The Ultimate Guide to Data Analytics in Singapore

the ultimate guide to data analytics


Are you talking about data analytics like every other discerning business owner or company? If you’re not, reconsider your school of thought. Perhaps you need to reawaken to the fact that data analytics is no longer the province of multinationals and big-box companies.

Let’s imagine that you know how analytics is transforming business processes and decision making. What all can agree is that having superior data analytics skills can position your business competitively. It’s the norm for modest businesses seeking growth successes in a disrupted world.

If your excuse to drift away from data analytics is your start-up status, smile. Small businesses are leveraging data analytics and it benefits to scale to levels they’ve only envisioned on paper. Since data is the new business fuel, it’s only wise to take some time to learn, internalise, and employ what data and analytics have in store for budding organisations and businesses.

Today, data science analytics is an essential business tool. Advanced computing and Internet of things (IoT) now enable businesses to leverage various metrics. There are many facets to understanding data analytics. Where can you start?

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What is Data?

A simple definition of data is raw information. Business and consumer activities online and offline amounts to business data. A specific definition points to data being demographic info, behaviour, trends, personal attributes, and business to customer interactions. Some big corporations gather data on the most unassuming activities online and leverage it to optimise delivery.

Data can be structured or unstructured

  • Structured data is highly defined in a way that makes it easily searchable. It’s stored in relational databases. Examples of structured data include addresses, names, dates, credit card details or stocks, and geolocation information.
  • Unstructured data has no pre-defined format. It’s stored in its native format and it’s only processed when the need to use it arises. Examples include videos, email, webpages, social media posts, presentations, chats, IoT data, photos, and audio files, among others. There’s no sin in using quantitative and qualitative data to describe the above. But, before we digress, let’s go back to data analytics.

What is Data Mining?

You, like many other business owners, keep asking, “How can I derive real business value from the massive data clusters?” The solution lies in data mining. What does it entail? Data mining is the technical process of sifting through, sorting, classifying, and organising datasets. Think of it like organising a huge store such that your customers and agents can point out a specific product instantly.

Data mining leverages techniques, such as clustering and classification. It requires the use of proper applications or tools with data filtering, clustering, and cleansing features.

These tools work on the data to help organisations get a better view of customers. You can leverage these tools to reinforce your strategies and make better use of your resources.

What is Data Analytics?

Data analytics is the calculated use of technology, processes, and people to extract unique insights from preferred data sets. The findings are then used to modify, enhance, or improve operations depending on the type of business, its niche, and its prevailing circumstances.

Data analytics, as a science, analyses raw information using automated techniques, processes, and algorithms. This is what makes mountains of data easy to consume. The techniques used reveal metrics or trends that may end up swallowed in the mass of data in evolving databases.

Data analytics is the swivel that is making more businesses wheel towards a data-driven approach in their core operations. Analytics can help you identify problems before they take over your business. It’s the data that helps you map out solutions for issues even before they occur.

Types of Data Analytics

Distinct businesses engage in different stages of data processing. They depend on the phase of the workflow and the analysis requirements. There are four analytic types:

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  • Descriptive analytics

    This is a basic form of analytics. The initial stage consists of resizing big data into fathomable sizes. Descriptive analytics seeks to identify the findings and underlying processes.

  • Diagnostic analytics

    Diagnostic analytics seeks to identify why a particular aspect or phenomena happened. This stage makes use of data mining, discovery, and correlations. Diagnostic analytics seeks to understand what happened. It determines the factors and triggers that lead to a particular scenario through probability and chance.

  • Predictive analytics

    Predictive analytics, as the name suggests, entails predicting possible future outcomes. Predictive analytics cannot accurately predict future occurrences. Instead, it estimates the chances that a given event may occur. It creates models that use current data to deduce a path that future data is likely to follow.

  • Prescriptive analytics

    Prescriptive analytics surpasses the other analytic facets. It helps to chart a preferred way forward riding on the other three. This model enables visualisation of outcomes if specific steps are taken. Also, it recommends individual courses of action that can lead to the desired outcome. It leans on a feedback system to anticipate the connection between an action and its outcome.

