#10 Data science as a service (DSaaS)

In today's rapidly changing business landscape, companies are constantly seeking ways to optimize their operations and make data-driven decisions. HR and People Analytics have become crucial tools for organizations looking to attract and retain top talent, increase productivity, and improve overall employee satisfaction. With the emergence of Data Science as a Service (DSaaS), companies now have the opportunity to leverage cutting-edge technologies and data analytics to gain deeper insights into their workforce. In this article, we will explore the benefits of investing in DSaaS for HR and People Analytics, and how it can transform the way you manage your human capital.

What is Data Science as a Service?

Data science as a service (DSaaS) is a type of outsourcing where data scientists from an external company use advanced analytics applications to extract information, which is then provided to corporate clients for business use.

A DSaaS provider receives data from clients, refines and analyzes it using advanced algorithms, and then returns the findings generated by the algorithms to the customers.

Clients are typically required to upload their data to a cloud database or a big data platform, where a team of data engineers and data scientists from the service provider can work with it.

Benefits of DSaaS in HR / People Analytics

To understand the advantages of DSaaS, let us look at the leading barriers to adoption of analytics in organizations. As per Deloitte’s Analytics Advantage Survey, the major barriers include lack of proper technology and talented resources, poor data governance and lack of push from top management. All these challenges are addressed by choosing DSaaS.

DSaaS offers organizations a potential solution for dealing with a shortage of skilled data analysts, including data scientists. As businesses increasingly rely on advanced analytics such as predictive modeling and data mining to gain insights for profit, the demand for trained data scientists has outpaced supply. DSaaS allows businesses to gain access to analytics resources for specific data science applications without having to hire or train their own data analysts. DSaaS is a cost-effective alternative that provides organizations with the benefits of advanced analytics without the need to invest heavily in building an in-house analytics team. By outsourcing data analytics, companies can focus on their core business operations while leveraging external expertise to drive data-driven insights that will lead to growth and profitability.

Some examples of Applications..

DSaaS applications include various types of analytics, such as:

1. Talent acquisition: identify the best candidate profiles for specific job roles by analyzing resumes, job descriptions, and other relevant data. They can also help to predict which candidates are most likely to accept job offers and stay with the company long-term.
2. Employee engagement: measure and analyze employee engagement levels, identify key drivers of engagement, and recommend strategies to improve engagement.
3. Performance management: evaluate employee performance using advanced analytics, identify areas for improvement, and develop personalized development plans.
4. Workforce planning: forecast future workforce needs, analyze current and potential skill gaps, and identify strategies to address those gaps.
5. Diversity and inclusion: measure and analyze diversity and inclusion metrics, identify areas for improvement, and develop strategies to promote a more inclusive workplace.

Types of DSaaS

1. Data Collection And Transformation Tools

Several no-code or low-code Data Science solutions are available in the market. They help companies automate the end-to-end process of extracting data from different sources and storing it in the desired format. ETL tools remove manual efforts while ensuring Data Integrity across the departments.

2. Data Analytics Tools

Over the years, Data Analytics Tools have removed the tedious process of writing codes for insights generation. Today, you can drag and drop to quickly process information for making informed decisions. Data Analytics Tools like Power BI and Tableau have not only simplified Descriptive and Predictive Analytics but also Sentiment Analysis with Text Data

3. Recommendation Systems

One of the most highly used Data Science solutions is Recommendation Engines. These systems allow companies to offer a personalized experience to customers. Highly used in Media, Entertainment, and eCommerce companies, Recommendation Systems are very complex in nature. Building Recommendation Systems from scratch would take several months and would require constant monitoring, resulting in increased operational costs for many companies. With several available industry-specific Recommendation Systems providers in the market, organizations can leverage solutions that would require little to no tuning while implementing

4. Chatbots

Today, Chatbots are everywhere and are probably the most widely used DSaaS. Chatbots are assisting companies in providing better customer service at scale with almost no human interaction. Developing Chatbots require expertise in Natural Language Processing and numerous datasets for training Virtual Assistants. Chatbots are the most accessible plug-and-play data solutions available for all types of organizations

5. Computer Vision Systems

Computer Vision solutions are being used for Identity Verification, Extract Information from Documents, Find Defects in Physical Products, and more. Pre-built Computer Vision models can be used within companies to speed up the business process that involves Verifications and Digitizing physical documents

6. Fraud Detection

Fintech has witnessed a revolution in recent years due to the advancements in the Data Science landscape. Financial Transactions can be verified for their genuineness with Machine Learning models automatically, which was usually carried out manually. Since the Fraud Detection process has been automated, millions of transactions are being processed within seconds, leading to the Fintech revolution. Fintech firms can embrace off-the-shelf Fraud Detection solutions to abide by the rules in the highly regulated industry

7. AutoML

While developing Data Science solutions, Data Scientists spend a lot of time evaluating different models to gain the best results. This slackens the workflow since it is a manual process. AutoML solutions in the market are critical to recommend the right algorithms for your Data Science projects. Although there are huge advancements in the AutoML, it still is in a nascent stage. Nevertheless, it still enhances productivity in Data Science projects

So why should you invest in DSaaS in HR & people Analytics?

1. Lack of in-house expertise: If your organization doesn't have experienced data scientists, then a data science as a service provider can fill the gap. These service providers have a team of expert data scientists who can help you with your data-related needs.
2. Limited budget: Hiring a full-time data scientist can be expensive, especially for small or mid-sized companies. A data science as a service provider can offer you flexible pricing options that can fit within your budget.
3. Scalability: If your data science needs are project-based or seasonal, then it may not make sense to have a full-time data scientist on staff. A data science as a service provider can provide you with the flexibility to scale up or down as needed.
4. Time constraints: Data science projects can be time-consuming, and your team may not have the bandwidth to take on the work. By using a data science as a service provider, you can free up your team's time to focus on other projects.
5. Access to technology: Data science as a service providers often have access to the latest tools and technologies, which can be expensive for an organization to acquire and maintain. By using a service provider, you can leverage their technology and expertise without the additional costs.
6. Faster time to market: Data science as a service providers can help you get your products and services to market faster. Their expertise can help you identify and prioritize the most important data-related tasks, and they can work on multiple projects concurrently to speed up the delivery process.

Limitations of DSaaS

While DSaaS can reduce operational costs and turnaround time for implementing a new initiative within organizations, it has several drawbacks.

• One of the most prominent issues is that not all solutions would cater to your business-specific needs. In such cases, you will have to develop solutions from scratch. Therefore, you cannot always rely on existing tools for all your Data Science requirements.
• In addition, since DSaaS are mostly Cloud-based, you will have to, on numerous occasions, provide your data to the tool provider, which might compromise Data Privacy. As a result, you should not embrace DSaaS for every requirement.

Conclusion

In this article, you learned about Data Science and Data Science as a Service. You also gained insights into the various components and challenges associated with Data Science. Moreover, you explored various types of Data Science as a Service.

DSaaS is gaining popularity among organizations to streamline the entire workflow of Data Science initiatives. As the DSaaS landscape matures, organizations will have more options available to reduce the dependency on experts and maintenance for enabling Data Science Infrastructure. In the future, DSaaS will revolutionize the way organizations implement Data Science for business growth.

Are you looking for specific expertise in solving People Analytics use cases, reach out! With Summ.link, we can help you with all talent questions!