How Big Data and AI can deliver results


The terms “big data” and “artificial intelligence (AI)” are frequently used when addressing the future of business. The potential for their application in various business sectors to evaluate data and gain the needed solutions instantly is vast.

Organizations have built up massive stores of data. However, simply storing and managing large amounts of data doesn’t provide the most value. Forward-thinking enterprises are increasingly using intelligent or advanced forms of big data analytics to extract more value from their data.

In particular, machine learning is being used to identify patterns and provide rational reasoning across vast sets of data, thus providing the ability to apply the next level of analytics needed to get value from their data.

Defining Big Data

Big data consists of high volume information assets that require innovative and cost-effective processing to provide insights to enable better decision-making. This data can then be reviewed to discover correlations or hidden patterns that would otherwise be inaccessible.

Also read: Big Data Basics: Understanding Big Data

Just having the computational power and storage space needed to amass vast amounts of data is not enough. There must be a way to make sense of it. Since no human can scan huge volumes of data to look for patterns or connections that can be used for strategic planning, artificial intelligence is employed for the task.

Data allows organizations to learn more about specific demographics and what motivates them. When consumers use technology passively or actively, data is generated which describes it.

This includes credit cards, smartphones, cameras and any electronic device which grows their data profile. When the analysis is done correctly, institutions can learn a great deal about the behavior and characteristics of an individual or group.

Such crucial info can then be used to improve services or products. As a result, corporations are now in a race to develop the most powerful, accurate and comprehensive data collection and analysis tools.

Origin of Big Data and Analytics

Since antiquity, humans had collected data when our ancestors presented information on tablets, parchment, and stone reliefs. The data collected today merely continues that phenomenon on a much larger scale. However, the concept of big data as we know it today started during the 1950s.

Although the term “big data” wasn’t used back then, corporations used simple analytics that came in spreadsheet numbers that had to be manually reviewed to discover trends and insights. However, this method was slow and painstaking.

Without computers, machine learning and AI, corporations were limited in their ability to gather the large amounts of data that is now accessible and make sense of it.

How Big Data and Analytics Works

Big data and analytics encompass multiple technologies which function simultaneously to aid institutions in gaining the greatest value from their information.

These technologies are predictive analytics, Hadoop, data mining, machine learning, and text mining in-memory analytics and data management.

Predictive Analytics: This technology utilizes statistical algorithms and data to determine future results based on historical data. Its goal is to help institutions plan for the future. It is very successful in areas such as marketing, risk analysis and detecting or preventing fraud.

Hadoop: An open-source software framework capable of containing vast levels of data. It can use commodity hardware to run applications and is considered an indispensable tool when dealing with continually growing data variety or volume. Because it uses a distributing based computational model, it can rapidly process large amounts of data. Another advantage of being open-source is that it is freely available.

Data mining: The mining of data will aid organizations in examining substantial levels of information to uncover connections and patterns. Such information can be applied towards further analysis to answer essential business questions. When data mining occurs, information will be rapidly sifted through to concentrate on the data used to make crucial decisions.

Machine learning: ML is best thought of as an AI subset that teaches a machine various learning methods, enabling it to rapidly generate models that can then analyze large data sets to provide faster results with greater accuracy. This can be accomplished even on bigger scales. When done correctly, institutions will recognize profitable opportunities while avoiding risks that would be difficult to quantify.

Text mining: The mining of text is similar to mining data but with a few differences. Here the goal is to evaluate text which is available on the web, including in e-books and comment sections, to develop insights that weren’t previously available. Text mining is closely associated with naturalized language processing (NLP). Text mining allows documents, blogs, emails, and social media feeds to be reviewed to make new and interesting discoveries.

In-memory analytics: When system memory data is analyzed, you will gain insight to respond to it quickly. In-memory analytics will significantly reduce processing latency and data prep. New scenarios can be tested to develop fresh models. This makes it easier for institutions to remain flexible while making wiser long term decisions.

Data management: It isn’t enough to own large data amounts. This information must also be organized and premium in quality. This is where data management comes in. It allows the data continually flowing in and out of organizations to be subject to processes that make it more decipherable and usable. Once this happens, the data can be applied in new and profitable ways.

These are the seven technologies that encompass analytics and big data. However, they must function together seamlessly through artificial intelligence to achieve the desired results.

As computational processing power grows and evolves, AI will accelerate accordingly. The industry’s current state indicates that it has already made significant strides and is prepared for broader adoption.

Merging Big Data with AI

It isn’t a coincidence that extensive data collection and AI have emerged simultaneously; one cannot exist without the other.

