Big Data: a waste of money or a profitable investment

Big data: a waste of money or a profitable investment


Facts, numbers, bits of information, all of it is data. Data has come to mean so much in recent years that many now consider it a form of capital

Over the last few years, there has been an accelerated disruption across industry sectors, as well as society and governments, showing great potential for even richer streams of data. In reality, people’s priorities are constantly shifting, especially given the recent combination of destabilizing events including a global health crisis and the geopolitical effects of the Russian invasion of Ukraine. These events only cause greater uncertainty but also opportunities for businesses looking to thrive in the current environment as well as realize value at scale. 

Understandably so, organizations alike are tasked with prioritizing ways to leverage data to accelerate growth and keep return on investment high amidst highly uncertain and fluid global realities and impacts. 

The smart use of data enables businesses to respond more quickly than competitors to shifts in customer and employee demands, as well as accelerate new product development, and improve channel and business model innovations.

In this article, we are going to dive deeper into data, specifically big data to understand how necessary, profitable, and smart it is to employ smart approaches and strategies to consume it effectively.

Put your data hat on and let’s get going!

What is the big data concept?

Around the 1960 and 1970s, the big data concept has been used to describe high-volume, high-velocity, high-variety data. In short, it refers to data under extreme circumstances and at massive scales. As explained earlier, the business risks and opportunities of big data have been exacerbated by recent global events, especially the explosion in data traffic thanks to the evolution of the internet and superior computing power which offer a rich source of insights that lead to better decisions but also increased organizational challenges on how to store, manage, and analyze big data.

Before diving deeper, and according to research firm Gartner, big data is defined as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation.”

Here’s another definition of big data from Oracle, “data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three Vs. Put simply, big data is larger, more complex data sets, especially from new sources big data. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems you wouldn’t have been able to tackle before.”

3 Vs in big data

The key concepts we focus on from these definitions are the very essence of big data: high volume, high variety, and high velocity, or the 3vs in big data. Big data is well, big, you already know that. But it also comes from a multitude of sources like audio, video, and image assets that demand to be cleaned, processed, analyzed, stored, and consumed at scale. Not to forget, big data is also generated at break-neck speed. Let’s explore the 3vs in big data in more detail:


This basically refers to the amount of data. Big data comes in high volumes of unstructured, messy data that can very well be of little value or incredibly valuable, which is why it needs to be analyzed with different methods. 


The speed at which data is received and processed. High-velocity data streams directly impact memory and computing power. 


There’s so much data, coming in so quickly, and from so many sources, which is why Variety is a critical V of big data. With the rise of big data, it’s only natural that data comes in even more unstructured data types including text, audio, video, and more, all of which require preprocessing to glean meaning.

But the 3vs in big data don’t stop there. While volume, velocity, and variety are king in big data, two more Vs have made an appearance, including value and veracity. Focusing on value, we refer to the fact that all data has value but it’s of no use until it’s properly discovered. Remember, value is now a form of capital, making value extraction even more significant. 

What is big data analytics? Big data analytics directly relates to how data is managed to support all of its uses, all of which are aimed at driving better decision-making, improved business processes and outcomes, and the discovery of new risks, challenges, and opportunities. As such, organizations are aggressively looking for ways to leverage new analysis methods to find relationships, patterns, or combinations of diverse data to improve decisions, processes, and outcomes.

For example, synthetic data is leveraged by using a sampling technique to real-world data via simulation scenarios where models and processes interact to create a new set of data that doesn’t directly come from the real world. Thanks to in-built machine learning algorithms embedded in data sets, it’s easier to know the data conditions needed to train machine learning models.

There are growing concerns over big data sourcing, big data quality, bias, and big data privacy, which have impacted big data collection, giving way to emerging approaches like small data or wide data. This all makes sense as by 2025, it’s expected that 70% of organizations will be compelled to shift their focus from big data to small and wide data to leverage available data more effectively, either by reducing volume or extracting more value from unstructured data sources. 

Why is big data important for your business?

