Academy Xi Blog

How to Become a Data Analyst

By Academy Xi

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Data Analytics explores the methods, processes, algorithms, and systems used to extract knowledge from data. Data analysts are investigators, storytellers, and most importantly, problem solvers who use raw data to draw actionable insights.

As the backbone of any well-informed decision, data has become vital in driving the strategy and future roadmap of many businesses. Statista found that the big data industry is expected to be worth US$49 billion in 2019, and by 2027 is expected to reach US $103 billion worldwide.  

As more industries become heavily data-reliant, new opportunities for data science roles have emerged including; data scientists, data analysts, data architects, data engineers, statisticians, and database administrators.

A snapshot of the data analytics industry:

  • Over 300,000 people were employed in data analytics in Australia between 2016 – 2017
  • Annual forecasted growth in the data industry is expected to be 2.4% YoY by 2022
  • 76% of companies expect to invest more in data analytics capabilities over the next two years
  • Forecasted average income for a Data Analyst is AU $130,176 by 2022.

How data analytics provides business value

With data becoming the new currency for business decisions and strategic roadmaps, some key benefits of data analytics include:

  • Helping businesses define decisions and goals: By dissecting previous performance, businesses can use data to prioritise their goals according to highest importance and optimal results. Rather than making decisions based on gut, data can prove what has worked well within a business and define what future goals they should work towards.
  • Adoption of best practices: Applying analytics to the design and control of processes enables businesses to optimise their activities to fulfil customer expectations and achieve operational excellence.
  • Test informed decisions: Effective data collection enables businesses to stay competitive by testing and validating informed decisions and anticipating market demand.
  • Reduce risk and fraud: Data Analysts are able to identify data patterns that can be used to make frameworks to detect fraud. These alerts help businesses track unusual activity and respond in an appropriate timeframe.
  • Deliver personalised experiences: By using data to tell stories, Data Analysts are able to empower sales and marketing teams to better understand their audience. With increased knowledge of buyer behaviour and motivations, organisations are then able to make personalised, well-informed solutions.

Key attributes of a Data Analyst

According to LinkedIn, data mining and statistical analysis were the second most in-demand skills requested by employers, with the most number of job openings, listed in 2016.

As the main driver of strategic business decisions and high industry demand, here are some key attributes of a successful Data Analyst:

  • Critical thinking: Before developing any hard skills such as programming, it is vital that a data analyst adopts a critical thinking mindset. To ensure useful insights can be drawn it is necessary that a data analyst has the ability to ask the correct questions in the first place. The role of the analyst is to then uncover and synthesise connections, make sense of the information connected, and present their findings in an easily digestible manner.
  • Understanding the data lifecycle: Familiarity or knowledge of the acquisition, management and pre-processing of data, as well as mathematical and statistical analysis, reporting and decision making is extremely useful. It allows a Data Analyst to interpret data and present it in a meaningful way that can be used to support business decisions.
  • Computer programming: Competency in programming languages such as R, Python, SQL, SAS, MATLAB and Excel are invaluable for Data Analysts, who use programming skills to extract, discern, and manipulate data. This information can then be presented into digestible visualisations in tools like Tableau.
  • Data visualisation and presentation: Data visualisation and presentation are hand-in-hand skills for a Data Analyst. The ability to tell a compelling story with data and drawing valuable insights is key to making any data useful.   
  • Machine learning: Machine learning and predictive modelling are increasingly becoming the most popular topics in the field of data science. To develop these skills, a Data Analyst is required to have proficiency in programming languages in order to make predictions and automate existing data systems. 

Data Analytics aligns results in a quantifiable commercial outcome that is realistic and applicable to each situation. This takes a lot of patience and creativity.” — Felipe Rego, Analytics Partner.


Opportunities in the world data analytics

On a global scale, 2.5 quintillion bytes of data are created each year, with over 90% of all the data that exists today only created within the last two years. That’s a lot of data!

With increasing demand for Data Analysts, there is a multitude of benefits of kickstarting a career in data including:

Huge job opportunities

  • With increasing demand for Data Analysts, there is a significant skills shortage in the market. With the number of unfulfilled roles set to rise, McKinsey predicted that in the US alone, there was a shortage of over 190,000 Data Analysts in 2018. For anyone on the front foot of driving a future-focused career, embarking on a career in data would avoid making their skills obsolete and assist in riding the next wave of digital change in the workforce.

Increased earning potential

  • As with the basic economics of supply and demand, a shortage in the number of Data Analysts in the market automatically means increased earning potential. For those Data Analysts currently in, or looking to transition into the field, an increasing trend of year-on-year income is set to continue.  

Expanding career development

  • Organisations of all sizes are beginning to prioritise data as an important part of their business operations. With new technology enabling increasingly sophisticated Data Analytics with large and diverse data sets, there is a multitude of different types of roles across various pathways. Anyone entering or progressing in the field can also choose from three types of Data Analytics to work in: prescriptive analytics, predictive analytics, and descriptive analytics.

Ability to work with some of the world’s biggest brands:

  • The world’s largest brands such as Apple, Amazon, and Uber are all looking into data to make well-informed decisions. For Apple, data is used to understand what additions and modifications customers need to deliver exceptional user experience. For Amazon and Uber, predictive algorithms are used to map out recommended purchases and travel routes.