Characteristics of Data Analysis

Characteristics of the data analysis process depend on various aspects, including velocity, volume, and variety. These characteristics make it different from the traditional analysis process.

  • It’s programmatic

    Instead of using manual data exploration options, it’s possible to employ codes to programmatically explore the same big data due to its sheer scale.

  • It’s data-driven

    Data analytics experts may turn to hypothesis methods to analyse data. However, you can skip the hypothesis and drive the data with a machine-based formula. It works for big data.

  • It’s iterative

    With superior computing capacity, data chunks can be iterated over and over until you get the desired outcome. With traditional analysis, you’d need a huge memory to make desired modes to work. If you have Infrastructure as a Service capacities, you enjoy more machine power to process and analyse more data quickly.

The Data Analysis Process

You gradually understand the anatomy of data and analytics. Relax, you still need to know the steps that companies and start-ups use to analyse it. It’s what the analysis process entails.

1.  Define a need

Nothing trumps the need for data analysis. It’s about what is happening. You can define the need based on aspects such as dwindling sales, soaring business, customer satisfaction, or cart abandonment reasons. You need a data management plan that ensures your data in and out tray is organised and accountable.

2.  Collect data

Collecting data isn’t difficult. You can harness structured data from internal sources. This can be your CRM, OR, and ERP systems, or market automation applications. You have a breadth of open-source tools that can help you collect external data patterns.

3.  Exclude duplicates and inconsistencies

After you collect your data, the chances are that there are redundancies and repetitive, inconsistent data. You need to get rid of those since they could misinform the process.

4.  Analyse data

Data analysis can take different forms. You can use your business intelligence tools to create reports, charts, or graphs. These make it easy to decipher and understand your data sets. Satisfied? If not, consider data mining for in-depth analysis.

5.  Take action

Congratulations, the last thing you need to do is to put your findings into use. The analysis needs to show you a particular course of action(s). If it doesn’t, consider reviewing your analysis processes to see if you might have missed critical pointers.

How Business Analytics Can Help Your Business

Are your customers switching allegiances due to poor user experiences? Do you know the right steps your business needs to take in the analytics process? Unfortunately, you have no reason to not leverage your experiences given the host of benefits it offers. Implementing data analytics to drive intelligent and effective business decision making has its benefits. But it’s not A-B-C easy. What you cannot ignore is that your Singaporean business needs it more than ever. Here’s what data analytics can do for your business:

  • Identify business opportunities

    Analysing critical data can reveal new opportunities that you might have ignored. You can identify untapped customer segments. Using an intelligence-based approach opens you up to endless opportunities that your competitors don’t know.

  • Efficient operations

    Data analytics does more than streamlining your operations and driving the return on investment (ROI). Analytics gives you a sneak preview of your audience. This makes it easy to target ads that resonate with them.

  • Find your audience

    Data analytics help you listen to what people say about your brand. Analytics provides you with social media chatter. If you combine that with customer behaviour, it’s easy to identify the ideal audience that you should be targeting. You can do it better than everyone else and keep customers coming in.

  • Effective marketing

    Data helps you understand your target audience better. Data analytics arms you with creative insights that help your venture better market to your customers. You can preview your campaigns and polished them for optimal results.

    With the right analytics tool(s), you can determine the demographics that will identify with your marketing campaign. It becomes easy to modify your targeting strategies and messaging. It results in more conversions and less effort.

  • Better customer service

    Data analytics literally gives you customer insights on a silver platter. This simplifies how you personalise your offerings to fulfil consumer needs. Even if it doesn’t lead to stronger customer relationships, it breeds brand loyalty.

  • Improved decision making

    Insights from data analytics can help companies revitalise their decision-making processes. You no longer rely on smart guesswork to make critical marketing, content product development, or planning decisions. Think of analytics as giving you a 360-degree view of your customers. Isn’t that adrenalin enough to drive your growth projections?

  • Delivering relevant products

    Effective data gathering and analytics help you to keep abreast with the ever-changing business landscape. You’ll be in the know as soon as new trends or technology hit the market. It enables you to innovate and develop new products that your customers are yearning for.

