Data Analytics

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.

Data mining is the process of digging through large amounts of data which allows businesses to discover patterns and predict future trends. Also known as ‘knowledge discovery in databases’, data mining is normally applied under three disciplines:

• Statistics
• Artificial intelligence
• Machine learning algorithms

Advances in computing speeds has allowed businesses to automate this process so that they can step away from manual and time-consuming practices. Businesses such as banks, retailers, insurers and manufactures all use data mining to discover patterns in everything from pricing, economic forecasting, competition and social media, so that they can discover how they are affecting their business models, operations and client relationships.

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

• Help you understand the needs and behaviours of your clients
• Improve customer satisfaction
• Enable your business to exploit new market opportunities
• Allow you to identify the strength and weaknesses of your product or services
• Allows your business to optimise its resources
• Reduce expenditures
• Increase logistical and operational effectiveness
• Improve revenue and profitability
• Improve financial forecasting and other KPIs
• Allow your business to be more proactive, stay agile and reduce risk by acting under reliable information
• Allow your business to automate time consuming processes and improve efficiency

At RSM we will:

• Look at your business requirements – Taking an in depth understanding of your strategic objectives and reporting needs, defining use cases and identifying gaps in your data.
• Evaluate your data source – Identifying and conducting an assessment on all your businesses data sources.
• Provide a data quality assessment – Identifying any opportunities for data cleansing and introduce governance to ensure the data is complete, correct and consistent.
• Provide reports and visualisations – Offering advice on reporting tools KPIs and metrics as well as assisting with the creation of reports.
• Produce Data integration / modelling – Taking data from multiple sources to link and create data models for reporting.
• Enterprise analytics solutions – Offering assistance with platform and technology selection and implementation, as well as giving practical advice on pre-built versus custom solutions.

We will help your business by defining your data analytics strategy and develop a roadmap with tangible KPIs and priorities deliverables so that you can:

• Understand your strategic objectives
• Understand your applications
• Understand your data
• Understand your technology