Top 10 Data Analytics Consulting Services in Singapore Using Data Science to Help Companies Make Better Decisions

How to Find a Data Analytics Consulting Service with Accurate Real-Time Data to Improve Business Decision-Making

Data analytics has become an integral part of several industries, including travel and hospitality. Collecting customer data quickly allows companies to detect where any issues lie and how to address them swiftly. Likewise, a lot of industries use vast amounts of structured and unstructured data along with analytics for fast decision-making processes. Furthermore, sectors such as the retail industry utilises massive quantities of data to meet the constantly shifting needs of buyers.

Companies can gain a competitive edge by incorporating data analytics into their business model; it has the power to identify and reduce costly inefficiencies, and provide insight for better decision-making. Additionally, analysing customer feedback and trends with data analytics allows companies to develop new—and improved—products that meet consumer needs more accurately.

Why is Data Analytics Important for Businesses?

Without data analytics process, searching through piles of unstructured data would be like finding a needle in the proverbial haystack. The implications are numerous—both businesses and consumers benefit greatly from this practice. With the help of advanced technologies such as data and text analytics, organisations can now uncover relevant patterns in large datasets while extracting meaningful insights from textual documents. With innovative applications powered by machine learning algorithms and automation tools, which are developed using specific programming languages, data analysts have access to powerful open-source analysis tools that allow them to gain valuable knowledge quickly.

Exploring the data opportunities involves focusing on specific applications. Below are the benefits that businesses can enjoy:

• Streamlined Marketing Campaigns. Businesses can gain an in-depth understanding of their target audience. That way, they can craft tailored campaigns—from social media outreach to individualised messages. This leads to more effective marketing efforts that provide much better returns on investment (ROI) and ensure that the message is heard by those who need it most.
• Better Decision-Making. Data analytics empowers companies to refine their decision-making prowess. With an in-depth view of their client base and the accuracy of their progress, they can utilise the perceptiveness gained from data analysis to make wiser choices and modernise project management.
• Enhanced Efficiency. By utilising data analytics, businesses can streamline their processes and become more cost-effective. Not only does this make them more efficient, but it also helps them with financial analysis. This allows companies to allocate resources strategically, for example, when creating targeted marketing campaigns that can deliver maximum ROI.
• Improved Customer Service. Businesses can not only comprehend customer wants and needs but also raise their customer service quality. Plus, having all customer information in a unified repository available to the entire support team will ensure that everyone provides an equal level of care.

How Should Businesses Use Data Analytics?

Exploring how data analytics is utilised in everyday business operations is an essential yet demanding task, especially considering the sheer magnitude of available data. It's vital to consider thoughtfully and judiciously the applications of this technology for businesses.

Gaining trustworthy insights without advanced data analytics practices can oftentimes be hard. Here is a thorough look at how firms incorporate data analytics and the essential queries they have to think about along each step of the process:

• Planning and Strategy. Prior to beginning any data analytics project, businesses need to strategise and set precise goals. It is also absolutely essential that they ask themselves the right questions first: Why should we collect this particular kind of data and what results do we wish to attain?
• Data Collection. Once companies have identified the aim of data analytics, they decide upon their corresponding data sources, relevant points to consider, and how to gather this information. For instance, businesses may opt for standard transaction and social media datasets or leverage high-tech approaches such as GPS technology or RFID chips.
• Data Relevance. Raw data doesn't reveal much on its own, therefore in order for businesses maximise their quantitative information-gathering efforts, they need to ensure it provides meaningful insights and is processed correctly. Gathering massive amounts of data indiscriminately with no purpose in mind isn't just inefficient—it can even be detrimental.
• Data Usage. Those wanting to leverage data analytics consider how and where they will find resources from. Regardless of the size of the business, any organisation can outperform their competition with the right metrics in place.
• Data Presentation. The significance of data visualisations in presenting results and making them more comprehensible cannot be underestimated. With the right software, businesses can illustrate their data as graphs and charts for presentation aids such as online tutorials, webinars, or even captivating infographics that are highly sought after on social media platforms.
• Data Insights. It's one thing to gain insights from data analytics, but in order for businesses to reap the full benefits of their findings they formulate an action plan that puts them into practical use. How can the company utilise its specific discoveries to enhance customer service? What strategies might be implemented to reach new audiences? By implementing a timely and effective action plan you could improve operations while gaining invaluable insight.

Data Analysis Process Explained

Data analytics helps companies in deciphering and analyzing raw data to improve their internal processes and gain insight into what their customers want. This is usually done in three stages, which are:

• Collect and Store: These include purchases made by customers, social media posts and interactions, Machine-to-Machine sensors, and everyday business operations
• Process and Organise: This means to compute, modify, handle, secure, and enhance data
• Analyse and Visualise: These are exploratory reports, predictive analytics and ad-hoc queries which can be easily tracked through interactive dashboards

Now we will dive into the intricate details of how each stage functions as cogs in the massive machine known as data analytics.

