Due to rapid technological advancement and digitalisation, enormous amounts of data are being produced every second. How much data can there actually be? Well, the whole digital universe was expected to reach up to 44 zettabytes in just 2020 alone. To put things into perspective, it means that there are 40 times more bytes than stars in the observable universe.
Data analytics come into the picture when dealing with these data. One might ask, “How does Data Analytics help business?” Continue reading to find out!
How Data Analytics can help business
Data analytics is the process of extracting valuable insights and findings from statistics and figures provided in the data. These insights would then help businesses make data-driven decisions that would lead to a larger profit. Businesses engaging in data analytics generally observe an increase in profits by 8-10%.
For example, Netflix reportedly saves approximately $1 billion year-on-year, using data analytics to further improve their recommendation engines and customer retention strategies.
So, what are the different methods of Data Analytics that businesses are utilising?
What Can Data Analytics be used for?
There are three types of Data Analytics, namely, Descriptive analytics, Predictive analytics and Prescriptive analytics.
Although each method is already powerful when used individually, they are very effective when used together.
Descriptive analytics involves the analysis of historical data using two methods – data aggregation and data mining, both which helps identify trends and patterns. Descriptive analytics is a representation of events that have occurred in the past, providing historical review of the business’s performance and answering questions such as “What happened?”
Descriptive analytics are usually represented visually using representations like line graphs, bar charts and pie charts, providing insights and acting as the groundwork for future analysis. A tool widely used to create these visualisations is Tableau. Descriptive analytics relies only on historical data and simple calculations, and tend to be applied in daily operations. One example would be annual revenue reports.
As its name suggests, Predictive analytics is centred around prediction and the understanding of what could happen in the future, answering the question “What might happen in the future?”. Similar to descriptive analytics, predictive analytics utilises data mining.
The difference is that predictive analytics has a few more extra steps, which includes statistical modelling and machine learning techniques. Predictive Analytics tries to forecast different outcomes in the future and the probability of each outcome based on historical data. For instance, forecasting customer behaviour and their purchasing patterns.
While descriptive analytics tells us what happened and predictive analytics tells us what could happen, prescriptive analytics is all about telling us what the next step is. This final type of analytics is the call-to-action for businesses, prompting relevant stakeholders to make informed decisions.
Prescriptive analytics requires specialised knowledge in mathematics and computer science since it involves the use of many statistical methods. This stage might be confused with both descriptive and predictive analytics, so just remember that prescriptive analytics emphasises actionable insights rather than data monitoring.
Prescriptive Analytics is held in high regards due to its nature of measuring possible implications of each decision, and then recommending the best course of action to take.
4 Real-world Examples of Data Analytics
You might be wondering, “How to use data analytics in marketing?”.
Marketing teams utilise descriptive analytics in order to track campaign performances, which is done through monitoring metrics such as conversion rates, cost per click, social media followers and more.
Next, predictive analytics is then used to refine marketing strategies, predict future market trends, consumer behaviour, online and offline engagements.
Lastly, when it comes to prescriptive analytics, marketing teams implement machine learning and AI marketing that enables them to take actionable insights in real-time by automating dynamic pricing, forecasting sales, content creation and real-time personalisation.
The finance sector is no stranger to data analytics as well. Descriptive analytics is used by the majority of business leaders and financial specialists, where they monitor and track financial metrics like the growth in revenue and expenses.
Some data analytics use cases in banking that involves predictive analytics include detecting possible security threats and duplicate payments, along with identifying credit card fraud and insurance fraud.
Prescriptive analytics allows businesses to optimise their decisions more efficiently, aligning them with their goals, such as reducing costs, increasing profitability and improving customer satisfaction.
Even though we visit clinics and hospitals time-to-time, one might still be curious and ask “What is data analytics in healthcare?”
Data analytics play a huge role in the healthcare industry, and can be used under various circumstances. For instance, it can be used to determine just how contagious a virus is by analysing the rate of positive tests in a specific population over a period of time.
For predictive analytics, it helps in forecasting the spread of seasonal diseases based on historical data. One example would be pharmacists expecting an appropriate amount of medicine to stock in anticipation of any impending outbreak of diseases.
Lastly, in prescriptive analytics, patients’ conditions are assessed, risk of relapse are determined. It allowing the medical team to incorporate specific preventative treatment plans after considering risks.
In this case, we associate “entertainment” with subscription streaming services like Spotify and Netflix. Both these companies use descriptive analytics to identify trends and what is currently trending and most popular with users and buyers. Through descriptive analytics, Spotify learns which artists or albums subscribers are listening to.
Spotify then leverages these insights with their ‘Discover Weekly’ playlist function where users are able to listen to songs of similar genres based on what the users listens to on a daily basis, what they are liking and what other listeners of the same genre listen to. The more the Spotify users, the more personalised the playlist is. Spotify even has a function where users can generate an automatic playlist based on context, a great example of how both predictive and prescriptive analytics work.
Where can I learn these industry-relevant Data Analytics skills?
If you are keen on gaining these industry-relevant Data Analytics skills, look no further! Vertical Institute provides an extensive Data Analytics course which covers the fundamentals of Excel, SQL and Tableau in great detail. Attend our live online classes led by industry experts where they will guide you through on how to apply data analytics. You also get to build a compelling portfolio and get professionally certified. Start today!
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