Are you new to data analysis and keen to find out what makes the industry tick? Read on to discover the methods and techniques that are the driving force behind one of the world’s fastest growing professions.
Data analysis is the process of organising, cleaning and examining raw data in order to draw conclusions and create meaningful solutions. In short, it’s about making sense of data to make well-informed, real-life decisions.
Data Analysts sit at the intersection of business, technology and statistics. With the primary goal of increasing efficiency and improving performance, Data Analysts discover patterns in data and make strategic recommendations.
Many businesses have a wealth of raw data at their fingertips, but data that sits untouched in a spreadsheet is of minimal value and translating it into actionable insights is easier said than done.
Data analytics helps a business tap into a vital resource and better understand its products, customers and competitors, as well as its own operational procedures and capabilities.
Armed with the knowledge data analysis brings about, businesses are able to identify inefficiencies and opportunities. Because data is not an opinion or a theory, it can act as an impartial source of truth when making important business decisions.
Customer analytics is the process of collecting and analysing customer data to gain insights toward customer behaviour. Customer analytics helps businesses make smarter decisions that build stronger connections with customers.
Companies use customer analytics to develop customer-centric business strategies related to marketing, product development, sales, and more. This could include planning your entire customer journey, or building personalised marketing campaigns.
The data analysis process can be broken down into a few simple steps:
✔️ Step 1: Identify – The first step is to define the problem you’re trying to solve and figure out what kind of data is likely to help you find that solution.
✔️ Step 2: Collect – Now that you know what data you’re after, your next job is to collect it. Data might be extracted internally, or gathered from external sources.
✔️ Step 3: Clean – Before it can be analysed, data needs to be cleaned. This involves removing any erroneous information that will distort your results, also known as ‘dirty data’.
✔️ Step 4: Analyse – With your data cleaned and prepped, it’s time to get down to the all-important analysis. There are a range of tried-and-trusted analytical techniques and approaches, some of which we’ll unpack in a little while.
✔️ Step 5: Interpret – Now you’ve analysed your data, you should be able to interpret the findings in relation to your original problem. This will allow you to make a data-backed recommendation to the relevant stakeholders.
Here’s a list of definitions for the most important types of data analysis:
Exploratory data analysis (EDA) techniques are used by Data Analysts to investigate data sets and summarise their main characteristics, often employing data visualisation methods.
This helps determine how best to manipulate data sources to get the answers you need, making it easier for Data Analysts to discover patterns, spot anomalies, test a hypothesis, or check any underlying assumptions.
Descriptive analytics is a simple, surface-level form of data analysis that clarifies what has happened in the past. This involves using data aggregation and data mining techniques.
For instance, a company that monitors its website traffic might mine that data and find a day when the number of visitors dipped dramatically.
Once an anomaly has been identified, a Data Analyst will then look at additional data sources which might tell them why this occurred. The analyst is searching for causal relationships within the data, which could mean using probability theory, regression analysis, filtering, or time series analytics.
Following our example, the Data Analyst might consult data about the company’s day-by-day advertising spend and discover that certain advertising channels were switched off on the day the website traffic decreased.
This is when Data Analysts start to come up with data-driven insights that a company can act on. Predictive analytics estimates the likelihood of a future outcome based on historical data and probability theory.
While predictive analytics can never be completely accurate, it does eliminate the guesswork from making crucial business decisions.
Using the example above, the Data Analyst could make a reasonable prediction that temporary reductions in advertising spend are likely to yield a short-term drop in website traffic.
Prescriptive analytics advises a business on which course of action to take and aims to take advantage of any predicted outcomes.
When conducting prescriptive analysis, Data Analysts will consider a range of possible scenarios and assess the consequences of different decisions and actions. As one of the more complex forms of analysis, this may involve working with algorithms and machine learning.
Using our example, the Data Analyst might recommend that the business maintains a more even day-by-day advertising spend in order to generate consistent levels of website traffic.
When conducting people-focused data analysis, you can normally only acquire data from a sample group, because it’s too difficult or expensive to collect data from the whole population you’re interested in.
While descriptive statistics can only summarise a sample’s characteristics, inferential statistics use your sample to make reasonable guesses about the larger population. Though data might have been collected from a hundred people, you could use inferential statistics to make predictions about millions of people.
With inferential statistics, it’s important to use random and unbiased sampling methods. If your sample isn’t representative of your population, then you can’t draw large-scale conclusions.
Here’s a simple breakdown of the most popular and useful methods that modern Data Analysts rely on:
This is an analysis method whereby a set of objects or data points with similar characteristics are grouped together in clusters. The aim of cluster analysis is to organise observed data into meaningful structures in order to make it easier to gain further insight from them.
This is a form of behavioural analytics that breaks data (normally attached to people) into related groups before analysis. These groups, or cohorts, will share common characteristics and experiences.
Cohort analysis allows a business to see patterns within the life-cycle of a customer, rather than analysing all customers without accounting for their position in a customer journey.
This method enables analysts to accurately identify which variables impact a data set of interest. It helps analysts confidently determine which factors matter most and which can be ignored, as well as how certain factors influence each other.
To give a very simple example, a person’s weight will definitely increase as their height increases. Analysts look for these kinds of causal relationships between statistics because they clarify how one factor will affect another, making outcomes predictable.
Used in machine and deep learning, neural networks are a series of algorithms that replicate the neuro functions of the human brain. Each of these neurons:
How data moves between neurons within a network and the calculations performed will depend on what data findings are uncovered along the way. Though a neural network makes decisions about what to do with data all by itself, it first needs to be trained with data inputs.
IBM Watson is powered by 2800 processor cores and 15 terabytes of memory. Its neural network was trained over a period of two years, as millions of pages of patient records, medical journals and other documents were uploaded to the system. IBM Watson is used to accurately diagnose rare illnesses and diseases.
This method involves analysing a sequence of data points collected over an interval of time. In time series analysis, analysts record data points at consistent intervals over a set period of time, as opposed to recording data points intermittently or randomly.
The advantage this offers over other methods is that the analysis can show how variables change over time. In other words, time is a crucial variable because it shows how the data evolves over the course of its life cycle, rather than just focusing on the end results.
Want to learn more about all things Data Analytics?
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If you have any questions or want to discuss your career prospects, speak to a course advisor and take the first steps in your Data Analytics journey.