In the last few years, the benefits of Big Data insight have been sought after by most, if not all industry sectors including banking, infrastructure, healthcare, hospitality, tourism, media, education, law enforcement, mining, manufacturing, telecommunications, security, retail, and more. As big data permeates into our daily lives, industry is determined to find real value in the abundance of our digital data.
Businesses across industry sectors are adopting big data processes to remain at the forefront of technology innovation and meet the needs of their customers. There is also a growing need for professionals with the ability to use data science tools and techniques on the job to reveal daily insights that can help businesses stay ahead of the competition and improve operations.
The applications of data science, data analytics and machine learning depends on a company’s business goals. Primary goals for most organisations are to enhance customer experience, reduce costs, improve targeting and optimise current processes and products. More recently, data privacy has also become a growing concern – making data security a priority for every business.
Employers everywhere want to achieve data driven outcomes. If you can provide them with domain knowledge combined with a job-ready data science skillset, you will be the value-adding professional that can meet their business needs.
Here are eight ways you could apply data science skills on the job in your next role:
1. Data Science skills will help you identify inconsistencies and detect fraud in data
Some of the biggest challenges the banking and finance industry face include: card fraud detection, tick analytics, trade visibility, and social analytics for trading. To overcome these challenges, professionals with data science skills are beneficial and necessary.
Big Data analytics can be used to monitor financial market activities. Network analytics and natural language processors can be used to detect illegal trading activities in the financial markets. Skills in predictive analytics, risk analytics, sentiment measurement, and trade analytics can be used to add more value to your professional profile and give you the sought-after ability to contribute towards fraud mitigation in the banking industry.
2. Using big data knowledge to create detailed customer profiles and improve target marketing
With intense competition in the industry, digital marketers face the pressing challenge of getting their message to their target audience before a competitor does. Consumers are looking for information using a variety of devices at all hours and on a number of platforms. With information overload, marketing professionals need to be able to identify the audience for their product and the locations where they are finding information online.
Data science and analytics skills are essential in today’s consumer driven market, especially if you want to understand consumer behaviour and identify trends and patterns to predict future behaviour. Using big data insight, companies across global industry sectors have the opportunity to create targeted marketing campaigns and reduce costs on mass advertising by focusing on consumers that are more likely to become customers.
3. Data science and analytics can help in better and faster decision making
Data science and analytics insight is changing the way businesses make decisions and big data has disrupted the existing business ecosystem. Data science tools and techniques are helping businesses in faster and fact-based decision making, while also optimising employee time and performance by providing more opportunities to problem solve and apply data insight on the job. Studies also show that organisations that are driven by data make better daily decisions and notice an increased efficiency in operations, customer experience and higher revenues.
For example, data analytics is acting as a unique value proposition to the Supply Chain industry. Supply chain managers are not only using data analytics to identify inconsistencies and inefficiencies in existing business processes in order to cut down on costs, but also analysing supply chain investments and making decisions based on risk modelling and data assessments. Supply chain managers can further use data science tools to make improvements in inventory management, procurement, logistics and channel management.
4. Re-developing existing products and new product development with the help of Big Data
With a data-focused, predictive and proactive approach, firms are using data science to develop new products, identify areas of improvement in existing product lines, and increasing the implementation rate of new product features and product extensions to ensure their products are meeting the changing needs of consumers.
Big data is driving industries to make new products and re-develop existing products based on findings about consumer demands, future profitability and production. There are plenty of benefits of using data science techniques in new product development including:
- Increased customer satisfaction
- Minimised risk associated with new product development
- Customer-focused products and services
- Customisable product offerings
- Increased customer lifetime value
- Enhanced customer engagement
In addition to conducting traditional market research to gain insights on consumer needs and wants, all professionals have the opportunity to enhance their roles and help businesses achieve their desired outcomes faster and more strategically using data science, data analytics, machine learning, data mining, and artificial intelligence tools and techniques.
