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There is no denying that Artificial Intelligence is taking the world by storm. Whether you’re in the IT business or not, your social media accounts are brimming with numerous conversations about the benefits and dangers of AI tech. Despite all this, people often overlook one missing link when discussing AI: its dependence on Big Data.

This blog will explain why Big Data rules the AI industry and why businesses need Big Data consulting services.

Understanding Big Data

According to recent IT surveys, the Big Data market is set to reach a market of $367 billion by 2029. But contrary to popular belief, Big Data has long been dominating the world of technology. In fact, its development can be traced back to the 1990s, when Carlo Strozzi developed NoSQL, an open-source relational database to restore and retrieve data in tabular modes. Needless to say, Big Data didn’t start the way it is today. Big Data began as a mere step to store data on interconnected sensor networks and connections–pretty much a computer-to-computer or device-to-device communication. However, due to the rise of the Internet of Things (IoT), data collection has witnessed unprecedented growth in recent years. Thus, what used to be the simple art and science of data collection has evolved into what we now know as Big Data.

But what is Big Data? The term loosely refers to the large data set from online resources available to the public. Their sources include social media platforms, online backend transactions, sensor networks, hardware devices, and other digital technologies. To put things into perspective, everything that you see online is considered a large network of information–from the tweets you read when you wake up, the Youtube videos you watch, and even this article you’re reading right now. All of these can be collected, processed, and analyzed to be repurposed by companies as data.

As of this writing, there are three main sources of Big Data:

Human- This includes intentional or unintentional user-generated data, including social media data, online reviews, search engine statistics, customer data, surveys, polls, and more. In short, almost everything people place on the internet about themselves.

Machine - Data generated from hardware such as machine sensors, software, robots, vehicles, smartphones, and other devices linked to IoT. 

Transactions - Data generated from business transactions such as sales, purchases, inventory processes, POS systems, and other financial transactions.

Needless to say, these sources create a vast network of data. For this reason, big data consulting companies further classify datasets into three types:

Structured - Data following rigid formats and are mostly collected in an organized manner. For example, contacts, zip codes, companies’ employees, credit card numbers, and geolocations. In short, datasets that follow the quote, “What you see is what you get.”

Unstructured - Consists of qualitative data, which requires advanced algorithms to organize. This includes rich media, surveillance data, geospatial data, weather data, and more. They are often drawn out with pattern identification, behavior analysis, and other data workflows.

Semi-structured - These are considered object-oriented databases. Data that fall under this category have the qualitative source of unstructured data while producing organized data. For example, HTML codes, graphs and tables, emails, and XML documents.

With all these in mind, there’s no denying that Big Data offers a great volume, velocity, and variety of data that businesses can use to scale their businesses. While this sounds great, it also entails the painstaking task of data management. 

The Role of Big Data in AI Development 

If AI is the rocket ship that will launch us to the future, Big Data is the fuel that allows it to maximize its capabilities. All AI algorithms require vast amounts of data to be able to map out an algorithm and create an intelligent workflow. Here’s the truth: an AI tool is only as good as the data it has.

Nowadays, the biggest challenge among AI developers is training an AI model with large datasets. While the existence of cloud computing has made it easy for developers to collect, organize, and analyze large datasets, tool training still requires rigorous planning. This includes choosing the right algorithm, preparing the data, validating sets, choosing the appropriate hardware, training the model, evaluating workflows, and fine tuning the overall work procedures. 

Aside from this, Machine Learning algorithms are also fully dependent on Big Data. This is due to the fact that an ML tool’s reliability and functionality are fully reliant on data–the more data an ML tool receives, the better it becomes at identifying patterns and behaviors. In short, an ML algorithm with a strong backend workflow will still fall short if it lacks data to process.

Moreover, most AI developers also find it challenging to facilitate deep learning techniques using Big Data. Say that a company has already collected enough data to work on; the AI models should now be trained to recognize patterns, classify objects, and make predictions based on preceding datasets and workflows. By analyzing vast amounts of data, deep learning algorithms can learn to identify features and patterns that are not visible to the human eye, thus creating a powerful AI solution.

All of these sound challenging–that is true. This is why big data consulting companies aid businesses in making an efficient and effective workflow for collecting and managing datasets for AI training. Without proper data management, ML algorithms and AI solutions will not be able to provide value for clients.

One of the best applications of Big Data in ML algorithms and deep learning techniques is the Mayo Clinic case study in 2016. The clinic’s algorithm used electronic health records (EHRs) of 22,000 patients between 2011-2012 to identify risk factors and health trends among patients to prevent severe health issues. While the algorithm provided satisfactory results, it was not until they included another data set of 10,000 EHRs from 2013-2014 that they got the most accurate diagnosis of infections. Since the algorithm is fed with more data, it’s able to assist doctors better in providing more accurate diagnoses.

With this innovation, numerous healthcare providers also realized the power of AI technologies in improving the healthcare system. Most importantly, it also shows how essential data management is when it comes to increasing the efficiency of AI solutions. 

Real-world Applications of Big Data and AI

While most people have just realized the power of AI due to the rise of ChatGPT, businesses and organizations have long been using Big Data in conjunction with AI to improve numerous lifestyle markets. Here are some of them:

Healthcare

Big Data in AI allows healthcare providers to provide personalized diagnostics and prescriptions in the most efficient way possible. By analyzing large amounts of patient data such as patient profiles, genetics, medical history, lifestyle factors, and other health-related factors, an AI tool can identify patterns among patients’ behaviors and provide more accurate diagnoses. This prevents the possibility of severe infections, ailments, and even contagious diseases. Because of the success of numerous AI solutions, recent studies suggest that the global AI healthcare market will expand at a compound annual growth rate (CAGR) of 37.5% from 2023 to 2030.

