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Understanding Big Data Analytics In Less Than 10 Minutes!

Analytics Have you ever wondered what exactly the difference between data and big data is? Both are collections of information, right? While that is true, a unique identifier for big data is its large volume. Big data is often used by businesses for obtaining customer insight. Such datasets are so extensive that traditional software like MS Excel is not agile enough to handle them anymore.


Not long ago, businesses were using traditional database methods to store quantitative information on spreadsheets like Excel; a program using grids, tables, and columns to organize the storage of data. Little did we know that the gradual inflow of data would accelerate at such a high speed that we would find ourselves in an explosion of information. It was around that time that we realized that the traditional data management systems were no longer capable of processing such massive volumes of information.

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The 3Vs That Define Big Data

Volume, Velocity, and Variety are the 3Vs that best define what big data stands for.


Whether or not a dataset is considered to be big data depends entirely on the volume of the information. Hence, this is the most important V defining big data. It is a simple rule: any information that is massive in volume is big data. For example, according to Fortunly, there are around 1.5 billion WhatsApp users in 180 countries! Just imagine the huge volume of data being generated on a daily basis. As traditional data management can no longer take the burden of this colossal amount of information, we rely on new methods (like Hadoop) to store and analyse the information.


In his recent letter to Amazon shareholders, Amazon CEO Jeff Bezos emphasized how “speed matters in business.” Velocity, which refers to the high speed at which data is received, helps businesses make the most timely decisions. Tesco, Sainsbury, and Marks & Spencer are some examples of retailers using big data analytics in retail and leisure industries.

If you were the owner of a retail business, your biggest concern would be to know which items go out of stock within minutes. For retail businesses, predictive analytics is practised on a large scale, which means they predict future consumer demand with present data available. Retailers can observe customer behaviour to predict what product should be stocked in future to meet the demand.


Big data always features different varieties of data. Social media alone has a diversified range of data, such as text messages, videos, audio files, pictures, and much more. When working with big data, it is important that you organise and structure the variety of information for later analysis.

Variety of data refers to two basic categories:

  • Structured Data: This can be easily defined and searched for by machine language. It is made up of quantitative information, e.g. name, address, telephone number, and billing information.
  • Unstructured Data:  It is unorganized in nature and is mostly text-based, e.g. emails, voicemail, ECG recordings, and business recordings.

Now that we know what big data is, it’s important to understand how big data and analytics integrate to extract value from the data. Before big data came into existence, businesses relied on basic data analytics, a traditional system to examine data for insights. With the cutting edge technology we have today, big data can be examined through big data analytics, a much faster way to draw out customer insights and behaviour patterns.

What Is Big Data Analytics?

A lot of people wonder what big data analytics is, and how it can be used to create value for their business. A simple definition of big data analytics is:

The process of examining big data for adopting appropriate business strategies and better decisions.

For example, if you were running an entertainment channel like YouTube, you would depend on big data and business analytics to answer some of the following questions:

  1. What kind of videos do viewers watch the most?
  2. How long is each video watched for?
  3. What are the viewer’s preferences?
  4. Which celebrity video is watched more?

Once we are able to obtain the answers to these questions, we can combine the powers of big data and analytics, and adopt a business strategy that would help lay out our marketing goals.

To further comprehend big data analytics, let’s take a look at its three major aspects:

  • Sources of big data analytics
  • Top 5 big data analytics tools
  • Innovative ways companies are using big data

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Sources Of Big Data Analytics

Big data can be split into three wide categories on the basis of the three primary sources that it is obtained from.

1. Social Data

Social media has completely turned our lives around. Every click, tweet, and comment that we make is stored and used for the purpose of big data analytics.

Are you still wondering how that happens? Well, social data gives an insight into user behavior patterns through various social media sites. Big data is obtained through the monitoring of how a user engages through different media channels, including Google, Facebook, LinkedIn, Instagram, and many others.

