{"id":83376,"date":"2024-10-29T13:33:36","date_gmt":"2024-10-29T12:33:36","guid":{"rendered":"https:\/\/www.pikon.com\/?p=83376"},"modified":"2026-01-07T11:56:50","modified_gmt":"2026-01-07T10:56:50","slug":"successful-start-in-big-data-analytics-for-your-company","status":"publish","type":"post","link":"https:\/\/www.pikon.com\/en\/blog\/successful-start-in-big-data-analytics-for-your-company\/","title":{"rendered":"Successful Start in Big Data Analytics for your Company"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"83376\" class=\"elementor elementor-83376 elementor-83316\" 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data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6c141a0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6c141a0\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0e2f2db\" data-id=\"0e2f2db\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f84d3ad elementor-toc--minimized-on-tablet elementor-widget elementor-widget-global elementor-global-75893 elementor-global-75856 elementor-widget-table-of-contents\" data-id=\"f84d3ad\" data-element_type=\"widget\" data-e-type=\"widget\" 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class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__header\">\n\t\t\t\t\t\t<h4 class=\"elementor-toc__header-title\">\n\t\t\t\tContent\t\t\t<\/h4>\n\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--expand\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__f84d3ad\" aria-expanded=\"true\" aria-label=\"Open table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-down\"><\/i><\/div>\n\t\t\t\t<div class=\"elementor-toc__toggle-button elementor-toc__toggle-button--collapse\" role=\"button\" tabindex=\"0\" aria-controls=\"elementor-toc__f84d3ad\" aria-expanded=\"true\" aria-label=\"Close table of contents\"><i aria-hidden=\"true\" class=\"fas fa-chevron-up\"><\/i><\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<div id=\"elementor-toc__f84d3ad\" class=\"elementor-toc__body\">\n\t\t\t<div class=\"elementor-toc__spinner-container\">\n\t\t\t\t<i class=\"elementor-toc__spinner eicon-animation-spin eicon-loading\" aria-hidden=\"true\"><\/i>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a284990 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a284990\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7980c81\" data-id=\"7980c81\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cdcccd7 elementor-widget elementor-widget-heading\" data-id=\"cdcccd7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What is Big Data Analytics?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e4d4837 elementor-widget elementor-widget-text-editor\" data-id=\"e4d4837\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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 <strong>Big Data Analytics<\/strong>. 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 \u20ac26.6 billion in revenue.<\/p><p>In this article, you&#8217;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\u2019ll introduce you to the basics of Big Data. In addition to understanding how Big Data Analytics works, you&#8217;ll discover which tools you can use for Big Data Analytics, as well as the potential benefits and challenges it may bring.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fada45 elementor-widget elementor-widget-heading\" data-id=\"3fada45\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Why is Big Data Analytics important?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8bc3470 elementor-widget elementor-widget-text-editor\" data-id=\"8bc3470\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>According to author and management analyst Geoffrey Moore, companies that don\u2019t analyze Big Data are \u201cblind and deaf, wandering the internet like deer on a highway.\u201d 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.<\/p><p>Big Data will continue to play an essential role in the business world. In their report <em>&#8220;The Data-Driven Enterprise of 2025,&#8221;<\/em> McKinsey &amp; Company mentions that data will become an increasingly integral and transformative factor for daily business operations. Furthermore, McKinsey &amp; Company characterizes data-driven companies with the following features:<\/p><ul><li>Data will be integrated into almost every decision.<\/li><li>Data will be processed in real-time.<\/li><li>Flexible data storage will provide \u201cready-to-use\u201d data.<\/li><li>Data inventories will be treated as products.<\/li><li>Chief Data Officers (CDOs) will generate added value.<\/li><li>Data-sharing platforms will become the norm.<\/li><li>Data management will be among the top priorities.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8193887 elementor-widget elementor-widget-heading\" data-id=\"8193887\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">What is Big Data?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ff9f75c elementor-widget elementor-widget-text-editor\" data-id=\"ff9f75c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Before we explore the workings of Big Data Analytics, this section provides an introduction to Big Data.<\/p><p>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 &#8220;big.&#8221;<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9c1e0ce elementor-widget elementor-widget-heading\" data-id=\"9c1e0ce\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Types and sources of Big Data<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba503b9 elementor-widget elementor-widget-text-editor\" data-id=\"ba503b9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>In general, datasets can be divided into three types\u2014structured, unstructured, and semi-structured.<\/p><ul><li><strong>Structured Data<\/strong>: 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.<\/li><li><strong>Unstructured Data<\/strong>: Unstructured data has no predefined form or structure and is therefore classified as qualitative data. In today&#8217;s business world, it is produced in large quantities\u2014as 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.<\/li><li><strong>Semi-Structured Data<\/strong>: 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.<\/li><\/ul><p>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.<\/p><ul><li><strong>Social Data<\/strong>: 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.<\/li><li><strong>Machine Data<\/strong>: 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.