New market dynamics combined with fiercer competition, supply chain challenges, and macroeconomic unknowns like inflation make it a necessity to manage uncertainty with the help of data, making it a critical core competency for success. 

Big data for business is critical to improve decision outcomes for all types of decisions, be it macro, micro, real-time, cyclical, strategic, tactical, or operational. By putting data to good use, we can unearth new questions and opportunities that you may have not even considered. Plus, you can detect innovative solutions to solve problems, which is increasingly important in a world that’s constantly changing in terms of demands and the speed at which it makes them. 

Big data helps harness data in such a way that is easy to identify and extract new opportunities and avoid risks. Using big data to your advantage can help you make smarter business moves, make your organization more efficient, turn higher profits, and reduce costs. All thanks to advanced big data analytics methodologies, techniques, and tools like:

  • Cloud computing. Used for scalability mainly, cloud computing comes in a subscription-based delivery model for fast delivery and efficiencies to power big data analytics. It removes physical and financial barriers ensuring organizations can align their IT needs and business goals.

  • Data management. It helps ensure data is of high quality and well-governed before it can be reliably processed. Via repeatable processes and high standards for data quality, data management helps organizations establish master data programs so every party involved is on the same track.
  • Data mining. Examine massive amounts of data to unearth patterns, anomalies, trends, combinations, groups, and more, so they can be used for further analysis and answer some of the business's most complex questions. Data mining software filters out messy and redundant data, detects what is relevant, uses relevant data to evaluate potential outcomes, and accelerates the speed at which decisions are made. 
  • Data lake and data warehouse. Methods of data storage where structured and unstructured data resides so it’s easy to access and use. Data lakes ingest massive amounts of raw, unstructured data in native format. Data warehouses store structured data in a central database. Both storage methods are complementary and are typically used jointly.
  • In-memory analytics. Method to analyze data from system memory as opposed to a hard drive disk to derive insights from data and act on them quickly. It removes latencies in data preparation and analytical processing to test new scenarios and create models.
  • Machine learning. A subset of AI that trains a machine on how to learn to quickly and automatically produce models that can analyze bigger, more complex data at scale. Precise machine learning models lead to faster, more accurate results, giving organizations higher chances of detecting profitable opportunities or avoiding unwanted risks.
  • Text mining. Technology that analyzes text data from the web, comment fields, books, and other text-based sources to discover actionable insights. This technology leverages both machine learning and natural language processing to sift through emails, blogs, social media feeds, and more.
  • Hadoop. An open-source software framework that stores data and runs parallel applications on hardware clusters. Its distributed computing model has made it the go-to software to process big data fast. Plus, it’s free and can use commodity hardware to process and store plenty of data.

Big data for business intelligence

Business intelligence focuses on technology-driven software that yields accurate insights and reports. In short, it analyzes raw data and transforms it into meaningful data through a set of processes and architectures that design strategies for your organization with the purpose of getting higher profits.

Gartner defines business intelligence services as “offerings to design, develop and deploy enterprise processes and to integrate, support and manage the related technology applications and platforms. These include business and infrastructure applications for BI platforms, analytics needs, and data warehousing infrastructure. Solutions include areas such as corporate performance management (CPM) and analytics, in addition to the traditional BI platform, data warehouse/data infrastructure, and data quality areas.”

Within that context, it’s easy to see how big data for business intelligence are both aimed at extracting data, sifting through it, and getting actionable insights from it. While similar in general terms, they do have key differences:

  • Purpose. Business intelligence helps the user make smart decisions via accurate reports generated from information extracted from the data source. Big data manipulate structured and unstructured data to improve customer outputs.
  • Environment. Business intelligence needs dashboards, enterprise resource planning databases, data warehouses, and an operating system to function. Big data typically works with Hadoop, Spark, Hive, and more.

Returning to points of convergence, we find that the big data benefits of using it with business intelligence and big data analytics lead to:

  • Smarter decision-making
  • Quick and accurate data-based reports
  • Cost efficiencies
  • Improved work processes

How to avoid wasting money on big data?

