In the modern business world, big data analytics is a critical discipline that helps companies make more informed decisions, accurately select development directions, and solve emerging problems. Of course, it takes time to do all that. But imagine what would happen, if it didn’t? In short, that’s what real-time big data analytics is all about. And today, we are going to take a closer look at this advanced data-related solution.

First off, let’s start with the basics. What is big data analytics all about? And why is this field of research so crucial to every dynamically growing organization?

The role of big data analytics

Big data is one of the IT world’s major buzzwords. It has been around for some time now and for a reason. You see, big data is a term that describes data that flows through almost every organization (not only large international corporations) and comprises information concerning your processes, customers, activity, marketing campaigns, financial records, HR, and several other elements. In a way, big data reflects everything that happens within your company and the market.

Big Data

Of course, data itself can’t do any good to your company. In order to draw useful business-wise conclusions, you have to analyze it. When analyzed, big data becomes a priceless source of information about your company, its current situation, challenges ahead, and development opportunities and directions. As a result, you obtain a fantastic source of all the information necessary to make accurate, informed decisions.

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You can think about big data analytics as your guide through data in your company process. It helps you in answering such questions as:

  • Why did X happen, and will it happen again?
  • How can we prevent Y from happening?
  • How will improving Z or doing N affect our company?

And many similar questions. We could say that big data analytics is all about finding satisfying answers to urgent, vital questions in your company. In other words, big data analytics is based on taking data input from various sources, analyzing it for patterns, interpreting the results, and finally, communicating these findings in a readable way. We say that big data analytics is a vital part of AI Software Development mainly because modern data analytics solutions use smart technologies like machine learning to enhance their work.

real-time big data analytics

Now, when it comes to real-time big data analytics, the word “urgent” is key. You see, in many instances, time is of the essence. Finding a good answer has to happen quickly. Otherwise, the opportunity will pass by, or this new challenge will turn into a straight-up crisis. If you run a company, you surely had one of these situations in the past–you had to make a decision and make it quickly. In such a stressful situation, it’s difficult to concentrate and make an informed decision, isn’t it?

Now, imagine that you didn’t have to. Imagine you had a reliable source of information that was ready to provide you with all the information you need within seconds or minutes. Sounds intriguing, doesn’t it? Many entrepreneurs would agree that such a source is worth every penny.

Real-time big data analytics: The definition

You have to understand that, depending on the source, you will find different definitions of real-time big data analytics. That’s because it’s not some sort of ready-made software or application. It’s rather a set of tools and techniques that can be used to accelerate the process of analyzing data and drawing conclusions so that the necessary information could be obtained almost instantly.

Quick side note: It is important for you not to mistake “real-time” for “instantaneous”. You see, in some situations, real-time insight can come after several hours. It’s hardly instantaneous, correct? But yes, in both cases, the goal is to receive insight as quickly as possible.

Woman working with data analysis

If we had to coin one universal definition of real-time big data analytics, we would say it’s an approach where big data is analyzed as soon as it becomes available, emerges, or is extracted from an IT system or application.

This means that everything happens much quicker, almost automatically, and the company doesn’t have to wait for days to get the needed information. And remember, we are not talking about one ready-made software. Sometimes, yes, it can be a separate application, while in other situations, it’s all based on a built-in feature that extends the capabilities of a typical data analytics program. Every company has their own approach and requirements, making real-time big data analytics more of a concept than a software product that can be purchased in some store.

