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.