1. What is data analytics?
Most companies are collecting loads of data all the time—but, in its raw form, this data doesn’t really mean anything. This is where data analytics comes in. Data analytics is the process of analyzing raw data in order to draw out meaningful, actionable insights, which are then used to inform and drive smart business decisions.
A data analyst will extract raw data, organize it, and then analyze it, transforming it from incomprehensible numbers into coherent, intelligible information. Having interpreted the data, the data analyst will then pass on their findings in the form of suggestions or recommendations about what the company’s next steps should be.
You can think of data analytics as a form of business intelligence, used to solve specific problems and challenges within an organization. It’s all about finding patterns in a dataset which can tell you something useful and relevant about a particular area of the business—how certain customer groups behave, for example, or how employees engage with a particular tool.
Data analytics helps you to make sense of the past and to predict future trends and behaviors; rather than basing your decisions and strategies on guesswork, you’re making informed choices based on what the data is telling you.
2. What’s the difference between data analytics and data science?
You’ll find that the terms “data science” and “data analytics” tend to be used interchangeably. However, they are two different fields and denote two distinct career paths. What’s more, they each have a very different impact on the business or organization.
Despite their differences, it’s important to recognize that data science and data analytics work together, and both make extremely valuable contributions to business.
Key difference 1: What they do with the data
One key difference between data scientists and data analysts lies in what they do with the data and the outcomes they achieve.
A data analyst will seek to answer specific questions or address particular challenges that have already been identified and are known to the business. To do this, they examine large datasets with the goal of identifying trends and patterns. They then “visualize” their findings in the form of charts, graphs, and dashboards. These visualizations are shared with key stakeholders and used to make informed, data-driven strategic decisions.
In short: data analysts tackle and solve discrete questions about data, often on request, revealing insights that can be acted upon by other stakeholders, while data scientists build systems to automate and optimize the overall functioning of the business.
Key difference 2: Tools and skills
Data analysts are typically expected to be proficient in software like Excel and, in some cases, querying and programming languages like SQL, R, SAS, and Python. Analysts need to be comfortable using such tools and languages to carry out data mining, statistical analysis, database management and reporting.
Data scientists, on the other hand, might be expected to be proficient in Hadoop, Java, Python, machine learning, and object-oriented programming, together with software development, data mining, and data analysis.
3. What are the different types of data analysis?
Data analytics is broadly classified into four types: descriptive, diagnostic, predictive, and prescriptive.
1. Descriptive Analytics – “What Happened?”
Descriptive analytics summarizes past data using data aggregation and mining to identify patterns. It helps present insights in an easy-to-understand way but does not explain causes.
2. Diagnostic Analytics – “Why Did It Happen?”
This type analyzes anomalies and investigates their root causes using probability theory, regression analysis, and time-series analytics. It focuses on identifying relationships between events.
3. Predictive Analytics – “What Will Happen?”
Using historical data and probability models, predictive analytics forecasts future trends, helping businesses make informed decisions. Though not 100% accurate, it reduces uncertainty.
4. Prescriptive Analytics – “What Should We Do?”
Prescriptive analytics recommends actions based on predictions. It employs machine learning, algorithms, and computational modeling to optimize decision-making and improve outcomes
4. What are some real-world data analytics examples?
Let’s now take a closer look at data analytics in action with some real-world case studies.
Data analytics case study: Healthcare
One area where data analytics is having a huge impact is the healthcare sector. Junbo Son, a researcher from the University of Delaware, has devised a system which helps asthma patients to better self-manage their condition using bluetooth-enabled inhalers and a special data analytics algorithm.
So how does it work? First, the data is collected through a Bluetooth sensor which the user attaches to their asthma inhaler. Every time the patient uses their inhaler, the sensor transmits this usage data to their smartphone. This data is then sent to a server via a secure wireless network, where it goes through the specially devised Smart Asthma Management (SAM) algorithm.
Over time, this unique algorithm helps to paint a picture of each individual patient, giving valuable insight into patient demographics, unique patient behaviours—such as when they tend to exercise and how this impacts their inhaler usage—as well as each patient’s sensitivity to environmental asthma triggers. This is especially useful when it comes to detecting dangerous increases in inhaler usage; the data-driven SAM system can identify such increases much more quickly than the patient would be able to.
What’s more, the SAM system has been found to outperform traditional models, with a false alarm rate that is 10-20% lower than that of current models, together with a 40-50% lower misdetection rate.
This case study highlights what a difference data analytics can make when it comes to providing effective, personalized healthcare. By collecting and analyzing the right data, healthcare professionals are able to offer support that is tailored to both the individual needs of each patient and the unique characteristics of different health conditions—an approach that could be life-changing and potentially life-saving.
You can learn more about this case study in the following journal article: A Data Analytics Framework for Smart Asthma Management Based on Remote Health Information Systems with Bluetooth-Enabled Personal Inhalers.
Data analytics case study: Netflix
Another real-world example of data analytics in action is one you’re probably already familiar with: the personalized viewing recommendations provided by Netflix. So how does Netflix make these recommendations, and what impact does this feature have on the success of the business?
As you might have guessed, it all starts with data collection. Netflix collects all kinds of data from its 163 million global subscribers—including what users watch and when, what device they use, whether they pause a show and resume it, how they rate certain content, and exactly what they search for when looking for something new to watch.
