What Is Data Analytics: A Complete Guide

In today’s fast-paced digital world, many companies are beginning to realise the importance of data to guide decision-making and support their corporate strategies. With the emergence of new technologies, and the proliferation of the internet and social networks, the number of digital data pools will only continue to grow.

However, this augmented availability of data begs the question of how one can leverage it to derive actionable insights or key performance indicators (KPIs).

Companies globally are generating vast volumes of customer-related data in the form of log files, web servers, and transactional data. Additionally, social media websites are adding to this pool of data.

But with data scattered throughout organisations, extracting integrated information in a timely fashion can also prove to be an overwhelming challenge. This is often because of a lack of data analytics capabilities. In some instances, business analysts may be forced to manually collate data before they can even start any analysis exercise.

What is data analytics?

Data analytics is a technical discipline that encompasses multiple diverse techniques and processes for deriving actionable insights from historical data. It helps to predict outcomes, evaluate performance, and design or optimise systems for better business decision-making.

As a scientific discipline, data analytics draws from computer programming, mathematics, and statistics to meaningfully convert raw data to describe, predict, and improve performance. To effectively carry out these tasks, there are several essential skills in data analytics that one needs to acquire.

As we shall see later, data analysts typically exploit various data management frameworks and techniques like data modelling, data mining, data cleansing, and data transformation to make sense of the data they have and derive conclusions and insights.

Benefits of data analytics

Following are some key advantages of using data analysis skills within an organisation:

Enhanced decision-making

Data analytics helps eliminate guesswork when making business decisions and facilitates marketing planning and product development. Organisations can exploit derived insights to make informed decisions that lead to better outcomes and customer satisfaction.

A single source of truth

Trustworthy analytics and insights can be consolidated into a single source to allow for consistency and accuracy with a unified view of metrics, and insights.

Improved customer service

Data analytics can allow you to customise your business offerings according to the client’s exact needs. Data can reveal extensive insights into customers’ interests and concerns to help companies to curate better products and service experiences.

With such a high granularity of personalisation, organisations can easily build stronger relationships with customers.

Improved productivity

Data analytics can augment operational efficiency by optimising resources, streamlining internal processes and boosting production. The idea is that with an enhanced understanding of one’s target audience, one should spend less time aligning the company’s needs to serve clients.

Data analytics vs Data science- what is the difference?

Despite being interrelated, data analytics and data science differ from each other.

Data analytics is essentially a subset of data science employed to understand the composition of an organisation’s data and present them in reports and visualisations. It uses existing data to produce actionable insights that drive decision-making. Hence, data analytics works better when there is a focus or end goal in mind that needs to be answered based on existing data.

While data science leverages the output of data analytic exercises to study and solve problems, it need not always answer specific queries, instead it parses through massive datasets in sometimes unstructured ways to expose insights.

Data analytics process

Data analysis can be broken down into four main steps:

Defining and understanding the problem

Understanding business challenges and defining organisational goals is typically the first step in an effective data analytics exercise. This essentially guides the following steps.

For example, an e-Commerce company could be facing issues like managing customer fraud, predicting the return of items, or providing relevant product recommendations.

Data collection

The next step is to collate and aggregate relevant historical business data and customer-related information to address the challenges that the business is facing. For example, this data can be the total units sold or the profit made.

Data cleaning

There is a chance that the historical data collected thus far is corrupted, messy or has unwanted or missing values. Hence, it is important that the data is cleansed of all unwanted, redundant or missing values prior to data analysis to ensure effective results.

Data exploration and analysis

After performing the above three steps, an exploratory data analysis should be conducted using business intelligence tools, data mining techniques, and predictive modelling. These techniques and tools are used in a combinatory fashion to aggregate data to derive actionable future outcomes.

Types of data analytics

Descriptive data analytics

Descriptive data analytics is a baseline exercise in data analytics and business intelligence (BI). It involves exploiting historical and current data from multiple heterogeneous sources to describe a specified historical state. In other words, descriptive data analytics seeks to ask the question: What happened?