There’s certainly a lot of hype around big data as a means for effective problem solving, but does it provide guaranteed success? While the hype can be a little blown out of proportion, we can’t turn a blind eye to the numerous benefits that can be gained by using big data to boost your business outputs.

To put it simply, big data alone does not guarantee success, return on investment or profits for that matter. But when used smartly, it in fact brings value and plenty of it. So, how do you ensure you’re not wasting money on big data? 

Big Data


One of the first things your organization can do is ensure they have the right technological foundation to digest, process, and consume data at scale. Second, regardless of how costly, powerful, and battle-ready data infrastructures are, organizations need to have legitimate production use cases to justify the use of big data successfully.

There are plenty of big data solutions out there but the key is that the tool you choose is able to grow with your scaling business needs. If big data is not used with revenue growth in mind, then is it really being put to good use? Of course not! Think of your revenue streams, your strategic business goals, and other business considerations that you want to achieve so you have a clear sense of what big data needs to achieve for you.

There’s a term out in the industry that’s picking up steam called Lean Big Data which focuses on making smart investments in big data technologies, going into detail on the pitfalls to avoid that if left unaddressed, can lead to failed big data projects.

Let’s explore these pitfalls and the strategies you can employ to avoid them and ensure you’re not wasting money on big data:

1. Using big data technologies with no actual big data to analyze

The term data-driven is so overused that most companies adopt it as part of their mission statement without fully realizing what it means and what it represents for their business. Additionally, some companies equate using big data with being data-driven, and unfortunately, that’s not always the case. When you have small data, there’s no financially-sound reasoning to employ big data technologies which can lead to streams of money losses because your organization won’t be able to access the benefits of scalability, speed, or computing power that big data can offer. Deciding to employ big data technologies when it’s not necessary can also lead you to iterate slower as your teams are learning a new and complex tool, generate a greater accumulation of technical debt, look to hire expensive big data specialists, etc. In simple terms, you should always strive to be data-ven but not necessarily with the help of big data technologies.

2. Using big data technologies with no real use case in mind

Without valid use cases identified, there’s no point in using big data technologies. Otherwise, your organization might end up with failed or high-cost projects. 

3. Overspending on big data technologies that you think will help you solve most of your problems

Big data technologies are not always cheap but there are certainly solutions out there that can help you economize your big data initiatives. For example, the Hadoop environment offers economizing properties like an open-source license, standard hardware requirements, scalability capabilities, high-level frameworks, and more. The more you spend on big data technologies, the higher the pressure and expectations, so it’s can be very easy to spiral down a bottomless pit of overspending. 

4. Not having a team with big data experience

Real production experience with big data can help organizations strategize in the right direction as well as avoid costly mistakes, hire the right resources, and correctly communicate with stakeholders. 

With a Lean Big Data mindset, you can focus time and resources on tasks that are actually centered around generating real value or validating reasoning such as data analysis, data science, dashboards, data-driven features, and more. Prioritize your organization’s data infrastructure with value in mind.

Modern organizations don’t sit on data, they use it in many progressive ways with many even leveraging data outside their control boundaries to make smarter and more agile business decisions.

Within this context, there are three data tracks to follow when leveraging data for business and ensuring big data investments are yielding value and positive financial results:

  • Diversity and dynamism. Adaptive AI systems help drive growth and innovation by coping with global market fluctuations. You can unlock the full potential of data with AI data management tools, automated, active metadata-driven approaches, and data-sharing competencies. 
  • People and decisions. Rich, context-driven data analytics generated from modular components by the business help make insights relevant to decision-makers
  • Trust. Trusted data helps achieve value at scale by managing risks and institutionalizing connected governance across systems, edge environments, and emerging technologies. 

Companies use big data examples

Now that we’ve hopefully painted a clear picture of how to use big data to your advantage and cost-effectively, let’s see which companies are using big data so you get real-life examples of how positive big data can be to your organization when used wisely. 

