Data Analytics: The Ultimate Guide 2022

Data Analytics Definition

Data analytics can be equated to an intelligence-gathering process, where raw data is collected and then analysed to produce actionable outcomes. 

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What begins as incomprehensible data is processed, sorted and analysed to make sense for a wider audience, not just data experts. This information can then be passed along the chain to inform and drive decisions. 

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While virtually any data can be subjected to data analysis, there are four basic types that we’ll break down in the next chapter. 

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Types of Data Analytics

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‍The four main types of data analysis are: Descriptive, Diagnostic, Predictive and Prescriptive. 

Let's look at each individually to determine what they mean and their function. 
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Descriptive Analytics 

Descriptive analytics is the most commonly used and most straightforward type of analytics. 

Its purpose is to summarise large sets of data in a clear and understandable way. It’s also important to note that descriptive analytics focuses on historical and past data. 
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Using facts and real data (not assumed or derivative data), analysts can present their findings to shareholders or management in durable, comprehensible forms, like graphs, pie charts or similar. 
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A typical use of data analytics would be for KPIs. In marketing terms, for example, we would use descriptive analytics to measure the performance of an email marketing campaign. 
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The report would explain how many people were reached, how many clicked through and the number of conversions. This data is then interpreted and used to drive decisions on how to structure the next campaign. 
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In summary, descriptive analytics is concerned with what has happened in the past.
 

Diagnostic Analytics 

The next logical step in the analytics process is to diagnose the data. Otherwise known as diagnostic analytics, this type refers to asking why something has happened. 
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Diagnostic analytics is used to perform a deep-dive on your collected data to answer questions, identify trends and extract valuable insights that will inform your next steps. 
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Continuing with our email marketing example, you might have discovered a notable drop in click-through rates from a recent campaign. 
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This forms the basis of your investigation: why have click-through rates dropped? 
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Before you can answer this though, you’ll need to understand a few concepts first: 
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1. Hypothesis Testing
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Hypothesis testing exists to disprove or prove an assumption. In the case of our email campaign, we might assume that the recent drop in click-through rates was because of a recent change to the subject line. 
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Hypothesis testing directs your attention to where you think the problem may have arisen. 
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2. Correlation vs Causation 

Correlation between two variables or sets of data can be either positive or negative. In other words, one influences the other in the same way. If one goes up, so does the other and if one goes down, the other follows suit. 
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This doesn’t necessarily mean that just because two variables are correlated, that one is responsible for the casual effect of the other. 
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Analysts have to remember to distinguish correlation from causation for more reliable conclusions. 
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3. Diagnostic Regression Analysis 

This refers to an in-depth analysis to determine the relationship between variables. How one variable affects another. 
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When analysing the data of two variables, this is referred to as single linear regression, and when it’s three or more variables, we call this multiple regression. 
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Once a team can understand the historical relationships between different aspects of their data, forecasts and predictions can be made for what to do next, with greater insight and accuracy. 
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Predictive Analysis 

Predictive analysis does what it says on the tin: using current and historical data, businesses make predictions about future outcomes and performance. 
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In terms of marketing, for example, analysts will forecast actionable strategies for what they believe will be a successful marketing tactic or channel, based on the performance of previous campaigns and trends. 
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If we go back to our original question at the start of the article about Netflix suggesting what you should watch next, this is an example of predictive analytics being used to enhance the customer experience. 
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To do this, predictive models use a series of techniques to produce forecasts, including: 

• Data Mining 
• Artificial Intelligence or AI
• Machine Learning 

When applied, predictive analysis increases the accuracy and success rate of its application because it draws on factual and statistical data that’s reliable. It’s a safer bet for where to pour your resources. 
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Prescriptive Analysis 

This is the final stage of the data analytics process and goes one step further than the prediction phase. This is where the best course of action is actually prescribed. 
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Machine learning is used again here to make recommendations on what to do next. 
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If we apply this to our email marketing campaign idea, prescriptive analysis interprets all of the historical and current data, plus the predictive forecasts for what may be popular or emerging as a trend, and surmises this data into an actionable strategy. 
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Prescriptive analysis allows businesses to make informed, data-driven decisions by eliminating guesswork or relying on instinct. 

Data Science vs. Data Analytics

Both types involve techniques and processes that overlap one another but possess fundamental differences in their approach and purpose. 
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In their simplest form, each term can be described in the following way: 

Data Analytics - Analyse and mine existing business data to create actionable insights 

Data Science - Discover new questions to ask, answer and areas of opportunity 

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So if data analysis focuses on understanding large data sets and providing insights, within specific areas and specific goals - data science is focused on creating and leveraging algorithms and statistical models that help drive that purpose. 