2. Machine-Generated/Internet-Of-Things (IoT) Data

Machine data is composed of all the digital information that we get from software installed in devices such as road cameras, smartwatches, smart meters, and satellites. Let’s take the smartwatch, as an example. Smartwatches track an individual’s health data by monitoring their blood pressure, seizures, and steps walked on a daily basis. This data can then be used by doctors who can offer their patients with better diagnosis owing to the easy access to their consistent medical record.

3. Transactional Data:

For the owner of a retail shop, transactional data is no less than a treasure chest. A large volume of data is available to them, including:

  • Purchases and returns made by the customers through payment slips
  • Information about the services that customers are subscribed to
  • Invoices for recording all the sales transactions and orders placed by the consumer
  • Consumers’ personal information gathered through loyalty cards programs for better customer relations.

As surprising as it may sound, even a small incentive, such as a loyalty card, has a big role to play in the collection of big data. Customers are given discounts in exchange for their personal information, helping businesses to determine the consumer preferences for future planning.

While all sources of big data are important, the most widely used method in the modern world is social media. Hence, it would be unfair to go ahead without shedding some light on this relationship.

Artificial Intelligence: How AI Is Changing Retail

The digital transformation of the retail industry has been going on for years. It has increased speed, efficiency, and accuracy across every branch of retail business, thanks in large part to advanced data and predictive analytics systems that are helping companies make data-driven business decisions.


None of those insights would be possible without the internet of things (IoT), and most importantly, artificial intelligence. AI in retail has empowered businesses with high-level data and information that is leveraged into improved retail operations and new business opportunities. In fact, it is estimated that $40 billion of additional revenue was driven by AI in retail in a 3-year span.

Retailers looking to stay competitive need look no further than AI in retail business. It is forecasted that 85% of enterprises will be using AI by 2020, and those who don’t risk losing insurmountable market share to their competitors.

What Technologies & Solutions Are Used for AI in Retail?

Artificial intelligence is a term that is thrown around in many industries, but many people don’t fully grasp what it means. When we say AI, we mean a number of technologies, including machine learning and predictive analytics, that can collect, process, and analyze troves of data, and use that information to predict, forecast, inform, and help retailers make accurate, data-driven business decisions.

These technologies can even act autonomously, using advanced AI analytical capabilities to convert raw data collected from the IoT and other sources into actionable insights. AI in retail also utilizes behavioral analytics and customer intelligence to glean valuable insights about different market demographics and improve many different touchpoints in the customer service sector of business.

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What Does AI in Retail Look Like?

Today’s dynamic retail industry is built on a new covenant of data-driven retail experiences and heightened consumer expectations. But delivering a personalized shopping experience at scale — that is relevant and valuable — is no easy feat for retailers. As digital and physical purchasing channels blend together, the retailers that are able to innovate their retail channels will set themselves apart as market leaders.

So, what exactly does that look like? Here are some examples of how AI in retail is reshaping the entire industry.

Inventory Management – AI in retail is creating better demand forecasting. By mining insights from marketplace, consumer, and competitor data, AI business intelligence tools forecast industry shifts and make proactive changes to a company’s marketing, merchandising, and business strategies. This also impacts supply chain planning, as well as pricing and promotional planning.

Adaptive Homepage – Mobile and digital portals are recognizing customers and customizing the e-retail experience to reflect their current context, previous purchases, and shopping behavior. AI systems constantly evolve a user’s digital experience to create hyper-relevant displays for every interaction.

Dynamic Outreach – Advanced CRM and marketing systems learn a consumer’s behaviors and preferences through repeated interactions to develop a detailed shopper profile and utilize this information to deliver proactive and personalized outbound marketing — tailored recommendations, rewards, or content.

Interactive Chat – Building interactive chat programs is a great way to utilize AI technologies while improving customer service and engagement in the retail industry. These bots use AI and machine learning to converse with customers, answer common questions, and direct them to helpful answers and outcomes. In turn, these bots collect valuable customer data that can be used to inform future business decisions.

Visual Curation – Algorithmic engines translate real-world browsing behaviors into digital retail opportunities by allowing customers to discover new or related products using image-based search and analysis — curating recommendations based on aesthetic and similarity.

Guided Discovery – As customers look to build confidence in a purchase decision, automated assistants can help narrow down the selection by recommending products based on shoppers’ needs, preferences, and fit.

