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What is Big Data Analytics: How to Operationalize & Examples
In this guide, we’ll look at what big data analytics is, including the principles it’s built on, before investigating why it’s useful, as well as the challenges users can face. We’ll also discuss the role of artificial intelligence (AI) in big data analytics, to help understand what the future could look like.
The world is full of data. From the payments you make to the way your customers navigate through your website, there’s data to be gathered at every turn. Even unstructured data , such as customer complaints or news articles, can become an insight to improve your decision-making.
But with so much input, it can be challenging to really harvest deeper level insights and make sure you’re getting the most out of your data. Often, businesses end up focusing on data sets in isolation due to their complexity, low quality and the fact that data is scattered across different systems, departments and geographies. Therefore, they struggle to gain a bigger picture and better understanding. Enter big data: the framework that has the potential to harness huge amounts of information from multiple sources and bring them together to provide meaningful insights.
What is big data analytics?
The term big data analytics refers to the tools, techniques and frameworks used to examine large amounts of data. Big data itself is simply raw information of extremely high volumes and complexity, gathered from multiple sources. It increases over time, and as a result, it’s far too complex for traditional methods of data management to process and analyze. Examples of big data include data that comes from global banking systems such as billions of transactions or corporation CRMs containing hundreds of thousands of customer information.
The purpose of big data analytics is to reveal patterns and trends that might otherwise be missed. Companies can then use these insights to make data-driven decisions, or to inform their strategy. The information gathered removes unnecessary guesswork and allows businesses to get as close to their target audience as possible. According to research by Accenture, 79% of users believe that “companies that do not embrace big data will lose their competitive position and may even face extinction”.
What are the five types of big data analytics?
There are five main types of data analytics:
This type of analytics focuses on what has happened. It’s a record of what has happened historically, but also what is happening at the moment – for example, a 360 view of your customer’s data.
This method looks at why something has happened. Rather than just noting what occurred, big data is used to explore the reasoning behind different events. This can, in turn, help increase understanding of a customer or other individual. For example, diagnostic analytics could be used to dissect if a particular marketing campaign boosted sales.
As the name suggests, predictive analytics is focused on what will happen. It uses data to give insights into potential future trends, so that businesses can get ahead and remain competitive. To do it, predictive analytics models use historical data – increasingly, machine intelligence and AI are also involved.
Closely linked to predictive analytics, prescriptive analytics aims to work out what to do, using data. By looking to the past, as well as examining suggested trends, decision makers can be more confident they’re making the right choice. It can be particularly useful if there’s several courses of action, or the way forward isn’t automatically clear. Plus, stakeholders are often reassured by the solidifying confirmation that data offers, in comparison to gut feeling.
The newest addition to the data analytics folder, cognitive analytics uses AI and machine learning to sift through unstructured data – documents, images, audio files and other items that aren’t in a format that a typical database can work with. Thanks to the technical capabilities of AI, this can happen continuously and in real-time, with little human input.
Examples of big data analytics
Big data analytics can be used in a whole range of industries. Its ability to adapt and look at so many different types of data makes it extremely valuable to organizations may have high volumes of data which is difficult if not impossible to compare and analyze otherwise, such as complex transaction flows or corporate directorship hierarchies..
Some typical use cases for big data analytics include:
Detecting money laundering
Managing supply chain risk
Analyzing patient data in healthcare to suggest potential diagnosis and come up with treatment options
Fraud detection and prevention
Spotting areas to reduce costs and streamline business operations
Comparison of a business area such as sales with larger data such as the weather, or world events
Price optimization
Forecasting what products could be popular in the future
The five V’s of big data analytics
There are five defining characteristics of big data. Originally, the initial work by Gartner (2001) proposed three characteristics, but these have been added to include the final two in more recent years. They are:
Volume
The indication of this characteristic is in the name “big data”. What makes this type of analytics stand out is the sheer amount of data points it encompasses.
Velocity
The modern world is constantly changing and evolving and so too is the data. Information for big data models is often available in real-time and therefore the analytics models must keep up with this in order to be useful. Velocity refers to the speed at which the data is generated.
