Big Data Analytics: Why Does it Matter and How Does it Work?

Author profile photo for CareerFoundry blog writer Elliot Gun

A company’s success is increasingly measured by its ability to mine its massive volumes of collected data to extract actionable insights for growth and optimization. As such, many have turned towards investing in this new business resource and have even called data the new oil.

But what exactly is big data analytics, and why is it important for aspiring data professionals to consider learning how to analyze big data?

We’ve spoken about the concept of big data before, so this article focuses on how it works in practice. We’ll look at the pros and cons of adopting it into your strategy, as well as what the future of big data analytics might be.

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  1. What is big data analytics?
  2. The big data analytics process
  3. Big data analytics: Benefits
  4. Big data analytics: Challenges
  5. The future of big data analytics

Ready? Let’s take a look at what big data analytics is all about!

1. What is big data analytics?

Big data analytics is a subset of analytics, where you apply similar analytical tools and concepts to large datasets defined as “big data” in order to surface hidden patterns, useful correlations, important market trends, isolate customer preferences, and many more possible insights.

One misconception is that big data analytics is merely about very large datasets, but it extends beyond the sheer volume of data.

Big data also includes the three (now four) “V”s: velocity, volume, variety, and veracity. These attributes are what determine whether a big dataset is useful for certain business goals. If your company wants to make more accurate forecasts about consumer demand, having more data in real-time would enable analytics leading to more informed business decisions. 

As big data is a rich but challenging resource to use, you cannot use existing methods to process it. Big data can include very large and diverse datasets across different data types, including structured, semi-structured, and unstructured data. Some of these datasets can even range from terabytes to zettabytes in size!

Many of the new techniques emerging use complex machine learning algorithms. These techniques are chosen based on your use case. For text-based data, you can use:

Many of these techniques are often used in combination with each other since big datasets contain different data types. 

2. The big data analytics process

While the exact stages may vary between specific use cases, a typical big data analytics project will include these three main stages.

It’s important to note that you may not proceed between these in consecutive order: Experienced analysts know that the analytics process looks more like an iterative cycle, where you return to previous stages to further tweak and enhance your dataset even as you draw findings from your dataset.

This allows for optimization at each stage and provides you with the most updated insights for decision-making. 

Data acquisition

Before any analytics project can begin, you’ll first need to find suitable data.

Big data analytics projects typically use data from a diverse range of sources. This can be from your company’s databases or publicly collected data.

They may range from social media feeds, Internet of Things devices, meta data, multimedia files, customer transactions, and many more. If you’re not sure where you can find an interesting dataset, we’ve collected a list of the best 15 open source datasets for you.

Sometimes the data may already be structured (e.g. data types are clearly defined). But other times the data is unstructured and will need to be processed prior to analysis.

An example includes data from social media posts, where you have both text and multimedia files per each record. However, you will most commonly encounter semi-structured data which is a mix of both types. 

Data preprocessing

Now that you have obtained your big dataset, there are a series of mini steps you’ll need to do before you can use it. We call this stage preprocessing as you will need to “process” your data to verify its quality and accuracy.

This is often a tedious and difficult step that many are tempted to skip–but always remember the saying “garbage in, garbage out”. Your findings are only as good as the quality of your data.

As data collected in the wild often includes errors, inconsistencies, and duplication, you’ll need to sift through it to remove and correct these records first. Next, you may need to convert the data to a suitable format (e.g. converting timestamps to machine-readable datetime format). Then, you can apply transformations in order to standardize and aggregate values into units that are better for your analysis.

Finally, you will then integrate these cleaned and transformed datasets into one single database for ease of analysis. At this stage you will need to consider how you will store and manage your data. This depends on the type and volume of your dataset, and many options exist to suit all project needs and budget, including NoSQL databases or cloud-based storage platforms. 

Data analysis

We’ve now arrived at the key stage in the analytics process. Here, you will choose from many analytical models and apply them to the big dataset with the goal of uncovering patterns, trends, correlations, and insights.

Your approach will be determined by whether you need descriptive or predictive information. If you’re looking to present a snapshot of consumer trends and how the business is poised to meet those needs, it may be sufficient to analyze the key performance indicators (KPIs) by aggregating your data and presenting summary statistics.

If you want to look at what the future could hold for your industry, you can turn to more advanced techniques in machine learning to help you present forecasted trends. 

3. Big data analytics: Benefits

In a digital world, businesses cannot do without big data analytics. Let’s take a look at the top two benefits from such an approach. 

Reducing Fraud

Large datasets that are generated in real-time allow companies to better identify any risks or anomalies that could help flag fraudulent activity.