<\/li><li><strong>Transaction Data<\/strong>: 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.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1f6e4ed elementor-widget elementor-widget-heading\" data-id=\"1f6e4ed\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">The 5 V\u2019s of Big Data<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3430342 elementor-widget elementor-widget-text-editor\" data-id=\"3430342\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The renowned data expert Doug Laney defines Big Data through the 5 V\u2019s\u2014Volume, 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.<\/p><ul><li><strong>Volume<\/strong>: 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.<\/li><li><strong>Velocity<\/strong>: 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.\u00a0 \u00a0 \u00a0<\/li><li><strong>Variety<\/strong>: Variety refers to the different types of data, as detailed in the previous section.<\/li><li><strong>Veracity<\/strong>: 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.<\/li><li><strong>Value<\/strong>: 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.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b7c8251 elementor-widget elementor-widget-heading\" data-id=\"b7c8251\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Use Cases of Big Data Analytics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d973e5e elementor-widget elementor-widget-text-editor\" data-id=\"d973e5e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Almost every company and industry can benefit from conducting Big Data Analytics and the insights gained from it.<\/p><p>In <strong>the transportation and logistics sector<\/strong>, 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 <em>Journal of Big Data<\/em>, 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-354387a elementor-widget elementor-widget-heading\" data-id=\"354387a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">How does Big Data Analytics work?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a3c8fb2 elementor-widget elementor-widget-text-editor\" data-id=\"a3c8fb2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Following the introduction to Big Data, this section explains how Big Data Analytics functions.<\/p><p>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 &#8220;non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.&#8221; 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.<\/p><p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88a9d6b elementor-widget elementor-widget-image\" data-id=\"88a9d6b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<a href=\"https:\/\/www.pikon.com\/wp-content\/uploads\/2024\/10\/GRAFIK-EN-1.png\" data-elementor-open-lightbox=\"yes\" data-elementor-lightbox-title=\"GRAFIK EN (1)\" data-e-action-hash=\"#elementor-action%3Aaction%3Dlightbox%26settings%3DeyJpZCI6ODM0MDksInVybCI6Imh0dHBzOlwvXC93d3cucGlrb24uY29tXC93cC1jb250ZW50XC91cGxvYWRzXC8yMDI0XC8xMFwvR1JBRklLLUVOLTEucG5nIn0%3D\">\n\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"750\" height=\"468\" src=\"https:\/\/www.pikon.com\/wp-content\/uploads\/2024\/10\/GRAFIK-EN-1.png\" class=\"attachment-medium_large size-medium_large wp-image-83409\" alt=\"\" srcset=\"https:\/\/www.pikon.com\/wp-content\/uploads\/2024\/10\/GRAFIK-EN-1.png 750w, https:\/\/www.pikon.com\/wp-content\/uploads\/2024\/10\/GRAFIK-EN-1-300x187.png 300w, https:\/\/www.pikon.com\/wp-content\/uploads\/2024\/10\/GRAFIK-EN-1-600x374.png 600w\" sizes=\"(max-width: 750px) 100vw, 750px\" \/>\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a001cca elementor-widget elementor-widget-heading\" data-id=\"a001cca\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Targeting<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3361969 elementor-widget elementor-widget-text-editor\" data-id=\"3361969\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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&#8217;s perspective.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-88cc8cd elementor-widget elementor-widget-heading\" data-id=\"88cc8cd\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data Selection<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e68a94 elementor-widget elementor-widget-text-editor\" data-id=\"9e68a94\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-50cca05 elementor-widget elementor-widget-heading\" data-id=\"50cca05\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data Processing<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c425c1 elementor-widget elementor-widget-text-editor\" data-id=\"4c425c1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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&#8217;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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-34da112 elementor-widget elementor-widget-heading\" data-id=\"34da112\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data Transformation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-12da5cc elementor-widget elementor-widget-text-editor\" data-id=\"12da5cc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-41890b1 elementor-widget elementor-widget-heading\" data-id=\"41890b1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data Analysis\/Data Mining (combined)<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f3e110 elementor-widget elementor-widget-text-editor\" data-id=\"6f3e110\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Now we arrive at the core step of the process\u2014Data 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7dda20b elementor-widget elementor-widget-heading\" data-id=\"7dda20b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Predictive Analytics<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e2a4059 elementor-widget elementor-widget-text-editor\" data-id=\"e2a4059\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>This type of analysis examines a company\u2019s 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aa17929 elementor-widget elementor-widget-text-editor\" data-id=\"aa17929\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p><em><strong>Deep Learning vs. Machine Learning<\/strong><\/em><\/p><p>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, &#8220;Data is the lifeblood of AI. A system must learn from data to fulfill its function.