Data Engineering & Analytics Archives - Page 4 of 9

Yashica Chopra
September 05, 2023
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Top 13 Data Engineering Trends and Predictions 2025

Data engineering is the process of building, deploying, and integrating data pipelines to streamline data flow within an enterprise. It is the foundation for business intelligence processes to run and deliver actionable insights. Here, we’ll discuss the top data engineering trends and predictions for 2025. Data engineering is a growing discipline in the global market. It involves the process of designing and building data pipelines to collect, transform, and transport data to end users (data analysts and data scientists) to derive actionable insights. The data pipelines have to connect all data sources to the central data warehouse or data lake. The success and accuracy of data analytics depend on how well data engineers set up the foundation. This requires high-level data literacy skills. Unfortunately, there is a gap between the demand and supply of qualified and experienced data engineers in the market. It’s one of the primary reasons many SMBs and large enterprises partner with offshore data engineering companies to adopt advanced data-driven technologies and processes for effective decision-making. Many experts feel that 2025 will be a vital year for data engineering. In this blog, we’ll take a detailed look at the various big data engineering trends and predictions that will transform the industry at different levels. 13 Top Data Engineering Trends and Predictions in 2025 1. Increase in Cloud Management Cloud has become a favorite for many businesses around the world. Small, medium, and multinational companies are moving their data and IT infrastructure from on-premises to cloud servers. Data Engineering in AWS (Amazon Warehouse Services), Microsoft Azure, Red Hat, etc., are in high demand. While some companies are directly building data pipelines on the cloud, others are migrating their existing systems to cloud servers. 2. Greater Budget Allocation for FinOps Another trend is the need for data cloud cost optimization. Top vendors like BigQuery and Snowflake are already talking about ways to optimize the data cloud cost and make cloud services more cost-effective to businesses from various industries and markets. Financial managers are becoming a part of data teams to ensure that their data engineering strategies and processes will deliver the necessary returns. While there aren’t enough best practices in the industry (data engineering is still in its early stages), data teams are finding ways to overcome the challenges and make their cloud-based data architecture more agile, flexible, scalable, and future-proof. The cost of ownership is also a crucial topic of discussion. 3. Usage-Based Data Workload Segmentation In the current scenario, companies are focusing on using a unified cloud-based data warehouse. For example, AWS data engineering is popular for offering data warehousing services to several business enterprises. However, the same type of database cannot be suitable for all kinds of data workloads. Experts predict that organizations will shift from data warehouses to data lakes where different databases and tools are individually organized and grouped into a unified setup. This can make the data architecture cost-effective and increase its performance. 4. Data Teams with Higher Specializations Though data engineers are in short supply due to the complexity of the job, data teams will continue to expand and include professionals with more specializations. For example, the data teams will have data engineers, data analysts, data scientists, analytical engineers, etc., to handle different aspects of establishing and using the data architecture in an enterprise. DevOps managers, finance managers, data reliability engineers, data architects, data product managers, etc., are other specializations we will see in future data teams. 5. Metrics Layers in Data Architecture In traditional data pipelines, the metrics layer (also called the semantics layer) is in the middle, between the ETL (extract, transform, load) layer and the cloud data warehouse. It defines the metrics for the values in the data tables and ensures consistency to eliminate errors during business analytics. Experts predict that the metrics layer will have an addition of a machine learning stack that has its own infrastructure. The ETL layer will continue to do its job, but the data will flow through the machine learning stack, which will help data scientists choose the right metrics for the given data. One day, the metrics layer and the ML stack will be combined to work as a single automated unit. 6. Data Mesh The concept of data mesh is one of the emerging DE trends discussed by many top companies. This new architectural model is said to help organizations overcome the limitations of traditional data warehouses and centralized data lakes. Date mesh is the decentralization of data governance and ownership. As discussed in the previous trends, domain-specific data platforms, tools, and databases will be established for greater efficiency. The idea is to build resilient, dynamic, and agile data pipelines that offer more autonomy, interoperability, and control to every member of the data team. However, establishing a data mesh requires more skills and tools. However, centralized data warehouses will continue to exist until enterprises can successfully build and deploy data mesh architecture. 7. Increase in Success of Machine Learning Models In 2020, a report by Gartner shows that ML models had only a 53% success rate. That too when they were built by companies with strong AI foundations and prior experience. It means even three years ago, only half the machine learning models could be deployed accurately and effectively. However, the success rate has been increasing over time. Soon, a greater percentage of the ML models can be successfully deployed by organizations. Of course, this will be possible when businesses overcome challenges such as misalignment of needs and objectives, overgeneralization, testing, validating issues, etc. 8. Changes in Cloud-Premises Architecture The architecture for data flow within an enterprise usually combines three different software applications. Databases from different departments (CRM, CDP, etc.) are connected to the data warehouse. The business intelligence and data visualization tools are connected to the other end of the data warehouse. Data flow occurs only in one direction. However, in modern data engineering, the data flow will occur both ways. The next-gen cloud data architecture will be bi-directional and allow data

Yashica Chopra
August 24, 2023
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Top 10 Benefits of Procurement Analytics – Why Is it Important?

