BENEFITS OF DATA SCIENCE FOR BUSINESSES

Benefits and importance of data science in business: A Guide

Data is steadily becoming one of the most critical disciplines in the modern business world.

When employed correctly, data science allows businesses to gain a competitive edge by empowering their employees to make informed decisions that promote business growth.

But what is data science? And why is data science important for business functions?

To understand the innumerable benefits of data science, it’s important to first appreciate the diverse nature of data science and the various roles that comprise a data science team.

Benefits of data science for businesses

Being able to locate hidden patterns within collections of data provides numerous opportunities for businesses looking to anticipate emerging trends and act accordingly.

Some of the proactive benefits of data science for businesses include:

Gathering business intelligence in real time

Data science and digital literacy in the workplace allows businesses to gather and analyse business intelligence in real time. Employees and managers can subsequently use this information to improve decision-making across the entire organisation.

Using data to define goals and identify targets

Without data, business objectives can become vague, ever-moving targets that are absent of any specific KPIs that can realistically be measured. Data enables organisations to set realistic targets that can be tracked continuously.

Guiding business decision making with evidence

Real-time data provides businesses with a more streamlined approach to monitoring and measuring performance. This in turn ensures that employees and management can base their decisions on concrete information rather than abstract ideas.

Improving customer service with data insights

Improved customer service is closely linked to data science. Data science provides organisations with the necessary tools to anticipate customer behaviour and offer a more personalised customer experience.

Enhancing data security

Collecting and managing data comes with a range of inherent risks that must be mitigated to ensure that the data is being used responsibly. Investing in data science can contribute to safer data practices within an organisation, ensuring that internal practices align with regulations and industry standards.

Predicting industry disruption with forecasting tools

When used correctly, data can map out a variety of future scenarios by capitalising on current trends and numbers. Analysing possible scenarios means that businesses can more effectively anticipate internal and external events. This ensures that they have adequate time to not only establish meaningful risk mitigation strategies, but also adopt a proactive approach to changing contexts.

Key aspects of data science

As data science is an interdisciplinary field of study, it comprises numerous areas that each serve a different purpose. In order to add true value to an organisation, a data science team must encompass expertise across the following disciplines:

Data collection

Data collection describes the processes of gathering and measuring data that will then be utilised to inform business decision-making, research or strategic planning.

Data processing

Data processing is when raw data is collected and manipulated into meaningful information. This is a complicated process that must take into account numerous factors to ensure that the produced information is accurate, unbiased and usable.

Data management

Data management is the practice of collecting, organising and storing data in a manner that allows it to be analysed within an organisation. As most organisations collect data at an extremely high volume, data management ensures that the collected data is kept secure and is maintained as efficiently as possible.

Data analysis

Data analysis refers to the process of inspecting, cleaning and transforming data into valuable information that can be used within an organisation to make informed decisions.

Data communication

Data communication is the transfer and reception of data via a transmission medium. Data communication is key to internal and external business functions as it allows digital or electronic data to be shared via two or more networks, regardless of geographical location.

Data science project life cycle

A data science project life cycle is a common methodology used for data science and other advanced analytics programs. It sets out six repetitive steps that must be completed in order to deliver a project. These steps are as outlined below:

Problem formulation

What problem is the business facing? And what solution is the business looking to achieve?

Understanding the problem that the organisation is facing is the first step in the data science project life cycle. In order to generate accurate results and uncover underlying problems, a data scientist must be fully aware of their client’s key concerns and requirements.

Data acquisition and processing

Once the data scientist has identified and examined the problem, it comes time for the data to be collected. Data collection may take various forms depending on the problem and the organisation’s requirements. Data scientists may have to collect data from numerous sources, such as digital libraries, social media posts or information already stored within an Excel spreadsheet.

Data analysis and preparation

After the data has been gathered, it must then be prepared. Data analysis and preparation ensures that we gain a full understanding of the implications behind the sets of data. This step also involves data cleaning – a process in which a data scientist identifies missing values, removes incorrect data, and checks for outliers or other inconsistencies.

