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Today every business owner requires valuable information and insights to stay relevant on the market. If you need to distinguish your target audience, find out what your clients want and predict their needs, big data is a major part of your precise decision-making. The correct data processing and analysis will lead to achieving these goals.


We are exposed to an astonishing amount of information. According to Northeastern University, the total number of data in the world was 4.4 zettabytes in 2013. In 2020, however, it is expected to rise to an enormous 44 zettabytes. Take a look at this:


Big Data Statistics



This information can bring value to enterprises, and now they are able to leverage AI algorithms to process it. As a result, companies are able to understand and influence clients’ behavior. In 2018 over 50% of companies on the planet already adopted big data. So what exactly is it?


What Is Big Data?

Gartner defines Big Data as “high-volume and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision-making, and process automation.”


To simply put it – Big Data is a term that includes every tool and process that helps to utilize and manage huge sets of data. The concept was created out of necessity to capture trends, preferences, user behavior into a single place so-called data lake when people interact with various systems and each other. Big Data can help companies to analyze and figure out motivations of the most important clients, while also providing ideas for the creation of new offerings.


Why Big Data Is So Important?

As we mentioned above, data is a crucial part of understanding the target demographics and motivation of the customers. When the customer interacts with the technology in an active or passive way, the data on his behavior could be created, describing his actions. Cameras, credit cards, smartphones, purchased products are all contributing to the expanding data profile. If done right, the analysis could tell a lot about personality, behavior, and events occurring in the customer’s life. Companies can use these insights to improve the product, adjust business strategy and boost marketing campaigns ultimately getting closer to the target client.


For years the experts have discussed big data and its business impacts, however only recently the technology evolved to the point, where it is actually achievable. Now the large datasets could be analyzed fast and efficient. 44 zettabytes in 2020? Well, the number of structured and unstructured data will increase in the coming years. Only collecting and processing it, companies could get the AI insight that will help to maintain high income and be ready for the future.


Even the smallest companies will adjust their operations to experience the benefits of big data. It will happen because data collection and interpretation is becoming more accessible than ever. Every day we find out about innovative technologies that are cheap and very easy to implement.


The Benefits of Big Data combined with the Artificial Intelligence

Artificial Intelligence and Machine Learning technologies breakthroughs are opening the possibilities for entirely new use cases with additional types of data. Those technologies now could easily handle processing pictures, voice and videos like never before. Big Data needs Artificial Intelligence to unlock its business value to the fullest. The more data AI receives the smarter it gets. We don’t need the concern about the storage space that much today, the bigger, the better!


3 Ways in which Big Data can benefit from AI

You probably heard that AI, and its most popular subdivision Machine Learning offers some kind of insight for business owners. Let’s find out exactly what AI/ML could bring to Big Data, resulting in business value.


  • The new methods for data analytics

Probably the biggest question that comes from implementing Big Data is to how actually handle this variety of information. For a long time, “SQL like” query languages where used by analytics to extract needed data, then they spent a lot of effort to achieve some useful insights using old fashioned approaches to deal with the data, but now AI and ML are the methods that could take care of this process.


  • Speeding up data analytics

People are still very important in managing data and its analysis, but Artificial Intelligence could make things a lot quicker and efficient. Letting AI technologies assist humans in the process of data analysis could ultimately result in faster decision-making for the company, with more insights.


  • Eliminating data problems

Every time people talk about Big Data the question on the quality of data pops up. The low quality of information means a little to no value. ML algorithms can definitely help with this because the secret of the ML projects is that 80% percent of the overall time is spent on cleaning or preparing information. ML algorithms are able to detect missing or outlier values, normalize data to common terminology and distinct the different records that describe the same thing with slightly different technology.


What Big Data Means For Business?

Implementing Big Data with Artificial Intelligence has already been vital for many top businesses to beat the competitors. It doesn’t really matter whether it is a new company or established leader on the market – they all use data-driven strategies to turn information into tangible value. It is possible to find a Big Data use case almost in every industry, from IT and Banking to Agriculture and Healthcare.