Procurement analytics is much more than just spend analysis. It can help all departments in the business to correct and strengthen their strategies and increase overall efficiency. Here, we’ll discuss the benefits of procurement analytics for an enterprise. Procurement is crucial for a business. It influences the various elements of the supply chain, right from production to quality control to distribution of the goods. With such an important role, it’s no surprise that businesses need to adopt the latest technologies to stay up to date in the dynamic global market and survive the competition. In many enterprises, the procurement teams are already handling cross-functional activities and working with teams from other departments. In fact, according to a survey by Procurious, more than 81% of procurement teams feel pressured to be more effective when responding to supply chain challenges. While this is alarming, the good news is that close to 80% of CFOs intend to maintain or increase enterprise digital investment. Procurement analytics is the process of using data and technology to help procurement teams make better, faster, and more efficient decisions. Instead of relying on guesswork or outdated reports, the teams can use real-time analytics to choose the raw materials, vendors, procurement methods, transportation routes, and so on. As per the report by Verified Market Research, the global procurement software market will touch $9.5 billion by 2028 with a CAGR (compound annual growth rate) of 7.6%. In this blog, we’ll read more about the benefits of procurement data analysis and how businesses can overcome challenges to fill the gaps in skills and technology. Why is Data Analysis Important in Procurement? Procurement analytics involves data collection from multiple sources (and ERP systems) and the analysis of this data to derive actionable insights for faster decision-making. The collected data is categorized into different segments based on the business KPIs. The analytical reports are shared using business intelligence or data visualization tools. However, procurement and spend analysis are much more than data visualization reports. It helps business enterprises make use of historical and current data to determine the best way forward in managing vendors, suppliers, and other members of the supply chain. The ultimate aim of procurement analytics is to optimize resources and increase ROI while enhancing customer satisfaction. Types of Procurement Analytics How can Procurement Analytics Benefit Your Business? CPOs (Chief Procurement Officers) have a great responsibility on their shoulders. They need to ensure the cost of procurement is within the budget without compromising quality and also maintain good partnerships with vendors. At the same time, CPOs have to consider various business goals and align the department goals accordingly. Fortunately, procurement analytics can provide a comprehensive solution to all these aspects and more. Let’s look at the various benefits of using purchasing analytics in the business. 1. Resource Allocation How to manage resources for the entire business? Are there areas in a business that is overspending? Can the organization reduce expenses without cutting corners? The answers to these questions can be found by relying on procurement analytics. That’s because the various activities of procurement analytics are interdependent and influence one another. For example, by monitoring inventory, the teams can determine the usage of different materials within the businesses. It helps track which items are necessary and which can be procured in fewer quantities to prevent overspending. By optimizing resource allocation, the business can improve the spending management indicators and increase opportunities to save costs. 2. Risk Management Countless things could go wrong during the procurement process. External factors can lead to delays, damages, and other issues, resulting in losses for the business. Even if an enterprise has been following the same procurement process for years, the element of risk will always remain. However, procurement analysis can help in managing this risk efficiently. It empowers the teams to identify bottlenecks and challenges that can and cannot be controlled by the business. Then, these issues are addressed individually to establish a risk management policy and minimize losses. For example, an organization with vendors from other countries has to create a risk policy to handle geographical border restrictions, weather-related delays, socio-political factors like wars, etc., to ensure that the supply keeps coming despite the adversities. 3. Data-Driven Decisions Data is the key to making informed decisions in today’s world. Data analytics help combine past and present data to derive meaningful insights for identifying trends, understanding the challenges, and becoming proactive to make the most of market opportunities. Procurement analytics will help the enterprise track the demand and supply in the market and adjust the orders accordingly. Demand forecasting reports help stay one step ahead of competitors and meet customer demands before other businesses can take advantage. 4. Supply Chain Transparency The supply chain network of a business can be lengthy and complex. Maintaining transparency throughout the process can be difficult without using the right technology. That’s where using analytics in procurement helps organizations. Data is collected from different sources and made available to teams from various departments across the business. This allows the finance, IT, marketing, and customer service teams to use the data to improve sales and customer experience. For example, customers around the world are becoming more aware of sustainability and eco-friendly practices. Many customers want to know where and how a business procures its materials. A transparent supply chain makes it easy to share this information with customers. In fact, some brands use it as a marketing strategy to attract customers. 5. Category Management Category management is a part of spend analysis and vice versa. The spend categories are created by grouping similar types of purchases under individual heads. Spend analysis gives the procurement teams the necessary data to segment the purchases and expenses by creating a basis for category structure. It is the starting point of category management and allows businesses to have a clear picture of how much they spend on each category and ways to optimize the expenses. Not only do procurement and spend analysis help with category management but also provide businesses with the necessary insights to link the strategies

Yashica Chopra
August 17, 2023
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10 Steps to Create a Data-Driven Culture