Variety
Big data comes from multiple sources, allowing companies to gain insights they may not otherwise have access to. The data can be structured, unstructured or semi-structured – we’ll go into the differences in more detail in the next section.
Veracity
The more data you examine, the more likely there are to be mistakes, purely as a result of the volume. However, not including all available data could mean that you miss out on valuable context or additional information. The term veracity relates to trustworthiness; a higher veracity indicates that the data can be trusted, with a lower veracity suggestion you should take any conclusions with the understanding that it might not be the full picture.
Value
The data you’re using must be relevant. If you’re looking at information based on other sectors, or for a different scale of business, you might draw conclusions that are correct, but aren’t helpful to your specific situation.
Some experts also include a sixth characteristic: variability. This refers to the fact that data is constantly changing, making it difficult to have a concrete interpretation. Additionally, data collection methods will vary as time goes on, thanks to emerging technologies.
What are the types of big data?
The term “big data” is used to describe a whole range of different types of information. It’s not restricted to needing to work inside a spreadsheet, or only be images, for example. In fact, there are three different categories of data that sit under the big data umbrella term:
Structured data. This sort of data is what most people will think of. It’s the type of information that can be stored in spreadsheets or databases, and is easy to search and work with. An example of this is a file of text and numbers, such as customer details.
Semi-structured data. As the name suggests, this is a middle ground between structured and unstructured data. It isn’t quite as rigid as structured, but equally it does have some tags that make it identifiable and easier to process. JSON and XML files sit in this category, but so do images that have some defining information such as a location tag and timestamp.
Unstructured data. Unstructured data is information that doesn’t follow traditional data formatting, making it hard to process in a standard database. It could be video files, text, images, social media data – in fact, a lot of data is unstructured.
How to operationalize big data analytics
With such a wide variety of data needing to be analyzed, there are some steps that first need to be taken in order to make the data workable, as well as to avoid any anomalies that might skew the results.
Collect
Firstly, the data must be collected in its original form. As we’ve discussed, this can be structured, semi-structured and unstructured, as well as a mixed combination. To get the most out of your big data tools, it’s important to connect data from different sources, so you get a full picture. If you leave some information out, your data will be incomplete, potentially leading to missed opportunities.
Process
Next, the data must be processed so that it can be analyzed. When there are lots of different types of format, it’s hard to gain insights without first making sure the tools used can correctly access and read the information. For example, raw data may need to be converted, or it collectively may need to be organized in such a way that the learning tools used to complete the analysis know where to find the data they need. Given the size of the datasets often used in big data, this step can often be very time consuming. Some solutions offer schema-less data ingestion which saves a lot of time and effort.
Clean
The third stage is to clean up the data once it has been processed. Much like you might tidy up a spreadsheet so that you could be sure the data is as accurate as possible, duplicates and irrelevant data inputs are removed to ensure they don’t skew the results.
Store
Many businesses store their big data on the cloud in a data lakehouse – a combination of a data lake (large repository of raw data) and a data warehouse (organised, structured data). This means that the data isn’t just an unorganised pool, but rather a large collection of information that can be pulled into big data tools. Data lakehouses also have the benefit of being easy to scale at a low cost.
However, it is also possible to store your data in a more physical format, such as servers or other on-premises options.
Analyze
Once the data is in the best form possible, you can complete the analysis. Machine learning and deep learning tools can get to work on the data, pulling out key insights and patterns at rapid speed. It might seem like it takes a lot of time to get to this stage, but by completing the earlier steps, businesses can ensure their data-driven insights are as accurate as possible.
Gain control of your data
Why is big data analytics important?
With so much data available in the modern world, big data analytics is crucial for businesses to make sense of all these different inputs. Rather than having to use a narrow picture approach, big data allows decision makers to look more widely, using technology to spot patterns and insights that might otherwise have been overlooked.
The speed at which insights can be gained is also another advantage of big data analytics. Especially in fast-moving industries, speed is key to keeping a competitive edge. In the healthcare industry, for example, this speed might also improve patient quality of life as soon as possible. Furthermore, the reliable and unbiased framework used in big data analysis can reduce human error, although insights are still best used as a starting point, rather than a definitive answer.