Use Cases

As we’ve just told you–primarily to make more informed business decisions without delays. Of course, that’s not the only application. Real-time big data analytics proves useful everywhere where time matters. For instance, this approach comes to the rescue concerning:

  • Cybersecurity threats
  • Monitoring the state and measuring the performance of critical applications, machines, and devices
  • Monitoring crucial measurements (e.g., the temperature in mine)
  • Making investment decisions (e.g., on the stock market)
  • Understanding and targeting customers: With real-time big data analytics, companies can create predictive models of market demand, pricing, and trends. As a result, brands and sellers have a deeper understanding of their customers, target them more effectively, and devise offers and marketing campaigns to get their attention.
  • Optimizing business processes: Actually, the scope of uses is quite wide here. Real-time big data analytics can help you optimize almost every process you can think of in your company.
  • Optimizing performance: Interestingly, although real-time big data analytics can be a standalone solution, it also works very well with other AI-related technologies. For example, when you combine real-time analytics with machine learning, you can accelerate the pace at which your ML-powered devices get better and smarter. In other words, ML-fueled machines and applications can put processed data to use immediately.

Database system

Additionally, we could say that big data analytics is extensively used in IT, banking, cybersecurity, and manufacturing.

The benefits

You already know a major benefit–real-time big data analytics makes analyzing data in your company significantly quicker. This means that you can make valid decisions and have more time to consider the pros and cons of an upcoming project or endeavor. Of course, the list of benefits is much longer. Let’s examine some of the common ones:

IMPROVED IT INFRASTRUCTURE MONITORING

For starters, thanks to real-time big data analytics, you can ensure your IT infrastructure works flawlessly and is protected from possible cyberattacks. Here, we ought to mention especially advanced enterprise-level IT security software solutions such as security event management (SEM) and security information and event management (SIEM). Both these solutions are designed to analyze information coming from logs in your IT infrastructure. SEM software looks at specific types of events, and SIEM is a more advanced combination of security information management and security event management[1]. Both SEM and SIEM analyze large sets of IT data in real time, primarily in order to detect possible threats, initiate necessary quarantines, and mitigate cyberattacks before hackers manage to cause some serious damage to your IT infrastructure.

Information security

Read more about Big Data Security Issues and Challenges

ENHANCE THE DECISION-MAKING PROCESS

In fact, real-time big data analytics has such a wide scope of applications, we could generally say that this approach improves the entire decision-making process. Whether you think about product development, customer engagement, sales levels, new projects, whatever comes to your mind, real-time big data analytics makes your work easier and more straightforward. And that’s what we believe is the biggest advantage of this solution.

COMPETITIVE ADVANTAGE

Without a doubt, companies that implement real-time big data analytics gain a significant competitive advantage. Just think about that: You have a versatile tool that allows you to make accurate decisions quickly. A tool that gives you access to all the relevant information exactly when you need it. And yes, so far, we talked chiefly about data coming from your company, but the truth is real-time big data analytics is so much more! You have real-time insight into the whole market, your target audience(s), and your competition’s activities as well.

Competitive advantage of real-time big data

It isn’t difficult to imagine that such a technology can be a massive milestone that will enable a much quicker development. And the good news is, still a lot of companies don’t discern the real potential behind big data analytics, so it is likely that you’ll be one of the early adopters in your niche.

REAL-LIFE EXAMPLES

Let’s consider a straightforward example:

The pricing strategy

Every company has to have one, correct? Typically, pricing is based on several elements:

  • Production costs
  • Profit margins
  • The value of the product itself
  • But also, on your competitors’ activities and prices

People creating strategy

Now, suppose one of your competitors all of a sudden changes their pricing and/or sales strategy. Surely, you’d like to know about that as quickly as possible, correct? The reason is apparent; such a shift can have an adverse impact on your sales. Perhaps now customers will go elsewhere. You don’t want to take that chance, so you decide to use pricing software that uses real-time big data analytics features. As a result, you get the necessary information quickly, 24/7. Such tools already exist, and we’ve mentioned them in a number of previous blog posts.

Retargeting

This element is particularly important in the e-commerce world. Every abandoned cart translates directly into lost revenue. The whole purpose of retargeting is to retrieve at least some of these abandoned carts. But you have to act swiftly! After a few days, your potential customer will have already bought a similar product at your competitor’s store. So, you need retargeting software that’s equipped with real-time big data analytics features so that you can remind customers about abandoned carts and invite them to finish their order as quickly as possible.