With proper planning and implementation, enterprises can effectively use data to make business decisions. However, a strong data culture is still a theory for many business organizations. Here, we’ll discuss the steps to create a data-driven culture in an enterprise. Businesses need to work with quality data to make effective business decisions. While we cannot ignore the importance of human expertise, combining both is the best way to boost a business in a competitive market. This requires using data and data analytics to make decisions. Statista reports that the global big data analytics market will reach $655 billion by 2029, while the predictive analytics market is estimated to touch $41.52 billion by 2028. A business organization can enjoy the benefits of data analytics and business intelligence by adopting a data-driven culture. Another report shows that 57% of leading brands are already using data to drive innovation. So how do you create an effective data-driven culture in your enterprise? Let’s find out in this blog. But first, let’s dive into the basics. What is a Data-Driven Culture? Is data the key to a data-driven culture? Absolutely! A data-driven culture is where the workforce uses statistics, analytics, facts, insights, predictions, etc., to make everyday business decisions and optimize their tasks. Team leaders, managers, and C-level executives use insights to understand various elements of work and how these affect business performance. Many components contribute to creating a data-driven culture in an organization. However, the major aspects are as follows: 1. Data Maturity Data maturity refers to the process of storing and retrieving data over time. It depends on your data governance policies and how well you manage and maintain the datasets with accurate tags, metadata, etc. 2. Data Leadership Data leadership defines the role of leaders and decision-makers in managing business data. These people understand the importance of quality data and help maintain a work culture where decisions are made based on data analytical reports. 3. Data Literacy Data literacy is the act of ensuring business data is structured, accessible, reliable, and accurate. It also involves training employees to understand and use datasets effectively for day-to-day work. Investing in a data-driven culture requires expert guidance and support. SMBs and large organizations partner with a reputed data analytics company to revamp their internal processes and work culture the right way. What are the Steps in Data Management? The data management framework has to be structured and aligned with the business process. Here’s how to implement successful data management in your enterprise and create a strong foundation for data-driven culture. Steps to Create a Data-Driven Culture Understand that creating a data-driven culture is not limited to technological investments. The focus is equally on changing the existing work culture to help employees use new technology and tools at work. The following steps will help you build an effective data-driven culture in your enterprise. 1. Start with the Top Management The top management and C-level executives play a vital role in influencing other employees. They need to understand the importance of data-driven solutions and establish it in the organization. When the management makes it a norm to use data and evidence for decision-making, employees will follow it over time. For example, the team leader or manager can allocate a few minutes at the beginning of a meeting to go through the analytical reports and observe whether the proposals are backed by data. Then, they can discuss the proposals and the reports to help other employees realize why they should work with data. When the top management sets an example, it becomes easier for employees to adapt to the changes. 2. Choose Metrics Carefully How do you analyze the business performance? It can get complicated and confusing without metrics. Every enterprise has its own metrics for analytics. However, there’s no guarantee that the existing metrics are correct or suitable for accurately measuring the business. For example, many businesses use competitor analysis because they need to keep track of what other brands in their industry are up to. An FMCG company will have to decide its pricing and marketing strategy based on customer behavior, market trends, and competitor’s offerings. Not factoring in either factor can result in skewed analytics, which invariably leads to wrong decisions. Define metrics carefully and make sure they are always aligned with the business vision, goals, and industry standards. 3. Don’t Isolate Data Scientists One major mistake many organizations make is to keep the data scientists and business leaders in separate units. While the analytical reports are shared between the teams, the people responsible are not always collaborating and usually work in isolated teams. This can affect the quality of analytics and will soon widen the gap between reality and analysis. Leading brands have managed to avoid this issue by eliminating the boundaries between data scientists and business leaders. The staff (team members) is rotated between different departments to keep communication flowing. Another method is to ensure that the top management has the necessary technical know-how to directly interact with data scientists and work with them. While it is not necessary to replace existing executives with AI and ML engineers, it is important to train them in the basics. 4. Provide Access to Data A common complaint from employees is that there don’t have access to data or analytics at work. It takes too much time and energy to obtain even the basic data, and this isn’t enough to make data-driven decisions. Despite democratizing the storage systems, analysts aren’t given access to information from other departments due to different constraints. This challenge can be handled by identifying the data related to the KPIs for the project/ quarter and providing enterprise-wide access to this information. For example, if the sales analyst has to create a demand forecasting report, they should have access to information about past sales, customer feedback, inventory, etc. While data security is a concern, it can be handled through effective data governance and by setting up authorized access employees based on their project requirements. 5. Assess Uncertain Aspects To build an

Yashica Chopra
July 18, 2023
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10 Common Data Analytics Mistakes to Avoid

