{"id":435,"date":"2019-07-24T09:55:00","date_gmt":"2019-07-24T11:55:00","guid":{"rendered":"https:\/\/answerrocket.com\/?p=435"},"modified":"2024-07-30T12:03:04","modified_gmt":"2024-07-30T14:03:04","slug":"data-analytics-machine-learning","status":"publish","type":"post","link":"https:\/\/answerrocket.com\/data-analytics-machine-learning\/","title":{"rendered":"Data Analytics and Machine Learning: Let\u2019s Talk Basics"},"content":{"rendered":"\n

As consumer data grows, so too do the opportunities to better understand and target customers and prospects.<\/p>\n\n\n\n

To capitalize on this data, businesses must frame their approach strategically. After all, having the data is not enough to:<\/p>\n\n\n\n

    \n
  1. Interpret and understand the story it\u2019s telling.<\/li>\n\n\n\n
  2. Determine which data is most relevant to which audience.<\/li>\n\n\n\n
  3. Instill a culture of data discovery in employees, especially when acting on hunches can be habitual.<\/li>\n<\/ol>\n\n\n\n

    Business leaders understand the value of data that\u2019s tailored to each function and the role analytics tools play in the overall employee experience of accessing that data.<\/p>\n\n\n\n

    In this sense, analytics software that organically promotes data-driven decision-making provides a competitive advantage.<\/strong><\/p>\n\n\n\n

    The advent of AI analytics<\/a> has changed the premise of the conversation. With the automation and augmentation capabilities of AI, analytics tools are no longer facilitators of data analysis but are capable of performing the actual labor that was once unique to humans.<\/p>\n\n\n\n

    These advancements mean that businesses have an incredible opportunity to capitalize on data (as we\u2019ve mentioned), but they must do so with an eye toward scale, change management, and curiosity culture.<\/p>\n\n\n\n

    In this article, we\u2019ll specifically discuss the advantages of machine learning analytics and how it fits into the larger picture of AI in business intelligence.\u00d7<\/p>\n\n\n\n

    Learn more about the state of AI in business intelligence with this in-depth eBook for business leaders.<\/a><\/p>\n\n\n\n

    The difference between traditional data analytics and machine learning analytics<\/h2>\n\n\n\n

    Data analytics is not a new development. From the beginning of business intelligence (BI)<\/a>, analytics has been a key aspect of the tools employees use to better understand and interact with their data.<\/p>\n\n\n\n

    However, the scale and scope of analytics has drastically evolved. Let\u2019s discuss these differences in more detail.<\/p>\n\n\n\n

    Data Analytics<\/strong><\/h3>\n\n\n\n

    Traditional data analytics platforms typically revolve around dashboards.<\/p>\n\n\n\n

    Dashboards are constructed of visualizations and pivot tables that illustrate trends, outliers, and pareto, for example. Technical team members like data analysts and data scientists play a role in constructing these dashboards; generally, the humans are still performing the bulk of the analysis, and the software helps facilitate the results.<\/p>\n\n\n\n

    Current state analysis with traditional data analytics software looks something like this:<\/strong><\/p>\n\n\n\n

      \n
    1. The data analyst starts with a core question, likely sourced from a business team. In this case, the question is \u201chow did market share do last quarter?\u201d<\/li>\n\n\n\n
    2. The data analyst accesses different spreadsheets from different locations.<\/li>\n\n\n\n
    3. The data analyst merges multiple spreadsheets manually.<\/li>\n\n\n\n
    4. The data analyst conducts analysis by filtering data based on their hypotheses around market share\u2019s performance. This process is constrained by time restrictions, so the analyst can\u2019t fully test every scenario. As the analyst iterates on their hypotheses, they may need to access data again.<\/li>\n\n\n\n
    5. The analyst presents the story, or the findings from their analyses. While these stories can be well-researched and accurate, they\u2019re not a complete picture of what\u2019s happening in the data and rely on the analyst\u2019s initial assumptions.<\/li>\n<\/ol>\n\n\n\n

      This process is labor-intensive, time-consuming, and often frustrating. Data analysts have advanced skill sets that they can\u2019t use effectively when they\u2019re spending their time stuck in a cycle of routine reports.<\/p>\n\n\n\n

      The limitations of this process have paved the way for machine learning to take hold in analytics.<\/p>\n\n\n\n

      Machine Learning Analytics<\/strong><\/h3>\n\n\n\n

      Machine learning analytics is an entirely different process.<\/p>\n\n\n\n

      Machine learning automates the entire data analysis workflow to provide deeper, faster, and more comprehensive insights.<\/strong><\/p>\n\n\n\n

      How does this work?<\/p>\n\n\n\n

      Machine learning is a subset of AI<\/a> that leverages algorithms to analyze vast amounts of data.<\/p>\n\n\n\n

      These algorithms operate without human bias or time constraints, computing every data combination to understand the data holistically. Further, machine learning analytics understands boundaries of important information.<\/p>\n\n\n\n

      If asked to identify changes in sales figures, the machine can learn the difference between a $200 fluctuation and a $200,000 increase, only reporting the latter because that\u2019s the info that actually impacts the company.<\/p>\n\n\n\n

      In other words, machine learning also tests out hypotheses to answer key business questions \u2014 but it can test all of them in a much shorter timespan. Think seconds instead of weeks. Then, it tells a data story that\u2019s accurate, exhaustive, and relevant to the person asking questions.<\/p>\n\n\n\n

      Practically, machine learning is invoked in techniques like:<\/p>\n\n\n\n