In the last 30 years, the rise of data, and how it is produced, consumed, and stored has dramatically revolutionised the way businesses make decisions. With the number of career opportunities set to increase, transitioning into a role in data presents a plethora of exciting pathways.

Learn how you can use data to make informed business decisions and present compelling stories with our upcoming Data Analytics course.

Academy Xi Blog

An Introduction to Big Data Analytics

By Academy Xi

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Data has become one of today’s most important resources for businesses regardless of size. It’s invaluable when it comes to decision-making, so much so that big data is today a multi-billion dollar industry, and growing at an exponential rate.

The value of data isn’t new. In fact, collecting data and using it to make informed decisions have been around for millennia.

The history of data analytics

Statistics — where data analytics is based on — can be traced back to Ancient Egypt for the construction of the pyramids. Data collection has been used for centuries with governments collecting information for censuses, using trends and insights for planning and taxation.

By the 19th century, businesses have followed suit with the likes of Frederick Winslow Taylor and Henry Ford using data analytics to gain insights they will then use when making changes or improvements to their processes.

In the 60s, data analysis was shifting from being completely manual to being aided by computers. By the 80s, the growth of data gave rise to data warehouses. By the 1990s we were data mining; the process of finding patterns using large datasets.

Fast forward over 20 years, and the rise of big data as we know today is being used across industries from government and healthcare to retail and lifestyle. A multitude of tools are being developed constantly so we can collect, store, and assess data efficiently.

Key technologies in Big Data

Many techniques and technologies are involved in data analytics, enabling businesses to maximise their data and get the right kinds of insights.

Here are some of Big Data’s key technologies:

Data management: Before data can be analysed, it must first be high-quality. Processes must be established to maintain the quality of data. Tools like NoSQL databases and other master data management systems are used to ensure reliable and efficient data management that can be accessed by the entire enterprise.

Data mining: With the exponential growth of data, data mining is used to discover patterns and get information that can be analysed and used to inform complex business decisions. Data mining tools make it possible for data to be sifted immediately, providing only relevant insights to help companies make speedy decisions.

Predictive analytics: Data is processed using algorithms and machine learning to assess future outcomes. Through predictive analytics, businesses can determine if they’re on the right track, helping prepare them for the future and to discover, evaluate, or solve problems through predictive scenarios.

How we collect, analyse, and use data has evolved. Along with technology, the importance of data and its analysis has also evolved.

The importance of data analytics

Data analytics provides businesses with quantifiable information, enabling smarter decision-making through having the means to measure success and track goals over time. By analysing data, they can find new opportunities for improvement and growth.

Business moves are smarter, profits are higher, operations are more efficient, and customers are happier.

According to Whizlabs, the importance of data analytics is apparent through four perspectives: data science, business, real-time usability, and the job market.

  • Data science: Big Data has variety, volume, and velocity with analytics being able to extract and prepare it to provide fresh insights for researchers, analysts, engineers, and businesses.
  • Business: Businesses can keep their focus on customers by interpreting Big Data to provide them with their needs. This improves aspects of a business from efficient workflows and customer satisfaction to increased profits.
  • Real-time use: Many industries benefit from Big Data analysis like banking, technology, energy, and consumer industries. The educational sector also benefits from data analytics in real-time through study.
  • Job opportunities: Big Data and analytics have opened a wealth of lucrative opportunities in the data sciences, making it a promising profession. Those interested in data can gain opportunities like Big Data Analysts, Big Data Engineers, Solutions Architects, and more.

Making data-driven decisions

In order to survive in business, you must remain competitive. These days, where organisations use Big Data and powerful data analytics strategies every day, simply using your gut to make decisions will not get you the desired results.

Instead, you need to develop a data-driven culture.

Your business acquires data by the second — whether it’s from internal processes or customer input. You have the raw materials you need to get the answers and the best insights to inform your business decisions. These answers and insights are obtained through analytics.

Despite the value of data analytics, only 40% of businesses use data-driven strategies, with a majority still opting to trust gut instincts and experience. Making the most out of data technologies can be your strategic edge above the competition.

But how do you start making data-driven decisions? Here are five steps you can follow:

Step 1: Have a plan.

What goals do you want to achieve, or questions you want to be answered using data? This informs the kind of data that will be relevant, and the technologies and strategies you will use to analyse data.

Step 2: Identify sources of data.

Which area of your business provides the most valuable data that will help you achieve your business goals and strategies? Is it customer service? Operations?

Step 3: Target and streamline data.

With the amount of data you receive, it’s crucial to know which is most beneficial and worth investing your data management budget on. Focusing on relevant data reduces chaos and inaccuracies in data reporting.

Step 4: Collecting and analysing.

Determine the tools and technologies you will need to effectively collect and analyse data, as well as the skills and expertise of those that will handle data and analysis. Depending on the size of your organization, this can range from one person using Excel to a team of data scientists and executive level heads using a combination of technologies to acquire insights.

Step 5: Take action.

The presentation of data and insights can be crucial in helping decision-makers take the correct course of action. Visual presentation of data and analysis will help make them more clear, concise, and digestible. Decision-makers will be able to take quick action and improve the business faster.