&#8221; This means Big Data and AI have a reciprocal relationship, and insights from Big Data can only be obtained through AI.<\/p><p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d55cab elementor-widget elementor-widget-heading\" data-id=\"4d55cab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Diagnostic Analytics<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8edc09d elementor-widget elementor-widget-text-editor\" data-id=\"8edc09d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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\u2019s revenue analysis. With Diagnostic Analytics, it might be discovered that sales declined because the payment page was not functioning properly for several weeks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bda2a9f elementor-widget elementor-widget-heading\" data-id=\"bda2a9f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Prescriptive Analytics<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d12ae6b elementor-widget elementor-widget-text-editor\" data-id=\"d12ae6b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a75707 elementor-widget elementor-widget-heading\" data-id=\"0a75707\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Descriptive Analytics <\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-57bc3b3 elementor-widget elementor-widget-text-editor\" data-id=\"57bc3b3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7946515 elementor-widget elementor-widget-heading\" data-id=\"7946515\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Data Interpretation<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d2814ad elementor-widget elementor-widget-text-editor\" data-id=\"d2814ad\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36f4044 elementor-widget elementor-widget-heading\" data-id=\"36f4044\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Reporting Results<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-43926c7 elementor-widget elementor-widget-text-editor\" data-id=\"43926c7\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p><p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fcae1be elementor-widget elementor-widget-heading\" data-id=\"fcae1be\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Which tools are available for conducting Big Data Analytics?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ebf7308 elementor-widget elementor-widget-text-editor\" data-id=\"ebf7308\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a17eeab elementor-widget elementor-widget-heading\" data-id=\"a17eeab\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">SAP Analytics Cloud<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f474122 elementor-widget elementor-widget-text-editor\" data-id=\"f474122\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p><p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9c25c68 elementor-widget elementor-widget-heading\" data-id=\"9c25c68\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">SAP HANA<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e542e6b elementor-widget elementor-widget-text-editor\" data-id=\"e542e6b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-424c399 elementor-widget elementor-widget-heading\" data-id=\"424c399\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">SAP BW\/4HANA<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6b2bfec elementor-widget elementor-widget-text-editor\" data-id=\"6b2bfec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p><p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d3696de elementor-widget elementor-widget-heading\" data-id=\"d3696de\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">SAP Datasphere<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-44f95f0 elementor-widget elementor-widget-text-editor\" data-id=\"44f95f0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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\u2019s 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05cb16b elementor-widget elementor-widget-heading\" data-id=\"05cb16b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">NoSQL-Datenbanken<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-af50ad6 elementor-widget elementor-widget-text-editor\" data-id=\"af50ad6\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>NoSQL databases, such as Hadoop\u2019s 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f5fdd74 elementor-widget elementor-widget-heading\" data-id=\"f5fdd74\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Apache Hadoop<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-46ad62d elementor-widget elementor-widget-text-editor\" data-id=\"46ad62d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cd9a7f9 elementor-widget elementor-widget-heading\" data-id=\"cd9a7f9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">MapReduce<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-59f373c elementor-widget elementor-widget-text-editor\" data-id=\"59f373c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-870185e elementor-widget elementor-widget-heading\" data-id=\"870185e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">YARN<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17e84a8 elementor-widget elementor-widget-text-editor\" data-id=\"17e84a8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>YARN stands for \u201cYet Another Resource Negotiator\u201d 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-28d8f47 elementor-widget elementor-widget-heading\" data-id=\"28d8f47\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Spark<\/h4>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e7566c elementor-widget elementor-widget-text-editor\" data-id=\"4e7566c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-17e964f elementor-widget elementor-widget-heading\" data-id=\"17e964f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Tableau<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-58a4d1b elementor-widget elementor-widget-text-editor\" data-id=\"58a4d1b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5d54128 elementor-widget elementor-widget-heading\" data-id=\"5d54128\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What are the advantages of Big Data Analytics?