big data can help improving social delivery.
Improve Healthcare – The industry has improved a lot – whether predicting epidemics, finding solutions for deadly diseases, improving quality of life or avoiding preventable deaths. Behind these improvements, big data and analytics play a crucial role. Today, people are aware of health potential of wearables. Big data contains the data by track sleep, track eating, mood, physical and emotional health. Once data is generated that could give information back to the users who generated it, and create personalized insights – health tips. Big data analytics also being used to decode the entire DNA strings to find and to understand the disease. In early days clinical trails were limited to small sample sizes but now it can be analysed on a wide range of data.
Transportation – Government authorities started to reorganize fruits of big data and analytics. They realize city infrastructure and public transport systems in order to encourage people to walk and use cycle more rather than drive cars in the city. Often in cities, people are suggested alternative route and appropriate driving time. How autorities provide these information? By using GPS system it tracks the public transport and traffic signals predict traffic volumes.
Improve social security – Effective social security is not just a product which can be built, use and improve. Big data is used in improving security enforcement. Different national security agencies leverage big data and analytics to find the terrorists and criminals pattern of crime. Big data is also being used to detect and prevent cyber-attacks, fraud detection, etc.
Improvement of cities – Everyone talks about smart cities. With the help of data about city, governments plan different schemes for development. How much money to be spent and in which area? These questions being answered by big data and analytics. Good part is – a number of cities around the world started exploring big data analytics to being a smart city.
Trading – Big data has vital use in trading specially in capital market. Big data is widely being used for analysing fraud pattern and minimize the fraudulent trading. By analysing different patterns experts predict the up and rise of the financial market combining various data points. Also, there are several real time and near real time reports that are being generated on trading and operations using big data and analytics.
Retail – Big data application in retail often enables them offering optimized price. And for that, retailers constantly finding innovative ways to draw insights from online and offline data sources. They are embracing big data and analytics to understand their customers, matching them to products or services; eventually convert them into dollars – in one or other form. Retailers analyse various data points – sales, pricing, social, economic and many other to find hidden pattern to serve their customers better.
Personal life – Big data and analytics is impacting every social touch point of a customer – the way we think, behave and act; everything is being monitored and analysed for increased customer service. Many dating and matrimony sites use big data – to analyse the listing characteristics, behaviour and reaction to find a perfect match. They also use these data to improve the matching algorithm.
Exploring big data and analytics for social delivery is rapidly evolving. It offers tremendous opportunities when explored and applied. It can help governments – drill down deeper on the opportunities of big data, and uncover powerful and effective methods for optimizing governance.
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 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.
“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.