{"id":12510,"date":"2020-12-04T12:09:05","date_gmt":"2020-12-04T11:09:05","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=12510"},"modified":"2022-01-05T20:06:44","modified_gmt":"2022-01-05T19:06:44","slug":"data-analytics-data-mining","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/big-data\/data-analytics-data-mining\/","title":{"rendered":"7 Key Differences Between Data Analytics and Data Mining"},"content":{"rendered":"\t\t\t\t
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  1. Data Science for Business Intelligence<\/a>
  2. 7 Differences Between Data Analytics and Data Mining<\/a>
  3. Using Data Analytics and Data Mining for Business Planning<\/a><\/ol>\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\n\n\n

    With every second we spend online<\/span>, mountains of data<\/span> is generated. For every social media post, Google search, and link clicked, there is a way in which our activity can be collected for data<\/span>. Experts within the data science<\/span><\/strong> field can utilize this, creating meaningful information for businesses<\/span>. Businesses<\/span> can draw on this invaluable data<\/span> to develop their customer base. This allows them to embrace new technologies and platforms – they might close sales using social media<\/a> alone, or use AI<\/span> to avoid cart abandonment. How? With the help of data<\/span> analytics<\/strong> and data mining<\/span><\/strong>.<\/p>\n\n\n\n

    While both data<\/span> mining and data<\/span> analytics are a subset of Business Intelligence<\/span><\/strong>, that\u2019s about all they have in common. One of the key differences between data analytics<\/span> and data mining is that the latter is a step in the process<\/span> of data analytics<\/span>. Indeed, data analytics<\/span> deals with every step in the process<\/span> of a data-driven model, including data mining. Both fall under the umbrella of data science<\/span>.<\/p>\n\n\n\n

    Data Science for Business Intelligence<\/h2>\n\n\n\n

    For business<\/span> owners, knowing their target audience\u2019s behaviors and being able to capitalize on that information is like gold dust.<\/p>\n\n\n\n

    That\u2019s where data science<\/span> comes in. Specialists can provide genuine insight into a business<\/span>\u2019s customers – they can delve deeper and further than any traditional marketing method. This is because they can base their understanding<\/span> on substantial evidence, rather than speculating about what the consumer may want. Data science<\/span> uses extensive research<\/span> to accurately forecast what steps a business<\/span> may need to take in order to capture their audience and improve customer retention<\/a>.<\/p>\n\n\n\n

    Both data mining and data analytics<\/span> are needed to help a business<\/span> strategize its next steps. Both demonstrate their own value to Business Intelligence<\/a>, but what exactly are the key differences between them?<\/p>\n\n\n\n

    7 Differences Between Data Analytics and Data Mining<\/h2>\n\n\n\n

    Team Size<\/h3>\n\n\n\n

    Data mining<\/span><\/strong> can be undertaken by a single specialist with excellent technological skills<\/span>. With the right software<\/span>, they are able to collect the data<\/span><\/strong> ready for further analysis<\/span>. At this stage, a larger team<\/span> simply isn\u2019t required. From here, a data mining specialist will usually report their findings to the client<\/span>, leaving the next steps in someone else\u2019s hands.<\/p>\n\n\n\n

    \"data<\/figure><\/div>\n\n\n\n

    However, when it comes to data<\/span> analytics<\/strong>, a team<\/span> of specialists may be needed. They need to assess the data<\/span>, figure out patterns<\/span>, and draw conclusions. They may use machine learning<\/span><\/strong> or prognostication analytics to help with the processing<\/span>, but this still has a human element involved.<\/p>\n\n\n\n

    Data analytics<\/span> teams<\/span> need to know the right questions to ask – for example, if they\u2019re working for a telephony company, they may want to know the answer to \u2018how is VoIP used in business<\/a>\u2019. A data<\/span> mining specialist can provide evidence of where it\u2019s used and how often, but data<\/span> analytics uncovers the how and the why.<\/p>\n\n\n\n

    Their goal is to work together to uncover information<\/strong> and figure out how the gathered data<\/span> can be used to answer questions and solve problems for the business<\/span>. <\/p>\n\n\n\n

    Artificial Intelligence<\/span><\/strong> advances are likely to bring serious changes to the analytics process. An AI<\/span> system can analyze hundreds of data<\/span> sets and predict<\/span> various outcomes, offering information about customer preferences, product development, and marketing avenues. <\/p>\n\n\n\n

    AI<\/span>-powered systems<\/span><\/strong> will soon be able to complete menial tasks for data<\/span> analytics teams, freeing up their time to take on more important work. It has the capability to dramatically improve the productivity<\/span> of data<\/span> scientists by helping to automate elements of the data<\/span> analytics process.<\/p>\n\n\n\n

    Data Structure<\/h3>\n\n\n\n

    When it comes to data<\/span> mining, studies are conducted mostly on structured data<\/span>. A specialist will use data analysis<\/span> programs<\/span> to research<\/span> and mine data<\/span>. They report their findings to the client<\/span> through graphs and spreadsheets<\/a>. This is often a very visual explanation, due to the complicated nature of the data<\/span>. Clients<\/span> are not typically data<\/span> mining experts, and they don’t claim to be!<\/p>\n\n\n\n