Finance

Fraud detection and risk management have become easier with the rise of numerous ML financial algorithms. In fact, IBM says that AI usage in fraud prevention is expected to reduce the number of false declines by a whopping 80%. Using transactional data, ML algorithms can easily flag and report high-risk transactions and suspicious activities, preventing a problem before it happens.

Retail

Thanks to customer segmentation using AI and Big Data, retail businesses can now provide better-targeted offers to consumers. In fact, in a recent article by McKinsey & Company, consumers don’t just want personalization, but they now demand it. For this reason, businesses that use customer personalization now see 40% revenue growth compared to their competitors, thanks to seamless upselling and cross-selling processes. 

Aside from this, Human-generated data from customer profiles, browsing behavior, and purchasing history enables retailers to create demand forecasting. Business managers can now identify patterns that will help them optimize their inventory and production procedures. As a result, the retail industry now has a clear chance of pushing for sustainable growth, as demand forecasting allows businesses to reduce waste significantly.

Manufacturing

Predictive maintenance and supply chain optimization has become a breeze for manufacturing companies. With the use of machine-generated data from production equipment, AI algorithms can predict production downtime and reduce maintenance costs. In fact, based on a recent study by Market and Markets, the predictive maintenance market from AI technologies is expected to expand to a whopping $16.3 billion in 2027. Furthermore, AI technologies help manufacturers improve supply chain efficiency and operational costs by managing production schedules and logistics.

Benefits of Big Data Consulting for AI Development

While Big Data management plays an important role in AI development, the truth remains that it’s only a part of the overall process. After creating their product or solution, AI developers are still expected to market their products, monitor sales, and manage their brands. 

For this reason, numerous companies are now relying on Big Data consulting firms to process large amounts of data needed for their AI products. A Big Data consulting company uses intelligent, distributed, and automated analytics tools to process data. These processes create more optimized data management procedures to help businesses extract valuable insights whenever needed. Based on a recent article published by IDC, the Big Data Analytics Consulting market is expected to reach a CAGR of 23.6% by 2026. The growing dependence of businesses on Big Data consulting companies should come as no surprise, as they take over pretty much all data-related tasks, including collection, analysis, and synthesis of data from the company and the entire market. 

Furthermore, Big Data consulting services also allow AI companies to ensure and maintain the quality of their data. In an article published by Harvard Business Review, bad data costs businesses $3 trillion a year, and that is on the US market alone. Big data analytics ensures the accuracy and completeness of datasets which further makes workflows efficient and with even less human interaction. With accurate datasets, the risk of data breaches is also significantly lessened. This results in heightened compliance with data privacy-related regulations among businesses.

AI and ML companies also rely on Big Data consulting companies to identify potential use cases for their products and solutions. These data insights lead to creating a solid go-to-market strategy that will not just ensure ROI but even open partnership opportunities. This is especially important in today’s market, as there is a growing need for AI solutions in almost all sectors.

Seamless integration with existing software and tools should also be a priority among AI companies. According to Statista, the number of devices linked to IoT will rise from 9.7 billion in 2020 to more than 29 billion in 2030. For this reason, it’s imperative for developers to ensure that their AI solutions can easily be linked and connected to numerous POSs, CRMs, cloud providers, and other IoT solutions to improve their marketability.

Most importantly, Big Data consulting firms make AI businesses future-proof, as they ensure the scalability and adaptability of companies. The IT industry has now become one of the most malleable sectors, as each AI solution presented on the market opens up another market. So, aside from using data for creating products, AI companies should also use data to ensure that they continuously provide value to their clients.

Future Trends and Challenges

Here’s the truth: there’s no stopping Big Data. As long as people continuously provide data dispensable to businesses, the market will continuously grow. In fact, we’re on the threshold of complete AI, big data, IoT, and blockchain convergence, which further unlocks new opportunities for innovation and growth.

The rise of edge computing further expands the Big Data industry. AI companies can now process data closer to the source rather than from a central location. By maximizing edge computing, businesses can improve the reliability of data, reduce management time and cost, and ensure a seamless data transfer.

However, the more data is collected and manipulated, the higher the risk of data breaches. Data privacy and security remain to be one the biggest challenges when it comes to big data analytics. Based on a study conducted by Gemalto, over 4.5 billion data records were compromised worldwide in the first half of 2018 alone. While numerous data security solutions are now being introduced to the market, most companies still fall short of putting these security solutions in place. Thus, preventing them from fully eradicating their data breaches.

Now more than ever, the world needs data governance and ethical management to ensure all data-related processes don’t violate consumer or human rights. While most companies now have Chief Data Officers, the world is in dire need of a centralized governing body among businesses to guide companies in observing data privacy laws and regulations. Remember that at the heart of all the complexities of data science is the market’s desire to connect with their respective customers, which will be virtually impossible if they start violating their consumer rights.

Conclusion

Big Data has evolved into a crucial building block for the advancement of AI, allowing for the development of more complex and precise models for a range of applications. For this reason, it’s almost impossible to overemphasize the importance of Big Data consulting services for realizing the full potential of AI solutions. To maintain a competitive stance in the market, AI companies should harness the power of Big Data. After all, both Big Data and AI will continue to influence how we live, work, and conduct business in the future.