Data analytics is a critical part of business processes in today’s world. However, mistakes can be costly and lead to losses. Here, we’ll discuss the ways to avoid common mistakes in data analytics. Data analytics is a part of the global industry, helping countless businesses derive and use actionable insights to make business decisions. More organizations now rely on big data analytics to detect patterns and trends in large datasets and uncover information not visible to human eyes. Statistics show that the big data analytics market will touch $103 billion in 2023, and around 97.2% of businesses are investing in big data and AI. However, quite a few barriers affect the adoption of data analytics in enterprises. Limited access to data, lack of training, not investing in the correct tools, wrong metrics, etc., are common issues that lead to incorrect insights or errors. In this blog, we’ll discuss data analytics and the common mistakes you should avoid when running data analytics. We will also discuss the importance of hiring a data analytics company to help businesses avoid these mistakes and achieve the desired results. What is Data Analytics? Data analytics is the process of collecting, transforming, and organizing data to derive actionable insights for decision-making. Raw data is used to arrive at meaningful conclusions that help optimize business performance and increase overall efficiency. It is a continuous step-by-step process that can be automated using AI tools. Analyzing and reporting data with artificial intelligence tools makes it easy for employees to access the insights in real time. SMBs and large enterprises work with offshore data analytical service providers to integrate different systems and streamline data flow. Employees at various verticals can use intuitive dashboards to access data visualizations and make faster decisions. Types of Data Analytics Before investing in data analytics projects, you should know about the different types of analytics and how these help your business. As the name suggests, descriptive analytics describes data patterns and trends to help find more information and insights. Data mining and data aggregation are used in descriptive analytics to draw conclusions from vast datasets. Large datasets are mined using predictive models to forecast future outcomes for a business. It also uses descriptive analytics to define and understand the predictions. Historical and present data is processed to detect patterns that forecast future opportunities and risks. Diagnostic analytics describes the techniques used for processing data. It takes descriptive analytics one step ahead but identifies the reason for the results. Data mining, drill-down, and correlations are some techniques used for diagnostic analytics. Prescriptive analytics uses the above three types of data analytics and focuses on data monitoring to derive more actionable insights. It combines mathematics, science, descriptive models, and predictive models to provide the necessary analytics to the business. What are the Common Data Analytics Mistakes to Avoid? While the concept of data analytics looks simple, it is easy to make mistakes that affect your business in the short term and long term. That’s why several enterprises partner with data analytics consulting firms to use their expertise and experience to successfully avoid the mistakes others make. Here are the most common data analytical mistakes business organizations make and methods to avoid them effectively. 1. Sampling Bias and Cherry Picking Data Data is the core of data analytics, and choosing incorrect or wrong sample data can lead to distorted insights. For example, sampling bias is one of the primary mistakes many organizations make. Sampling bias is when you choose non-representative samples. If you want to know how people feel about your product, you should choose a sample with your customers and non-customers. If your sample includes only your loyal customer base, you will not know how others view your product and whether they are even aware of your brand. Similarly, cherry-picking is where you intentionally choose a sample that will align with your hypothesis. If a sales manager wants to prove that their campaign was successful, they might present only those reports that support their claim. In both instances, you will not be aware of the actual market condition. To avoid this, you should gather data from multiple internal and external sources. Get data from social media mentions, websites, emails, chats, surveys, customer feedback, etc., to include more representations in your sample and use it for analytics. 2. Wrong Sample Size or Market Here’s another vital question to consider when collecting data for analysis. Does the sample market align with your business industry? Businesses use large datasets to derive insights because a smaller sample size can lead to inaccurate conclusions. However, you should also focus on where your data comes from. Demographics are important when finalizing the data sources. For example, a business selling hearing aids should focus on people with hearing difficulties to get their views and feedback. The sample market is highly specific in this case. To avoid these mistakes, you should first define your business vision, mission, and goals. Be clear about what you offer so that you can identify your target market and proceed to list out data sources. 3. Not Standardizing Data Raw data comes in various formats, structures, and types. It is collected from different sources like the cloud, spreadsheets, SaaS applications, social media, etc. Some data can be in tabular format, while some could be in percentages, fractions, and more. You cannot directly run analytics using these datasets if you want accurate insights. Not setting up a definite ETL process is one of the administrative mistakes examples in data analytics. First, you should standardize the collected data. Establish ETL processes (Extract, Transform, and Load) to clean the data and format it uniformly. Label the datasets and add tags for easy categorization. Then, proceed to use analytical and business intelligence tools to derive insights. This also removes duplicate data and minimizes errors. 4. Vague Goals and Objectives What is the purpose of running data analytics? Each department in your business has different goals and objectives. For example, the sales team needs analytics to understand market preferences and customer behavior. The HR

Yashica Chopra
June 05, 2023
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10 Ways Data Analytics is Revolutionizing Restaurant Industry