Actually, real-time big data analytics play a significant role in the entire e-commerce world. Sellers use this technology to:

  • Optimize their stock
  • Look for new vendors and distributors
  • Extend their offer
  • Improve customer service

Frequently, these changes are possible thanks to accurate predictions generated from social media data, website analytics data, web search trends, and even weather forecasts.

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Supply chain optimization

Primarily in the COVID-19 times, retailers have to keep their supply chains optimized and effective. Again, real-time big data analytics comes to the rescue. You see, when it comes to supply chains, there are a lot of vital data sources, including GPS, traffic, and IoT data (e.g., radio frequency identification sensors). All that information can be used to track goods or delivery vehicles and optimize their routes by integrating live traffic information. As a result, you can accelerate your deliveries or at least avoid potential delays. If you run a logistics company, you surely understand the value of these improvements!

Supply chain

BENEFITS FOR YOUR STAFF

You can think about it as a side bonus, but it’s still important. You see, real-time big data analytics features allow your team to save a lot of time, as the majority of the analytics work is automated. Therefore, your experts and data scientists can focus on other, possibly more important tasks and projects.

The architecture of real-time big data analytics

So far, we talked about the nature of real-time big data analytics and the benefits this approach entails. Now, let’s concentrate on the architecture that’s needed to make the most of it.

Big data analytics

Shortly put, from the technical standpoint, it’s all about dividing data into smaller chunks that can be analyzed more quickly. The vast majority of real-time analytics solutions are cloud-based. That’s because, in the cloud, you can use containers that are designed to both navigate complex systems and automate application deployment no matter what the scale is.

FOUR LAYERS

In general, when it comes to real-time big data analytics, we have to take four data layers into account:

  1. The decision layer
  2. The integration layer
  3. The analytics layer
  4. The data layer

It all starts with the data layer, which is a foundation for the whole process. Here, you’ll need a database management system (for example, NoSQL or Hadoop) and data tools (e.g., Apache Spark). The analytics layer creates an environment for analytics of data coming from the data layer. Finally, the integration layer is responsible for maintaining the entire infrastructure (e.g., user dashboard). The decision layer is the last element that allows users to see the results.

The challenges of real-time big data analytics

Before you start estimating future profit potential, there are some challenges that have to be dealt with. As we’ve already said, real-time big data analytics is no ready-made plug-and-play solution. You have to make necessary adjustments first. First off, your IT architecture has to be fully compliant with your goals concerning using data. In other words, the IT infrastructure in your organization should be tailored to achieve better performance.

Next, all the processes within your company should be improved and congruent to your goals. What good will additional input do, no matter how beneficial, when your internal processes do not permit you to make any use of it? In other words, make sure you will be able to implement decisions based on the information coming from big data analytics.

The next element is the budget. We frequently say that on our blog–every AI-related technology comes with a significant upfront cost. Your company has to be ready for it. But all of these technologies, including real-time big data analytics, become profitable in the long run. Most likely, you won’t see the results instantly. It takes weeks and sometimes months to build a data analytics solution that fits your company and its needs.

A person calculates a budget

And finally, you need to have a decent strategy. The process of implementing real-time big data analytics to your company is complex and multi-faceted. It would help if you had someone who could guide you through it and propose optimal ways to achieve the desired goal. And this is where Addepto comes into play. We are an experienced AI consulting company. Big data analytics consulting and data science is what we’ve been doing every day for over ten years now. We have built data analytics infrastructure from scratch for tens of companies representing various niches and sectors. We know what to do to make this entire endeavor effective and beneficial. With our help, you will be able to achieve all of your goals.

If you’d like to find out more about real-time big data analytics and find out how this solution can help your company, feel free to drop us a line! We are always keen to talk about data science. The Addepto team is at your service, and that’s all you need to make your data-related projects work!

[1] Justin Stotzfus, Techopedia, https://www.techopedia.com/7/31201/security/whats-the-difference-between-sem-sim-and-siem, Accessed June 4, 2021.

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