International Data Corporation (IDC) predicts that by 2025, the amount of data generated worldwide will reach 163 zettabytes, growing at a CAGR of 44%. Not just that, Gartner predicts that by 2025, AI-driven automation will reduce data preparation time by 95%, enabling organizations to analyze vast amounts of data in real-time.
Walmart, the world’s largest retailer, has embraced the power of big data and advanced analytics to enhance its operations and gain a competitive edge. In the early 2000s, they started collecting an enormous amount of information from their stores, including sales records, inventory levels, and customer demographics.
With advanced analytics, it was able to uncover valuable insights into its customers and operations. For example, they optimized their supply chain by using predictive analytics to predict product demand and adjust inventory levels, resulting in reduced waste and lower costs.
In addition, Walmart used big data and advanced analytics to personalize the shopping experience for their customers. By examining customer data, they made individualized product suggestions and offered tailored promotions, improving customer satisfaction and loyalty.
Data has revolutionized the way businesses make decisions. Big data is the vast and diverse data that organizations gather from various sources, such as customer interactions, internal systems, and online platforms.
Advanced analytics uses big data to extract meaningful insights and make predictions. This is done by utilizing complex algorithms and statistical methods. By harnessing the power of these technologies, organizations of today can:
- Gain a comprehensive view of their operations
- Visualize Customer behavior
- View Market trends
- Improve efficiency in processes, and
- Make more informed decisions
In this blog, we will see what big data and advanced analytics is, their relationship, and their relevance in today’s organization.
What is Big Data?
Big data can be defined as a large and complex set of data that exceeds the processing capacity of traditional data processing systems. It typically includes data from various sources, such as social media, sensors, and transactional systems, and can be structured, semi-structured, or unstructured.
The goal of big data is to provide insights and drive decision-making by analyzing and drawing meaningful conclusions from this vast amount of information. The size and complexity of big data require advanced analytical tools and techniques, such as machine learning and predictive analytics, to process and analyze it effectively.
Here’s an example of how Netflix has effectively leveraged big data to gain a competitive advantage. The company has access to a wealth of data from its subscribers, such as their viewing history, search patterns, and ratings. This information is then analyzed to make informed decisions about the content and user experience.
Moreover, big data also enables Netflix to personalize the user experience. By examining each subscriber’s viewing history and search patterns, they can make individualized suggestions, leading to longer viewing sessions and increased customer satisfaction.
Big data and advanced analytics are used in conjunction with one another. Let’s see how they’re related.
Relationship Between Big Data & Advanced Analytics
Big data and advanced analytics are closely related concepts that provide valuable insights and drive decision-making.
The relationship between big data and advanced analytics can be likened to a puzzle. Big data provides the pieces, while advanced analytics helps to fit those pieces together to uncover a clear and comprehensive picture.
By working together, big data and advanced analytics can help organizations to make data-driven decisions, improve operations, and drive innovation.
Amazon has effectively leveraged the power of big data and advanced analytics to improve its operations and drive growth.
Amazon generates massive data from its e-commerce platform, including customer shopping patterns, search histories, and purchase histories. Amazon uses advanced analytics to make sense of this vast amount of data and support the following use cases:
- Make personalized product recommendations
- Increase sales and improve customer experience.
- Optimize supply chain and logistics operations
- Reduce delivery times, build efficient delivery routes, and minimize waste
So far, we’ve seen the uses of Big Data and Advanced Analytics and how organizations use them to gain competitive advantage. There is another field where the data can be used to forecast the future using critical metrics tracked. This is called Predictive Analytics. When deployed in conjunction with advanced analytics and big data, predictive analytics can help organizations take a growth leap in the highly competitive market.
So what is predictive analytics, and what are its uses?
Predictive Analysis
Predictive analysis uses data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.
This approach involves analyzing vast amounts of data from various sources to identify patterns and relationships and then using these findings to make predictions about future events. Predictive analysis can be applied to various industries and applications, including finance, healthcare, marketing, and supply chain management.