Conversational Support – AI-supported conversational assistants use natural language processing to help shoppers effortlessly navigate questions, FAQs or troubleshooting and redirect to a human expert when necessary — improving the customer experience by offering on-demand, always-available support while streamlining staffing.

Personalization & Customer Insights – Intelligent retail spaces recognize shoppers and adapt in-store product displays, pricing, and service through biometric recognition to reflect customer profiles, loyalty accounts or unlocked rewards and promotions — creating a custom shopping experience for each visitor, at scale. Stores are also using AI and advanced algorithms to understand what a customer might be interested in based on things like demographic data, social media behavior, and purchase patterns. Using this data, they can further improve the shopping experience and personalized service, both online and in stores.

Emotional Response – By recognizing and interpreting facial, biometric, and audio cues, AI interfaces can identity shoppers’ in-the-moment emotions, reactions or mindset and deliver appropriate products, recommendations or support — ensuring that a retail engagement doesn’t miss its mark.

Customer Engagement – Using IoT-enabled technologies to interact with customers, retailers can gain valuable insights on consumer behavior preferences without ever directly interacting with them. Take the Kodisoft interactive tablet for example – Kodisoft developed a tablet to be used in the restaurant setting for customers to use to browse menus, order, and play games. Supported by the IoT Hub and machine learning, this tablet has leveraged consumer data and behavior trends, allowing companies to increase engagement and success with customers.

Operational Optimization – AI-supported logistics management systems adjust a retailer’s inventory, staffing, distribution, and delivery schemes in real-time to create the most efficient supply and fulfillment chains, while meeting customers’ expectations for high-quality, immediate access and support.

Responsive R&D – Deep learning algorithms collect and interpret customer feedback and sentiment, as well as purchasing data, to support next-generation product and service designs that better satisfy customer preferences or fulfill unmet needs in the marketplace.

Demand Forecasting – Mining insights from marketplace, consumer, and competitor data, AI business intelligence tools forecast industry shifts and make proactive changes to a company’s marketing, merchandising, and business strategies.

Customized Selections – Taking customer service to the next level, many retailers are using AI to help them provide unique, personalized experiences for customers. And, there’s big money in providing such services. “Brands that create personalized experiences by integrating advanced digital technologies and proprietary data for customers are seeing revenue increase by 6% to 10% — two to three times faster than those who don’t,” according to a study by the Boston Consulting Group.

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Why You Need AI in the Retail Industry

Aside from the business intelligence and sheer speed that these technologies can provide, the digital transformation in retail is simply setting successful businesses apart from unsuccessful ones. There are countless benefits that can be credited to artificial intelligence in retail business, but here are five primary ones that retailers can count on.

1.     Captivate Customers – With a plethora of innovative competitors providing shoppers with immersive shopping experiences, traditional retailers need to engage customers in a personalized and relevant manner that is unique and inspiring across all touchpoints.

2.     Create Exciting Experience – To drive continued interest, retailers need to differentiate their products and offer consumers compelling service and experiences. By integrating predictive analytics to gather more market insight, retailers can lead with innovation rather than react to change.

3.     Create Insights from Disparate Data – Faced with an onslaught of information from all aspects of their business from supply chain to stores to consumers, retailers need to filter through the noise to transform these disparate data sources into consumer-first strategies.

4.     Synchronize Offline & Online Retail – Digital and physical shopping channels typically operate under a different set of initiatives and approaches but treating these channels as distinct business units adds friction for customers seeking a seamless shopping experience and leads to operational inefficiencies.

5.     Empower Flexible Logistics Networks – In order to service a wider range of customer demands that are moving from mainstream to niche, retailers need to rethink their traditional supply chain in favor of adaptive and flexible ecosystems that can quickly respond to consumers’ shifting behaviors.

Implementing the systems to support AI in retail can seem overwhelming, but it doesn’t have to be. With a technology solutions partner like Hitachi Solutions, you will be supported and guided through every step of the process, and even after deployment. Reach out to one of our experts to learn more about Hitachi Solutions for retail business.