5. Data science and Machine Learning tools can enhance overall customer experience
Companies are striving to provide customised and personalised service offerings to their audience for a better customer experience. AI and Machine learning, specifically natural language processing, predictive data analytics and deep learning, are giving businesses the opportunity to develop and use a variety of features to assist customers with decision making and enhance their customer experience. Some of today’s most widely-used customer focused AI developments across industry include: virtual voice assistants, online chatbots, and task-oriented advanced robotics.
Data science tools and techniques can also be used to monitor changing customer needs by generating prescriptive, practical insights through data analysis and data visualisation. For example – data science and predictive analytics are used when your content streaming service suggests a personalised playlist for you to watch – these are informed recommendations based on data collected about your recent behaviour on the site!
Businesses are investing in professionals with the ability and willingness to understand customer data to improve the customer experience. Data visualisation tools like Microsoft PowerBI and Tableau enable data professionals to identify trends and patterns in customer data and efficiently present findings to stakeholders. This ongoing insight helps stakeholders make better, faster decisions and enables a stronger customer experience.
6. Data science and analytics tools are used to compile data from various sources to extract meaningful information
Every competitive business or organisation is focused on collecting, storing, organising, managing, extracting insight and making sense of big data related to their customers, products and processes. However, it is also crucial for very business to ensure their big data is being acquired and handled correctly and suitably to meet ongoing business needs.
The education industry is facing the big challenge of efficiently collecting, storing and managing vast amounts of data that is available at their disposal. Data science enables educational institutions to collect, analyse and channel staff and student data insight to improve the quality and relevance of content, delivery, outcomes, measure the effectiveness of teachers, and identify student trends to facilitate a better educational experience for teachers and students.
Educational institutions that are investing in the latest learning and student management systems, data management programs / analytics tools and techniques, and employing staff with data skills, are benefiting from optimised learning, teaching and administrative processes.
7. Big Data tools and technologies can be used to improve employee performance
Irrespective of the size of the business, hiring professionals with data analytics skills is becoming a necessity for decision makers and stakeholders. The rapid advancement of data technology is also an opportunity for professionals today to adapt and prepare for when their role will require them to use data skills on the job. Keeping up with industry demands by gaining data skills training will make you more attractive to future employers and will demonstrate your potential to evolve with changing business needs.
For example, the use of big data tools and techniques has become quite popular among Human Resource departments for employee management, engagement, employee performance and productivity tracking, improving employee retention and optimising resource allocation.
HR managers are increasingly making use of employee tracking systems to monitor performance, build better relationships with employees and contractors while also empowering them to work within the system. Performance data collected through a tracking system enables employers to understand the strengths and weaknesses of each employee, and gives them the opportunity to share this insight with the team to maintain transparency, improve processes to increase productivity and promote a positive environment.
8. Data science tools are used across industry sectors to predict future trends and patterns from historic data
The opportunity to use data science skills on the job is not limited to the traditionally highly technical streams such as engineering or IT, there are increasing opportunities for professionals to employ data skills working in a variety of industry sectors including hospitality, HR, law enforcement, banking, retail and entertainment. Retailers often use these techniques to forecast inventory, manage shipping schedules, and even alter store layout based on data about consumer preference and interest to increase sales. One of the ways law enforcement uses data science and analytics is to study crime rates across different suburbs at different times and based on data patterns and clusters, they employ additional or pre-emptive protection to those areas with higher rates of crime.
Data science and predictive data analytics enables organisations to access accurate and reliable insights about future trends and it is becoming common practice in today’s data driven market for businesses to base their processes on real-time and historic data insight. This involves using data mining, statistical modelling, machine learning algorithms and artificial intelligence to forecast events and detect patterns and trends based on the available data.
If you are seeking a career in data science and looking to upskill to expand your career prospects, it is important to understand how different businesses are evolving by utilising data science, data analytics, machine learning and AI. This will help you to identify how your existing skillset combined with the ability to use data science tools and techniques, will enable you to sharpen your problem solving skills and produce better results on the job.
Now is a strategic time to enhance your future job prospects and prepare for a data driven career. If you’re ready to upskill, equip yourself with job-ready training in the most in-demand data science and analytics tools and techniques through one of our accelerated industry-focused training programs. Learn more about upskilling to data science here or schedule a call here.