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Data Analytics Explained

You may have been doing basic data analytics without even knowing. For example, when you compared the benefits of owning an IPhone versus a Samsung Galaxy (or any other preferable brand) based on information from different sources, whether you realized it or not, you have ultimately gathered, inspected, cleansed, transformed and analyzed data that helped guide your decision making process of acquiring a new phone – and this is exactly what data analytics is about.

Businesses are also known for their extensive use of data analytics to guide decision making. E-commerce giants such as Amazon and Alibaba often study data to help predict when and where clients might need certain products. This serves to help cut costs associated to shipping, warehousing, labour, among others.

Governments on the other hand, may use data analytics to support policy, budgetary, and operational decisions. For example, government agencies will often use data analytics to forecast natural occurrences that may impact citizen’s lives. Chiefly, agencies such as the National Oceanic and Atmospheric Administration (NOAA) in the US, assist the government in predicting a hurricane’s trajectory by looking at current and historical data. Such information, is used to allocate appropriate resources to affected areas, ultimately resulting in lives saved and decreased damage costs.

    But What Exactly is Data Analytics?

    Data Analytics is the study of data (often through statistics) to better understand a topic and/ or support individuals, businesses and governments throughout their decision-making process. Thanks to this science, information can be analyzed in several different ways in order to expose patterns and trends, resulting on more efficient business practices and better decision making.

    Data Analytics has been traditionally divided into 4 segments:

    • Descriptive analytics focuses on understanding what happened in the past for a determined period of time. For example, a company looks at how many cellphones they sold last year to determine how many to manufacture this year.
    • Diagnostic analytics looks into past data and tries to understand why things happened the way it did. For example, a company wants to understand why the sale of cellphones increased last year.
    • Predictive analytics is concerned with using past data to forecast what may happen in the future. For example, a company uses statistical analysis to look at previous cellphone sales and forecasts the number of sales in the upcoming year.
    • Prescriptive analytics examines the past and provides guidance to what may happen in the future. For example, a company looks to understand how many cellphones were sold last year, has a forecast of how many cellphones they are likely to sell in the upcoming year and has guidance on how to reach the desired number of sales.

    In order to make data driven decisions, data analytics uses the following process to guide the decision-making process:

    • Identify the problem in question: frame the issue and identify possible alternative issues.
    • Review previous findings: see how and if people have previously solved the problem and what are their approach to the solution.
    • Model the solution and select the variable: formulate your hypothesis and infer how it might affect the outcome.
    • Collect Data: gather primary and secondary data related to your hypothesis – i.e. data collected by you or by someone else.
    • Analyse Data: assess if the data is appropriate, run a statistical model, rinse and repeat. There are several different software that can be used to support this process. To name a few there is SQL Server Analysis Services (SSAS), SAS Analytics, Microsoft Excel, R, Tableau, Power BI, among others.
    • Present and act on the results: present your results as if you are telling a story with your data. Remember, a message is only as good as it’s ability to be understood. Spending a lot of time analyzing your data and delivering a poor presentation can have an impact on end results.

    Common terms often used in data analysis:

    Raw Data – Refers to information that has not been changed, processed or filtered to the point where it can be used for analysis, presentation and decision making. For example, every action taken throughout the process of purchasing something online creates data. Without connecting the dots (raw data) of some or all the process, that data on its own has no meaning.

    Unstructured data – Refers to data that does not conform to a structure that allows for easier data processing and analysis. Pictures, e-mails, sounds, video and text without the right structure will often fall under this segment. For example, a company has pictures of multiple consumers purchasing a variety of products, the picture may provide information regarding demographics, age, purchased product, amount spent among other areas. However, pictures are not structured in a pre-defined manner that facilitates data analysis, making it difficult to dig into the information without spending significant resource.

    Structured data – Refers to data that has been compiled in a way that facilitates the data modelling process, which is often found in text format and easily examined. An example is provided below:

    Note: With the advances in technology driven by the Fourth Industrial revolution, data analytics derived from pictures, videos & audios are starting to become more commercially viable due to decreased costs of data storage and advancements in tools such as Amazon Rekognition & Google Vision AI.

     

    Data Cleansing – The process of finding, fixing or removing inaccurate or corrupted data to support the data analytics process. An example can be seeing below:

    Data Report – A data driven analysis that has defined, created and optimized information in an accurate manner to help support the decision-making process.

    Source: UN data visualized in Tableau

    Ad Hoc Data Analysis – Data driven analysis with the aim of digging into a segment of a dataset or prior results to answer specific questions that may have been raised. For example, the data report above can be used by the government to determine key factors that may lead certain regions to have a higher rate of homicide.

    Data Filtering – Closely related to ad hoc data analysis, data filtering looks to set filters (conditions) to dig further into already available data, focusing on areas relevant to the author or decision maker.

    Data Alert – A set of pre-assigned specifications/limits (whether fixed, formula driven or otherwise) implemented in reports or data tool(s) that raises alerts when the data goes outside defined ranges or is expected to do so based on forecasted information.

    Pattern Recognition – The discovery of patterns and irregularities in the data driven by man-made rules, heuristics, machine learning or “Artificial Intelligence” to help drive decision making (E.g. data clustering, anomaly analysis)

    Data Analytics Process Automation aims to automate repetitive tasks associated with data analysis to help decrease costs, improve the bottom-line, ensure information arrives to the right people at the right time and frees employees’ time to do more value-added tasks. Data analytics process automation can help structure data, cleanse it, create the reports automatically, recognize patterns, and set alerts among many other areas.

    Training & Workshop – Ensures that teams are well equipped to do quality data analytics work. It can also serve to help operational teams manage data analytics tools and automated systems, leading to long-term organizational success. For an example of a data analytics and visualization report, please refer to ‘Canadian School Expenditure Report

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