  • Developing marketing campaigns

    Haven’t your datasets told you what your competitors are up to? Then what are you waiting for? The insights you glean enables you to craft and send the right messages to the audience you want to draw to your new business. Data analytics will help you to create new offerings and enhance brand recall through creative marketing campaigns.

  • Expansion planning

    You deserve a pat on the back if you’re about to expand. If you have tons of consumer, procurement trends, equipment supply, and delivery, don’t sit on it. You can exploit such data to inform you and assist you in planning for the expansion.

  • Creating your business plan

    If data can tell you how you’re performing, it can show you how you’re likely to perform in the next phase you’re planning to launch. Insights from data help businesses to forecast sales, trends, and turnover. Such information is crucial if you want a winning business plan for the next venture.

  • Mitigating risk and fraud

    Security and fraud analytics are part of the bigger analytics picture. These are what keeps your business safe from internal and external fraud incidences. Done the right way, analytics can help unearth inconsistencies that encourage fraud.

Steps for Successful Data Analytics

There’s no need to overemphasise how data analytics grants you the competitive edge. You already know that. The idea is not to hoard all the data you can get. It’s about deriving value that can drive your ROI and profitability. YOU need to know the steps that lead to success with data analytics.

1.  Define business use cases and outcomes

Always align data analytics with business-centric growth plans. Your strategies should work with practical suggestions that are proven through progressive measuring and tracking of crucial metrics.

2.  Appoint data experts from your business and IT departments

Selecting experts from different departments in your organisation contributes to the success of your data efforts. Make sure they are individuals with the right aptitude and knack for progressing the analytics cause.

3.  Establish proper infrastructure, tools, and architecture

The right analytics tools, data architecture, and experts assist your business in wading through the data project successfully. However, you’ll need to find the right tools that your personnel like to use.

4.  Get the right data/analytics resources

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You have the right analytics team and your project is set to launch. However, make sure that they have the resources they need if they require additional support.

5.  Manage the project in phases using measurable indicators

You can’t take all of data analytics in with one massive bite. Start small in manageable and measurable segments. Always prioritise a specific area. You will realise that this approach helps to reinforce data usability and quality.

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6.  Involve data integrity protocols

It pays to assess your data proactively to discern critical elements and areas that require adjustments. It’s the only way you’ll glean insights that enhance decision making. You need to establish data quality controls moving forward. Consider making this a companywide responsibility.

Data Analytics Technology

Data analytics isn’t another new fad. But the volumes of new data require advanced tech if you expect valuable insights. Today, some technologies make big data analytics useful to organisations. They include:

  • Machine learning and Artificial Intelligence

    Machine Learning (ML) and Artificial Intelligence (AL) are always intertwined. They power algorithms that can decipher data while mimicking human intelligence. Machine learning aids in digesting and deciphering data to give predictions. There’s no human contribution involved in the process. MI-based algorithms can be programmed to deduce small data clusters and advances as more data is added.

  • Data management

    Data cannot be analysed if there are no data flow and organisation procedures in place. You must maintain high quality for the data you collect and you need a data management platform for that.

  • Data mining

    The process of categorising, clustering, and classifying data to get valuable insights is indispensable. It’s the only way you can navigate data sets to glean relevant information. Today’s data mining technologies enable companies to execute analytics quickly.

  • Predictive analytics

    Predictive analytics is the technology that enables you to assess cumulative data to foretell future occurrences and possible outcomes. Predictive analysis employs machine-based logic and algorithms to provide accurate results and insights.

Examples of Data Analytics Tools in the Singapore Marketplace

  1. Xplenty
  2. Skytree
  3. Azure HDInsight
  4. Talend
  5. Tableau Public
  6. OpenRefine
  7. Splice Machine
  8. Apache Spark
  9. Plotly
  10. Elasticsearch

Top Data Analytics Trends in Singapore in 2020

  • Domination of augmented analytics

    Other than business intelligence and machine learning, organisations are opening up to augmented analytics. It empowers experts to draw more insights from data clusters. Already, vendors are infusing augmented analytics into their products to improve user experiences.

