Big data analytics Overview

Big data analytics is the process of extracting value from data sets that are too big to be processed with traditional methods. By harnessing the power of big data and combining it with advanced technologies, we can gain insights into our business that were previously impossible.

Big data analytics analyse large amounts of data to uncover hidden patterns, unknown correlations and other useful information. The goal of big data analytics is to identify patterns in large amounts of unstructured information such as social media posts or text messages. It can be used for many purposes including:

• Predicting customer behavior
• Identifying fraud and cybercrimes
• Predicting future trends

Data analytics can be used in many industries, including: Retail and e-commerce Financial services Healthcare Manufacturing Automotive.

Benefits of big data analytics

The benefits of big data analytics include:

• New insights. Big data analytics allows you to capture and analyze all kinds of information that would have been difficult or impossible before. This includes social media posts, log files, sensor data and more. You can use these tools to gain a better understanding of your customers’ needs and wants by examining their buying behavior on social media platforms such as Facebook and Twitter; how they’re shopping online through Google Shopping Express or Amazon; what they’re searching for on Google Search Engine Optimization (SEO); even where they live within the country itself!
• Improved decision making. By leveraging this new technology you’ll be able to make better decisions faster than ever before – which will ultimately save time & money in the long run by making faster decisions about what products/services are most wanted by customers based upon previous purchases made by them over time too.

Challenges of big data analytics

Big data analysis can be tricky because of the sheer volume, variety and velocity of data available. These challenges include:

• The sheer volume of data: With so much information flowing into companies from a variety of sources, it’s important to sort through the information to find what’s most relevant for your business.
• The variety of data types: Data comes from many different sources including social media platforms like Facebook or Twitter; sensors embedded in products such as cars or refrigerators; customer surveys; employee feedback surveys; video surveillance cameras; scientific experiments run by researchers at universities around the world (and elsewhere). Each type has its own logic behind how it works—and each requires different tools to analyze effectively.

Solutions to challenges of big data analytics

The most common challenge with big data analytics is the sheer amount of data you need to analyze. If you’re trying to solve a problem that’s too big for your current infrastructure, it’s important to scale up as quickly as possible.

To help meet these needs and more, there are several options available:

• Use a cloud-based big data analytics platform. A typical solution will include tools for processing different types of data (e.g., relational or text), along with tools for visualizing results in easy-to-understand charts and graphs—both on mobile devices or computers screens.
• Use a hybrid cloud platform that combines both on-premise hardware (with servers) and cloud resources (with servers). This provides the best combination of flexibility and performance because each type has its own strengths.
• Use an open source software product like Hadoop MapReduce Framework (HMRF) or Apache Drill which can be integrated into existing applications such as SAP BusinessObjects BI platform.
• Leverage prebuilt modules built into these platforms so they don’t require any additional coding effort whatsoever.

Disadvantages of big data analytics

There are some disadvantages to big data analytics. The first disadvantage is cost. Big data analytics can be expensive, and often requires a large amount of money and resources to carry out the analysis. This can be especially problematic if you’re trying to implement this kind of technology into your organization or business model.

You might also have issues when it comes time for you to use these results because they may not fit your expectations or needs as well as they should have done if they had been done properly in the first place.

Another disadvantage is data quality issues with big data analytics processes since there will always be errors present within any database system no matter how good it may seem at first glance and even though we all know this already from our experience working with databases ourselves over time (which means that everyone knows about how difficult it is finding good information), most people still find themselves making assumptions based on past experiences only instead of looking deeper into what could potentially happen next time around when using new methods such as those mentioned above…

harnessing big data is substantial

Big data analytics is a new field of study and research. It is not a panacea, but it can be used in many applications. The potential from harnessing big data is substantial, however there are a number of challenges that need to be overcome before large-scale adoption takes place.

The first challenge is that most people do not understand what “big data” even means and how it works. They may have heard about it but don’t know enough about its benefits or drawbacks yet; they also may not know where their organization stands on the topic yet either! This means that you need to educate your audience on the basics while also providing them with real examples so they can see how this technology could work for them personally (or at least indirectly).

Secondarily: You’ll need someone who has experience working with large amounts of information–this could be an expert from another industry like medicine or finance; perhaps even yourself if possible? Someone who knows what questions should be asked when trying out this methodologies’ efficacy within their own environment(s) would be ideal because otherwise there’s no way for us.

Conclusion

We’re not done yet, but we have covered a lot in this blog post. Big data is a very exciting field with many challenges, however there are also many opportunities for big data to make our lives better. The next step is up to you!