    \"data<\/figure><\/div>\n\n\n\n

    So, data<\/span> needs to be fairly simply interpreted into graphics<\/span> or bar charts<\/span>. As with the earlier phone company example, if the client<\/span> needs to know the data<\/span> behind how many people click the link to \u2018what is a VoIP number<\/a>\u2019 on their website, this should be displayed in easy to read charts<\/span>, not complicated documents. <\/p>\n\n\n\n

    A data<\/span> mining specialist builds algorithms to identify a structure<\/span> within the data<\/span><\/strong>, which can then be interpreted. It\u2019s based on mathematical and scientific concepts, making it excellent for businesses<\/span> to gather clear and accurate data<\/span>. <\/p>\n\n\n\n

    This is in contrast to data<\/span> analytics, which can be done on structured, semi-structured<\/span>, or unstructured<\/span> data<\/span><\/strong>. They\u2019re also not responsible for creating algorithms like a data<\/span> mining specialist. Instead, they are tasked with spotting patterns<\/span> within the data<\/span> and using them to brief the client<\/span> on their next steps. <\/p>\n\n\n\n

    This can then be applied to a company\u2019s business model<\/span><\/strong>. The marketing team may want to see their customer and industry data<\/span> laid out in front of them. If they can understand the behaviors of a competitor’s consumer, they can then apply it to their own strategies.<\/p>\n\n\n\n

    Data Quality<\/h3>\n\n\n\n

    The way that the data<\/span> needs to be presented for data<\/span> mining compared to data<\/span> analytics varies. While data<\/span> mining is used to collect data<\/span><\/strong> and search for patterns<\/span>, data<\/span> analytics tests a hypothesis<\/strong> and translates findings into accessible information. This means the quality of data<\/span> they work with can differ.<\/p>\n\n\n\n

    A dating mining specialist will use big data<\/span> sets and extract the most useful data<\/span> from them. Therefore, because they\u2019re using vast and sometimes free data set<\/span>s<\/a>, the quality of the data<\/span> they are working with isn\u2019t always going to be top-notch. Their job is to mine the most useful data<\/span> from this, and report their findings in ways businesses will understand.<\/p>\n\n\n\n

    However, data<\/span> analytics involves collecting data<\/span> and checking for data<\/span> quality<\/strong>. Typically, a data<\/span> analytics team member will be working with good quality raw data<\/span> that is as clean as possible. When the data quality<\/span> is poor, it can negatively impact the results, even if the process<\/span> is the same as with clean data<\/span>. This is a vital step in data<\/span> analytics, so the team<\/span> must check that the data<\/span> quality is good enough to start with. <\/p>\n\n\n\n

    Hypothesis Testing in Data Analytics and Data Mining<\/h3>\n\n\n\n

    A hypothesis is effectively a starting point that requires further investigation, like the idea that cloud<\/span>-native databases<\/span><\/a> are the way forward. The idea is constructed from limited evidence and then investigated further. <\/p>\n\n\n\n

    A key difference between data<\/span> analytics and data<\/span> mining is that data<\/span> mining does not require any preconceived hypothesis or notions before tackling the data<\/span>. It simply compiles it into useful formats. However, data analysis<\/span> does need a hypothesis to test<\/span>, as it is looking for answers to particular questions.<\/p>\n\n\n\n

    \"data<\/figure><\/div>\n\n\n\n

    Data mining<\/span> is about identifying and discovering patterns. A specialist will build a mathematical or statistical model<\/span> based on what they derive from the data<\/span>. Because they don\u2019t lead with a hypothesis, a data<\/span> mining specialist typically works with large data<\/span> sets to cast the widest net of possibly useful data<\/span>. This gives them the opportunity to whittle down the data, ensuring the data they\u2019re left with at the end of the process is usable and reliable. This process works much like a funnel, starting with large data sets and filtering it into more valuable data.<\/p>\n\n\n\n

    In contrast, data analytics tests a hypothesis, extracting meaningful insights as part of their research. It helps in proving the hypothesis, and it may use the data mining discoveries in the process. For example, a business may start with a hypothesis such as, \u2018Having a free sample link at checkout will lead to an improved conversion rate of 15%\u2019. This can then be implemented and tested on the website. <\/p>\n\n\n\n

    The data analytics team will work to test the hypothesis statement by analysing each visit to the website. They may even conduct split tests<\/strong> into A\/B<\/span><\/a> for link placement, where \u2018A\u2019 leaves the sample link at the top of the page, and \u2018B\u2019 at the bottom. This gives an even closer insight into consumer behaviour when purchasing items, and lets the business know the best place to position the free sample link.<\/p>\n\n\n\n

    Forecasting<\/h3>\n\n\n\n

    One of the tasks of a data mining specialist is forecasting<\/strong> what may be interpreted from the data. They find data patterns and note what it could lead to by using reasonable future predictions. <\/p>\n\n\n\n

    Understanding how the market may react to certain products or technologies can be important for brands and businesses across many sectors. Implementing a new technology such as a TCPA dialler<\/a> brings both risks and benefits, and data can help a business decide if it\u2019s the right solution for them.<\/p>\n\n\n\n

    Therefore, the work undertaken during the data mining process can prove to be essential for businesses that rely on forecasting trends. <\/p>\n\n\n\n

    As well as this, a data mining specialist will make sense of data by analyzing:<\/p>\n\n\n\n