Tools for data analysis

Whether it’s only one person doing data analysis or a whole team, there are indispensable tools that will help parse data and generate reports and statistics.

Two tools, in particular, are Excel and Tableau.


Microsoft Excel is a basic but powerful tool for data analysis used by many. It can perform simple to complex data analytics, helping users from creating simple pie charts with conditional formatting to more complex histograms, run SQL databases, search vlookups and pivot tables.


Tableau features an intuitive interface and can do everything from data sourcing through any data source, preparation and exploration of data, as well as analysis and presentation. Tableau functionality includes data visualisation and even social media sharing.

Other tools for data science

Aside from the above tools, more complicated data may benefit from more robust tools like programming languages. Here are five of the most widely used:

  • Python: one of the most popular data science languages, Python is easy to learn, has good visualisation capabilities and is scalable and faster than any other coding language. Data science and data analytics libraries also use Python. With its large community, it’s easy to find answers online too.
  • R Programming: through data wrangling, R Programming is able to clean and process messy data for easier understanding and analysis. It has many tools for data visualisation, analysis, and representation and is compatible with machine learning. It is also open source, making it a cost-effective option for organisations regardless of size.
  • Java: one of the oldest languages, Big Data tools are likely written in Java. It has a wide library with multiple tools for data science, enabling you to easily solve Big Data problems. It also has the JVM or Java Virtual Machine, a great platform for writing identical code for multiple platforms.
  • Microsoft SQL: this is a relational database management server that can store and retrieve data. It has capabilities for different workloads, whether it’s a small single-machine application to a larger Internet-based one with multiple users.
  • Scala: a high-level programming language that means “scalable language”. It is open source and like Java, runs on the JVM platform and can operate seamlessly, making it flexible and compatible with Big Data tools that also run on Java.

These are only a few of many programming languages that can aid in the analysis of data. Other languages like Julia, MATLAB, C++ and more have their own advantages and can be more preferable depending on your organisation’s needs and your data scientist’s expertise.

Through the use of tools and technologies for data science, businesses will be able to truly take advantage of data analytics.

Businesses will enjoy cost reductions through cost-effective storage options for large amounts of data, and data-driven improvements to make business more efficient. Not only will decision-making be better, but it will also be faster—even immediate.

Ultimately, data and analytics provide businesses with the ability to identify what customers want and the best way to give them exactly what they want.

Turn your data into actionable insights, and well-informed business decisions with our part-time Data Analytics course.

Academy Xi Blog

Reskilling in 2020: How to manage choice paralysis

By Saga Briggs

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Reaching the decision to reskill or upskill is one thing; taking the necessary steps to do it is quite another. In an age where the question is no longer “What do you want to become?” but “What do you want to learn?”, we have even more options to choose from and even more potential for choice paralysis. Do we learn software development? UX design? Digital marketing? Should there be coherence to our skillsets and experience, or should we be well-rounded and widely familiarised?
Benefits of upskilling and reskilling include future-proofing your career, positioning yourself for promotion and raises, staying abreast of industry trends, and gaining the knowledge and experience to fall back on consulting or freelancing should you ever need to.

Companies like Amazon, Walmart, PwC, and JPMorgan Chase have upskilling programs underway, and Quartz estimates that most Fortune 500 companies will have launched a program in the next year.But when it comes to taking charge of your own professional future, and deciding whether to reskill in areas that may be brand new to you, it’s important to know what will and will not be worth your time, effort, and money in the end.

According LinkedIn’s 2020 Emerging Jobs Report, the top 15 Emerging Jobs in Australia are:
1. Artificial Intelligence Specialist: teaching machines to learn
2. Cybersecurity Specialist: keeping computer information systems secure
3. Marketing Automation Specialist: generating leads and analysing workflow
4. Robotics Engineer (Software): building and deploying robotics software
5. Site Reliability Engineer: applying software engineering to systems administration
6. Customer Success Specialist: understanding customers’ needs and managing relationships
7. Data Scientist: part computer scientist, part mathematician
8. Data Engineer: managing pipelines, data workflow and management
9. Growth Manager: using growth hacking to acquire more clients at scale
10. Chief Strategy Officer: developing strategies that deliver growth across a business
11. Anti-Money Laundering Specialist: train employees how to manage fraud and establish compliance policies
12. Product Owner: market analyst, project manager, product designer, business strategist
13. Service Designer: designing experiences for clients and employees
14. Full Stack Engineer: front-end and back-end web development
15. Automation Consultant: introduce software and digital processes into organisations to allow functions to run more efficiently

The U.S. is also seeing demand for Behavioural Health Technicians, Sales Development Representatives, Robotics Engineers, Chief Revenue Officers, Cloud Engineers, and Javascript Developers.

In Europe, employers are hiring Data Protection Officers, Human Resources Partners, Commercial Real Estate Agents, Community Relations Managers, Big Data Developers, Building Information Modeling (BIM) Specialists, Enterprise Account Executive, and Content Designers.

In Asia, add to that DevOps Engineers, Partnership Specialists, Clinical Specialists, E-commerce Specialists, Creative Copywriters, Robotic Process Automation Consultants, Growth Managers, Digital Marketing Specialists, and Lead Generation Specialists.