Data analytics in the food and beverage industry is revamping how restaurants, diners, food courts, etc., provide services to their customers. Many problems in the sector can be solved using AI and ML technology. Here, we’ll discuss how data analytics and advanced technologies are revolutionizing the restaurant industry. The restaurant industry is among the most competitive markets around the world. Food is a part of our lives, and so is community living. Restaurants bring both aspects together with ease. Despite the competition, the restaurant industry is not with its issues. Like every other niche, it had to adapt and change to stay relevant in the global market. For example, the lockdowns during the Covid-19 pandemic resulted in a multitude of problems for restaurants. According to National Restaurant Association, 92% of restaurant operators said that the increasing cost of food is a major issue. However, many operators are optimistic and aim to establish positive growth in 2023. The food industry is expected to reach $997 billion, mainly due to the increase in food prices. Furthermore, close to 50% of restaurant owners predict fierce competition in 2023 and the coming year. Technology plays a major role in giving restaurants a definite edge over competitors. The global restaurant POS market size is estimated to grow at a CAGR (compound annual growth rate) of 6.4% from 2021 to 2028. Many small and large restaurants rely on data analytics service providers to plan and implement a data-driven model to make the most of analytics and insights to increase profits and customer base. In this blog, we’ll read about the role of data analytics for restaurants and how it can solve various problems in the food industry. How Can Data Science Help Restaurants? Data science can help restaurants make use of their raw data and derive actionable insights. These insights can be used to make faster and more effective decisions at different levels. Many restaurant operators hesitate to invest in data science as they don’t know where to start. Data science can help with the following: Ways Restaurant Analytics is Transforming the Industry 1. Greater Efficiency The AI and ML tools used to derive analytics for restaurants help the operators to increase the overall efficiency of the place. From identifying the right sources of ingredients to increasing transparency in the supply chain and identifying ways to minimize costs without compromising quality, data analytics can help restaurants in becoming more efficient and thus enhance customer experience. For example, automating reservations allows the staff to pay more attention to improving the diner’s service. Using AI devices to take orders will minimize the risk of human error and even help the staff recommend dishes based on what the customers order. 2. Streamline Marketing Restaurant data analytics is incomplete without focusing on marketing strategies. Every business needs a strong marketing plan to attract new customers and retain existing ones. A restaurant business has to understand what the customers want and establish communication channels to reach out to a wider audience. Special offers, discounts, suggestions based on previous orders, etc., can be set up based on insights provided by data analytics. 3. Quality Control Big data analytics helps restaurants ensure that they maintain quality standards at all stages. This includes the procurement of raw ingredients, kitchen cleanliness and maintenance, cooking, packaging, delivery, dining services in the restaurant, staff behavior, etc. Food has a short shelf life which affects the quality. This can be prevented or minimized by monitoring the supply chain movement, storage, and production quantity. 4. Supply Chain Management More customers are paying attention to how and where the ingredients are being sourced by restaurants. This is especially true for vegan consumers who prefer a transparent supply chain and want the restaurant to explicitly mention the sources. Similarly, diners and food courts that rely on local and international products can use data analytics for supply chain and inventory management to ensure there are no delays or issues with stock quantities. 5. Customer Sentiment Analysis Sentiment analysis is the process of identifying the various emotions customers feel when they interact with the restaurant business. From the ‘like’ on a social media post to online reservations, dining experience, communicating with the staff, response to food served, reactions about pricing, etc., are all factored in this analysis. It helps restaurants understand the strengths and weaknesses of the business from the customer’s point of view. The best way to derive customer sentiment analytical reports would be to hire a company offering customized data analytics solutions in the industry. 6. Demand Forecasting Restaurant predictive analytics helps the chefs finalize the menu based on customer preferences. If a restaurant gets more customers in the evening and has a maximum number of orders for pasta, it’s an indication that the dish is popular and should be definitely available during rush hour. This will help chefs plan the day’s work and adjust the meals cooked for other times. Even the portion of each serving can be decided based on this. It also minimizes food wastage by the restaurant. 7. Predicting Product Shelf Life Food products are perishables. While some taste good only for a few hours, other items can last a week or more. For example, cookies last longer than cooked risotto. Data analytics assists restaurants in correctly determining the shelf life of each product and also lists the guidelines for consumers to prevent health issues (or lawsuits). 8. Personalized Customer Experience Every customer wants a personalized experience from the businesses they interact with. From food portions to special requests to multiple payment options and so on, it’s vital to meet the customers’ expectations to ensure loyalty. This is possible by processing their information (previous orders, payments, recurring visits, feedback, etc.) to derive actionable insights. Customer data and feedback are a goldmine for restaurants. 9. Restaurant Layout Optimization How can the restaurant’s dining room be organized to increase capacity but also ensure privacy and prevent the room from appearing stuffed or suffocating? This is one of the biggest concerns for many restaurants, especially the ones with limited space availability. How many

Manpreet Kaur
May 29, 2023
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11 Key HR Metrics to Track: Boost Your HR Operations