Model building

Model building is the process of taking the prepared data and selecting the appropriate type of model that will best implement the desired results. The type of model will depend on whether the problem assessed in Step 1 was a regression problem, a clustering-based problem or a classification problem.

The 3 primary data model types used in data science are:

1. Dimensional
2. Relational
3. Entity-relationship (E-R)

Once the type of model has been determined, the data scientist must then select the most appropriate machine learning algorithm.

Model deployment and data communication

Model deployment is the final step in the life cycle. This step requires the data scientist to select the most appropriate channel and format to represent the data. The model must be representative of the information encapsulated in the data.

What skills will your data scientists need?

To maximise the benefits of data science for businesses, it’s necessary to note that there are various skills an expert data scientist will need to possess. Some of the areas that a data scientist must be proficient in include:

Mathematics

At its core, data science is a mathematical construct. In order to produce reliable results, a data scientist must have a thorough understanding of core mathematical concepts such as arithmetic, linear algebra, geometry, probability and, of course, Bayes Theorem.

Statistics

Closely related to mathematics, statistics plays a key role in data science as it provides data scientists with the necessary insights to make inferences and predict forms. Understanding probability and statistics ensures that data scientists can identify underlying relationships/ dependencies that could potentially exist between two variables.

Programming

Programming forms the basis for data science by acting as the primary tool that a data scientist will use to transform raw data into discernible facts. Python, Java and R are some of the most common programming languages used in data science.

Database management

Database management is the process of storing, organising and accessing data from a computer system. A Database Management System (DBMS) is the software that a data scientist will use to manage their data. Subsequently, it’s crucial that a data scientist is proficient in using the DBMS that is required in their role.

Big data analytics

Big data analytics refers to the process of uncovering patterns and correlations evident within large amounts of raw data. With organisations collecting an unprecedented amount of data, the importance of big data analytics in business is becoming increasingly visible as organisations capitalise on emerging technologies and capture more sophisticated insights.

Machine learning and deep learning

Machine learning is a branch of data science that centres around the field of artificial intelligence. Through machine learning, algorithms are employed to extract data and predict likely future trends. Data scientists often use machine learning in their work to generate faster and more accurate results.

Meanwhile, deep learning is a subfield of machine learning. This subfield is based on artificial neural networks with representation learning.

The skills associated with machine learning and deep learning are closely related to programming, statistics and probability, prototyping and data modelling.

Data visualisation

Data visualisation is the practice of translating data into a visual representation that can be deciphered by individuals without data literacy skills. Visualisation can include graphs, charts, maps or any other type of infographics. Data visualisation makes it easier for our brains to register patterns and identify outliers in large datasets.

Other transferable skills

Other transferable skills that are necessary to excel in the field of data science include:

• Comprehension
• Problem-solving
• Communication
• Multi-tasking
• Leadership
• Teamwork

Examples of data science used in industry

We all experience the aftermath of data science in our daily lives. But how is data science useful in the context of your specific organisation or industry?

To help you answer that question, here are some examples of how data science has been employed across various industries to boost business performance and deliver personalised customer experiences:

The quintessential data science example: how Netflix recommends relevant shows

Have you ever wondered how Netflix always seems to know what your next favourite TV show is before you do?

With over 151 million subscribers, it’s safe to say that Netflix has access to a lot of data across the entire globe, including individual viewing habits and watching patterns. Netflix uses this data to create detailed profiles of each of its subscribers that delineates how and when individuals watch specific content. This means that Netflix knows much more about your viewing habits than just the genre of movies you prefer to watch.

To enhance their user profiles, Netflix also uses information such as:

• When and where a user pauses their show
• Whether a viewer binge watches a show
• How long it takes for a viewer to finish watching a show

The data that Netflix gleans from the above points ensures that the company doesn’t have to spend excessive amounts of money on advertising to guarantee that you are aware of new content that’s relevant to your interests. The viewing suggestions Netflix provides to your account are supported by an algorithm that monitors your online behaviour and then uses this information to provide a customised viewing experience.