When taken together, the top skills needed for these jobs are as follows:
Python, CRM, Salesforce, SaaS, Amazon Web Services, Apache Spark, Data Science, Machine Learning, Account Management, JavaScript, Information Technology & Services, and Marketing & Advertising.

With these in mind, it appears well-worth your time to 1) learn programming, 2) become a machine learning expert or data scientist, 3) specialise in service-based IT, and/or 4) study account management or sales.

You’ll notice that half of those skills are service-based or human-centred. And, in fact, the report notes that demand for soft skills will increase as automation becomes more widespread:
“Skills like communication, creativity, and collaboration are all virtually impossible to automate, which means if you have these skills you’ll be even more valuable to organisations in the future,” writes Guy Berger, LinkedIn’s Principal Economist. “If you have these skills, make sure they’re on your LinkedIn profile so hiring professionals can find you for relevant opportunities.”

Let’s take a look at how you might reskill or upskill in both “hard” and “soft” areas.

Reskilling in machine learning

The number-one most in-demand job for 2020, anywhere in the world, is Artificial Intelligence Specialist. Businesses are recruiting AI Specialists for a wide range of projects, from designing crop-harvesting robots to modeling risk in finance.

Element AI in Montreal estimates that in the United States, there are around 144,000 AI-related job openings but only about 26,000 developers and specialists looking for work. One reason may be that people who are, in fact, qualified for these jobs don’t realise they are. But that’s starting to change.

“The most common jobs that AI Specialists held prior to labeling themselves with the title include ‘software engineer,’ ‘data scientist,’ ‘research assistant,’ and ‘data engineer,’” says Jonathan Vanion, writing for Fortune. “This suggests that people may be updating their job titles to include artificial intelligence, to put themselves in a better position to capitalise on the current AI boom.”

This advice is gold: Call yourself what you want to be, not what you are, and you’ll grow into the role. After all, hiring managers often post unreasonable lists of qualifications that describe no real candidate.

“Employers’ list of desired skills often doesn’t make much sense—for instance, a request for ten years’ experience with a framework that has existed for only two,” says Jilles Vreeken, machine-learning researcher at the Helmholtz Center for Information Security and the Max Planck Institute for Informatics in Saarbrücken, Germany. “So make sure you tick lots of the boxes, but don’t feel inhibited if you don’t tick every one.”

To get started with machine learning or AI, you’ll most likely need to know Python and have a solid grasp on data science and deep learning fundamentals. Traditionally, AI Specialists have a degree in computer science, though it doesn’t have to be a PhD. A background in math and statistics is helpful.

“Focus on learning the fundamentals: good linear algebra, probability, and software engineering skills,” says Ian Goodfellow, a research scientist at OpenAI. “The state of the art in machine learning changes from one year or even month to the next, but the fundamentals stay the same for decades.”

That said, you don’t have to be an AI Specialist to work in AI.
Many AI companies are hiring subject matter experts in a wide variety of fields to guide AI development in specific industries like healthcare, insurance, and finance.

“AI designed for simple tasks like image recognition can be trained on data alone, but AI assigned to more specialised tasks needs input from human experts,” explains Antony Brydon, CEO and co-founder of Directly. “Post-doctoral researchers, data scientists, and other AI specialists may be brilliant people with diverse skill sets, but rarely do they have insight into the nuances of product design, the delicacies of customer service, or the subtleties of concierge hospitality.”

According to Nature, businesses such as LinkedIn, Kixeye, and Nextdoor are already using non-technical experts to develop their AI platforms.
The point is that working in AI may not necessarily mean programming machines. It could instead mean consulting AI companies in your area of expertise, like mental health or banking. Still, you could gain an edge as a consultant by studying the fundamentals of AI yourself.

Reskilling in programming

The top programming languages and frameworks mentioned in the LinkedIn report are Python, JavaScript, React.js, Node.js, CSS, and AngularJS. You can learn any of these online, at your own pace. You may even be able to learn a language on the company dime, depending on your employer’s professional development policy.

To get started, be prepared to learn. Voraciously.
“You must have an insatiable appetite for knowledge,” says professional programmer Rob Walling. “This usually means reading a programming book every few weeks in the early days, and moving on to more conceptual books like The Pragmatic Programmer, Code Complete, and Facts and Fallacies after 6-12 months of full-time coding. I can’t stress enough the value of reading, nor the value of immersing yourself in code early in the process.”

Consider doing a software apprenticeship, which involves learning to program alongside a mentor. You’ll pick up not only the fundamentals but insights, knowledge, and insider tips that normally take years to uncover. You may also get a job out of it if you’re a savvy networker.

Next, sign up for a course in the programming language that’s most often required for the job you want. For instance, if you want to work in machine learning or back end development, choose Python. You can learn it as a beginner programmer, although it helps to know programming basics at least in theory. If you want to become a UI Designer or Front End Developer, choose JavaScript or CSS. Both are “core technologies” of the web, so you won’t waste your time with these.

Estimates for “time to proficiency” for Python are eight weeks. For JavaScript, nine months. Tackle both if you want to become a Full Stack Engineer.
Finally, some words of wisdom from Data Scientist Freddie Odukomaiya: “Pick one programming language and stick to it. Don’t go back and constantly change your choice of language to study. If you do, you will slow your progress down.”