The business experts are acknowledging that Big Data and Artificial Intelligence can create some new ways for growth and expansion. There is even a possibility that the new type of business will be getting popular soon – the data analyzing and aggregation companies for particular industries. The sole purpose of those organizations will be processing the enormous flows of data and generating insights. Before it actually happened, businesses should empower their Big Data capabilities very intensively. In the past estimation was made based on the retrospective point of view. Leveraging real-time analysis, Big Data can empower the predictions and allow strategists to test assumptions and theories faster.


What Are The Implications Of Big Data?

The level of understanding of the current infrastructure and client engagement will determine the opportunities for businesses to unlock hidden insights and get the advantage over competitors. Big Data and Artificial Intelligence can offer multiple opportunities for growth, now we would like to talk about the three most important:


  • Quicker and more precise decision-making

If you combine AI insight, the speed of data analytics technology and the access to new sources of information you will get never before imagined level of informed decision-making, based on smart and accurate analysis.


  • Improved automation

One of the benefits of big data is the ability to automate processes resulting in an efficiency boost. The price of cloud computing is lowering, making massive data storage more affordable. Adding scalable IT infrastructure automated data collection will be more simple than ever.


  • Deeper Insights

Big Data introduces the entirely new levels of uncovering hidden opportunities. Organizations couldn’t analyze that large sets of data in the past, but now the ability to do that could result in unexpected business value. Giant datasets could be easily used in innovating product development. Getting hold on the patent in already existing and developed markets could be a crucial factor determining the position of the company in the industry.


Artificial Intelligence and Big Data Applications

Let’s take a closer look, and find out what value Big Data powered by Artificial Intelligence can bring to the existing business in various industries.


AI in Retail

The first usage is making sense of data silos in the organization. While retail company evolves and grows, it became much harder to make sense out of all the information coming to the different departments. Data is not located in one place, like some kind of warehouse. Every Like, tweet or comment is an element in the bigger picture of customer behavior. The goal for AI and Big Data in Retail is to collect all this information in one place and make sense of it. By introducing multiple data points and using advanced Machine Learning techniques it is possible to create a model for a customer journey and predict customer behavior.


The second application is the creation of an immersive shopping experience for the customer with seamless transitions from offline to online shopping. Augmented Reality, Artificial Intelligence, and Big Data provide customers with the opportunity to try products at every touchpoint. Smart Fitting Room concept is a perfect example of this. When customers try on models of clothing, the system could provide recommendations based on styles and preferences. The platforms from Alibaba are using “Fashion AI” technology, which leverages over 500,000 pieces of data on fashion and advice from stylists to offer personalized recommendations. Alibaba also uses a digital screen that is named “magic mirror”. This solution allows customers to try on different clothing virtually, and then buy it via Alibaba’s payment service by scanning QR code on the screen.


Finally, AI and Big Data can help to create a supply chain, that is data-driven and transparent. Machine Learning can find the most important factors fast and drive the success of the chain.


When all links in the supply chain will become smart, business owners will receive benefits like:

  • Real-time forecasting of inventory levels
  • Checking the quality of supplies
  • Predicting the demand
  • Planning the production
  • Optimized delivery and transportation


Hema Xiansheng from Alibaba is a great example of a supermarket working with this technology. Every product in the supermarket is digitized. Customers can scan barcodes and get complete information on the product. At this time the real-time recommendation is offered, considering the previous purchase history. All of this is achieved by an effective combination of large database and AI-driven analysis and suggestions system.


AI in Healthcare

Artificial Intelligence, Machine Learning, and Big Data in Healthcare are already implemented in numerous ways in hospitals around the globe. In the context of radiological diagnostics, cognitive computing systems are being used to facilitate repetitive processes and create sample analyses. Learning and predictive functionality is added, which moves it to the Machine Learning territory.


