Introduction
We are currently at an early stage of exploring Big Data. But it is never too early to adopt the best practices of any work. It is essential to have a plan, explore various options, and know why a particular technology or tool can better suit your needs. Adopting good practices can significantly enhance your work output, knowledge, and experience. Using best practices can help in a more straightforward justification of your work. It can help get funds for your work and, at the same time, boost the credibility of your work and the organization. Now we will get to know ten best practices for Big Data.
Ten Best Practices
-
Know Your Objectives
After conducting some experiments with your big data to know the possibilities for exploration, understand what you want to achieve from your big data. It is advisable to have a combined discussion with the business and the IT teams to set well-defined short and long-term goals. These decisions based on your big data can help organizations grow tremendously.
-
Plan a Road Map
Once you know your objectives, it is time to make a plan of action. It is crucial to set possible benchmarks. Taking on very high targets can lead to failure and demotivation. You don’t need a 20-year-long road map. Start with a year or two long road map, with clearly defined objectives involving all the stakeholders.
-
Explore Your Data
Almost all the companies are a warehouse of data! But most companies don’t know how to utilize this data efficiently to predict and improve their products or services as most of the time; they don’t know what information they have. It is crucial to understand the type of data, its ownership, who controls it, its current usage, duplicate data, and any third-party company involved. This exploration can lead to beautiful insights, which is the foundation of the Big data analysis strategies.
-
Understand What Data You Don’t Have
After exploring the available data, next is the time to think about what other data you will require for your goals. We can be creative to think about more data possibilities and new inventions.
-
Explore the Technology Options available
Now, when you have well-defined goals ready, you know what data you have and what data you need, it is time to execute your plans to action. It is crucial to explore various technology options available like Hadoop, spatial databases, cloud storage, etc., to make informed decisions.
-
Plan for Security of your Big Data
Data security is often the most critical task of Big data, but people are generally unaware of the complexities of securing big data. Data should be free from internal or external risk factors. It is crucial to mask people's private information so that it is not misused.
-
Establish a Data Governance plan of action
Data governance means a trustable information resource for all the stakeholders and employees. Accountability for managing data is the most crucial feature of good data governance. It is essential to have specific rules for data protection. For example, patients' personal information must be masked in healthcare data to maintain anonymity.
-
Plan for Data Stewardship
It is easy to assume that you got the right numerical results for your data analysis, as numbers are usually lucrative representations. It is crucial to check the assumptions in different data sources you used and whether they can be combined to predict sales for a new product.
-
Keep Testing Your Assumptions
New data sources can improve your predictions and performance. It is crucial to assess whether you are getting the right results. If the results are different than what you expected, it is essential to explore the cause of such results.
Keep checking the reliability of your data and your assumptions.
-
Continually Study Best Practices and Leverage Patterns
Big data is an emerging field, and continuously new technologies are being launched. It is crucial to explore different best practices and technology options and leverage them for the greater good.
116+ registered 