Reskilling in data science

To become a Data Scientist, you do not necessarily need a degree in a field like engineering, computer science, mathematics, or statistics. What matters most of all are your skills and experience. This is especially true in a market where demand vastly outweighs supply.

DJ Patil, who built the first data science team at LinkedIn before becoming the first Chief Data Scientist of the United States in 2015 and coined the term “Data Scientist” with Jeff Hammerbacher (Facebook’s early data science lead) in 2008, tweeted the following in 2016:

“Data science doesn’t care about what you majored in or if you even got a degree. It’s what you do with data that matters.”

You could, in fact, be a data journalist (emphasis on the “journalist”) and get hired as a Data Scientist. It’s all about your familiarity with three things: statistics, programming, and business.

“A data scientist needs to know statistics and math to analyse patterns in data and to manipulate it with different treatments,” says Roger Huang, CEO and co-founder of CyberSecure. “They need to use programming skills to deal with data at scale that can take up terabytes of space. They need to understand business fundamentals in order to communicate their findings and drive other teams to action based on their insights.”

What this means is that you don’t have to start from scratch as a computer science student. You can simply take a course in statistics, learn a programming language, and enrol in an online business or entrepreneurship course to learn the basics of each. You can then combine these skills to work on projects with fellow learners and mentors. Remember, you can always volunteer to complete your first project for a company for free to gain experience.

“The one thing a bootcamp can deliver that a really good university can’t is a really thorough understanding of how to build something from ideation to deployment—that’s a full-fledged capability I don’t see often from Ph.D.s,” says Daniel Carroll, a principal data scientist at Aetna. “If you can bring that to light in a CV or interview, that will make you shine.”

Reskilling in account management

Account Managers and Customer Success Specialists are in high demand around the world. If you like to work with people, or at least to manage relationships, then this is a promising field for you. Businesses know they need to become more service-oriented as basic processes go digital, so they will be looking for candidates with strong customer service, communications, and HR management backgrounds.

It doesn’t matter if you’ve only worked as an Account Manager in one industry—your skills are transferrable. And if you’re just beginning your journey in account management, there’s no reason for concern. As you may have guessed by now, you don’t need a specific degree for this field either.

LinkedIn’s Emerging Jobs report lists Customer Success Specialist as an especially fruitful area. Unlike Customer Service, which is more reactive, Customer Success focuses on working proactively to understand client needs on behalf of the company.

“Australian consumers have some of the highest service expectations in the world,” the report explains. “So, it is not surprising that Australian companies have been early adopters of customer relationship management (CRM) tech. According to Gartner, in 2018, local companies spent a record A$2.3 billion on CRM technologies, up 20 percent on the previous year and outpacing the global growth of 15 percent. The rise of Software as a Service (SaaS) business has been a big contributor to the growth in this job. Product adoption & utilisation and customer retention & growth are key success factors for this job.”

The main skills needed for this job are account management, CRM, customer retention, strategy, and Salesforce. If you have experience in any of these areas, or in project management, you’re qualified to apply for a Customer Success Specialist role. Another skill, which we’ll explore below, can push you in the account management direction: Service design.

Reskilling in service design

Service design is an increasingly popular field for professionals in Australia, and will draw higher demand more globally in the next few years. As businesses move from product-orientation to service-orientation, they will need more people to understand how clients interact with those services to help improve their experience.

“Just as an industrial designer designs chairs and water bottles, a service designer designs experiences,” explains the report. “A good service designer can work with a broad sweep of the organisation to ensure that its service is designed to make life more enjoyable for its users.”

Top skills needed for service design include design thinking, user experience design, user research, user-centred design, customer experience design, and customer journey mapping. However, they are not requirements for getting started. If you have a background in the service industry, you can tack on a UX course and start redesigning your CV to help usher in your dream job. You can also take a service design course which, on top of experience in the hospitality or restaurant industry, will set you up for success in your job search.

Upskilling in communication

Good communication skills create a positive experience at work, build team morale, and help you build your own leadership skills. They’re not about taking the floor and delivering monologue after monologue until your conversation partner’s eyes glaze over. They’re about slowing down and being present, listening closely, asking questions, being clear, anticipating confusion, knowing when and when not to crack a joke, using body language that’s aligned with the message you want to send, and making the people around you feel comfortable.

“Good listeners ask questions, challenge assumptions, and generally check for understanding throughout a conversation,” writes Celeste Mora, Grammarly’s Senior Content Strategy Manager. “They cultivate a two-way conversation where they’re constantly trying to improve their understanding of what the other person is saying.”

Communication experts also recommend “over-communicating” sometimes, as we tend to assume people understand us more than they actually do. Balance that, however, by being as clear and concise as possible in your messages. You’ll be rewarded for it:

“Twenty years ago, the smartest person in the room at work was the one who had gathered more and better information than anyone else,” says Dean Brenner, president of The Latimer Group, an executive coaching agency in the U.S. “Today, the smartest person in the room…is the one who can simplify all the things that are going on and create a path through the complexity and toward a simpler solution.”