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Data scientists create questions and data analysts find answers to an existing set of questions. 
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In reality, they coexist and are codependent but they have distinguishable roles and skillsets: 
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Data analytics skills  
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• Excel and SQL (structured query language) databases 
• Business intelligence tools such as Microsoft Power BI
• Knowledge of statistical tools such as Python, R or SAS

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Data science skills 
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• Big data tools such as Spark and Hadoop
• Adept in programming languages such as Python, R or SAS
• Expertise in SQL databases 

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Whilst you may not be required to understand and be proficient in every skill, depending on whether you’re a data analyst or a data scientist, it’s still worth gaining a basic understanding of how these programmes and tools work. 

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Full marks for effort! 

Data Analytics Tools
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In order to segment, dissect and organise data you’ll develop familiarity with some tools and programming languages that will make it all possible. 

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There are literally dozens of analytics tools out there. Some will cater better depending on the analyst’s skill level and role but we feel these are a few of the top contenders when it comes to analytics:

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Google Analytics - analyses data and produces insights that help steer campaigns For example, it can tell you which channel had the highest conversion rate over a fixed period.  

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Analyze Data in Microsoft Excel - understand your data through natural language queries without having to write complex formulas. Excel analyses data and provides answers with visuals such as tables and charts. 

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Microsft Power BI - a leading business intelligence platform that enables users to create and share reports, visualisations and dashboards. Automated machine learning models can also be built through Power BI and integrates with Microsoft Azure.  

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R - an open-source programming language that allows technical analysts to build virtually any type of data for analysis. R is heavily focused on statistics and data visualisation. 

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Python - another open-source programming language that in addition to analysing and visualising data, can also be integrated with third-party machine learning and data visualisation packages. 

Big Data Analytics

The idea of analysing large volumes of data is nothing new. 

Decades ago, business data would be collected, organised and manually sorted through to predict future outcomes and strategies. Today, these same businesses can make immediate decisions with the power of modern technologies to speed up the process, keeping them agile and competitive. 

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So what is it? Big data can be defined as the process of uncovering patterns, trends and correlations in large data sets to help drive intelligent business decisions. 

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The sheer amount of data being collected in real-time is colossal. When you consider all of the touchpoints that a business collects data from, the reality of this becomes apparent. From social media, chat services, and online purchases to in-house operations like financing teams, sales teams and marketers. 

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Data is being absorbed through every facet of a business and this is where big data analytic tools come in. The following technologies are at the summit of big data analytics: 

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 - The process of analysing large sets of data to uncover patterns and trends Data Miningthat can be turned into actionable insights. 

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Data Management - The process of collecting, organising and storing large amounts of data for analysis and to maximise business operations. 

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Machine Learning - A subset of AI that teaches a machine how to learn. Machine learning automatically produces models that can analyse complex data and deliver accurate results, quickly. 

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Text Mining - The process of assessing any text-based source like books and web-based text to reveal business insights. 

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Hadoop - An open-source software framework that allows for the processing of large sets of data using simple programming models. 

Artificial Intelligence and Data Analytics

Artificial intelligence (AI) can be defined as the ability to teach machines to mimic human intelligence and decision making. 

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If you recall our email marketing example used above, let’s apply AI to one of the components of an email; the subject line. 

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Using AI, we can feed all of a company’s past subject lines and data like conversions rates, click-through rates and bounce rates. We can then teach the AI tool to disseminate which messages were most successful and it will write its own subject lines using these parameters. 

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This is just one example of AI automating processes to save time, resources and optimising performance but when we stop and look around, AI is everywhere. 

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On our streaming services like Netflix, in e-commerce retailers like Amazon, in our digital assistants like Alexa and Siri, even in the cars we drive using safety technologies like autonomous driving and Collision Alerts. 

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AI is used in data analytics to achieve a level of speed, scale and accuracy that would simply be too time-consuming for a human to match. 

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What does this mean for businesses? It allows them to forecast, predict and monitor operations in real-time, to maximise market opportunities and increase profitability. 

Machine Learning and Data Analytics

 
Regardless of what Hollywood has conditioned us to believe, AI and machine learning do not reflect a wartorn future between robots and time-travelling resistance leaders fuelled by evil computing overlords. 
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Machine learning is in fact, a subset of AI. The major difference between machine learning and artificial intelligence is that the former does not require direct programming to predict an outcome. 

In fact, we made a video about it. Check it out...

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As the computer encounters more data, it can use algorithms to improve its learning and predict outcomes. 
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There are four types of machine learning algorithms: 

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Supervised learning - The algorithm is provided with known datasets with desired inputs and outputs. The algorithm then decides how best to arrive at these inputs and outputs, corrected by an operator until it learns the best method. 