• Data Infrastructure. The data infrastructure is the foundation of any successful business, and those that make it a reality are storage engineers, business intelligence (BI) developers, warehouse analysts, network administrators, server admins and data architects. This process starts with constructing the technology to collect and save important information.
• Data Management and Processing. After data is collected and securely stored, companies have qualified IT professionals to create and maintain the proper pathways of processing and organising data within their company. Specialisations, such as database administrators, systems analysts, developers, data architects and engineers are perfect for this job.
• Data Interpretation and Visualisation. After data is collected and organised, it is interpreted to turn it into actionable insights. This necessitates the use of advanced analytics such as data modelling and data analytics techniques used by skilled scientists or business/data analysts. By using these strategies, they can create new datasets which can then be utilised for decision-making purposes.

Data Analytics Tools

Choosing the right tool for data scientists and analysts is equally daunting with so many options. To narrow down the choices, the following questions are raised: how widely used is this application and what are the cost efficiency, learnability or marketing campaigns that surround it? Below is an introduction of some popular data analytics tools available on the market today:

• SQL or Structured Query Language. Developed for the express purpose of managing data stored in relational database management systems, a special-purpose programming language can make light work of complex storage and retrieval tasks.
• Microsoft Excel. This seamlessly collects and analyses data because of the user-friendly spreadsheet tool that packs a powerful punch.
• Python. Initially created as an OOP language for software and web development, Python has been continually enhanced over the years to make it a powerful tool for data science.
• R. This is a programming language created specifically to perform statistical modelling, visualise data, and analyse results efficiently.
• SAS. For business intelligence, data organisation, and predictive analytics, this is a statistical software suite go-to solution.
• Power BI. Microsoft offers three distinct versions of their business analytics solution to suit any company's needs: Desktop, Pro and Premium.
• Tableau. A revolutionary BI tool designed specifically for data analysts provides the capability to visualise, analyse, and comprehend data in a single platform.
• Apache Spark. For developers, researchers and data scientists looking to process big data efficiently and accurately, this integrated system allows users to quickly and precisely analyse large volumes of information.
• Google Analytics. A web analytics service providing comprehensive metrics and reports to measure website traffic.

Types of Data Analytics Applications

Data analytics can be broken down into quantitative and qualitative analysis: the former requires examination of numerical data with measurable variables that may then be measured or compared statistically; while the latter is more interpretive, zeroing in on comprehending non-numerical material like audio/visuals, phrases, ideas and themes.

EDA and CDA

Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA) are two main methodologies used in data analytics. EDA is designed to uncover patterns and relationships within data, while CDA applies statistical techniques to verify whether theories regarding a dataset are true or false. This dichotomy can be compared to that between detective work—EDA— with the legal judgment of jury during court trial—CDA. The first time this contrast was highlighted was by John W. Tukey in his 1977 book Exploratory Data Analysis.

BI

By leveraging BI and comprehensive reporting at the application level, corporate executives and workers can access actionable data concerning key performance indicators, operations execution, customer behaviour patterns, and more. In prior times this information had to be sought out by IT developers for end-users to receive it; nowadays organisations can utilise self-service BI tools that allow their own employees to build reports themselves, and conduct ad hoc queries whenever required.

Data Mining

Data mining is a sophisticated form of data analytics in which large databases are carefully investigated to recognise trends, patterns, and correlations. Predictive analytics takes it one step further by anticipating customer behaviour, equipment failure, and future business events. With advanced data analysis such as these two methods, businesses can gain invaluable insights into their operations.

Machine Learning

Machine learning can rapidly process data sets at a much faster rate than humanly possible, making it an invaluable tool for any kind of data analysis. Big data analytics utilises text mining and various other algorithms to analyse structured, semi-structured, and unstructured data in order to draw meaningful conclusions about the datasets. Moreover, text mining allows effective discovery of hidden insights from large bodies of documents such as emails or articles with minimal effort.

CRM Analytics

Mobile network providers analyse customer data to anticipate potential attrition and take steps to retain customers in the face of competition. To strengthen their relationship with clients, companies use customer relationship management (CRM) analytics to categorise people for targeted marketing campaigns and equip call center agents with current caller information.

Data Science

As automation continues to grow, data scientists will be required to prioritise business needs and strategic leadership while delving deep into the world of learning. Conversely, data analysts operating in the realm of business intelligence will need to place greater emphasis on creating models along with other repetitive tasks. In a broader sense, data scientists focus mainly on providing insightful analysis whereas data analysts concentrate their efforts towards resolving specific queries.