The end goal of descriptive data analytics is to identify trends, probabilities and patterns from existing data that offer insights and present them graphically on reporting dashboards. The output (the data visualisations) from descriptive analytics is important for interpreting the insights that might be derived from predictive and prescriptive analytics.

Diagnostic data analytics

Diagnostic data analytics seeks to ask the question: Why did it happen?

Fundamentally, this exercise examines the root cause of a problem to determine why something happened. Thus, its central goal is to investigatively understand the cause of events and behaviours.

Predictive data analytics

Predictive data analytics seeks to exploit past data to predict the future and answer the question: What might happen?

With this forecasting ethos, predictive analytics utilises techniques like data mining (using linear regression) and machine learning to analyse historical and current data.

Prescriptive data analytics

This technique is dedicated to ascertaining the right action that needs to be taken to solve a problem: What should one do? Like predictive analytics, prescriptive analytics can exploit machine learning, business rules, neural networks and algorithms.

What are data analytics techniques?

As we have already established, data analytics is a discipline that encompasses many diverse techniques.

Data analytics techniques primarily seek to reveal actionable trends that would otherwise be lost in a web of massive information. Examples of such techniques are:

Regression analysis

It is a statistical technique that focuses on analysing the relationship between variables in order to determine how a change in one might affect the change in another.

Factor analysis

These techniques focus on extracting a large data set and then shrinking it into smaller datasets. It helps to discover hidden trends that would otherwise be a challenge to discover in larger datasets.

Cohort analysis

It involves breaking a sizeable dataset into groups of similar data to enable data analysts to further dive into the numerical intricacies relating to a specific subset of data.

Cluster analysis

This machine learning method is used to classify objects into relative groups called clusters. Cluster analysis can be exploited to reveal structures in data. For example, to investigate why specific locations are associated with particular food orders when handling restaurant data.

Time series analysis

This technique focuses on the analysis of a sequence of data points collected over an interval to solidify the relationship between the value of a data and the time of occurrence. This technique can help spot cyclical trends, or even project financial forecasts.

Sentiment analysis

This advanced data analytics technique exploits natural language processing, text analysis, and computational linguistics to understand the expressions of data. It is a subset of artificial intelligence that seeks to interpret and classify qualitative data to understand how customers feel about a product or service.

Data analytics tools

Data analysts today have a variety of state-of-the-art software tools to help extract data, store information, process data, and derive actionable findings.

In previous times, they were restricted to Microsoft Excel spreadsheets.

However, the tide has shifted as they currently have programming languages like R for statistical analysis or graphical modelling.

Other popular analytics tools include:

  • Python: An open-source programming language that allows users to extract, summarise, and visualise data.
  • Looker: Google’s advanced data analytics and BI platform.
  • Power BI: Microsoft’s data visualisation and BI tool for creating and distributing reports and dashboards.
  • Qlik: A suite of data analytics and integration platforms for exploring data and creating data visualisations.
  • RapidMiner: An enterprise data science platform for supporting multiple analytics users across a broad AI lifecycle.
  • Jupyter Notebook: An open-source web app for creating interactive documents by combining live code, equations, visualisations, and narrative text.
  • Sisense: An interactive AI-driven self-service business intelligence platform.
  • OpenRefine (formerly Google Refine) is a Java-based open-source desktop app for cleaning large datasets.
  • Apache Spark: An open-source unified analytics engine/software framework for big data processing and creating cluster computing engines.
  • QuickSight: An analytics cloud service from Amazon capable of integrating with cloud data sources.

Which industries use data analytics?