HR metrics are quantitative measures that track and analyze various aspects of an organization’s human resources to drive informed decision-making and improve the success of the organization. Are you ready to unlock the power of HR metrics and take your human resource management game to the next level? From employee turnover to training costs, data analytics can provide valuable insights to help you make informed decisions and drive your company’s success. So without much ado, let’s deep dive into top key HR metrics to track in 2025. What are HR metrics? HR metrics are measurements that identify how effective the human resources practices and processes are within an organization. They make it easier to track recruiting, onboarding, and other HR-specific activities. It also helps obtain valuable data about employees such as employee satisfaction, salary, and tenure. Examples of HR metrics ● Employee Absence Rate: Percentage of workdays missed by employees due to unplanned absences. ● Employee Growth Rate: The rate at which the organization’s workforce is expanding or contracting ● Staff Diversity and Inclusion: Level of representation and inclusiveness of diverse individuals within the organization. ● Healthcare Costs per Employee: Average cost of healthcare benefits provided to each employee. ● New Hire Turnover Rate: Percentage of newly hired employees who leave the organization within a certain period, typically measured within the first year. These metrics can help HR professionals identify areas for improvement, track progress, and make data-driven decisions for the growth and success of an organization. Here’re the Top 11 Key HR Metrics to Track 1. Employee Retention Hiring and retaining skilled employees is the hallmark of a successful company. A high employee retention rate indicates a stable workforce and that employees are satisfied with the job. To apply the right retention strategies, HR needs to track employee retention rate, one of the most important HR metrics in 2025. Measuring this key performance indicator helps you monitor and improve the onboarding process, leadership or employee engagement, satisfaction issues, and problems with employee productivity. 2. Cost per Hire Do you keep your hiring costs in check? Is your recruitment process efficient? If not then cost per hire (CPH) is one of the timeless HR metrics to measure recruitment expenses. CPH gives you the details of the average amount spent to acquire new talent as well as reach out to all new prospective candidates. Included are both internal and external costs such as interviewing costs, advertising, relocation fees, and more. CPH is a great indicator of cost-effectiveness. So, keep a track of your expenses and optimize over time. 3. Time to Hire Does it take too long for your company to hire a new employee? Then, it is a sign of a poor talent acquisition process! Reasons could be anything from bad promotion to unclear job descriptions or ineffective screening of prospective candidates to time-taking onboarding process. And, this is why ‘time to hire’ is the crucial one on the list of HR metrics. Measure this KPI to quickly fill in an open position and avoid losing a star employee to your competitor. 4. Quality of Hire How valuable are your new hires? Are they contributing well to salary hikes and client satisfaction? About 43% of HR professionals find it difficult to hire the right candidates because of competition from other employers. The ‘quality of hire’ is the right HR metric to gauge this. It encompasses several parameters like retention rates, performance appraisal scores, and other employee indicators. Measure this KPI to get a clear idea of your return on investment. 5. Absenteeism Rate If you want to make sure that your team stays on track, measure the absenteeism rate. Start tracking and managing attendance. Whether it is poor management, work dissatisfaction, or poor workplace policies it is sure to spike unscheduled absences and drop your company’s productivity. Taking the help of a data analytics firm and their Power BI experts can smoothen the tedious job of analyzing large datasets like patterns of missed time with the help of sophisticated software. 6. Employee Productivity This is the right metric if you are trying to figure out which employees are productive and which ones need more training. As an HR manager, tracking this key performance indicator is a must to address poor work output issues. 7. Employee Turnover Rate Keep an eye on employee turnover – it shows how many people are leaving and why. Treat high rates as red flags. To keep costs low and productivity high, create strategies to retain employees. To track turnover, simply record departures over a period of time and divide them by the average number of remaining employees. 8. Benefits Administration Does your company offer rewards and recognition? Are they serving your employees’ needs? Are you aware of your underutilized benefits? Or what benefits to keep, add or remove? Here comes the role of the benefits administration metric – one of the core HR metrics to track how effective are your company’s benefits. Hire a data analytics company for easy tracking and management of employees’ health insurance and other benefits. 9. Employee Net Promoter Score (NPS) Your employees represent your organization to customers, business partners, and vendors. Tracking employee Net Promoter Score (NPS) measures employee loyalty and their likelihood to recommend your company to others. A high NPS indicates a strong company culture, and it lets you know if your employees enjoy working with you. Improving this metric can help you attract top talent and make your company culture even better. 10. Training Costs Per Employee Training programs are crucial for improving employee productivity. How much do you invest in training each employee? How effective is your employee training program? To measure this, training cost per employee is an important HR KPI. And you can better analyze the return on investment for employee training programs. You can then take action to change the program if it is not worth it or doesn’t fetch desired outcomes. 11. Diversity Metrics Are you planning to build a diverse

Yashica Chopra
May 16, 2023
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Top 12 Benefits of HR Analytics: Revolutionizing Human Resources

