{"id":49132,"date":"2015-06-24T00:00:00","date_gmt":"2015-06-24T00:00:00","guid":{"rendered":"https:\/\/www.techopedia.com\/tackling-big-data-analytics-pain-points\/"},"modified":"2015-07-30T12:27:14","modified_gmt":"2015-07-30T12:27:14","slug":"tackling-big-data-analytics-pain-points","status":"publish","type":"post","link":"https:\/\/www.techopedia.com\/2\/31144\/trends\/big-data\/tackling-big-data-analytics-pain-points","title":{"rendered":"Tackling Big Data Analytics Pain Points"},"content":{"rendered":"<p><a href=\"\/\/www.techopedia.com\/definition\/27745\/big-data\" target=\"_blank\">Big data<\/a> comes in a variety of forms and structures. In recent years, <a href=\"\/\/www.techopedia.com\/definition\/28659\/big-data-analytics\" target=\"_blank\">big data analytics<\/a> has had a significant impact on business decisions, and while it can be of immense vale, it does come with some pain points.\n<\/p>\n<p>In this article, I will discuss those analytics pain points, but first, let&#8217;s focus on some characteristics of big data.\n<\/p>\n<h2><span id=\"big_data_characteristics\">Big Data Characteristics<\/span><\/h2>\n<p>Big data can be defined by several characteristics:\n<\/p>\n<ul>\n<li>Volume \u2014 The term big data itself refers to size, and volume refers to quantity of data. The size of data determines the value of the data to be considered as big data or not.\n\t<\/li>\n<li>Velocity \u2014 The speed at which data is generated is known as velocity.\n\t<\/li>\n<li>Veracity \u2014 This refers to correctness of data. The accuracy of analysis depends on the veracity of the source data.\n\t<\/li>\n<li>Complexity \u2014 Massive amounts of data come from multiple sources, so data management becomes a difficult process.\n\t<\/li>\n<li>Variety \u2013 An important thing to understand is the category to which big data belongs. This further helps in analyzing the data.\n\t<\/li>\n<li>Variability \u2014 This factor refers to the inconsistency which the data can show. This further hampers the process of managing the data effectively.\n\t<\/li>\n<\/ul>\n<p>Now let&#8217;s discuss some of the pain points.\n<\/p>\n<h2><span id=\"lack_of_proper_path\">Lack of Proper Path<\/span><\/h2>\n<p>If data comes from different sources, then there should be a proper and reliable path for handling massive data.\n<\/p>\n<p>For better solutions, the path should offer insight into customer behavior. This is the foremost motivation for creating a flexible infrastructure for integrating front-end systems with back-end systems. As a result, it helps in keeping your system running.\n<\/p>\n<h2><span id=\"data_classification_issues\">Data Classification Issues<\/span><\/h2>\n<p>The analytics process should start when the <a href=\"\/\/www.techopedia.com\/definition\/1184\/data-warehouse-dw\" target=\"_blank\">data warehouse<\/a> is loaded with massive amounts of data. It should be done by analyzing a subset of key business data. This analysis is done for meaningful patterns and trends.\n<\/p>\n<p>Data should be classified correctly before storage. Randomly saving data can create further issues in analytics. As the data is large in volume, creating different sets and subsets could be the right option. This assists in creating trends for handling big data challenges.\n<\/p>\n<h2><span id=\"data_performance\">Data Performance<\/span><\/h2>\n<p>Data should be handled effectively for performance and decisions should not be made without insights. We need our data to perform effectively for tracking demand, supply and profit for consistency. This data should be handled for real-time business insights.\n<\/p>\n<h2><span id=\"overload\">Overload<\/span><\/h2>\n<p>Overload can occur when trying to keep large amounts of data sets and subsets. The key pain point here is to select which information is kept from different sources. Here, reliability is also an important factor while selecting which data to keep.\n<\/p>\n<p>Some types of information are not needed for business and should be eliminated to avoid future complications. An overloading issue could be resolved if some tools are used by experts for making an insight to create a big data project success.\n<\/p>\n<h2><span id=\"analytical_tools\">Analytical Tools<\/span><\/h2>\n<p>Our current analytical tools provide insights into prior performance, but tools are needed for providing future insights. Predictive tools could be optimal solutions in this case.\n<\/p>\n<p>There is also a need to give analytical tool access to managers and other professionals. Expert guidance can boost the business to a higher level. This leads to proper insight with less assistance given for IT support.\n<\/p>\n<h2><span id=\"right_person_at_the_right_place\">Right Person at the Right Place<\/span><\/h2>\n<p>The motto for many HR departments is \u201cthe right person at the right place,\u201d and it is same for big data as well. Provide the data and analytics access to right person. This could assist in getting proper insights for predictions related to risk, costs, promotions, etc. and could convert analytics into actions.\n<\/p>\n<p>The data collected by companies through emails, sales, tracking and cookies are of no use if you can\u2019t analyze it properly. Analysis is important for providing what the consumer wants.\n<\/p>\n<h2><span id=\"forms_of_data\">Forms of Data<\/span><\/h2>\n<p>There is a large amount of data collected, which can be structured or unstructured and from varying sources. Improper handling of data and lack of awareness about what to save and where to save it can hamper the handling of big data. The usage of each form of data should be known to the person handling it.\n<\/p>\n<h2><span id=\"unstructured_data\">Unstructured Data<\/span><\/h2>\n<p>Data coming from different sources can have an unstructured form. It could contain data which is not organized in a standard, predefined manner. For example, emails, system logs, word processing documents and other business documents can all be data sources.\n<\/p>\n<p>The challenge is to store and analyze this data correctly. A survey stated that 80% of the data generated daily is unstructured.\n<\/p>\n<h2><span id=\"conclusion\">Conclusion<\/span><\/h2>\n<p>Data in an enterprise is difficult to manage due to its large size and the need for higher processing capacity. Traditional databases can\u2019t process this efficiently. An organization can make better decisions if it can successfully manage and analyze massive data with ease.\n<\/p>\n<p>It could be petabytes of data storing details of employees of an organization from different sources. If not organized properly, it could become difficult to use. The situation is worsened if even more unstructured data is coming in from different sources.\n<\/p>\n<p>Big data has the potential to improve business decisions and analytics. Today banking, services, media and communications are investing in big data. The above pain points should be taken into consideration while working with massive amounts of data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Big data comes in a variety of forms and structures. In recent years, big data analytics has had a significant impact on business decisions, and while it can be of immense vale, it does come with some pain points. In this article, I will discuss those analytics pain points, but first, let&#8217;s focus on some [&hellip;]<\/p>\n","protected":false},"author":7870,"featured_media":49133,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_lmt_disableupdate":"","_lmt_disable":"","om_disable_all_campaigns":false,"footnotes":""},"categories":[581,586,592],"tags":[],"category_partsoff":[],"class_list":["post-49132","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","category-emerging-technology","category-identity-access-governance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v24.2 (Yoast SEO v24.5) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Tackling Big Data Analytics Pain Points - Techopedia<\/title>\n<meta name=\"description\" content=\"Big data comes in a variety of forms and structures. 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