Contents
What is Big Data Analytics?
Companies are generating more data than ever, hour by hour. According to estimates by the International Data Corporation, the volume of digital data will rise to 284 zettabytes by 2027. To put this in perspective, one zettabyte equals a billion terabytes. When such vast amounts of data are collected, reviewed, and analyzed, we refer to this as Big Data Analytics. Through this process, companies can identify market trends, insights, and patterns in their data, enabling them to make sound business decisions. According to the European Commission, companies in Germany alone that use data-driven business models to provide and produce their products, services, and technologies generated €26.6 billion in revenue.
In this article, you’ll learn how your company can use Big Data Analytics to efficiently increase Return on Investment (ROI). Starting with a brief explanation of the relevance of Big Data Analytics, we’ll introduce you to the basics of Big Data. In addition to understanding how Big Data Analytics works, you’ll discover which tools you can use for Big Data Analytics, as well as the potential benefits and challenges it may bring.
Why is Big Data Analytics important?
According to author and management analyst Geoffrey Moore, companies that don’t analyze Big Data are “blind and deaf, wandering the internet like deer on a highway.” It is only through Big Data Analytics that companies can identify opportunities for improvement and optimization within their massive datasets. This leads not only to cost reduction but also to smarter operations and the development of improved, customer-specific products and services, which in turn results in higher customer satisfaction and increased revenue.
Big Data will continue to play an essential role in the business world. In their report “The Data-Driven Enterprise of 2025,” McKinsey & Company mentions that data will become an increasingly integral and transformative factor for daily business operations. Furthermore, McKinsey & Company characterizes data-driven companies with the following features:
- Data will be integrated into almost every decision.
- Data will be processed in real-time.
- Flexible data storage will provide “ready-to-use” data.
- Data inventories will be treated as products.
- Chief Data Officers (CDOs) will generate added value.
- Data-sharing platforms will become the norm.
- Data management will be among the top priorities.
What is Big Data?
Before we explore the workings of Big Data Analytics, this section provides an introduction to Big Data.
Big Data refers to vast amounts of data generated by sources such as computers, smartphones, and electronic sensors. These datasets are so large that traditional databases cannot capture, manage, or process them. However, it is not only the volume of data that classifies it as Big Data; its complexity and diversity also contribute to its classification as “big.”
Types and sources of Big Data
In general, datasets can be divided into three types—structured, unstructured, and semi-structured.
- Structured Data: This type of data has a clearly defined structure and is typically organized in tables with relationships between rows and columns. For instance, SQL databases or Excel files contain data in structured form. Since this type of data is easy to organize and manage, it alone does not meet the definition criteria for Big Data.
- Unstructured Data: Unstructured data has no predefined form or structure and is therefore classified as qualitative data. In today’s business world, it is produced in large quantities—as videos, audio, text, open customer comments, and much more. Traditional relational databases would be unsuitable for storing and managing this volume of data. Instead, it is stored in data lakes, data warehouses, and NoSQL databases.
- Semi-Structured Data: This is a mix of structured and unstructured data. For example, emails or devices using timestamps or geotagging contain both structured and unstructured information. Since these data lack a fully functional structure but still have some structural characteristics, their management and processing are much more complex. However, they can yield significantly more detailed insights.
The number of data sources producing Big Data is continuously and rapidly increasing. To maintain an overview, the origin of the data volume can be divided into three main types.
- Social Data: Unsurprisingly, social media platforms with posts, images, and videos generate a high volume of data, especially given that an estimated 2.72 billion people were active on social media by 2023.
- Machine Data: In companies, machines and IoT devices are typically equipped with sensors that capture and process data from devices, systems, etc. Additionally, there are weather and traffic sensors that send and receive data hourly. According to an estimate by the International Data Corporation, there will be over 40 billion IoT devices by 2025.
- Transaction Data: Transaction data volume is growing faster worldwide than any other type and, due to its semi-structured nature, is considerably more complex to manage and process. For example, one major international retailer processes over a million customer transactions every hour.
The 5 V’s of Big Data
The renowned data expert Doug Laney defines Big Data through the 5 V’s—Volume, Velocity, Variety, Veracity, and Value. According to Gartner, Big Data consists of informational resources with high volume, high speed, and significant variety. Only when all five characteristics are met can data be classified as Big Data.
- Volume: Volume represents the amount of structured and unstructured data collected, indicating the scale of the data. To store, manage, and retrieve the many terabytes of data, specialized databases focused on capturing Big Data are needed.
- Velocity: This refers to the speed at which data is generated, received, and processed. With the right database technology, companies can access and analyze data in real time.
- Variety: Variety refers to the different types of data, as detailed in the previous section.