Types of Data Analytics

Data analytics is a vast field, with four specific forms—descriptive, diagnostic, predictive and prescriptive. Each type serves its own purpose in the data analysis process and plays an essential role in modern business.

Descriptive Analytics

This provides valuable insight into the past by helping key stakeholders answer questions about what happened. By developing key performance indicators (KPI) and metrics such as ROI, they help track successes or failures in a variety of industries. Gathering relevant data, processing it, conducting an analysis and visualising the results are essential elements that comprise this process—ultimately offering KPI outcomes.

Diagnostic Analytics

Diagnostic analytics can help provide clarity to why something happened. This type of analysis builds on the more basic descriptive analytics by digging deeper into existing findings and exploring performance indicators in further detail to uncover what either caused improvement or decline. It entails three steps:

• Identify anomalies in data, such as unexpected changes within metrics or particular markets; determining what these variations mean; and ultimately arriving at an explanation for them.
• Compile relevant data related to these anomalies for further analysis.
• Employ statistical methods to uncover correlations and patterns that explain these irregularities

Predictive Analytics

This offers the ability to answer questions about potential upcoming events. Analysing past data allows allows for recognition of patterns and deciding if they are likely to repeat themselves. Predictive analytical tools provide firms with valuable insights into what may happen down the line by utilising advanced statistical analysis methods like neural networks, decision trees, regression and more.

Presctiptive Analytics

This offers the answer to which actions should be taken. With its predictive insights, this allows companies to make data-driven decisions, allowing the company to move forward with confidence despite uncertainty. Prescriptive analytics utilises machine learning approaches that can reveal trends within large datasets. By studying past choices and situations, analysts can estimate the probability of different results occurring.

What to Look for in Data Analysts

Data analysts are expected to have good understanding of the different types of data analytics, the software used to process and interpret it, data visualisation tools, machine learning operations, and the relevant coding languages. It is essential to ensure that they possess the right mix of skills and experience. A good data analytics expert also have the following:

Data Analysis Skills

SQL is undoubtedly the most essential skill to acquire if you want to become a data analyst. Additionally, knowledge of advanced programming languages such as R or Python offers analytical capabilities that Excel can't provide—an important feature when it comes to analysing large and sophisticated datasets. Econometrics is also important as it involves using statistical and mathematical frameworks to investigate current economic structures and identify trends based on historical data. Data analysts aiming for roles in the finance industry, particularly at investment banks or hedge funds will find it essential that they have a thorough understanding of econometrics.

Specialisation

Data analysts can be specialised in a certain field and have an understanding of the industry-specific terminology and any relevant business processes. A data analyst can be any of the following:

Database Administrator: This role focuses heavily on using SQL scripts to modify data stored in relational databases and often involves some systems administration as well. As such, it's important that you have strong skills in database administration, SQL programming and scripting, and systems management.

Data Analyst: As the most in-demand role, data analysts are sought after by companies looking to make more informed decisions. They are equipped with a comprehensive set of skills that range from data analysis and business process optimisation to effective visual storytelling through data visualisation.

Data Scientist: Data scientists are one of the newest positions on the tech job market, as data is still a relatively new field even among its other technology pillars – infrastructure, cybersecurity, and software development. What sets them apart from traditional data analysts is their ability to use statistical modelling to forecast future insights.

Data Architect: These individuals may come from any professional field, but they have a basic understanding of the data landscape and general familiarity with technological functions. As data architects are responsible for crafting appropriate systems and making important business decisions, it is essential that they possess relevant clusters of skills like project management, system design, implementation, and communication capabilities.

Data Analytics Certificate

Generally, there are three different levels of certifications available: basic, intermediate, and advanced. Basic certification is typically aimed at entry-level data analysts, while intermediate and advanced certifications are designed for experienced professionals. The Singapore government has recognised a few certifications as industry standards. The National Infocomm Competency Framework, or N-Infocomm, provides a comprehensive list of data analytics certifications that are accepted, such as the Certified Data Scientist and Big Data Engineer, and Professional Certificate in Business Analytics.

Data Visualisations

As visual creatures, humans comprehend charts or graphs more rapidly than a spreadsheet. Thus, data visualization is the ideal solution to present trends and patterns in an easily-understandable format. Besides knowledge of statistics, visualising data is crucial for any effective exploration of stored information. Oftentimes, there are aspects of the data that will go undetected when only examining numerical values alone; however, with visualisations, you can spot these discrepancies and gain further understanding.

By utilising numerous tools and methods, raw numbers can be transformed into powerful insights that motivate decision-making and effective management. With this type of understanding at your fingertips, you will know the necessary to develop solutions with confidence.

Explore our list of data analytics firms in Singapore to help process your business information.