Brenner adds that when you’re talking about something you do or something you know, most people want to know one thing: how it can apply to them.
“The real mistake is to assume that everybody cares about the nitty-gritty of the data as much as you do,” Brenner says. “What you have to realise is everybody’s listening to what you’re saying and thinking in their heads about how they can apply it to what they’re doing.”

An online course in written or spoken communication could be an interesting option as you head into 2020. Foreign language courses are always useful too, as employers will increasingly be looking for multi-lingual candidates who can liaise with global clients.

Upskilling in creativity

How do you become more creative?
Research shows one big predictor of creative output is a trait called “openness to experience.” Part of the Big Five personality scale, openness to experience is pretty much what it sounds like: exploring new places, meeting new people, going outside your comfort zone, taking up new activities and skills, and generally exposing yourself to new stimuli. Some people are naturally more comfortable with this way of being than others, but it can be learned and honed like any other skill. The more you seek variety and witness the creative fruits of your efforts to do so, the more comfortable you’ll become with this way of being and the more incentive you’ll have to continue with it.

“Multiple psychological studies suggest that a crucial trigger of creativity is the experience of unusual and unexpected events,” says psychologist Scott Barry Kaufman. “Unexpected events can certainly mix emotions, and mixed emotions… can increase sensitivity to unusual associations and ideas.”

A 2012 study published in the Journal of Experimental Social Psychology concluded that any life experience, from the traumatic to the joyful, can lead to creativity “as long as it diversifies your experiences and pushes you outside your normal thought patterns.”

For the study itself, researchers placed participants in a virtual reality world where they took a three-minute tour through a university cafeteria. They were divided into two groups: experiencers of a weird reality where events violated the laws of physics or experiencers of a normal reality with standard laws of physics. For example, people in the first group might walk up to a suitcase resting on a table only to watch the size of it decrease, then increase as they walked away.

Afterwards, researchers asked participants to take a test of cognitive flexibility where they came up with as many answers as possible to the question, “What makes sound?”

Participants who had been immersed in the weird virtual reality scored higher on the test, coming up with more creative answers, than those who had been immersed in a normal version of the virtual world.

Kaufman says the “core feature” of an experiment like this is “actively experiencing a violation of how things are supposed to happen.”

These findings have implications for team managers as well.
“The latest research on the role of emotions in creativity suggests that instead of focusing exclusively on bringing out positive emotions among employees—or attempting to dispel negative emotions—managers may want to consider additional factors,” Kaufman says, “such as whether the environment brings out emotional ambivalence (Is the environment unusual? Will it tap into a wide range of seemingly contradictory emotions?) and motivational intensity (Will it broaden or narrow someone’s focus?) when trying to stimulate creativity.”

Other research suggests that taking walks, doodling, feeling at ease, solving problems, tapping into mixed emotions, cultivating interdisciplinary thinking, and switching up your environment can all help you boost your creativity.

Simply put, employers want creative teams because, in an age of automation, creativity is a distinctly human trait. It won’t become obsolete—at least, not any time soon. Time to skill up.

Upskilling in collaboration

Collaboration will trump competition as the world becomes more globalised. We’re already witnessing the rise of the collaborative economy with the phasing out of solo entrepreneurship and siloed teams, the move toward partnerships between companies and consumers, and the democratisation of income through startups like Uber and Airbnb. Organisations know that collaboration is the faster route to growth, and that’s why they’re hiring people who can help build relationships with customers and partnerships with other businesses.

“The real driver of success in the collaborative economy is not technological innovation,” says Andrew Reid, founder and president of Vision Critical. “Collaborative startups like Lyft and Instacart aren’t getting billion-dollar valuations because they’re more technologically advanced than the big transportation and hospitality services that existed long before them. These startups are dominating because they’ve uncovered insight about today’s customers. Ultimately, that understanding of the customer is what will give companies sustained competitive advantage in the age of the collaborative economy and beyond.”

As customers themselves demand more authentic experiences from companies, what it means to “understand the customer” will increasingly require interacting with that customer and building a real relationship with them. If you plan to work in the customer-facing side of a business, you’ll need strong collaboration skills.

You’ll also need to be a team player, enthusiastic about learning from and teaching your colleagues. Teams that work well together simply get more done, and are able to be more creative during brainstorming sessions and meetings. Collaboration doesn’t mean being best friends with the people you work with; it means knowing how to bring out people’s strengths, make people feel heard, challenge people respectfully, stay receptive and open-minded, accommodate different personalities and communication styles, and guide idea sharing in a productive direction.

To become a skilled collaborator, try gaining experience as a community or events manager, customer success specialist, human resource manager, or employee experience designer.

Upskilling in emotional intelligence

Yes, this is a valid soft skill. Yes, employers are looking for candidates who have it. What does it look like, exactly?

As defined by Psychology Today, emotional intelligence is “the ability to identify and manage one’s own emotions, as well as the emotions of others.”

It’s made up of three skills: “the ability to identify and name one’s own emotions; the ability to harness those emotions and apply them to tasks like thinking and problem solving; and the ability to manage emotions, which includes both regulating one’s own emotions when necessary and helping others to do the same.”

How do you improve at something that requires so much sensitivity, especially in a professional environment?