With the help of data analytics, Netflix are then able to connect all of these individual data points to create a detailed viewing profile for each user. Based on key trends and patterns within each user’s viewing behavior, the recommendation algorithm makes personalized (and pretty spot-on) suggestions as to what the user might like to watch next.
This kind of personalized service has a major impact on the user experience; according to Netflix, over 75% of viewer activity is based on personalized recommendations. This powerful use of data analytics also contributes significantly to the success of the business; if you look at their revenue and usage statistics, you’ll see that Netflix consistently dominates the global streaming market—and that they’re growing year upon year.
As you can see from these two case studies alone, data analytics can be extremely powerful. For more real-world case studies, including how Coca Cola uses data analytics to drive customer retention, and how Pepsi Co uses their huge volumes of data to ensure efficient supply chain management.
5. What tools and techniques do data analysts use?
Much like web developers, data analysts rely on a range of different tools and techniques. So what are they? Let’s take a look at some of the major ones:
Data analytics techniques
Before we introduce some key data analytics techniques, let’s quickly distinguish between the two different types of data you might work with: quantitative and qualitative.
Quantitative data is essentially anything measurable—for example, the number of people who answered “yes” to a particular question on a survey, or the number of sales made in a given year. Qualitative data, on the other hand, cannot be measured, and comprises things like what people say in an interview or the text written as part of an email.
Data analysts will usually work with quantitative data; however, there are some roles out there that will also require you to collect and analyze qualitative data, so it’s good to have an understanding of both. With that in mind, here are some of the most common data analytics techniques:
Regression analysis
This method is used to estimate or “model” the relationship between a set of variables.
You might use this to see if certain variables (a movie star’s number of Instagram followers and how much her last five films grossed on average) can be used to accurately predict another variable (whether or not her next film will be a big hit). Regression analysis is mainly used to make predictions.
Note, however, that on their own, regressions can only be used to determine whether or not there is a relationship between a set of variables—they can’t tell you anything about cause and effect.
Factor analysis
Sometimes known as dimension reduction, this technique helps data analysts to uncover the underlying variables that drive people’s behavior and the choices they make.
Ultimately, it condenses the data in many variables into a few “super-variables”, making the data easier to work with. For example: If you have three different variables which represent customer satisfaction, you might use factor analysis to condense these variables into just one all-encompassing customer satisfaction score.
Cohort analysis
A cohort is a group of users who have a certain characteristic in common within a specified time period—for example, all customers who purchased using a mobile device in March may be considered as one distinct cohort.
In cohort analysis, customer data is broken up into smaller groups or cohorts; so, instead of treating all customer data the same, companies can see trends and patterns over time that relate to particular cohorts. In recognizing these patterns, companies are then able to offer a more targeted service.
Cluster analysis
This technique is all about identifying structures within a dataset.
Cluster analysis essentially segments the data into groups that are internally homogenous and externally heterogeneous—in other words, the objects in one cluster must be more similar to each other than they are to the objects in other clusters.
Cluster analysis enables you to see how data is distributed across a dataset where there are no existing predefined classes or groupings. In marketing, for example, cluster analysis may be used to identify distinct target groups within a larger customer base.
Time-series analysis
In simple terms, time-series data is a sequence of data points which measure the same variable at different points in time.
Time-series analysis, then, is the collection of data at specific intervals over a period of time in order to identify trends and cycles, enabling data analysts to make accurate forecasts for the future. If you wanted to predict the future demand for a particular product, you might use time-series analysis to see how the demand for this product typically looks at certain points in time.
Data analytics tools
Now let’s take a look at some of the tools that a data analyst might work with.
If you’re looking to become a data analyst, you’ll need to be proficient in at least some of the tools listed below—but, if you’ve never even heard of them, don’t let that deter you! Like most things, getting to grips with the tools of the trade is all part of the learning curve.
Here are the top ones:
Microsoft Excel
Excel is a software program that enables you to organize, format, and calculate data using formulas within a spreadsheet system.
Around for decades, this tool may be used by data analysts to run basic queries and to create pivot tables, graphs, and charts. Excel also features a macro programming language called Visual Basic for Applications (VBA).
You can learn the ropes with our guide to the top data analysis features in Microsoft Excel.
Tableau
Tableau is a popular business intelligence and data analytics software which is primarily used as a tool for data visualization.
Data analysts use Tableau to simplify raw data into visual dashboards, worksheets, maps, and charts. This helps to make the data accessible and easy to understand, allowing data analysts to effectively share their insights and recommendations.
SAS
SAS is a command-driven software package used for carrying out advanced statistical analysis and data visualization.
Offering a wide variety of statistical methods and algorithms, customizable options for analysis and output, and publication-quality graphics, SAS is one of the most widely used software packages in the industry.
RapidMiner
This is a software package used for data mining (uncovering patterns), text mining, predictive analytics, and machine learning.
Used by both data analysts and data scientists alike, RapidMiner comes with a wide range of features—including data modeling, validation, and automation.
Power BI
Power BI is a business analytics solution that lets you visualize your data and share insights across your organization.
Similar to Tableau, Power BI is primarily used for data visualization. While Tableau is built for data analysts, Power BI is a more general business intelligence tool.
