How can data science skills help you in your current job?

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, media, education, manufacturing, retail and so on. As big data permeates into our daily lives, the industry is progressing towards finding real value in our digital data. There has been a growing need for individuals with data science skills to join businesses across industry sectors that are adopting big data processes to remain at the forefront of technology innovation. 

The application of data science skills depends on a company’s business goals. Primary goals for most organisations are to increase customer experience, reduce costs, better targeting and increasing efficiency of current processes. More recently, data privacy has become a growing concern, hence, making it an important goal for many corporations. 

Keeping this in mind, make sure you have a good understanding of how your company is growing in terms of adopting data science and data analytics. This will help you understand how you can add more value to your current job by applying your data science skills. 

Listed below are the eight ways data science skills can be applied to your current job – 

1. Data science skills will help you identify inconsistencies and detect fraud in data

Some of the biggest challenges the banking and finance industry faces today are: card fraud detection, tick analytics, trade visibility, and social analytics for trading. To overcome these challenges, professionals with data science skills are beneficial to any organisation. 

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 current job profile to contribute towards fraud mitigation in the banking industry.

2. Using big data knowledge to create detailed customer profile and improve target marketing

With the intense competition in the industry, digital marketers face the pressing challenge of getting their message to the target audience. Consumers are looking for information in a variety of devices and on a number of platforms. With information overload, it is the marketers who need to identify the audience for their product, know where they are looking and how to approach them in order for the consumers to notice it. 

Data analytic skills play a very important role in helping marketers understand consumer behaviour and predict future behaviour. With big data insights on consumer behaviour, companies can create targeted marketing message and reduce costs on advertisements by focusing on those consumers who are more likely to become customers. 

3. Data science and analytics can help in better and faster decision making

Data science and analytics are changing the way businesses make decisions. Big data has disrupted the existing business model and ecosystem. Data science tools and techniques are helping businesses in faster and fact-based decision-making. Studies also show that organisations which are driven by data make better strategic decisions and also notice higher efficiency in operations, enhanced 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 the existing business model in order to cut down on costs, but also analysing supply chain investments and making decisions based on risk modelling and 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

Big data is driving industries to make new products and re-develop existing products based on findings of consumer demands, future profitability and production. There are plenty of benefits of using data science techniques in new product development including:

• Increase in customer value
• Minimise risk associated with new product development
• Customer-focused products and services
• Customisable product offerings
• Increased customer lifetime value
• Enhanced customer engagement

Engineers, marketers and data scientists are using data mining, artificial intelligence tools, predictive analytics and other data science tools along with traditional market research to gain insights on consumer’s needs and wants. With a predictive and proactive approach, firms are using data science skills to develop new products, identify areas from new features to existing product lines and new product extensions. 

5. Data science and machine learning tools can enhance the overall customer experience

Companies are thriving to provide customised and personalised service offerings to their audience for a better customer experience. AI and machine learning bring along a variety of other features to enhance customer experience such as – virtual reality, AI-chatbots, voice assistants, and so on. For instance, Spotify uses Hadoop, a big data analytics tool, to collect data from its users across the world, then analyses that data to provide informed music recommendations for the individual user. 

It is very common in today’s industry for marketers to require information from data science such as: an understanding of data analysis, generating prescriptive insights, A/B testing, management of data pipeline and much more. Marketers should have knowledge about the importance of metrics, how to obtain those metrics, identify trends and extract actionable insights. In order to be a competent and valued marketer, it would be a smart move to learn and gain data science skills in order to help your organisation meet goals quickly and efficiently. 

6. Data science and analytics tools are used to compile data from various sources to extract meaningful information

The education industry is facing a big challenge of collecting, storing and managing data that is available at their disposal. The staff and institutions need to get their hands onto the latest data management and analytics tools to channel the data into the right directions. 

For instance, the staff at the University of Tasmania, having over 26,000 students, uses an advanced Learning and Management system to track the student log-ins into the system, the amount of time spent on each page in order to understand the overall progress of the student over a period of time. Data science and analysis tools are also used to measure the effectiveness of teachers, for a better student and teacher experience.

7. Big data tools and technologies can be used to improve employee performance

Irrespective of the size of the business, data analytics skills have become a crucial aspect for the decision-makers. Apart from the importance for marketing professionals, engineers and data professionals, Big data has also become quite popular among Human Resource departments for employee management, engagement, employee performance, productivity tracking and improving employee retention. 

HR managers are now making use of an employee tracking system to monitor performance, build better relationships with the employees and empower employees to work within the system. Employers collect the data through the tracking system to understand the strengths and weaknesses of each employee, simultaneously sharing it with the employees to maintain transparency and a positive environment. 

8. Data science tools used by industries to predict future trends and patterns from the data

Predictive analytics, a category of data science and analytics, is the use of past or historical data to predict future trends and outcomes using statistical modelling and machine learning algorithms. Predictive analytics makes use of a range of technologies such as data mining, big data, machine learning, artificial intelligence, etc to forecast events and detect trends and patterns based on available data. 

Data science tools help organisations with accurate and reliable insights about future trends. Retailers often use these techniques to forecast inventory, manage shipping schedules, and even alter store layouts to increase sales. Law enforcement uses data science tools to study crime rates across different suburbs at different times of the year and employ additional protection to those suburbs with higher rates of crimes. 

Soon, all industries will base their entire business processes on big data and artificial intelligence. We need to be equipped in advance for the technological changes and adapt to keep up with the growing trends. Data science is not limited to professionals working in highly technical streams such as engineering or IT, even professionals from HR, law enforcement, and banking need to make use of data science skills in order to improve their performance. 

Waste no more time! Get your hands on the latest data science tools and technologies and become a competent professional ready to face the world progressing towards big data and IoT. 

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