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Advanced Analytics

    Advanced analytics is a broad category of inquiry that can be used to help drive changes and improvements in business practices. 

    While the traditional analytical tools that comprise basic business intelligence (BI) examine historical data, tools for advanced analytics focus on forecasting future events and behaviors, enabling businesses to conduct what-if analyses to predict the effects of potential changes in business strategies. 

    Predictive analytics, data mining, big data analytics and machine learning are just some of the analytical categories that fall under the heading of advanced analytics. These technologies are widely used in industries including marketing, healthcare, risk management and economics.

    Uses of advanced analytics

    Advanced data analytics is being used across industries to predict future events. Marketing teams use it to predict the likelihood that certain web users will click on a link; healthcare providers use prescriptive analytics to identify patients who might benefit from a specific treatment; and cellular network providers use diagnostic analytics to predict potential network failures, enabling them to do preventative maintenance.

    Advanced analytics practices are becoming more widespread as enterprises continue to create new data at a rapid rate. Now that many organizations have access to large stores of data, or big data, they can apply predictive analytics techniques to understand their operations at a deeper level.

    Advanced analytics techniques

    The advanced analytics process involves mathematical approaches to interpreting data. Classical statistical methods, as well as newer, more machine-driven techniques, such as deep learning, are used to identify patterns, correlations and groupings in data sets. Based on these, users can make a prediction about future behavior, whether it is which group of web users is most likely to engage with an online ad or profit growth over the next quarter.

    In many cases, these complex predictive and prescriptive analyses require a highly skilled data scientist. These professionals have extensive training in mathematics; computer coding languages, like Python and the R language; and experience in a particular line of business.

    Advanced analytics has become more common during the era of big data. Predictive analytics models and, in particular, machine learning models require large amounts of training to identify patterns and correlations before they can make a prediction. The growing amount of data managed by enterprises today opens the door to these advanced analytics techniques.

    Advanced analytics tools

    There are a variety of advanced analytics tools to choose from that offer different advantages based on the use case. They generally break down into two categories: open source and proprietary.

    Open source tools have become a go-to option for many data scientists doing machine learning and prescriptive analytics. They include programming languages, as well as computing environments, including Hadoop and Spark. Users typically say they like open source advanced analytics tools because they are generally inexpensive to operate, offer strong functionality and are backed by a user community that continually innovates the tools.

    On the proprietary side, vendors including Microsoft, IBM and the SAS Institute all offer advanced analytics tools. Most require a deep technical background and understanding of mathematical techniques.

    In recent years, however, a crop of self-service analytics tools has matured to make functionality more accessible to business users. Tableau, in particular, has become a common tool. While its functionality is more limited than deeper technical tools, it does enable users to conduct cluster analyses and other advanced analyses.

    Data Analytics and its type

    Analytics is the discovery and communication of meaningful patterns in data. Especially, valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming, and operation research to qualify performance. Analytics often favors data visualization to communicate insight.

    Firms may commonly apply analytics to business data, to describe, predict, and improve business performance. Especially, areas within include predictive analytics, enterprise decision management, etc. Since analytics can require extensive computation (because of big data), the algorithms and software used to analytics harness the most current methods in computer science.

    In a nutshell, analytics is the scientific process of transforming data into insight for making better decisions. The goal of Data Analytics is to get actionable insights resulting in smarter decisions and better business outcomes.

    It is critical to design and built a data warehouse or Business Intelligence(BI) architecture that provides a flexible, multi-faceted analytical ecosystem, optimized for efficient ingestion and analysis of large and diverse data sets.

    There are four type of data analytics:

    1. Predictive (forecasting)
    2. Descriptive (business intelligence and data mining)
    3. Prescriptive (optimization and simulation)
    4. Diagnostic analytics