In summary, this technology allows businesses to make decisions from a wider range of data, more quickly. The forecasting abilities of big data when combined with AI tools also ensures leaders can look to the future with the support of solid data to back up their instincts.
The challenges of big data
Although big data analysis certainly offers a lot of opportunities, there are still some challenges to tackle. Firstly, collecting, storing and maintaining quality in your data takes time, money and effort. Whilst costs are dropping, and the benefits are often worth it, especially for larger companies, there’s still an element of initial investment needed.
Then, time is needed to filter and clean the data as we’ve discussed, in order to make it usable and beneficial. You’ll need to carry out data matching to remove any unnecessary entries, as well as check for inconsistencies, which can be challenging when handling large quantities of information. Many modern big data tools are able to help you through this process, but in some cases, you may need to consult a data expert in order to get the most from your information. This will also likely require collaboration across teams, which can be a barrier, both as a result of internal politics and particularly if different groups gather and store information in different ways. Maintaining quality is key.
Finally, once you’ve chosen the right tools and got the data up and running, you’ll need to be vigilant about ensuring security. With so much sensitive information in one place, in a different place to where it is usually stored, it’s easy to slip up. However, it’s vital that businesses remain on top of the latest security enhancements and have a regular process in place to check this.
Artificial Intelligence (AI) and big data
Artificial intelligence (AI) refers to the concept of getting a machine to act in the same way as a human might – you’re just speeding up and automating the process. Alone, AI can use big data information to create more personalized marketing materials, analyze customer behavior, and anticipate future products that could be popular. The friendly user interface of most AI tools also means that you don’t have to be a data scientist to gain insights.
However, business can unlock far more potential by using both AI and machine learning together.
Machine learning and big data
Big data isn’t just about the volume of information. As we’ve discussed, value is one of the five Vs of big data, and this is where machine learning can come in. The strength of machine learning lies in its ability to look at historical data, draw conclusions and then look to the future, using pattern recognition to show what is working well (or isn’t), as well as predicting new trends.
Rooted statistical methodology, machine learning is a highly valuable tool when it comes to data-driven decision making. The benefit of using it in big data analytics in particular is that the wide range and volume of information allows it to draw conclusions based on a bigger pool of evidence. The more data you train the machine learning model on, the better it gets.
By using both AI and machine learning, businesses can look at data in a human-driven way, but also in a way that also uses pattern recognition. The result is a tool that is faster and more efficient than a human, but also one that spots things that a human user may miss, simply due to the volume of data involved.
Big data FAQs
What is big data used for?
Big data is used for gaining insights across multiple, rapidly growing data sources. Analytics tools, machine learning and artificial intelligence can help businesses make more informed decisions, using data to identify trends and patterns, as well as forecasting what might come next. Big data tools can also be used to create a more personalized customer experience, suggesting products and services based on historical information.
How do big data analytics and traditional data analytics differ?
Traditional data analytics has been useful for years, and in many cases, still is helpful. If the data being examined is structured and limited, statistical analysis and structured query language (SQL) allow businesses to gain insight via data points, graphs and tables. There’s a lot of value in traditional data analysis, but the key limitation is the type of data that this framework can handle. However, its smaller scale and lower cost can appeal to businesses who are starting out on their data journey.
In comparison, big data analytics vastly increases the pool of information that companies can pull from. Previously untapped resources such as social media content, customer service emails, website visit insights or even sensor data in factories can be pulled in real-time and cross-examined – there’s no need to just look at one source of data at once. Additionally, big data analytics can handle a bigger volume of data and has measures in place to help the technology scale as needed, more quickly, without disruption from faults.
What’s the difference between cloud computing and big data analytics?
Whilst cloud computing can enable big data analytics, the two are separate concepts. Cloud computing refers to storing large amounts of data on a remote server, rather than onto the computer directly. In comparison, big data analytics refers to analyzing large datasets, pulled from multiple sources, in order to aid decision making. Cloud computing can become useful for big data analytics as it can handle the large amounts of information required, scaling in response to demand.
Useful links
We’ve discussed a lot in this guide, but there might still be more you want to discover about big data analytics. Browse these handy sources to learn more.