What is Big Data?
Big Data is just what this term means i.e. extremely large volume of data sets both structured and unstructured. The importance of Big Data is not its size but its analysis to find the patterns, trends and associations leading to better planning, better strategic business moves and better decisions.
Implementing Big Data Projects
The most important task in managing and implementing big data is to adopt an approach that takes into consideration all the pressing points of your business. Here are some of the best practices to follow for increase in chance of success:
Analyze and list down the goals
First and foremost, gather, analyze and understand the business requirements. Select the specific business goals that can lead to great impact on business. Keep in mind that to make a list of goals you want to address with big data, always analyze from business perspective not engineering perspective.
Try creating a proof of concept taking one of the goals from selected list, where you want to improve decision-making but which doesn’t lead to a great impact in terms of engineering and business output.
Finalize the design, once you find an approach compatible and feasible with your business model. Make value to the customer a priority.
Collaboration between business units and Data teams
All the concerned data teams and business units must work together to meet business goals. Business units should have at least a high-level understanding of what the business can achieve (and cannot achieve) with data. Data scientists should be able to correlate data and models with what the business users are trying to achieve.
For clear understanding between both the units, effective communication is the key. Investing in integration capabilities can enable knowledge workers to correlate different types and sources of data, to make associations, and to make meaningful discoveries.
Have All the Right Data
Make sure that you have all the necessary data – internal and external. Also ensure that the data is effectively leveraged. Determine all the questions you want answers for and make sure that everything can be answered from the analysis of the collected data.
If any question remains unanswered, the missing data must be acquired. Sometimes some important minor factors can easily be missed in analysis, so test and review the results of the data to be sure that all the important factors are captured.
Use agile approach aligning with cloud based architecture
The effective approach to deal with a big problem is to divide it into smaller issues and deal with one issue at one time. Also usually, big data projects start with a specific use-case and data set, initially focusing on high-value opportunities.
Over the course of implementations, with more understanding of the data, organization needs evolve to expand the utilization and impact of big data analysis on remaining features.
Use agile and iterative implementation techniques that deliver quick solutions based on current needs instead of a big bang application development.
The cloud-based architecture offers dynamic storage and better processing capacity that can facilitate with scalability of the application.
Make sure the data represents the entire set
The size of big data is not the most important but it is important to make sure that your data represents the entire set and is not skewed towards a subset. Non-representative data sets may lead to incorrect conclusions.
Another thing to keep in mind is that analysis should be performed without any bias. Make sure you are not searching for, interpreting, and recalling information in a way that confirms your beliefs or hypotheses while giving disproportionately less attention to information that contradicts it.
Bias influences the data sets, tests, and outcomes. Data insights should be tailored by role and included in the applications, devices and channels where decision makers spend their time, and in a way where the insights are aligned or joined with their existing presentation technologies. Along with providing insights at the point of action, master the governance, security and privacy of your data assets.
Challenges in implementing Big Data
Hadoop analytics platform is the foundational technology supporting every big data initiative. Hadoop provides many benefits but implementing on-premise Hadoop is extremely difficult.
It is very challenging to use Hadoop and manage the software for data professionals who are not familiar with it. In addition, Hadoop often requires extensive internal resources to maintain.
As a result, many companies that adopt Hadoop end up allocating the majority of their resources to the technology instead of the big data problem they are trying to solve.
The Scalability Challenge
Big data projects can grow and evolve rapidly. On-premise Hadoop analytics platforms rely on commodity servers, and that physical environment results in scalability problems and storage limitations.
For addressing these issues, more physical servers need to be added. It can lead to more expenditure on resources and can also be disruptive to the project. So look for a cloud-based Hadoop solution for faster and easier scalability to accommodate growing data demands.
The Data Talent Shortage
Successfully implementing big data is largely dependent upon getting the right people with the right skills.
Big data implementation typically require sophisticated teams of developers, data engineers, data scientists and analysts who have the knowledge and skills required to identify actionable insights that create value and competitive advantage.
Putting such a team together can be a painstaking and expensive process. Organizations looking to implement a successful big data initiative that can solve the talent shortage would do well to consider partnering with a big data cloud vendor. Many cloud vendors provide their own educational resources as well as the bulk of the management that the big data implementation may require.
AppPerfect provides customized, scalable, secure and robust solutions for all your big data requirements in any of the following monitoring solutions – Hadoop, Spark, Storm, Hbase, Map-Reduce, Solr, Hive, Pig.
AppPerfect's Big Data Implementation Services has the following features:
- End-to-End Installation, Administration and Configuration of Hadoop and other big data tools.
- Developing map reduce programs according to your business needs.
- Providing SQL query like interface to analyze and visualize your consolidated data.
- Trend analysis.