In simple words, the field of data science and analytics deals with collecting, analysing and interpreting data to help organisations make decisions regarding their functions. This field deals with modern tools and techniques to uncover patterns and derive information pertaining to business decisions. For example, a banking or finance company can use a person’s previous billing data to know their creditworthiness. This field is majorly used in organisations dealing with fraud detection, risk mitigation, target advertising, etc.
But before answering the question “Why do organisations need Data Scientists?”, it is crucial to understand the importance of data in today’s time. The primary and most important function that data serves is to generate insights that organisations can then interpret to plan their next plan of action. In order to do this, the field of science combines subject expertise from various multidisciplinary subjects like programming, math, and statistics. This obviously helps draw sound conclusions, which is why data science is becoming more and more significant.
Southwest Airlines was once able to use data to alter the resources consumed by decreasing the amount of time their jets stood idle on the tarmac, saving $100 million. In conclusion, no business today could possibly or should envision a world without data.
But why would organisations need Data Science and Analytics?
The concept of a data-driven company is well entrenched in any business culture. In other words, data culture is gradually supplanting business culture. And now is the perfect opportunity to start out in front of the game. Let’s look at various ways data science and analytics can help an organisation.
1. It helps expand the business.
In order to understand complex data, top-performing companies mix analytics and data visualisation technology. To help them adopt a data-driven strategy, businesses are looking for data scientists who can assist them in finding new markets that might be intrigued by the organisation’s products.
It can highlight recent patterns or identify the inventory items in an organisation that will increase sales rapidly. But to do that, it’s critical to have a clear grasp of the organisation’s present customers.
2. It provides individualised results.
Without a data scientist’s help, it becomes difficult to track shifting client behaviour pertaining to one specific organisation. For instance, Airbnb, which helps visitors and hosts find and rent lodgings, recently studied user behaviour during online searches in order to deliver more tailored results. Bookings and reservations both went up as a result.
Therefore, with data analysis, organisations can collect information about the purchasing patterns of their clientele and improve the overall business strategy.
3. It sustains the business.
Data analysis provides more data-driven and fact-oriented information by taking emotions and precedence out of the equation. Consequently, a data scientist on the team will always assist in choosing the best course of action based on the gathered data, eliminating prejudices and prior history from the decision-making process.
4. It improves forecasting.
Given the constant change in our everyday lives, data science can produce more meaningful research by spotting trends that may not always be apparent. Organisations can use forecasting to establish data-driven plans and business decisions.
Financial and operational decisions are made based on current market conditions as well as predictions for the future. The prediction of future trends and developments involves gathering and pattern recognition analysis of historical data.
What are the services offered by Data Science and Analytics companies?
The service-industry matrix could be broadly classified into five segments.
1. Sales and Marketing Analytics
With the most recent developments in artificial intelligence, machine learning, and cloud-based technologies, Data Science and Analytics services can assist in approaching sales and marketing optimisation more strategically to create top-line & bottom-line growth. It can quickly and efficiently improve sales, marketing, and customer service.
Salespeople and marketers can quickly transition from product planning to commercialisation using Data Science and Analytics services. They would require adaptability and swiftly integrated internal and external communication.
2. Financial Risk Management
In order to manage financial risk, different assets and liabilities must be assessed both now and in the future. On the one hand, financial institutions must make money and can only do that by taking risks. This is a crucial realisation. On the other hand, the risk manager’s job is to limit these risks. Limiting risk-taking could be expensive or even hinder lucrative economic endeavours. Risk managers may contribute to the emergence of new hazards and dangers while managing challenging and occasionally conflicting needs.
When it comes to financial risk management, one must adhere to the conventional heuristic principles such as “never put all your eggs in one basket,” and so forth. Thus, it is necessary to consider various risk components using the appropriate technique at the appropriate time with the appropriate strategy by integrating external and internal aspects with a 360-degree connected data framework. To lessen economic hazards, data science and analytics services can help businesses comprehend, quantify, and forecast risk and uncertainty.
3. Customer Analytics
Customers are a brand’s most important asset and most prominent supporters. By combining human intelligence with cutting-edge machine learning techniques, organisations can create clients for life. The strategy employed by data science services is to pool strengths and subject-matter knowledge in order to offer a complete customer experience.
In order to create the integrated, end-to-end, user-friendly experiences that customers want, which will lead to higher revenue and growth, data science and analytics services can help you close gaps in your customer experience strategy and journey.
4. Operational Analysis
Business owners will be able to view the function of their daily operations in detail if they have complete transparency when accessing real-time data. Proactive planning and the necessary adjustments can then be implemented. This stream of predictive analytics services provides early warnings and failure diagnostics. It helps businesses minimise operations and maintenance expenses while decreasing equipment downtime, enhancing dependability, and improving performance.
These services allow you to manage dynamic change and gain visibility across your supply chain to help you turn business disruptions into opportunities for growth and profit. The use of operational analytics helps you achieve better decision-making in your organisation.
5. HR Analytics
Failure to adopt a 360-degree approach to HR data might impede progress. The effectiveness of workforce analytics depends on an all-encompassing strategy.
By utilising HR data with an analytical framework, these services would help an organisation to advance any workgroups. Data Science and Analytics services provide the full range of HR Analytics solutions and frameworks, including but not limited to job analysis, recruiting, selection tool validation, talent acquisition, performance management surveys, etc., starting with assessing the As-Is landscape and continuing with HR Analytics Consulting.
An organisation can improve the effectiveness of their strategic, operational, and tactical decision-making by using HR Analytics solutions, with an emphasis on analytics to transform information into insights, insights into actions, and actions into outcomes.
What to look at when you hire a Data Scientist?
To choose a data scientist for an organisation, it is vital for an individual to know that many alternative job names include a job description for a data scientist sitting behind them. They comprise Analytics Consultants, Data Engineers, and AI Developers, among many others. Although there are differences in the minutest ways, they all take aspects of each other. Overall though, there are certain hard and soft skills that an ideal job description of a data scientist must possess, such as:
- Programming knowledge (Python, Java, SQL, etc.)
- In-depth knowledge of machine learning and deep learning
- Familiarity with tools that facilitate programming knowledge like Apache Spark, Apache Hive, and Apache Pig, along with Hadoop
- Generating reports and dashboards, which requires business intelligence and data visualisation abilities
- Skills to convey and communicate information clearly
- Critical thinking, adaptability and flexibility, patience and perseverance, and effective communication with the stakeholders could be specific soft skills that a recruiter might want to look for in a data scientist.
According to the most recent research and needs, data specialists and data scientists possess the technical know-how to manage complex problems and the curiosity to discover what questions need to be answered. There are many data science and analytics services, but you need to identify your business goal before seeking the service that will help you achieve it effectively.
Before choosing a data science and analytics company, you need to ensure that the data scientists have certain technical skills, which might include statistical analysis and computing, machine learning, data visualisation, programming, etc. They are a mixture of trend forecasters, computer scientists, and mathematicians and operate in both the commercial and IT sectors, which makes them highly sought-after, and much needed for your business growth.