To cultivate better self-awareness, Mark Manson recommends understanding yourself and your behaviour on three levels: 1) know what you’re doing, 2) know how you feel about it, and 3) know what you don’t know about yourself.

“Schedule time in your day to get away from [distractions],” he advises. “Do your morning commute with no music or podcast. Just think about your life. Think about how you’re feeling. Set aside 10 minutes in the morning to meditate. Delete social media off your phone for a week. You’ll often be surprised by what happens to you.”

Next, learn to channel your emotions well. This means reserving judgment over the way you or another person feels and focusing on how to respond.

“The whole point of this is to be able to channel your emotions into what psychologists call ‘goal-directed behavior’—or what I prefer to call ‘getting your shit together.’”

Learning to motivate yourself can also boost emotional intelligence. Manson recommends acting early to get things simmering:

“If you don’t feel like anything motivates you, do something,” he writes. “Draw a doodle, find a free online coding class, talk to a stranger, learn a musical instrument, learn something about a really hard subject, volunteer in your community, go salsa dancing, build a bookshelf, write a poem. Pay attention to how you feel before, during, and after whatever it is you’re doing and use those emotions to guide your future behavior.”

Once you’ve built a healthy amount of self-awareness, you’ll be able to tune into the emotions of others to create relational health around you.

“You do this by connecting and empathising with others. By both listening to others and sharing yourself honestly with others—that is, through vulnerability.”

Finally, Manson recommends “infusing your emotions with values.”

“Emotional intelligence is meaningless without orienting your values,” he writes. “You might have the most emotionally intelligent CEO on the planet, but if she’s using her skills to motivate her employees to sell products made by exploiting poor people or destroying the planet, how is being emotionally intelligent a virtue here?”

Being clear about your own values will allow you to guide your emotional energy in a positive, constructive direction.

It’s a new decade. Time for a new you (or maybe just the 2.0 version). As challenging as it may be to decide which direction you’ll go in, rest assured that you can’t, really, make a wrong choice when it comes to upskilling or reskilling in any of these areas. The only mistake you can make is to take no action at all. So give yourself permission to do what feels right, whether it “makes sense” now or not. The reason you chose that new skill will become crystal clear when you land your dream job later on down the road.


If you want to future-proof your career and learn in-demand skills, we’ve got a variety of short courses available in Human-Centred Design, Business and Marketing and New and Emerging Tech.

Saga Briggs is a journalist covering trends in learning, creativity, intelligence, and educational technology. Follow her @SagaMilena 

Academy Xi Blog

Ten Growth Marketing Hacks

By Academy Xi

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Growth Hacking (or growth marketing) is the process of rapidly experimenting, iterating, and improving a business’ marketing strategy to optimise growth. The fascinating thing about Growth Marketing is how varied it is; there’s no one-size-fits-all approach.

Many startup founders and marketers are faced with the mind-boggling challenge of growing a sales funnel without spending bucket loads on expensive marketing activities. But without the right hacks and skills, the solution is mere guesswork, simply crossing your fingers in hope of a good return on investment.

If you don’t have extra cash to splash on extensive marketing activities, growth hacking can help you unlock sustainable and scalable growth from the ground, up.

With emerging technologies and exponential disruption, let’s explore some examples of valuable, easy-to-implement growth hacks that you can utilise in your business:

Growth Hack 1: Dropbox Reciprocates Value

The key to a business’ success is to always be giving.” — Jordan Harbinger

We all know that one friend — the who borrows money from you or asks for a favour without offering anything in return. A brand’s relationship with their customer should be similar to human relationships, based on the same behaviour of ‘give and take’.

One of the most famous examples of clever growth marketing was Dropbox who added a ‘Get Free Space’ button on the front page of their service. If someone referred a friend, both them and their friend received 500MB free space. Sign-ups increased by 60%. Now that’s win-win.

Growth Hack 2: Hotmail Leverages the Power of Referrals

Referral marketing is an effective, less expensive tactic that results in a strong source of lifetime-value (LTV). Research reveals that 71% of marketers agree that referral marketing costs less than traditional customer acquisition, that prioritises the acquisition of new customers.

Back in the 90s, Hotmail massively expanded email use with an intriguing offer at the bottom of their emails: ‘PS: I love you. Get your free e-mail at Hotmail.’ Their strategy was simple and still effective — and at the time generated over 3,000 new clients per day.

The practice of ‘referral marketing’ is a popular hack, and a key part of the ‘pirate funnel’ of growth marketing:

Referral Tip: Use some attention-grabbing copy or images to capture people’s interest.

Growth Hack 3: Airbnb relied on other sites

It might sound counterproductive, but leveraging high-traffic websites can be an effective way to get in front of your customers. An example of a company that has made this work is Airbnb, as they dramatically increased their user base by exploiting an existing high-traffic channel.

Through a smart email integration, Airbnb encouraged Craiglist users in America (similar to Gumtree in Australia) to replicate room ads on Airbnb. This integration worked in a number of ways — directing traffic from Craigslist to Airbnb, and automatically posting any Airbnb ads on Craigslist, effectively doubling Airbnb’s outreach.

To identify existing high-traffic customer channels, step into the shoes of your users:

  • “Where do my users hang out on social media or online?”
  • “How are my users currently solving this pain point?”