Generally speaking, all major sectors utilise data analytics in one of the following ways:

  • Managing risk
  • Optimising operational flow
  • Detecting fraud
  • Detecting anomalies
  • Managing customer data
  • Personalising and customising customer experiences
  • Performing market research
  • Analysing operations
data analytics

Photo by Lukas

Data analytics underpins most quality control systems and corporate sectors globally. For example:

  • The travel and hospitality industry exploit data analytics techniques to expedite turnarounds by aggregating customer data to figure out where the problems lie. And if any, determine how to fix them.
  • The health sector exploits high volumes of structured and unstructured datasets to make quick patient related decisions.
  • Similarly, the e-Commerce space currently exploits large amounts of data to meet the dynamic demands of shoppers in order to recommend products, and increase profits.
  • The banking sector leverages data analytics to uncover fraudulent transactions, probable loan defaulters, and to understand customer churn-out rates.
  • The logistics sphere exploits data analytics to optimise routes to ensure that deliveries reach on time in a cost-efficient manner.

When harnessed correctly, data analytics can be a source of competitive advantage. As such, more organisations are exploiting it to drive digital transformation and identify new business opportunities whilst using insights to prioritise business actions.

Data analytics examples

According to the Arizton Advisory and Intelligence research firm, Singapore’s data analytics market is projected to grow to approximately US$ 3,586 million by 2027, registering a compound annual growth rate (CAGR) of 15.1%.

Data analytics has seen practical application across different verticals in Singapore including finance, education, smart medical treatment, e-commerce, and even transportation. In fact, more SMEs are exploring ways to utilise data analytics to predict sales, improve customer relationships, and ultimately, generate increased revenues and profits.

Data analytics can also help Singapore businesses to implement new models that reduce operational costs by identifying more efficient operational approaches. This can lead to new and better products and services.

For instance, Singapore warehouse owners typically record downtime, and long work queues for different machines. Fortunately, they can exploit data analytics to better plan the workloads and identify delivery miss-matches in real-time to enable warehouse machines to operate closer to peak capacity. Thus, better optimising the inventory levels and supply chain activities.

Similarly, Singapore Customs is exploiting data analytics to interdict illicit trade, and fraudulent transactions, in order to ensure that legitimate trade continues to flow unhindered. In practice, it employs data analytics tools for accurate profiling and targeting to enhance the effectiveness of compliance checks.

Furthermore, some data analytics exercises have been trialled in Singapore for student profiling and academic counselling purposes whilst leveraging student learning behaviours, exam performances, social forums, and teaching effectiveness data.

The idea is to reduce the risk of students discontinuing learning or underperforming academically, thereby leading to an increased attrition rate.

Relatedly, some Singaporean hospital franchises like SingHealth are actively exploiting a combination of statistical analytics, machine learning, and AI to enhance operations and provide better patient care and outcomes. 

For example, SingHealth leverages predictive analytics to help doctors optimise the length of hospital stays, and thus, reducing patient costs. Furthermore, its AI driven tools enable its operating theatres to be more efficiently utilised, with shorter wait times for patients.

Concluding remarks- The Future of Data Analytics

Data analytics seeks to eliminate cognitive and personal biases from the business decision-making process by leveraging data as the primary input for decision-making. When executed effectively, data analytics can create a competitive advantage for any business entity.

However, as the world advances technologically, data analytics shall radically change the way we live and conduct business in the future. We can already see signs of data analytics elements present in our current technology devices. For example, helping us to avoid traffic-jam via Google Maps.

It’s exciting to think that all our devices will soon be fully connected to exchange data within the “Internet of Things” to deliver enormous sets of data. For example, sensor data like weather, location, health, error messages, and machine data will enable more advanced diagnostic and predictive analytics capabilities.

Additionally, it shall become easier to link all sorts of data from different sources with each other to get insights on a real-time basis. Voice recognition technologies could also reach new heights.

As the future of data analytics will be powered by AI and machine learning, investing in a Data Analytics course can ensure your organization stays ahead of the curve, predicting outcomes without needing explicit programming.

Since machine learning algorithms can be trained on a small sample of data, existing systems will continue to advance as they gather more data. Thus, becoming more accurate as time goes on.

So, it may be prudent for anyone seeking to get into the data analytics space to also pick up artificial intelligence and machine learning competencies.

Reach out to us for more information on how to get started with your data analytics journey through our training courses in Singapore.

Reviewed by

Comments are closed.