Demystifying Data Analytics
What is Data Analytics?
In this increasingly tech-enabled world, everything from washing machines to doorbells connects to wifi. There has never been a more exciting time to learn about data! According to Forbes, more than 50% of companies are analyzing Big Data (a fancy term for massive datasets).
Data Analytics is the process of manipulating and analyzing data to understand and uncover insights that can influence business decisions. Companies use these insights to target and adjust their efforts to reach their target audience. Customer and social analysis are among the primary goals for data analytics in most companies.
Data Analytics vs. Data Science
Like data analytics, data science is also a hot topic but involves an entirely different set of skills and tools. Data science is the study of data, with the focus of using algorithms, mathematics, and techniques to extract useful data from non-traditional data sources. While data analytics relies on readily available data, data science focuses on how to extract this data into a usable form that can be processed and analyzed with data analytics software.
Data scientists use tools like the coding language R and Hadoop framework to “scrape” or extract raw data from a website. In data science programs, students often learn how to “scrape” public websites with lots of raw data, such as Twitter or Google. Examples may include writing an algorithm to produce all results in a web directory or finding all of the records containing a specific word on a public site.
Another way to look at the difference between data science and data analytics is to view data science as preparing data before analysis, and data analytics as reviewing data after the fact. Data scientists are experts in data extraction, cleaning and preparation while data analysts are experts in uncovering trends and actionable insights. Despite being similar in name, data science and data analytics involve entirely different processes, tools, and functions of data.
Data Analytics Jobs
Data analytics jobs fall into different categories depending on your knowledge of programming and statistics. Most data analytics jobs fall within the business analyst role, in which data professionals work with Excel and Tableau. Some may have additional skill in a programming language or other specialized software.
Most data analytics professionals must incorporate data from a variety of tools to form one coherent story. A campaign targeted to customers including website traffic, social media, and other forms of marketing can form three separate data sources. To determine which strategies are most effective and identify new approaches that can lead to even more customer engagement and sales, data analysts often import data into one program.
Business analysts typically use tools, such as Microsoft Excel or another tool to analyze data and Tableau or another visual tool to communicate actionable insights to decision makers. Statisticians take data analytics a step further by using R, SPSS, or other statistical analysis tools to determine risk or make predictions. Other data analysts work within large databases, such as SQL or Microsoft Access.
Data Analytics Tools
Microsoft Excel
Despite being a popular tool that most individuals have on their personal and business computers, very few people harness the complete power of Microsoft Excel. Microsoft Excel is a data analytics powerhouse that offers support at every stage of the data analytics process from cleaning and analyzing data to performing analysis and displaying results in charts and graphs. Its most advanced users write macros (complex instructions that automate computer tasks) using Visual Basic for Applications (VBA), a coding language used to program Excel.
Microsoft Excel can do the following tasks:
- Reformat data
- Use formulas to calculate new values from existing data
- Forecast financials using equations and functions
- Complete math functions and statistical analysis functions like ANOVA and regression
- Display results and analysis using pie, line, and bar charts
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SQL
SQL (whether it’s pronounced ess-que-el or SEQUEL is up for debate) is a coding language used to store, organize, and retrieve data from relational databases. With SQL you can analyze data for an e-commerce store by joining multiple datasets or tables to gain new insights. For example, you can query and merge two datasets, one containing customers and another containing orders to determine all of the customers that bought a specific product and their particular location.
It is important to note that SQL is not a database itself, but rather a command language used to communicate with a relational database, most commonly Microsoft SQL server.
In recent years, programmers have created adaptations of SQL to use with modern object-oriented or non-relational databases. With SQL, you can perform the following tasks:
- Create a database
- Sort and/or filter data within a database
- Query a database to find specific information about a product or user
- Join two different datasets to view information that meets specific criteria
- Convert output data or export it to external applications
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Tableau
Tableau is a powerful tool that allows you to visualize your data analysis. The benefit of Tableau is that non-technical professionals and decision-makers can immediately notice trends and data insights without pouring over detailed reports or data sets. Tableau is useful in any industry with access to data and is a popular tool in both the public and the private sector.
With Tableau, you can do the following:
- Connect raw datasets from almost any program including Microsoft Excel, SQL, Microsoft Access, and SPSS
- Use Tableau Prep® to clean and analyze data for further analysis
- Analyze trends and stories within your data
- Create responsive insights that can be customized by colleagues
- Manipulate interactive charts, graphs, and other representations of data
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