  • Rise of IoT, ML, and AI

    Singaporean executives can attest to cloud computing being on top of the analytics games in the next few years. A majority of them foresee the impact of IoT, AI, and ML on their analytics activities.

  • Data-driven upskilling

    In Singapore, organisations are not fixated on basic analytic strategies and capabilities. The shift is moving towards upskilling their workforce with analytical savvy personnel who can improve decision making based on data

  • Data as a Service (DaaS)

    Whoever thought that DaaS would become a highly sought after resource? Today, businesses are investing in DaaS to mitigate the enormous data volumes in different verticals.

  • Graph analytics

    Small and established Singaporean businesses struggle to find the nexus between disparate data points. Thanks to graph analytics, most of them can now deduce data in simplified visual formats. Graph analytics is transforming sophisticated data interpretation and is here to stay.

  • Internet of Things meets data analytics

    Singaporean ventures that employ Internet of Things (IoT) in their analytics processes have been experiencing exceptional results. IoT data analytics are helping businesses to decipher the data coming out of IoT connected devices.

Tips for Choosing Data Analytics Tools

Annually, there’s a plentiful release of data analytics tools that offer a range of features and functionalities. Data isn’t your average resource to handle manually. But you need to know how to go about procuring data analytics tools.

  • Check business objectives

    You know too well that your IT tools need to sync with the existing infrastructure and data architecture. But you need to define your business objectives before you invest in a given tool. You’ll need an application that gives you easy access to data and reporting features that help meet your objectives.

  • Pricing

    It is possible that you build and maintain a proprietary data analytics tool. Even so, you need to appraise the costs. If you purchase such a tool, consider maintenance, hidden fees, or subscription costs. Like always, don’t go with the cheapest.

  • Assess user interface and visualisation

    Your workforce will be using the said analytics tool to help augment businesses decisions. Don’t make it a nightmare for them and you. A tool with a user-friendly interface makes it easy for your non-tech staff to execute and decipher dashboard reports. You don’t want the wrong interpretation since you know how it can affect decision making.

  • Advanced analytics

    The analytics tool you want to invest in should help identify data patterns, trends, and possibilities. Consider one with features that are beyond simple numerical calculations. It’s better if it supports graph analytics.

  • Mobility

    Do you want to access your datasets and analyse them remotely? If yes, you can prioritise tools that support mobile analytics. Even if you’ve not gone global yet, you’ll need to analyse data on the go as you scale.

  • Customisation

    You know your businesses is unique, and the data you collect is equally so. You must select a tool that meets your specific analytics needs. You might need bespoke analytics set up and you know too well that a generic, one-size-fits-all solution won’t suffice.

What Does a Data Analyst Do?

You’re probably asking yourself what kind of an expert handles data analytics. A data analyst does. But what exactly is their mandate?

  • Data analysts collect, systematically collates, interprets, and filters complex data to help different organisations and businesses.
  • Data scientists employ data analytics techniques and software to make swathes of disparate data useful for businesses facing a decision-making crunch.

If you didn’t know, this is how organisations end up making informed decisions. They gain witty muscles to optimise campaigns, content, strategies, and develop products that boost their brand recognition.

Key duties

Don’t be fooled to thinking that data analytics experts work with a magic wand. On top of the application they leverage, they come packed with superior skill sets. Their duties include:

  • Employing computerised models to extract core data meanings
  • Refining and deleting shady data
  • Performing preliminary analysis to determine data quality
  • Running algorithmic analysis to discern data meaning
  • Executing end analysis for additional data screening
  • Preparing reports based on findings and presenting them to decision-makers

Key skills of a data analyst

  • Distinct numerical and mathematical aptitude
  • Proficiency in programming languages, such as Python, SQL, or Oracle
  • Data modelling, interpretation, and analysis knowledge
  • Accuracy and troubleshooting skills
  • Methodology, logic, and critical thinking skills
  • Attention to detail
  • Interpersonal and teamwork skills
  • Excellent communication skills


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