As the world becomes increasingly digitalised, businesses are being given access to a wealth of data which was not previously available to them. Leveraging this data has become a ‘must have’ capability as organisations look to harness the power of new, innovative tools, in order to make informed decisions and gain that all important competitive edge. Through the use of advanced data analytical tools, businesses can convert mass raw data into useful, long-term insights.

The benefits of data analytics are many, and yet countless businesses have still to unlock their full potential due to lack of understanding or the right capabilities caused by data governance challenges and an over reliance on spreadsheets or even data being spread out across multiple unconnected systems.

RSM’s data analytics consulting service helps clients to leverage their data by turning it into an organisational asset. Our team of experts facilitate this by analysing, connecting and consolidating client data, using a variety of different tools, to create a single source of truth that can be trusted throughout an organisation. In turn, this enables clients to drive actionable insights and make effective business decisions based on credible and robust data. 

At RSM, our team of data analytics consulting specialists help clients to identify and capture valuable and meaningful insights from data, transforming them into business advantages through a blend of profound knowledge and technical expertise. Our advisers deliver insights and impact for middle market clients through a range of agile and transformational infrastructure and solutions.


Our expertise enables us to:

  • Assess the current availability and quality of data (Data source evaluation)
  • Highlight opportunities for data cleansing and introduce appropriate governance to ensure data is complete, correct and consistent (Data quality assessment)
  • Work to define the right data strategy to support the overarching strategic and operational strategy 
  • Identify the right platform and technology solution which best fit the organisation  - from the underlying platform to the front-end visualisations, and from off the shelf products to custom built enterprise solutions (Enterprise analytics solutions selection and implementation)
  • Demonstrate how to link data from multiple sources to create data models to enable better reporting (Data integration and modelling)
  • Support the implementation of the chosen solution in an agile, iterative manner, allowing clients to quickly benefit from robust reporting and data analytics
  • Be on hand to provide support and advice at every step of the implementation
  • Through understanding individual reporting needs, we can recommend appropriate reporting tools, KPIs and metrics, and aid with report creation (Reporting and visualisations advice)

Case study: Data analytics adoption in Singapore

A recent study has found that almost 70% of Small and Medium Enterprises (SMEs) operating in Singapore have not adopted any data analytics within their organisations, and half of those have no plans to do so in the future. In this study, RSM Singapore’s Adrian Tan reveals the findings of a survey of almost 600 SMEs conducted with the Singapore Institute of Technology, and the Institute of Singapore Chartered Accountants, and how they have been using this data to help RSM middle market clients to understand the benefits of using data analytics.

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Frequently Asked Questions (FAQs) 

Data analytics are the tools that are used to analyse raw data so that businesses can make informed decisions to strategies and performance. There are varying tools and processes used in data analytics, many of which are automated through algorithms. These algorithms can quickly reveal specific trends and metrics in mass data that would otherwise be lost.

The best data analytical tools will provide a range of statistical procedures. This allows teams and business leaders to look back and evaluate previous information and look into the future for scenario planning with predictive modelling.

Data analysis is the process whereby information is cleaned, transformed, and modelled so that it can be used to make informed business decisions. These processes will extract useful information from large amounts of data which would otherwise be lost. Teams and business leaders will often conduct this process when evaluating previous business performance, as well as using it to look forward when scenario planning or strategising.

Business intelligence is a term that covers the analytics, tools and processes which are used to optimise performance and make informed business decisions. Business intelligence is an umbrella term which covers:

  • Data visualisation
  • Reporting
  • Data mining
  • Performance metrics
  • Data preparation
  • Statistical analysis
  • Descriptive analytics

Business intelligence helps companies take an understanding of mass data so that it can be input into an intelligent enterprise model. This strategic approach allows teams and business leaders to identify useful information which would otherwise be lost in mass data.

Exploratory data analysis or EDA is where a researcher will conduct the first steps in data analysis before any statistical techniques have been applied. EDA is not considered a strict process, but a ‘philosophy’, whereby researchers will be getting a ‘feel’ for the data, often using their own judgement to discover what the most important elements are.