Or we could mention the Deep Learning Model from Stanford University or Google Brain. Technology here is used to predict the approximate time of death for patients with life-threatening conditions and building the strategies for the best treatment, as a result.


Robotic systems like surgical robots are another way to use AI technology. They could be the only possible solution for high-precision surgeries in the future, expanding the possibilities for treatment. Robots could also make a difference in rehabilitation periods, after the operation, offering logistical supports for the hospitals. With the evolution of AI technology and the availability of information, the contexts for the usage of medical robots will only increase.


AI in Healthcare


AI in the Food Industry

One of the best ways to implement Artificial Intelligence and Big Data in the Food Industry is for Data Prediction. It is a relatively new market, and there are already startups that tap into it.


Ida from Connecterra is using AI to help farm owners to predict and prevent some health problems with the cattle they own. While protecting the actual assets, the data gathered from the farm helps to keep cattle healthy and producing higher-quality beef and milk. The Yield startup is monitoring agriculture and aquaculture environments to create predictions and offer insights.


The American startup Brightseed is doing something really unique and intriguing. AI and Big Data-powered predictive analytics are used to discover beneficial plant compounds. The deal is that the less than 0.1% of possible plant compounds already are discovered. These compounds will be used to create bioactive compounds that will make foods more nutritious and healthy. This will ultimately revolutionize the food industry as we know it.


These are just a few of the use cases in the food industry, but it is expected to be much more:


  • AI will improve restaurant-management software to the extent that it will allow managers to be informed on product availability and correct pricing, adjusting to trends and customer preferences beforehand
  • Knowing your tastes, time of the day and you travel plans, a restaurant could suggest that you will visit soon and offer you some food to order, that you might like
  • Mobile Applications will analyze supply chains of restaurants and provide recommendations on ones with the freshest ingredients for the food
  • Considering trends food manufacturers will be flexible to produce food and beverages that will satisfy the current tastes of the customers in the shortest period of time


AI Insight in Business Analytics

Analytics help to build a strategy for businesses based on information that is coming. It takes time and effort to create structured dashboards and reports, but even more to effectively use it. Based on data analysis you can be alerted on problems, find new solutions, receiving ideas for new opportunities. But sometimes it can be really hard to determine what is important, and what’s not, and how to deal with it in the massive stream of information. Machine Learning can help in the analytics process, recognizing unusual patterns in the processes instantly and giving you suggestions on what to do next, providing valuable AI insight.


According to Capgemini Digital Transformation Institute there already some tangible benefits for businesses that had implemented AI:

  • 75% of organizations had increased their sales of new products and service offering by more than 10%
  • For 78% of those organizations, operational efficiency had increased by over 10% too
  • 79% of organizations had generated new insights and significantly improved their analysis
  • More than 10% is also the number for 75% of organizations improving customer satisfaction


There are basically four Artificial Intelligence and Machine Learning strategies for Big Data analytics.


Descriptive analytics in simple terms just describes what had happened. It’s summarizes everything and provides reports on the situations. Data categorized in historical context will help to make educated guesses and what will come next.


Diagnostic analytics breaks down all received information making an effort to answer the question “Why and how did certain events have happened?”. The investigation is performed by comparing various sets of data.


Predictive analytics is for recognizing patterns, matching events to those patterns to predict the events that will occur in the future. Narrative Science study claims that around 25% of enterprises are already implemented predictive analytics.


Prescriptive analytics is aimed to suggest the best ways to take action in a particular situation.



In 2022 one of five workers, doing generally non-routine tasks will be replaced by Artificial Intelligence, as Gartner predicts. Will it happen? Only time will tell. But we can say with certainty that competitors will replace businesses that ignore AI/ML technology that can handle Big Data. SPD Group is providing AI development that helps businesses grow and also offers Fraud Detection and Prevention functionality, elevating business security on an entirely different level. If you are interested in Artificial Intelligence and Machine Learning development contact us!