This is an especially key concern in the financial industry, where companies may lose money due to being liable for fraudulent transactions. Banks can use big data analytics to identify and predict potential risks early, take proactive steps to get ahead of them, thus resulting in significant cost savings.

Similarly, insurance companies can better identify fraudulent claims at scale, reducing the need for extensive paperwork and manual investigation by staff. 

Data-driven decision making

Most business analysts are drawn to big data analytics as it provides a systematic way to obtain actionable insights that can be turned into business strategy.

Traditional methods such as through qualitative research or by analyzing much smaller datasets may not be able to provide as in-depth an analysis of important trends.

For example, a retailer could look into how their massive databases tracking every customer’s transaction over time can provide a holistic understanding of each customer’s buying habits. They can segment this further to see when products are purchased at certain times, which better informs marketing campaigns, as businesses can invest in sales promotion right when customers are most likely to make a purchase.

This is a clear example of how big data analytics significantly reduces the cost of marketing campaigns while adding to revenue. 

4. Big data analytics: Challenges

Big data analytics may feature many opportunities for business efficiency and growth, it also contains some challenges that must be taken into consideration.

Let’s take a look at two issues that have surfaced in recent years. 

Data privacy

If you have been following the news on technological developments in data and artificial intelligence, it will not surprise you to learn that there are increasing calls for companies to deploy guardrails around the use of data that they collect.

This concern will only become more important over time since more and more companies are involved in collecting big datasets as part of their everyday business practices. Further, many are worried not just about the privacy of their data but how securely it is protected. It’s not technically easy to ensure that big datasets are stored and managed in a safe way.

Many companies lack staff who know how to implement robust security measures in place to prevent data breaches. Fortunately, the industry has started to respond to this need with innovative ideas.

For example, SOC 2 is an industry framework that certifies a businesses’ readiness when it comes to conducting security audits, having an incident response plan, and having access controls internally regarding who can access which database or code repository. 

Data quality

GIBO is an industry acronym for good reason. Data that is inaccurate, missing, or simply out of date provides a weak foundation for making business decisions.

It’s very difficult to maintain the quality of big datasets due to the four Vs. Data is being recorded in large amounts, at high speed, and in different formats. Without a plan to ensure that data is cleaned and transformed prior to its use with data analytics tools, any recorded data may not be usable. Hence, it is critical that analysts carefully clean data and remove any errors during the preprocessing stage.

This can be a complex process, but analysts can turn to more advanced tools to help with cleaning large volumes of data more quickly. 

5. The future of big data analytics

The field of big data analytics is just getting started, and there are many anticipated advances on the horizon. As the generation of big data gets more widespread, and its storage becomes cheaper, big data analytics will likely increase in prominence over time. 

Costly but worth it in the future

Real-time analytics is currently relatively costly and challenging to implement in most businesses, but we can expect this to change in the future. Real-time big data analytics requires that businesses create a system that can process and analyze data as soon as it is generated. Doing so will mean the creation of infrastructure and employing workers with a strong technical background to maintain this system.

Although this is expensive to set up, the payoff can be high: real-time big data analytics will enable more accurate and faster decision-making. For example, real-time analytics in finance can allow for instantaneous fraud detection. 

Technical regulation

However, technological regulation could also shape the trajectory of this field. Due to the concern about data privacy and security, many governments have turned to regulation to ensure better data governance.

For example, Europe introduced the General Data Protection Regulation (GDPR) back in 2018, which primarily governs rules on how companies host and process personal data.

In the United States, the California Consumer Privacy Act (CCPA) works to ensure that Californians have the right to know what personal data is collected by companies and the right to have it deleted. Regulations like these will likely become more common across jurisdictions, which will impact the way in which big data analytics can be used. 

6. Summary and next steps

We have taken a look at the importance of big data analytics and how it already has changed and will continue to shape the future of business operations worldwide.

Big data analytics has redefined the way that we approach business challenges and opened up many opportunities for how we can better streamline operations for cost savings, extract better insights to improve customer experiences, and better anticipate future trends with more accurate forecasting. 

It can be tricky to learn how to apply the tools of big data analysis in everyday data projects. But the best way to approach any complex project is to proceed in steps. Think about how you will acquire your data: will it be a combination of your company’s proprietary dataset and public sources of information? How will you process it before analysis?

Make sure to conduct quality checks during preprocessing, as data quality is just as important as your chosen machine learning algorithms. Consider how you will protect the security and privacy of your data, and whether your project aligns with the prevailing legal guidelines on data collection and usage. 

If the world of business analytics interests you but you don’t know where to start, why not try CareerFoundry’s free data analytics short course? It covers the basics of data analytics as a field and will give you a good idea of whether or not it’s a career path you’re interested in pursuing further.

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