- Veracity: Veracity pertains to the accuracy, quality, reliability, and uncertainty of the data. While structured data may suffer mainly from syntax and typographical errors that affect accuracy, the challenges with unstructured and semi-structured data are significantly more complex. Factors such as data origin and social noise can also impact data quality.
- Value: Value defines how useful the collected data ultimately is. Big Data gains higher value only after analysis, enabling companies to remain competitive and increase customer satisfaction through improved service.
Use Cases of Big Data Analytics
Almost every company and industry can benefit from conducting Big Data Analytics and the insights gained from it.
In the transportation and logistics sector, Big Data analysis can optimize route planning and load consolidation, ensuring increased shipping speed. Energy and utility companies also benefit from Big Data Analytics. By analyzing data generated by smart meters, insights can be used to improve energy efficiency, forecasting, and pricing. According to the Journal of Big Data, Big Data analysis also plays a significant role in the financial sector, particularly in trading and investment, tax reform, fraud detection and investigation, risk analysis, and automation. Here, enhanced customer satisfaction, improved experience, and data security can be achieved, among other benefits.
How does Big Data Analytics work?
Following the introduction to Big Data, this section explains how Big Data Analytics functions.
A well-known process for generating new knowledge from Big Data is the Knowledge Discovery in Databases (KDD) process. According to Fayyad, Piatetsky-Shapiro, and Smyth, the Knowledge Discovery in Databases process is a “non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” Through the KDD process, hidden relationships, patterns, and trends can be extracted from data, enabling companies to use these insights to improve customer experience, enhance decision-making, and optimize operations and strategic planning.
The process consists of seven steps that can be repeated in several iterations with feedback and adjustments. These steps ensure security and minimize the risk of generating meaningless or illusory patterns. The following section provides a closer look at each step.
Targeting
In the first preparatory step, information about the problem area is gathered. Additionally, a detailed understanding of the application domain is developed, and relevant prior knowledge is acquired to define the goal of the entire process from the customer’s perspective.
Data Selection
Since not all data is relevant for analysis, a subset of data is selected in this step, focusing on the defined goal. This ensures the quality of the dataset.
Data Processing
In this step, the selected data is extracted from the data repositories and consolidated into a single dataset. To avoid skewing results and improve the data’s effectiveness and reliability, the data is checked and cleaned for inconsistencies, errors, redundancy, and outliers. Another cleansing step involves addressing missing values in the data.
Data Transformation
Next, the data is summarized, aggregated, and transformed into the desired format, making it easily accessible, understandable, and processable by algorithms. Often, a data reduction step is also performed to filter out data with low informational value.
Data Analysis/Data Mining (combined)
Now we arrive at the core step of the process—Data Mining. In this step, based on the goal defined in the first step, an appropriate method and suitable algorithm are applied to extract patterns, trends, and relationships from the data. Data Mining serves as an umbrella term for many different methods and can be divided into various analytical techniques that use different Data Mining techniques and algorithms. These are briefly outlined below.
Predictive Analytics
This type of analysis examines a company’s current and historical data to predict future events and identify potential opportunities and risks. Predictive Analytics utilizes Artificial Intelligence, such as machine learning and deep learning, to forecast customer behavior, product demand, and market trends. This enables organizations to make and plan strategic decisions proactively. For example, companies in the manufacturing sector can apply machine learning models, trained on historical data, to predict if or when a machine might malfunction or fail.
Deep Learning vs. Machine Learning
As discussed in Section 1.2.2, Big Data is characterized by enormous, heterogeneous datasets that are not only complex but also generated at a rapid pace. For efficient and meaningful processing and analysis, Artificial Intelligence is required. According to analyst Brandon Purcell from Forrester Research, “Data is the lifeblood of AI. A system must learn from data to fulfill its function.” This means Big Data and AI have a reciprocal relationship, and insights from Big Data can only be obtained through AI.
Subfields of Artificial Intelligence include Machine Learning and Deep Learning, both of which use algorithms for data analysis. Machine Learning uses small, structured data sets that are defined by a human expert with specific features before analysis. Based on these features, the algorithm can independently recognize patterns in the data. Deep Learning can be considered an extension of machine learning. The algorithms used for pattern recognition in data are based on artificial neural networks. In Deep Learning, the features are determined by the algorithm itself, eliminating the need for manual data preparation. Thus, the Deep Learning process is best suited for large, unstructured datasets and for complex tasks like digital assistants, self-driving cars, or credit card fraud detection.
Diagnostic Analytics
By applying Diagnostic Analytics, companies can understand and trace the root causes of their problems. Big Data technologies and tools allow users to extract and restore historical data, enabling them to analyze a current problem and prevent its recurrence in the future. Common techniques used here include Data Mining techniques, drill-down methods, and data exploration. One possible use case could be a clothing company’s revenue analysis. With Diagnostic Analytics, it might be discovered that sales declined because the payment page was not functioning properly for several weeks.