Human resources department can hugely benefit from using data analytics to make effective decisions about hiring, compensation, employee training, performance, etc. Here, we’ll discuss the benefits of HR analytics for a growing business. The human resource department has always been vital in connecting employees with the organization. They are essential in taking care of employees’ needs and ensuring that the company continues to be productive and profitable. For years, the HR department relied on its own resources. However, times are changing. HR teams now use the latest technology to enhance their services in the enterprise. The HR software market is estimated to grow at a CAGR of 10% to reach $33.57 billion by 2028. As per Tidio, 67% of HR professionals said that AI will positively impact recruitment. Data and analytics are gradually becoming an integral part of the HR department. This led to a new discipline called HR analytics. Many BI companies offer offshore HR analytical services to help businesses adopt data-driven decision-making in their human resource departments. In this blog, we’ll read answer questions like what is HR analytics and why is it important, and the importance of HR Analytics in an organization. What is HR Analytics? HR analytics is defined as the process of collecting human resource data from multiple sources and analyzing it to increase the performance of the company’s workforce. HR analytics companies offering these services also term it as people analytics, workforce analytics, or talent analytics. Using data science in HR gives your business an added advantage over competitors and helps in gaining an in-depth understanding of your employees, their abilities, requirements, etc. Broadly speaking, HR analytics allows you to leverage the following: Benefits of HR Analytics 1. Talent Acquisition Recruitment or talent acquisition can be streamlined by tracking the data related to hiring KPIs (cost per hire, quality of hire, candidate experience, application completion rate, etc.). It not only reduces the time taken to recruit a candidate but helps the HR teams find the right candidates for the right roles. This further reduces the employee turnover rate and increases the retention rate. 2. Evidence-Based HR The HR department has been traditionally relying on supposed trends about what’s effective for the business and employees. This includes missing information, biases, etc. However, using data to make day-to-day decisions eliminates errors and strengthens the stand of the HR teams. For example, the HR department can use employee feedback to determine what kind of incentives they should offer instead of assuming or copying a competitor. 3. Employee Performance Management Performance appraisal shouldn’t be a year-end activity. The HR teams can use analytics to monitor the KPIs of employee performance and provide them feedback at regular intervals. Here, instead of using information shared by immediate supervisors (which can be biased), the department uses data collected from the systems (collaborations, project status, errors reported and solved, etc.) 4. Compensation and Payroll Management Payroll and compensation management is time-consuming and complex in many businesses. How do you ensure pay equity in your business? How do you value your employees’ productivity? For example, around 62% of Staples employees said that they wouldn’t mind a lower pay package if they got better perks. What additional perks can you provide to make your employees feel valued? HR analytics help find answers to these questions. 5. Workforce Planning Your business won’t be the same forever. As you achieve your goals, the business expands and enters newer markets. This means your workforce should be capable of handling additional work. When to recruit new employees? Should you have an internal hiring team or rely on staffing companies? HR analytics help the human resource department stay one step ahead and be ready to take advantage of market opportunities. 6. Save Time, Money, and Resources Budget and resources are a constant issue for many businesses. HR analytics can help the teams support their plans and projects to enhance employee performance through training, upskilling, etc. Similarly, the teams can discontinue programs or schemes that aren’t delivering the expected results but are draining the company’s resources. Budget allocation and management will be more effective when you make decisions based on analytical reports. 7. Analyzing and Bridging Talent Gaps One of the most interesting benefits of HR analytics tools is their ability to analyze the talent gap in your business and identify the exact areas where you need to hire new employees or train existing ones. This is done by analyzing employee information, such as education, experience, performance, current skills, etc. It also reduces the cost of recruitment by helping you hire only the necessary candidates. 8. Upskilling and Reskilling Training and development are vital in any organization. HR analytics gives insights into new upskilling and reskilling opportunities to boost employee performance. The HR teams can pick the right employees for specific training programs based on their interests, performance, and abilities. For example, an employee displaying leadership traits can be trained to become a better team leader or manager. 9. Internal Mobility Internal hiring or mobility can be very advantageous for the business. It increases retention rates and employee satisfaction. It also reduces the need to hire new candidates and provides greater work opportunities for existing employees. HR analytics and AI tools can help the teams map an employee’s career graph in the organization based on their strengths and weaknesses. This will allow them to achieve their goals without leaving the company. 10. Workplace Ethics Management Harassment, bias, misconduct, irresponsible behavior, etc., can reduce overall productivity. These issues can affect employees severely and have to be controlled by the HR department. HR analytics can help the teams collect information about previous complaints and identify potential employees who need workplace ethics training to prevent such incidents from repeating. 11. Supporting DEIB Goals DEIB (Diversity, Equity, Inclusion, and Belonging) goals have gained prominence in recent times. How can the HR teams ensure that their company has a diverse and inclusive workplace without compromising merit? HR analytical tools collect information from within the organization and recruitment platforms to provide a comprehensive report about how to make the

Pawan Chabra
February 06, 2023
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8 Advantages of Outsourcing Data Analytics: Get More from Your Data