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9e0ff4e elementor-widget elementor-widget-text-editor\" data-id=\"9e0ff4e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>When applied correctly, Big Data Analytics offers significant advantages for companies:<\/p><ul><li><strong>Decision-Making<\/strong>: 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.<\/li><li><strong>Customer Experience<\/strong>: Data-driven algorithms allow for more targeted marketing strategies, which ultimately lead to increased customer satisfaction and improved customer experience.<\/li><li><strong>Risk Management<\/strong>: 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.<\/li><li><strong>Product Development<\/strong>: The development and marketing of new products and services can be significantly simplified when data on customer needs and preferences is collected and analyzed.<\/li><li><strong>Cost Reduction<\/strong>: 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.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b9261f5 elementor-widget elementor-widget-heading\" data-id=\"b9261f5\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">What are the challenges of Big Data Analytics?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-849b0e2 elementor-widget elementor-widget-text-editor\" data-id=\"849b0e2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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:<\/p><ul><li><strong>Data Accessibility<\/strong>: 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.<\/li><li><strong>Ensuring Data Quality<\/strong>: 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.<\/li><li><strong>Ensuring Data Security<\/strong>: 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.<\/li><li><strong>Finding the Right Tools and Platforms<\/strong>: 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.<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd44cf9 elementor-widget elementor-widget-heading\" data-id=\"fd44cf9\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Conclusion<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2c3c44 elementor-widget elementor-widget-text-editor\" data-id=\"c2c3c44\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>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.<\/p><p>However, it should be noted that a single Big Data Analytics tool is often insufficient to meet a company\u2019s specific requirements; rather, a combination of various Big Data Analytics tools is essential for extracting insights from Big Data.If you\u2019d like to know which Big Data Analytics tools are best suited to your company\u2019s 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.<\/p><p>If you\u2019d like to know which Big Data Analytics tools are best suited to your company\u2019s 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.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-96b0f85 elementor-section-height-min-height elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"96b0f85\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b9e786e\" data-id=\"b9e786e\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-9402497 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9402497\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-b9c5852\" data-id=\"b9c5852\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-48ba32c no-table-of-content elementor-widget elementor-widget-heading\" data-id=\"48ba32c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Contact us!<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d2a2ef elementor-widget elementor-widget-text-editor\" data-id=\"8d2a2ef\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Do you have further questions about big data analytics? Arrange a web meeting with our experts or ask us your question in the comments section.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-951cc4a elementor-section-height-min-height elementor-section-boxed elementor-section-height-default\" data-id=\"951cc4a\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-inner-column elementor-element elementor-element-76c03cd\" data-id=\"76c03cd\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-68a88d1 elementor-widget__width-initial elementor-widget elementor-widget-pikon-author-box-widget\" data-id=\"68a88d1\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"pikon-author-box-widget.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-widget-image-box elementor-position-left elementor-vertical-align-top\"><div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><img decoding=\"async\" src=\"https:\/\/www.pikon.com\/wp-content\/uploads\/2023\/03\/Martina_klein-1-150x150.jpg\" alt=\"Martina Ksinsik\" title=\"Martina Ksinsik\" loading=\"lazy\"\/><\/a><\/figure><div class=\"elementor-image-box-content\"><div class=\"elementor-image-box-title\">Martina Ksinsik<\/div><div class=\"elementor-image-box-position\">Customer Success Manager<\/div><div class=\"elementor-image-box-phone\"><a href=\"tel:+4968137962113\"><i class=\"fas fa-phone\"><\/i> +49 (0) 681 379 62 - 113<\/a><\/div><div class=\"elementor-image-box-mail\"><a href=\"mailto:martina.ksinsik@pikon.com\"><i class=\"fa-fw far fa-envelope\"><\/i> martina.ksinsik@pikon.com<\/a><\/div><div class=\"elementor-image-box-description\"><p><div class=\"activecampaign-inline-container \"><div class=\"activecampaign-inline-form\">[borlabs-cookie id=\"active-campaign-email-marketing-form\" type=\"content-blocker\"]<div class='_form_230'><\/div><script type='text\/javascript' src='https:\/\/pikon.activehosted.com\/f\/embed.php?static=0&id=230&6A806B7B06E71&nostyles=0&preview=0'><\/script>[\/borlabs-cookie]<\/div><div class=\"activecampaign-inline-button\"><a class=\"cta-button\" href=\"#\">Schedule a meeting<\/a><\/div><\/div><\/p><\/div><\/div><\/div><\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Find out how your company can successfully start and benefit from big data analytics.<\/p>\n","protected":false},"author":81,"featured_media":83419,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[22],"tags":[451,755,454],"class_list":["post-83376","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-all-posts","tag-analytics","tag-planning","tag-sap-analytics-cloud","no-featured-image-padding"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Successful Start in Big Data Analytics for your Company - PIKON SAP Consulting International<\/title>\n<meta name=\"description\" content=\"Find out how your company can successfully start and benefit from big data analytics. 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