Prescriptive Analytics
Prescriptive Analytics builds on Predictive Analytics by combining the results of Predictive Analytics with optimization techniques, simulations, or rule sets to recommend actions for optimizing future outcomes. Various possible actions and their potential effects on the predicted event or outcome are considered. For example, to maximize the profit of an airline, Prescriptive Analytics could create an algorithm that automatically adjusts flight prices based on factors such as customer demand, weather, destination, and oil prices.
Descriptive Analytics
This type of analysis focuses on summarizing historical data. Through aggregation, Data Mining, and visualization techniques, trends, patterns, and KPIs are identified. This allows companies to better understand the current or past state of their systems or processes and make informed decisions based on historical information.
Data Interpretation
After data analysis, the discovered patterns are evaluated and interpreted in light of the goal defined in the first step, using various visualization tools. Some of these visualization tools are also mentioned in Chapter 3. The analysis results are presented in the form of charts, graphs, dashboards, etc. Visualization helps to communicate complex insights clearly and accessibly within the organization.
Reporting Results
In the final step of the KDD process, actions such as system changes are taken based on the knowledge gained from the process. This knowledge becomes actionable, allowing changes in the system to be measured. Additionally, organizations can make data-driven decisions, which, in turn, affect business strategies, processes, and operations.
It is important to note that Big Data Analytics is not a linear but an iterative process. As new data is continuously generated, it must be analyzed regularly, and the business strategy should be refined based on these results.
Which tools are available for conducting Big Data Analytics?
The question now arises as to which tools a company can use to conduct Big Data Analytics. With a wide range of such tools already available on the market, we will focus on the most important ones here.
SAP Analytics Cloud
When it comes to Predictive Analytics, SAP Analytics Cloud (SAC) is an excellent choice. This cloud-based Software-as-a-Service platform, with its capabilities in artificial intelligence and machine learning, allows for trend prediction and efficient planning processes. SAC also offers a live data connection, enabling access to data without the need to replicate it in the cloud. This means data remains securely stored on the HANA database, while only SAC functions are in the cloud. SAC can access data from various SAP systems, such as S/4HANA, SAP Datasphere, or SAP BW/4HANA, as well as from non-SAP databases.
The main features of SAC include data creation and analysis (Business Intelligence), planning, predictive modeling, and Augmented Analytics. For Business Intelligence, SAC provides self-service dashboards that require no programming knowledge and allow for visual data presentation. With Augmented Analytics, real-time analysis processes can be automated, which would otherwise require Data Scientists. In summary, SAC combines analysis, planning, and forecasting within a single user interface.
SAP HANA
SAP HANA is a powerful in-memory database where the processing and analysis of Big Data occur in the main memory (RAM). Additionally, the use of multiple processors and computing cores enables parallel data processing, significantly boosting performance. Thanks to optional column-oriented data storage, the database volume is substantially reduced. This is achieved by storing data content in a column-oriented rather than row-oriented structure, which minimizes storage needs and enhances processing performance. Furthermore, this in-memory database offers functionalities for application development, advanced analytics solutions, and flexible data virtualization. As a result, key SAP products like SAP S/4HANA, SAP BW/4HANA, and SAP Datasphere now run on SAP HANA.
SAP BW/4HANA
SAP BW/4HANA is a flexible and advanced data warehouse solution based on the powerful SAP HANA in-memory database. Companies choosing this data warehouse platform can benefit from accelerated data analysis, optimized reporting capabilities, and the ability to respond to data changes in real-time. It also supports integration, such as through Operational Data Provisioning (ODP), and the processing and analysis of Big Data from various sources. These sources can include not only SAP systems but also newer sources like social media or IoT devices with unstructured data.
SAP BW/4HANA is distinguished by its flexible and modern data modeling architecture. It uses powerful data objects, such as the Composite Provider, for Big Data modeling and analysis. For the integration, processing, and analysis of unstructured data, SAP Data Hub Intelligence can also be integrated into the SAP BW/4HANA system. SAP Data Hub Intelligence further enables complex data processing tasks, such as data quality checks, data preparation, and data cleansing.
SAP Datasphere
SAP Datasphere is another tool for applying Big Data Analytics. This Data Warehouse-as-a-Service solution, based on SAP HANA Cloud, is the evolution of SAP Data Warehouse Cloud. With SAP Datasphere, it’s possible to extract and process structured, semi-structured, and unstructured data from various sources, such as on-premise systems, cloud applications, and IoT sensors, in real time without physically storing it in SAP Datasphere. To ensure data quality, the platform offers data cleansing and validation capabilities. Additionally, the platform features an Open Data Architecture, supporting integration with other Big Data technologies like NoSQL databases, Hadoop, Spark, Tableau, and Power BI, as well as SAP systems like SAP Analytics Cloud, SAP BW/4HANA, and S/4HANA. Data-driven decisions can also be automated with machine learning models, and advanced analytics functions provide enhanced data insights.
