Key Pointers You Must Do Prior to Starting Your Big Data in Cloud Initiatives

In the current business landscape, big data is the new fuel to the most vital resource which has the power to change each industry. Companies that have not taken benefit of the big data in cloud they are gathering are getting left behind. So, it is not a surprise that several companies are excited to jump on the cloud big data analytics bandwagon.

Keeping in mind the complete scale of big data in cloud and the belief that it will be bigger and bigger, new-age businesses are now looking to the cloud analytics for their big data requirements. The cloud gives several different benefits such as cost savings, scalability and flexibility which can assist businesses of all sizes and niches take benefit from their big data initiatives.

Still the reasons for shift to the cloud big data analytics platform can be frightening ones, but having a strategy and take a systematic approach towards your big data cloud migration is very important. 

Below mentioned are a few vital steps to be considered by the new-age companies to make sure having a smooth journey to the cloud for achieving your big data objectives.

Explore the Key Pointers You Must Do Prior to Starting Your Big Data in Cloud Initiatives:

1. Find your Primary Goal: Getting started a big data project just for the reason to discover new possibilities, without a clear goal is a massive waste of time, effort and sources. New-age business enterprises often do not have a clear idea about the insights they actually want to take out from their data and in what ways it impacts their businesses. To increase your chances of success, you must find the key objectives you want to attain from your big data projects. Below mentioned are a few questions to ask:-

• What are the over-bending objectives you want to achieve through analyzing your data in a better way?

• In what ways these objectives fit in with your overall business plan?

• What are the business units that will be impacted the most?

• How exactly does a successful big data execution appear like?

It is very important to set up a definition for big data in cloud project success directly so that everybody knows in which direction to work towards. As you explain your goals, you can explore the details of your big data execution deeply.

2. Know your Data Storage Infrastructure Requirements: Understand your big data and the database infrastructure necessary to store and evaluate it. Your analysis must comprise a few factors like the kind of data you are going to store and analyze, how much data you have to handle and how fast you require analytical results. In case the kind of data that you are storing and analyzing is mainly systematic and properly structured, a SQL (structured query language) database is possibly the perfect option. SQL databases are ideal for relational data like accounting data, client data, retail stock data and other types which can be smartly structured into rows and columns. Obviously, nearly all cloud computing providers have the cloud versions of SQL databases. Though the rise of big data and the prevalent implementation of distributed computing have surfaced the way for an extremely flexible database technology- NoSQL. NoSQL is lot more flexible and is apt for other kinds of unstructured data like social media, sensor and other types of manually and machine-generated data. NoSQL is also good at processing and storing data immediately and scales horizontally that makes it perfect to ingest and deal with the huge volumes of data. Big data includes structured and unstructured data and both types of data can easily divulge important insights. Deciding to make use of a SQL or NoSQL database relies on your specific situation. Another infrastructure consideration to make is whether you require a data lake (a central repository which can store both structured or unstructured data as it is, without any sort of processing or modeling)  or data warehouse (is central data repository which combines data from multiple sources like databases, data lakes, transactional systems and other sources). The data from different sources in data warehouse have to be properly processed and standardized so that you can easily and speedily run reports through your big data analytics platform.

3. Identify Correct Cloud Big Data Analytics Platforms: As you have finished a comprehensive evaluation of how your data must be stored and managed on cloud, it is the time to choose the tools which will enable you extract analytical insights from your data in the best possible manner. Due to the massiveness of your data, a distributed data processing tool is required to effectively handle and process all of your data in cloud. Hadoop and Spark have surfaced to be two most commonly used open source tools which enable for distributed processing of huge data groups. Hadoop and Spark can definitely work together to achieve the velocity and competence you require. These tools could be installed on premise, but are just perfect to deploy big data in cloud environments. Nearly all the top cloud analytics providers have service offerings which can run Hadoop or Spark groups. Immediate data monitoring and analysis can make decision-making easy. Once integrated with real-time alerts, then real-time data analysis can assist your company make fast operational decisions which can enhance customer satisfaction. Few of the top streaming big data analytics platforms can be used for real-time data analysis and are especially designed to collect and analyze real-time data both from and in the cloud, making them perfect applications to build the strong foundation of your real-time data analysis channel. Clear and detailed graphs and visualizations make it simple for the data analysts and decision makers to evaluate data in no time.

4. Find Out Your Safety & Compliance Needs: The more volumes of data you have, the more valuable insights your company can dig out, but you must be very careful about ensuring the safety and privacy of this data. Putting your esteemed customers’ personally identifiable information exposed & in danger might lead to bearing heavy financial losses, regulatory permits and harm your reputation. Big data has exceptional safety needs due to its massive volume and diversity, distributed processing, dispersed storage and assorted infrastructure and analytics tools. As your big data is in the cloud, you need to work closely with your big data cloud services provider to bargain powerful safety SLAs. There is surely no one-size-fits-all cloud big data analytics platform when it comes to the safety of big data. IT professionals suggest a combination of strategies customized for your big data analytics platform. A few technologies and tools which can go into your big data safety solution might include:-

• Encryption tools which work with various kinds of data and storage formats.

• A centralized key management system which includes the equipment, guidelines and procedures about management of keys.

• Strong and strict user access control rules.

• Proper systems to detect and prevent fraudulent activities.

The key here is to get everyone in your company like the top management, data team and IT security team to consider data security to be a common objective which needs determined efforts to achieve.

5. Select the Best Cloud Analytics Model as per Your Needs: One of the primary and important decisions you have to make while moving your big data in cloud is whether to make use of a public, private, or hybrid cloud model. You have to balance expenditures, technical competence and safety and compliance needs before finally taking a decision.

6. Bring Together the Right Skills: Creating a big data team is one of the biggest challenges you have to face. There is an articulated scarcity of big data professionals—an issue that will not go away real soon. To create your own team you need to make a considerable investment, particularly if you do not have the essential in-house tem with right skills. This is an important step if your company is determined to take on a data-driven decision making procedure. Big data is not just about the data and technology and the people side of the equation is very important. Start with looking at your current team has to be one of your first moves. People in your company that already know your business inside out is surely qualified contenders to be a part of your highly enthusiastic team. To create your full big data team, you might require hiring whatever technical skills you lack in-house. A perfect big data team must be teamed with some key members like cloud engineers, data scientists, software developers, business analysts, data engineers and architects. As you create your team, you have to ensure that they realize their responsibilities in their individual duties and evangelizing a data-driven movement in your entire company. If creating your whole team from the beginning is an extremely frightening task then, you must also consider third-party big data in cloud managed services. With the correct outsourced data team, you might reap ROI faster as you do not have to spend a lot of time straight to recruit their team members. After you reach a steady state with your freshly created outsourced team, you can continue creating your in-house team for the future.

7. Put Your Final Plan Into Action: If you have done your groundwork and followed the steps delineated above then, you must put your final plan into action. This needs getting your data ready, getting your big data analytics platform in place and conversing vision, jobs and duties to your big data team. Make small beginnings through concentrating on your recognized goal, but notice other potential use cases for big data which might be ascertained in the process. While the execution might primarily involve the cloud big data team and other project stakeholders, your company must motivate a data-driven culture, so that any sort of success with your early big data initiative could be parlayed into the future projects.

A perfectly thought-out strategy is important to success of your big data in cloud initiative and it all begins with defining your goals. Just after that you can comprehend the infrastructure and tools you require, the cloud model to execute and who you will work with and then create your team and execute the plan. Consider and employ the above mentioned considerations will certainly put you in an excellent position to achieve your big data objectives.