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Semi-supervised learning - Using a combination of labelled data (data with tags so that the machine can understand what it is) and unlabeled data (data without definitive information), the algorithm learns to label the unlabeled data. 

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Unsupervised learning - Without the instruction of a human operator, the machine identifies patterns and correlations between data for itself. 

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Reinforcement learning - Through a trial and error approach, the machine tries to find an optimum outcome within a given set of rules and parameters. 

A career in Data Analytics

The constant need for businesses to attract, nurture and convert prospective customers have emphasized how data analytics empowers companies to do exactly that. 

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Data analysts are responsible for interpreting masses of data into meaningful insights that drive decision-making at the highest echelons of a business. This has led to increased demand for analysts and generous salaries to boot. 

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In the UK, even entry-level data analysts can expect to break the £30,000 p/a mark, with senior roles venturing beyond £100,000+. 

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Data analytics is a fast-moving and exciting field with plenty of opportunities to grow and lend your talents to many different fields. 

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So what skills are required to become a data analyst? 

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While some technical proficiency is expected due to the nature of the role, there are a few soft skills that are considered to be great assets to have, if you decide to pursue a career as a data analyst. 

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Data Analyst Technical Skills 
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Many of the following technical skills can be learnt through educational courses and training:
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1. Preparing data for analysis - gathering, arranging, processing and modelling structured and unstructured data. 
2. Data visualisation - using graphical elements like graphs and charts to display data in a digestible way to team members. 
3. Knowledge of statistical programming languages - typical examples used for data analysis are Python and R. 
4. Knowledge of machine learning and AI - understanding how and when to implement machine learning for the business and the ability to train and deploy AI solutions. 

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Data Analyst Soft Skills 
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1. Critical thinking - tackling problems from differing perspectives to objectively analyse and hypothesise results. 
2. Intellectual curiosity - think beyond surface layer assumptions with a creative spark that drives business success. 
3. Business sense - understand the needs and requirements of the business and tailor your approach to problems, and their results, with this in mind. 

Data Analytics Certification

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The value and importance of data analysts are evident in how the world’s leading companies like Netflix, Apple and Google communicate with their customers and tailor those experiences. With frankly obscene amounts of data being collected and deposited every second, there’s a real demand for skilled analysts to interpret the data. 

If you’re interested in a career path to data analytics there’s quite literally never been a better time and there are a few choices for how you can progress. 

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It’s worth saying that right off the bat, it is entirely possible to self-teach aspects of data analytics. You may already have a working knowledge of Google Analytics or Excel and this can be compounded with some practice and direction. 

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Again, some of the analytics tools like R and Python, are open-source, so these programming languages can be freely downloaded and rigorously studied. 

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Learning the core fundamentals of Descriptive, Diagnostic, Predictive and Prescriptive analytics can also be self-taught, however, putting these principles into practice will likely require access to larger software platforms that sit behind a paywall or corporate licensing. 

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We would never deter the enthusiasm of a self-motivated knowledge seeker but it’s worth noting that companies looking to gain a competitive edge, will probably opt for a certified candidate. And all it really comes down to is the fact that a certified candidate will have real-world experience, with hands-on training, and that is a huge plus for hirers, as well as reduced risk on their behalf. 

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Simply having this experience and structured depth of knowledge behind you puts you in the best position possible for a career as a data analyst. 

At the Growth Tribe, we offer a Data Analytics Diploma that encompasses all of the need-to-know skills required to do this. 

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You’ll benefit from live-hosted sessions for maximum engagement and retention, as well as  one-to-one coaching with feedback to sharpen those skills.

Our Data Analytics Diploma features a series of modules that take you from A-Z: 

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1. Data-Driven Mindset 
2. Exploring Your Data (Live)
3. Data Cleaning & Prep 
4. Data Wrangling with SQL (Live)
5. Statistical Reasoning 
6. Warehousing & Self-Service Analytics 
7. Data Visualisation Fundamentals 
8. Fundamentals of Machine Learning (Live)
9. End-to-End Project Walkthrough 
10. Linear Regression Analysis (live)
11. Your Path as a Data Analyst 
12. Live Presentations 

For a full breakdown of what you can expect to learn and the pricing options, head over to the Growth Tribe course page. 

Final Thoughts

We hope you’ve enjoyed this ultimate guide in data analytics and answered some of your burning questions. 
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Data analytics is a large field with a myriad of applications across multiple sectors, but when we drill down to the core, it’s all working from the same principles and it’s all fascinating. 

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The way in which businesses harness their customer data to create new, innovative relationships and improve business intelligence is modern society at its best. 

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There’s a huge demand out there for more data-driven minds like yours, we look forward to seeing what you’ll do next.