Advancements in machine learning (ML) have led to a new world where data can be used in ways never before thought possible. This technology can rapidly process videos, text, images and even voices. AI analyzes the more this data, the more efficient it becomes.

There are three core ways that AI benefits big data, and this is through enhanced data analytics, more incredible processing speed and the elimination of data challenges.

Enhanced data analytics

Managing big data effectively is one of the most significant challenges faced by organizations. SQL type languages have been incorporated in extracting desired data for several years.

Afterwards, a great deal of time and energy was needed to collect key insights that often involved older methods lacking efficiency. This has changed as machine learning, and artificial intelligence are now preferred.

Greater processing speed

When it comes to data processing, speed is everything. While humans are still used to analyzing and managing data, artificial intelligence is faster. It can aid people in analyzing data which results in faster insights and the ability to make critical strategic decisions that can take organizations to new heights.

Eliminating data challenges

Many challenges and problems come with the acquisition, management and processing of data. The biggest involves the quality of the information received. No organization wants to spend large amounts of time, money and resources acquiring data that is ultimately worthless.

For this reason, machine learning algorithms are now being used to clean up and prep information. For example, ML can detect outlier or missing values, standardize data based on terminology, and distinguish between different records expressing identical concepts using differing technology.

How does Big Data Benefit Organizations?

There are three additional ways that big data assists corporations, and this is through more rapid decision making, new services and products and lower costs.

  • Rapid decision making: In-memory based analytics allows institutions to review data to make decisions faster than their competitors. This gives them a significant advantage which may ultimately capture greater market share.
  • New services and products: You can develop services tailored to their needs once you know what your customers or clients want by using analytics. In turn, this will lead to greater brand loyalty and profits.
  • Lower costs: Technologies such as cloud analytics and AI will substantially lower costs, especially when assessing and storing new data sources. Furthermore, it can analyze existing business processes to determine unique ways to reduce costs while boosting efficiency.

Which Industries are using AI and Big Data the most?

While most industries can benefit from big data and analytics, there are six that have seen the most significant benefits: retail, banking, healthcare, manufacturing, government, and life sciences.


Customer support has evolved dramatically over the last decade. Consumers have become more sophisticated and now want retailers to know what they desire and when. By using analytics and big data, retailers can do this since they will have access to vast stores of data that can be assessed to predict trends and make recommendations for new products.


Financial institutions have always had access to large volumes of data, but understanding it was a different matter. Now they can take this unstructured data, use AI and analytics to organize it, and then evaluate the information to provide better banking services while maximizing the efficiency of their operations and protecting client accounts from cyber threats.


Those employed in the medical sector, whether doctors or nurses, must routinely make difficult decisions that are a matter of life and death. As such, having access to timely and accurate information is critical. However, medical professionals were limited in what they could accomplish in the past due to technological limitations.

Today, those in the medical industry make better decisions for patients and have a greater awareness of logistics and the availability of life-saving medication.


Companies in the manufacturing sector have the challenging task of taking raw materials and fabricating finished products that can be sold for profit. As you can imagine, doing this requires identifying and solving problems involving mechanical malfunctions, supply chain issues, and motion applications.

For this reason, data analytics is indispensable for this sector, as it can help identify problems well in advance while enhancing the efficiency of daily operations and lowering costs.


Managing and leading a nation of millions, tens of millions or hundreds of millions of citizens has never been an easy task. Governments must maintain stability and operate within budgets without compromising their citizens’ productivity or quality of life.

Additionally, law enforcement must protect civilians from criminal elements while prosecuting those who break laws. The military must protect against external adversaries. Achieving all this requires a holistic view that extensive data analytics can provide.

Governments will better understand their citizenry and their needs and desires while identifying potential threats that could lead to destabilization.

Life Sciences

Science is the foundation that makes modern technology possible. However, scientific research has historically been costly and slow, especially in the medical sector. It isn’t unusual for trials to fail for many different reasons.

Thankfully, the advent of analytics, IoMT (Internet of Medical Things) and artificial intelligence has opened the door to considerably speeding up scientific research.

Big Data and AI: Understanding the challenges ahead

While the worlds of big data and artificial intelligence might seem overwhelming, they are necessary for businesses to remain competitive. However, implementing robust blended Big Data and AI systems presents its own set of difficulties.

Big data and AI are inextricably linked. The latter’s success is dependent on the former for success while also assisting enterprises in unlocking the potential of their data repositories in previously inaccessible or onerous ways.

Today, organizations want as much data as possible – not only to have a deeper understanding of the business problems they’re attempting to solve but also because the more data we feed the machine learning models, the better they grow.

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