In the rapidly evolving landscape of scholarly publishing companies need committed individuals with the right talent to help grasp the intricacies of the field.

At PHI Learning, our hiring process is designed to ensure we bring on board individuals who are not only skilled but also aligned with our mission and values. Our meticulous hiring process ensures that we help onboard individuals who are genuinely interested so as to create a mutually beneficial relationship between the company and the new hire.

We believe that our stringent process is helpful to everyone — the company and the new entrant, who, we know, must be looking for a suitable place to invest their efforts in too!

The Hiring Process at PHI Learning

Our recruitment process begins with advertising vacancies through social media, job search websites, and online newsletters. Once interested applicants send us their CVs, these are thoroughly reviewed and shortlisted. The process involves:

1. An initial email interaction
2. A telephone interview
3. An in-person test for relevant roles
4. A final in-person interview at our office

During the interview, candidates are introduced to our company, products, mission, and culture. They are asked about their commitment to our vision, their knowledge and experience related to the publishing industry, and their passion for the industry. These questions are asked across all touchpoints during the hiring process – via the phone during the initial telephonic interview, via email, and during the in-person interviews to ensure compatibility with the role.

At this stage, it is impractical to emphasize the candidate’s commitment to the role — both from the perspectives of the interviewer and the interviewee.

Many-a-times suitable candidates who appear quite energetic, knowledgeable, and carrying a fresh perspective, lose out when asked about the company and its products.

Remember, your interviewers are, after all, making the effort to conduct this interview because of how invested they are in the company and its success. If they didn’t care about the company as much as you care about the skills you have written about on your resume, they wouldn’t be here interviewing you!

So impress your interviewers by talking about what matters to them the most.

At this stage, it is helpful to provide information about the company’s products through your own prior research and link it to how the skills you have developed are relevant to the development of the company’s products.

At PHI Learning, we advise candidates to demonstrate a genuine interest in the company as this shows their commitment and understanding of the role.

Leveraging Recruitment Analytics

Interested in learning more about recruitment strategies to ensure your company has hired the right talent to meet its aims and objectives?

PHI Learning’s title HR Analytics: The Future of HR offers a comprehensive overview of HR analytics, vital for HR professionals aiming to enhance their strategic capabilities. It covers key aspects such as recruitment, performance management, employee engagement, and DEI analytics, along with ethical considerations and practical tools like employee attitude surveys and KPI dashboards. The book provides valuable insights into predictive analytics, machine learning, and statistical modeling, addressing both ethical and legal aspects of HR analytics. It highlights emerging trends such as Agile HR Analytics and emphasizes the need to stay updated.

The above example outlines just one out of many criteria – commitment to the company values – which may be important to recruiters. This book equips HR professionals with essential tools and knowledge to effectively utilize HR analytics so that they can hire appropriately aligned individuals.

In today’s information rich world, we are seeing more and more data-related analysis skills in business analysis jobs. Some data skills are critical for business analysts while others are better suited to other job functions, such as data analyst, financial analyst, reporting analyst, marketing analyst, and product management.

In this article, we’ll look at the set of skills required for both data analysis and data modeling, describe how data modeling can require some data analysis, and explain how skilled business analysts complete this level of analysis without technical data analysis skills.

Data Analysis Evaluates the Data Itself

Data analysis is a set of tools and techniques to gain insight from an organization’s data. A data analyst might hold the following job responsibilities:

• Create and analyze meaningful reports (possibly using a third-party reporting, data warehousing, or business intelligence system) to help the business make better decisions.
• Merge data from multiple data sources together, as part of data mining, so it can be analyzed and reported on.
• Run queries on existing data sources to evaluate analytics and analyze trends.

Data analysts can be expected to have hands-on access to the organization’s data repositories and use technical skills to query and manipulate the data. They may also be skilled in statistical analysis and probably pursued some math classes in higher education.

Common alternative job titles for this type of role include Report Analyst, Data Warehousing Analyst, Business Intelligence Analyst, or even Product/Marketing Analyst. The common thread among this diverse set of job titles is that each role is responsible for analyzing a specific type of data or using a specific type of tool to analyze data.

Data Modeling Evaluates How an Organization Manages Data

In contrast, data modeling is a set of tools and techniques to understand and analyze how an organization should collect, update, and store data. Data modeling is a critical skill for a business analyst that is involved with discovering, analyzing, and specifying changes to how software systems create and maintain information.

A data modeller might:

• Create an entity relationship diagram to visualize relationships between key business concepts.
• Create a conceptual-level data dictionary to communicate data requirements that are important to business stakeholders.
• Create a data map to resolve potential data issues for a data migration or integration project.

A data modeller would not necessarily query or manipulate data or be involved in designing or implementing databases or data repositories.

Data Modeling Can Require Some Data Analysis

You often need to analyze data as part of making data modelling decisions, and this means that data modelling can include an element of data analysis. You can accomplish a lot here with very basic technical skills, such as the ability to run simple database queries. This is one reason that you can see a technical skill like SQL in a business analyst job description.

To view the full article: http://www.bridging-the-gap.com/data-analysis-data-modeling-difference/

Learn more with PHI Learning’s MICROSOFT EXCEL 2019 : DATA ANALYSIS AND BUSINESS MODELING, Sixth Edition by Wayne L. Winston. Buy now Online:  https://www.phindia.com/Books/BookDetail/9789389347180/microsoft-excel-2019-winston