Marketing: how DoorDash optimised their marketing spend

DoorDash is an American food delivery service that uses data science to reach and attract new customers. Data science allows DoorDash to strike the perfect balance between marketing spend and business growth, ensuring that they do not overspend on unprofitable campaigns. This is achieved through optimising campaigns in line with historical performance.

DoorDash’s marketing team relies heavily on data to update bids and budgets in a way that boosts the performance of optimised marketing campaigns. Data is gleaned through a custom-built Marketing Automation Program that is powered by machine learning. This program automatically allocates budget to each campaign according to its current performance and observed historical data.

Healthcare: how health startup Babylon creates personalised health experiences

Babylon Healthcare is a startup British digital health service provider that uses AI to offer accessible online healthcare. Babylon migrated its applications to a Kubernetes platform at the same time its infrastructure team began to use Kubeflow – a toolkit for machine learning on Kubernetes. Relying on machine learning to run clinical validations meant that validations could be completed within an astounding 20 minutes, rather than the 10 hours that they used to require.

Finance: how insurance company Thélem assurances automates fraud detection

Thélem Assurances is a French insurance company that was interested in launching an AI initiative to identify and reduce fraudulent insurance claims, which were costing the company millions of dollars. With the help of IBM consulting, Thélem created an algorithm that could be deployed to detect fraud. Now, with the help of a cloud-based AI, Thélem detects five times more fraudulent claims than they previously did. This has dramatically reduced business costs and time.

Travel: how an online travel agency identified alternate revenue channels and drove sales

In order to generate revenue, online travel agencies (OTAs) must evaluate the potential that their current revenue channels hold and proactively bridge any gaps between sales performances and marketing expenditure.

One of the leading OTAs in the industry was focusing disproportionately on their online channels as this was the primary source of the company’s revenue. However, fixating on their online presence came at the cost of the performance of the company’s offline channels. This was contributing to a decrease in revenues and market share.

With the help of WNS, the OTA was able to use its collected data to obtain a more thorough understanding of customer profitability and the performance of different channels. These insights enabled the OTA to take the proper steps to ensure that the right people were deployed to manage the right channels. It also ensured that the OTA was able to focus its efforts on customers that held the most lifetime value, which in turn allowed the company to determine strategies to maximise sales.

Customer service: how an Australian software company provides conversational voice AI solutions to a variety of industries

Curious Thing is an Australian software company that creates AI solutions to support all stages of the customer journey. This technology has helped organisations across a diverse range of industries drive outbound engagement via automated customer service. Curious Thing streamlines business’ customer service by collecting data from every customer conversation, which can then be used to re-engage with customers, share updates or remind customers of impending payments.

Transport: how a tech company provides a one-stop solution for bus ticketing and live tracking

Chalo is an Indian transport technology company that created an app to track buses live and generate mobile tickets. Chalo collects real-time transportation data to provide users with reliable updates on where their bus currently is, and how long it will take to reach their bus stop.

As a number of bus operators in India provide bus tracking information publicly, Chalo collects this data to generate accurate insights into public transportation timing. The company also uses Google Maps to display this information in a readable format for customers. In the context of India where public transport has generally unpredictable scheduling, Chalo provides a proactive solution to a very pertinent problem. This has resulted in the company growing exponentially and they now operate in 22 cities across India.

Start developing your organisation’s data science capabilities

In a world overflowing with powerful technology and fleeting trends, it is incredibly important that a business effectively capitalises on its collected data to gain full insight into its current, historical and future performance. However, integrating data science into your organisation does not have to be overly complicated or require complete restructuring.

Rather, data science plays a crucial role in everyday business operations, meaning businesses can infuse their organisation with data science, rather than implementing it with a top-down approach. This falls in line with the fact that any role can be enhanced with additional knowledge that is backed by real-time data.

DeakinCo. offers a wide variety of learning solutions focused on improving technical skills within your current workforce, including data science. Reach out to the team at DeakinCo. to learn more about how our customised approach to learning and development could help integrate powerful skill sets into your organisation.

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