One of the biggest examples of big data in the tech industry is Spotify, one of the world’s largest on-demand streaming music services worldwide. Known for always being on the cusp of next-generation tech infrastructure, Spotify is keen on pushing technological boundaries with the use of big data and artificial intelligence.

Spotify fully fits the bill when it comes to justifying the need for big data technologies with more than 182 million premium subscribers worldwide as of the first quarter of 2022. With these many millions of people using the music streaming platform, it’s safe to say the company has access to an incredible amount of intel, ranging from what songs are most played, from where are listeners tuning in from, the device they’re using, and lots more. As such, Spotify is data-driven for sure and is well known for making good use of their data to drive decisions. 

As they continue to collect data points, Spotify uses the cleaned-up and already-processed version of those data points to train algorithms and machines to apply insights and learnings to boost the experience of users and the business. For instance, take the Discover Weekly feature that offers listeners a personalized playlist of unheard music every week, all based on what the algorithms are trained to deduce the listener will most likely enjoy.

Another big example is Netflix, one of the world’s biggest subscription-based video and media streaming services and production companies. Netflix boasts about 222 million subscribers worldwide and is famed for its cutting-edge and advanced use of big data to enhance service-critical areas such as their world-renowned recommendation system which is completely data-driven. 

Netflix’s recommendation system humanizes the platform in such a way that users feel taken care of and influenced on what content to stream next. Using advanced data techniques and analytics, Netflix is able to offer its subscribers personalized movie and TV recommendations, and it certainly pays off as the company holds a 93% customer retention rate compared to Hulu (64%) or Amazon Prime (75%).

Via data collection, Netflix employs data analytics models to unearth subscribers’ behaviors, patterns, interactions, and responses and then use those data points as the foundation to create detailed subscriber profiles. For example, the streaming giant looks at how much time a subscriber spent watching a show, what the subscriber is searching for, whether the subscriber paused or not on a specific scene, and if they played the scene repeatedly, the device the subscriber is using to watch the content, or how long it takes them to complete a show. And more, much more. 

With so much data, it comes as no surprise that their recommendation system is so successful. Another use of big data analytics by Netflix is determining if original content is greenlit or not based on data about touch points extracted from their subscriber’s profiles database.

These high-profile examples are the tip of the iceberg with countless more examples of companies using big data successfully such as Amazon, Google, Starbucks, Burberry, Apple, North Face, Facebook, Instagram, Tiktok, American Express, and McDonald's, to name a few. 

At Svitla Systems, we are deeply experienced in working with numerous clients to employ big data technologies and analytics techniques successfully, seeing firsthand the positive results of helping our clients ensure the use of big data is justified, properly applied, and budget-friendly. One of many good examples is the one of a genealogical analysis website client who reached out to develop an enhanced version of their existing search system to offer subscribers more customizable services. 

In the case of our client, we helped them design a new search service from scratch by modifying their existing search system to make it easily configurable and tailored to fit their unique business needs in a data-driven environment. As a result, our client is now leveraging the latest and greatest big data technologies and frameworks in their improved version of their search service. 

Big data engineering services

Big data is like oil, and just like oil, it’s slippery and hard to get a hold of. The true value of big data lies in how to extract it, refine it, and use it to power your business and revenue goals. 

After having gone through the key and diverse aspects of big data, the hope is that you conclude this piece with a greater understanding of how to make profit-wise decisions when it comes to big data and how to avoid some of the biggest pitfalls of big data. 

At Svitla Systems, we’ve worked with nearly every industry under the sun in big data projects where the end game is always to deliver value to our clients. With some of the brightest minds in big data expertise, our engineers put every unique need of the client to create a balancing act between reducing costs, minimizing risks, and ensuring the use of the most intelligent, appropriate, and cost-effective technologies for each solution. 

If you’d like to learn more about our big data capabilities and offerings, please reach out to one of our representatives who will be glad to give you all the details on how we’d love to go and win “big” with your project.

by Svitla Team

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