NoSQL-Datenbanken
NoSQL databases, such as Hadoop’s HBase, are non-relational database management systems that can be used for processing semi-structured and unstructured data due to their flexible schema. In addition to decentralized storage of large datasets, they also allow for real-time analytical queries.
Apache Hadoop
Apache Hadoop is an open-source framework based on Java, designed to store and manage large datasets across a network of connected computers, known as clusters, rather than a single computer. This enhances performance as data analysis is processed in parallel. The framework is freely accessible and capable of handling large volumes of various data types, including structured and unstructured data, making it a valuable foundation for any Big Data operation.
MapReduce
MapReduce is a programming model developed by Google and serves as the central engine of Apache Hadoop. This algorithm enables the coordinated processing of large datasets by splitting computation-intensive tasks into smaller sub-tasks that are distributed across multiple computers. This parallel processing increases computational speed.
YARN
YARN stands for “Yet Another Resource Negotiator” and is one of the main modules of Hadoop, complementing the MapReduce algorithm. As a resource management platform, it is responsible for scheduling jobs and tasks executed across different cluster nodes.
Spark
Spark is an open-source processing system that does not have its own storage system. Instead, it focuses on real-time workloads such as graph processing, machine learning, and interactive queries.
Tableau
Tableau, an end-to-end data analysis platform, enables Big Data preparation and analysis. The platform is also characterized by visual self-service analytics, allowing employees to ask questions about protected Big Data and share real-time insights across the company.
What are the advantages of Big Data Analytics?
When applied correctly, Big Data Analytics offers significant advantages for companies:
- Decision-Making: Big Data Analytics enables the analysis of both structured and unstructured data, allowing companies to gain deeper insights into customer behavior, market trends, and other critical factors. With this newfound knowledge, companies can make more informed and strategic decisions.
- Customer Experience: Data-driven algorithms allow for more targeted marketing strategies, which ultimately lead to increased customer satisfaction and improved customer experience.
- Risk Management: The AI-powered analysis of Big Data allows for the quick detection of anomalies and unusual patterns, helping prevent risks like fraud or security breaches that could otherwise have far-reaching and immediate consequences.
- Product Development: The development and marketing of new products and services can be significantly simplified when data on customer needs and preferences is collected and analyzed.
- Cost Reduction: Extensive Big Data analysis enables companies to optimize processes, identify inefficiencies, and reduce costs to levels that would be otherwise unattainable with smaller datasets. For example, in manufacturing, Big Data Analytics can optimize production by analyzing data from sensors on the factory floor, reducing downtime and maintenance costs.
What are the challenges of Big Data Analytics?
While Big Data Analytics offers many advantages, it also presents some challenges. To fully benefit from Big Data Analytics, companies should consider the following points:
- Data Accessibility: As the volume of data continues to grow, collecting and processing data becomes increasingly challenging. A unified and cohesive data infrastructure can ensure easier data retrieval and integration for practical analysis.
- Ensuring Data Quality: With Big Data, organizations spend more time than ever searching for errors, duplicates, conflicts, and inconsistencies. Incorrect analyses and decisions can be prevented through data cleansing and validation as well as proper data management.
- Ensuring Data Security: As more sensitive data is collected and analyzed, concerns about data protection and security also increase. Before using Big Data Analytics, companies should ensure data protection against breaches, unauthorized access, and cyber threats to safeguard customer privacy and business integrity.
- Finding the Right Tools and Platforms: With the large number of Big Data Analytics tools available on the market and new tools being continuously developed, it becomes increasingly difficult for a company to choose a tool that meets its specific needs. Often, the right solution is also a flexible one that can adapt to future infrastructure changes.
Conclusion
As mentioned at the beginning of this blog, companies should be able to derive value-adding insights from Big Data Analytics in the coming years. Only in this way can they remain competitive and stay up-to-date with market trends, especially considering the rapid development of AI. With the Big Data Analytics tools presented here, particularly those from SAP, companies gain valuable insights into their data, which can be leveraged for data-driven business decisions, efficiently boosting their return on investment.
However, it should be noted that a single Big Data Analytics tool is often insufficient to meet a company’s specific requirements; rather, a combination of various Big Data Analytics tools is essential for extracting insights from Big Data.If you’d like to know which Big Data Analytics tools are best suited to your company’s needs to maximize the value from your data, feel free to contact us via our website. We offer expert, success-oriented support to assist you.
If you’d like to know which Big Data Analytics tools are best suited to your company’s needs to maximize the value from your data, feel free to contact us via our website. We offer expert, success-oriented support to assist you.
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