Growth Hack 4: Pinterest Generates Buzz

Another method of rapidly expanding your user base is through creating a sense of exclusivity. With one of the top human motivators being exclusivity and fear of missing out (#FOMO), crafting exceptional, seamless user experiences go hand-in-hand to form the ultimate growth hack.

Pinterest pinned their way to the top by following Facebook’s lead and starting out as invite-only. After they built a long waiting list, Pinterest generated a whirlwind buzz within the community. To keep the flames of their initial hype burning, Pinterest cemented their position in the market through the introduction of limitless scrolling.

By including this small feature, the impact on the user experience was phenomenal. Based on the insights captured, Pinterest encouraged longer uninterrupted interactions, and significantly increased the enjoyability of their app.

Growth Hack 5: Slack unlocks ‘Job to be Done’

As part of the Human-Centred Design philosophy within User Experience (UX) Design or Service Design, each product or service has a ‘job’ (need or purpose) that a customer has ‘hired’ it to fulfill.

One remarkable company that has hacked their way to success is Slack, a messaging and task management software. Slack defined their product based on a pain-point. Customers who are disillusioned with time-consuming internal email communication. By leveraging existing communities, the team invited 8,000 people to try the software, without charging them if they didn’t convert afterward. Little did Slack anticipate that their success would snowball into over 8 million daily active users today.

Once you understand what jobs people are striving to do, it becomes easier to predict what products or services they will take up and which will fall flat.” ― Stephen Wunker, author of Jobs to Be Done: A Roadmap for Customer-Centred Innovation


Growth Hack 6: LinkedIn prioritises SEO

With 93% of all online interactions starting with a search engine, it is obvious that your site’s SEO shouldn’t be neglected. While meta descriptions and canonical URLs can be difficult to grasp, tackling your site’s SEO offers invaluable benefits.

LinkedIn is an example of a company at the forefront of site optimisation. Through a simple change; allowing users to create public profiles that show up organically in search engine results, LinkedIn completely changes the rules of the SEO game.

Before our extensive social media presence, valid search results, when searching a name, company or title, were few and far between. LinkedIn, therefore, had some valuable search-result real-estate, and as a result, were able to create a multi-million dollar viral loop in the business.

Growth Hack 7: YouTube makes it easy to share

From the earliest of days, word-of-mouth is naturally one most powerful marketing tools available. Customer awareness is one of the most difficult stages of the marketing funnel to crack, and any effort to increase awareness puts companies in good stead.

The key to YouTube’s success was to focus on making it easy to share videos and create a community. YouTube presents you with an embed code to easily share on your social media channels, website, or blog. Video suggestions that show up encourage further engagement, making a user more likely to share another video suggested to them.

Sharing Tip: Employ tools like Share Link Generator to encourage people to share your content and expand your outreach. As a personal recommendation carries more social proof than any paid marketing effort, build your brand’s reputation from the bottom up by people that we trust most — our peers.  

Growth Hack 8: Twitter optimises their signup process

For most products, only less than 16% of the market can be classified as an early adopter or innovator and it can be challenging for businesses to get their product in front of the majority.

Twitter experienced the same situation and was able to solve this problem. When Twitter first launched there was a lot of interest and people signed up and shared Twitter across social networks by the thousands. But Twitter hit a roadblock when new users starting dropping off and disengaging.

By thoroughly exploring the key factors that led to the continued use of Twitter, they identified that people were more likely to stick around if they had the following conditions:

  • They followed at least 5-10 people
  • They had selected interests
  • They had created a network

Twitter shaped their sign-up process around this, encouraging users to invest in their account. This dramatically increased engagement.

Growth Hack 9: Tinder thinks outside-the-building

While stepping outside of the building isn’t a growth hack per se, a number of companies have capitalised on growth opportunities with a physical presence.

Tinder swiped their way to success by using offline, in-person strategies to grow user interaction with their app. By identifying their target market, Tinder uncovered that universities were a great place to reach their ideal persona. Through a number of free organised university parties, Tinder significantly increased their customer awareness and acquisition, with only one requirement to attend — that you created an account.

Growth Hack 10: Whatsapp built a great product

The heart of any successful growth hack is to simply build a great product. It may sound cliche but having a great product is a straightforward way of securing user acquisition.

Whatsapp employed clever strategies to enable them to gain 400 million users without spending a dollar on user acquisition. Understanding and delivering key value is the momentum they needed to lead Facebook into buying them out in 2014 for $22 billion. How did they do it? Put simply, they had a great product.

Whatsapp did their research, understood their users’ needs, and continued to iterate until they perfected their product. Nothing else was needed, and they spread like wildfire now with over 450 million daily users in 2018.

Creating a great product tip:  Uncovering what your users want may seem like a monstrous task. Research methods such as developing user personas will uncover who you’re solving for and what their functional, social, emotional, and personal drivers are.

From the outside, it’s hard to tease out what these different examples have in common. The main takeaway should be that no one principle, technique, or offer will suit every business type. The key to growth hacking within any business is to complete thorough testing to determine the approach that will work, and then creatively tackle challenges based on your insights.

Learn how to grow businesses with our range of  Digital Marketing courses.