Some examples of EDA are:

  • Checking for mistakes or missing data
  • Gaining insights into the structure of the data
  • Uncovering a parsimonious model which details the data with a minimum number and predicts variables
  • Checking assumptions
  • Creating a list of anomalies
  • Finding parameter estimates
  • Identifying the most important variables
  • Ranking a list of relevant factors

Confirmatory data analysis or CDA, is the process whereby the evidence from the data is evaluated and the assumptions are challenged. This is where businesses will work backwards from their conclusions and challenge the merits of the results.

CDA will include processes such as testing hypotheses, forecasting, variance analysis and regression analysis. This will allow business to test their findings to ensure quality and risk assurance.

Predictive analysis is whereby a business will evaluate historical data and past performance via statistical algorithms and machine learning techniques, so that they can predict, and forecast future outcomes. Businesses will use predictive analytics to solve difficult problems and uncover new opportunities. Some examples are:

  • Anticipate if your client will leave
  • Financial forecasting
  • Optimising marketing campaigns
  • Risk assurance
  • Improving operations

Having forecasted the risk ahead of time, predictive analysis allows businesses to prepare a response and influence the outcome.

Text analytics is the process whereby you transform large amounts of unstructured text into quantitative data to discover insights, trends, and patterns. When used in conjunction with data visualisation tools, this method allows businesses to understand the story behind the numbers and make better decisions. Businesses will use text analytics through a number of different technologies to analyse customer and employee sentiment to identify fraud and compliance risks.

Structured data is the data that lies within a fixed field within a record or file. This can also consist of data that is included in databases or spreadsheets. Businesses will build data models that will define what types of data can be stored – this can be anything from data types like currency, alphabetic, name etc, to restrictions on the data input like character length or restrictions on certain terms like Mr or Mrs.

The advantage of using structured data is that it can be easily input, stored, and analysed. Businesses will often use this type of data for functions such as financial or operational as it allows them to organise large amounts of data in one place, stepping away from prehistoric filing cabinets.

In contrast to structured data, unstructured data cannot be easily stored in a fixed field of a database or spreadsheet. Inherently this makes unstructured data more difficult to analyse and sift through. Examples of unstructured data consist of:

  • Photos
  • Video
  • Audio
  • Text
  • Presentations
  • Webpages
  • Open-ended survey responses

Despite it being harder to analyse, businesses will now employ the help of artificial intelligence which uses analytical tools to reveal trends in large amounts of unstructured data. Unstructured data is becoming more important to businesses as they look for a competitive edge, by considering every piece of data that they have available.

Data integration is the process whereby you take data from different sources and combine it into one single source of truth. Data integration will often take place during the exploratory data analysis phase, where a researcher will include steps such cleansing, ETL mapping, combining data and transformation.

Data integration will often involve a few elements including a network of data sources, a master server and client data. During this process, the researcher will access the master server for data, extract that data and then consolidate it into a single cohesive data set.

Data visualisation is whereby data is formatted and displayed via visual graphics. This will include examples of visual graphics such as:

  • Charts
  • Graphs
  • Maps
  • Data visualisation tools

Data visualisation allows teams and business leaders to easily discover trends and insights in large amounts of data. This visual data can then be used in team and client meetings to ensure business decisions are being based on fact.

Data driven decisions is whereby a business will use analytical facts, metrics, and data to help them come to a strategic, business decisions which helps them achieve their objectives and initiatives. This operation can be fulfilled by anyone from a business analyst to a sales executive and is an intricate part of any modern business, as they endeavour to leave no stone unturned in the search for the next strategic opportunity.

  • Large amounts of unconnected data from different sources
  • No existing formal data governance
  • Poor data quality
  • Too much reliance on spreadsheets
  • Time consuming manual and repetitive tasks
  • Teams operating in informational silos
  • Confusion over the correct numbers
  • No ability to look further into the numbers
  • Lack of data to offer insights and support decision making

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