UPS, the package delivery company, leverages predictive analytics to enhance its operations. By collecting information like delivery times, traffic patterns, and weather conditions, UPS generates predictive models that help optimize its delivery routes.
This leads to more efficient deliveries, reduced fuel consumption, and improved delivery times. To enhance customer service, the company also uses predictive analytics to gather customer data and insights, like feedback and complaint patterns.
Overall, UPS’s utilization of predictive analytics improves its business operations and elevates the customer experience, making them the leader in the market.
Using AI, Big Data & Advanced Analytics For Your Organization
Integrating AI, Big Data, and Advanced Analytics can significantly benefit organizations looking to improve their operations and gain a competitive edge. Here are a few ways these technologies can be utilized:
Customer Profiling:
Organizations can gain valuable insights into customer behavior, needs, and pain points by analyzing large amounts of customer data. This information can enhance the customer experience, boost customer satisfaction, and drive revenue growth.
Predictive Maintenance:
Organizations can use data from IoT devices, machinery, and other sources to predict equipment break down . This allows for proactive maintenance, reduced downtime and extended equipment lifecycle.
Fraud Detection:
Advanced analytics can assist organizations in identifying fraudulent activity by analyzing patterns in vast amounts of data. This can help prevent financial losses and enhance the security of the system. For example, TerraPay, a leading cross-border payments platform, leverages the user identity datasets created on Scribble Data’s Enrich Intelligence Platform coupled with advanced analytics and ML models to detect suspicious transaction activity and bolster their anti-money laundering (AML) efforts.
Supply Chain Enhancement:
Big data and advanced analytics can optimize supply chain processes by analyzing supplier performance, delivery times, and inventory levels. This results in cost reduction, improved efficiency, and enhanced customer satisfaction.
Synergy between AI, Big Data, and Advanced Analytics
By combining big data with advanced analytics, organizations can create a synergy that results in powerful insights to solve complex business problems. Here’s how these tools work together:
Machine learning algorithm
Machine learning algorithms are essential in integrating AI, big data, and advanced analytics. These algorithms help organizations to automate the process of extracting insights from large data sets, making it possible to gain valuable insights in real time.
Machine learning algorithms can also be used to make predictions based on historical data, which can help organizations make informed decisions.
Data visualization tools
Data visualization tools are crucial in bridging the power of big data and advanced analytics. These tools enable organizations to visualize large and complex data sets effectively. Thus, uncovering patterns and relationships that would otherwise be difficult to detect.
Data visualization tools also provide a visual representation of the data, making it easier for stakeholders to understand and interact.
Data Products
A data product can be considered a “container” that can help a business directly solve a persistent business problem. Data products can be of varying maturity levels, from BI (Business Intelligence) systems, simple tables, and derived datasets to complex tools built on machine learning models, and can support internal and external users. At Scribble Data, we see advanced analytics, machine learning, and big data being used to solve a wide variety of long-tail use cases with our low-code app store.
Other tools and techniques
There are many other tools and techniques that organizations can use to harness the power of AI, big data, and advanced analytics. For example, Natural Language Processing (NLP) and large language models such as GPT-3 have numerous applications in advanced analytics –from interpreting large datasets and tables for easier consumption by humans to text classification, text summarization, classification of named entities (such as people, places, and organizations), sentiment analysis, and more.
By effectively integrating these tools and techniques, organizations can create a seamless workflow that leverages the strengths of AI, big data, and advanced analytics to drive value and achieve their business objectives. These technologies enable organizations to uncover new insights, make better decisions, and drive innovation and growth.
Conclusion
As we’ve seen with the above examples, incumbents have been harnessing the combination of AI, Big Data, and Advanced analytics to make decisions and streamline their operations.
However, to fully realize these benefits, organizations need to have a well-thought-out plan encompassing data management, integrating various data sources, application of advanced analytics, and the use of AI to automate decision-making processes. With a comprehensive approach, organizations can unlock the full potential of these powerful technologies and maintain a strong position in today’s data-driven market.