Data analytics is increasingly becoming an integral part of any business. Irrespective of the industry or the scale of business, it is important to have a strong foundation in data analytics. With the rising amount of data being generated from various sources, its effective use is critical for success. Businesses often find it challenging or overwhelming to put in place the mechanisms for effective data management. It can be due to the current priorities, lack of technical expertise, or due to lack of time. The fact remains that the business is unable to harness the capabilities of data. Some companies opt to outsource data analytics to ensure that the focus remains on core activities and the organization is still able to move ahead with the times. The global data analytics outsourcing market stood at $3 billion in 2021. It is expected that the market will grow at a CAGR of 21.50% over 2022-2030, reaching a valuation of $70.68 billion by the end of 2030. Outsourcing data analytics can bring a variety of advantages to a business. Apart from being able to access the latest solutions, there are several other benefits of outsourcing big data analytics to a professional partner. What is Data Analytics? Data analytics is the science with which a business can utilize raw data to derive meaningful insights and conclusions to support business growth. Data analysis can help a business increase productivity and performance in a variety of areas like marketing, logistics, finance, sales, customer service, etc. Data analytics help a business gather data from various sources and uncover patterns that can help generate useful insights. With the right frameworks and structure, data analytics can emerge as a strong competitive advantage for any business. Why Should You Outsource Data Analytics? We are living in the era of outsourcing. Businesses are outsourcing all types of IT and business functions, including strategic processes. The reason for outsourcing data analytics can be different for each business. However, at the core, it is important to understand that data is already playing an important role in business operations and its prominence is only expected to grow further. As a result, all businesses need to consider data analytics as an important aspect. With the evolving technology, businesses utilize software systems with modern technologies like artificial intelligence, machine learning and automation capabilities. Establishing these systems from scratch in an organization can be time-consuming, expensive and challenging. However, with data science outsourcing, any business can make the most of the evolving technological trends and stay ahead of the competition. Key Advantages of Data Analytics Outsourcing Outsourcing data analytics has the potential to accelerate the growth of any business. Here are the key advantages: 1. Professional Assistance By deciding to outsource data analytics, a business has a chance to access skill sets that may otherwise be in short supply in the market. It is a known fact that talent is in short supply in the IT industry. As a result, a business is better placed to have access to professionals who have deep expertise in areas like cloud computing, advanced analytics, big data, data science and data lakes. A business can close the gap in the skillset landscape by outsourcing data analytics. With the explosion in the volume of data across structured and unstructured sources, a business can’t meet its increasing requirements using a traditional data center setup. Businesses need modern cloud platforms so that they can build data lakes and manage data loads. However, it becomes challenging to operationalize these changes with an in-house IT team that offers limited talent and skill set. Outsourcing the data analytics function can expand the net of skill sets available for business growth. 2. Industry Expertise While some analytics functions are universal, some industry-specific functions can prove to be critical in effective data management. For instance, sectors like healthcare and financial services can have specific analytics functions that are required to build capabilities in the area of data analytics. Therefore, getting an industry partner that has deep expertise in that particular sector can help accelerate the data transformation journey for a business. For instance, a partner that has the capabilities to deliver high-quality data analytics service in the retail industry will be able to build analysis points like customer lifetime value analysis, market basket analysis, store sales analysis and profitability analysis. In other words, the partner will be able to bring to the table techniques that are very specific to the retail industry. At the same time, by opting to outsource data analysis, a business can benchmark its growth against its peers or the industry in general. Over time, this can emerge as a solid competitive advantage for any business. 3. Easily Scalable Apart from the fact that outsourcing services can help a business achieve data analytics skills quickly, it is equally helpful in creating a scalable ecosystem that may otherwise not be easy or possible to create with in-house capabilities. Effective data analytics requires a level of scalability and complexity that may not be possible to achieve in-house. As a professional data analytics partner will have analytical assets like frameworks and accelerators, it is possible to scale the data analytics processes quickly. With a third-party handling data analytics function for a business, it is also easier to follow a quick path to analytics maturity in a business. 4. Ensuring Compliance With the increasing amount of data, it is becoming increasingly important for businesses to comply with various regulations regarding data capturing, processing, storage and usage. With a professional data analytics partner on your side, the business is better equipped to understand and handle compliance requirements. Businesses need to create easily audited data which can be achieved much more easily with an external outsourcing partner. For instance, with the General Data Protection Regulation (GDPR) and other similar versions in other markets, a business must be on the right side of the law to ensure seamless functioning. 5. Greater Use of Data It is often said that data is the

Kavika Roy
October 21, 2022
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How Can Data Analytics Enhance Investment Decisions to Maximize Private Equity Returns?

As the data coming from company portfolios becomes vast and complex, a standard approach to private equity (PE) business analysis crumbles. It is no longer sufficient to get precise and data-rich insights to measure, track, and grow the performance of your company portfolios. As technology keeps evolving and businesses and their underlying operational models become more complex, making profitable and smart private equity investment decisions becomes even more daunting. Some key questions arise: The other key pain points that need a data-driven remedy include operational efficiency, increasing market valuation, performing due diligence, etc. Furthermore, the ongoing pandemic has also led to a paradigm shift in the PE investment sector and investors are more positive about potential portfolio companies that can adapt to the market changes as well as stay on the growth path. All this compiles into a highly relevant and massive use case umbrella for data analytics or private equity analytics. Combine that with automation and digital transformation and we have a very thought-provoking discussion topic: the rise, challenges, and applications of analytics and automation in the PE sector. Let’s explore the various ways analytics can help both companies as well as investors to make well-informed investment decisions. Data Analytics For Private Equity Investment Decisions: An Introduction A recent study published by Deloitte identified the huge potential and different applications data analytics offers in private equity investment decision-making. The study outlined seven key points that make data analytics extremely relevant and crucial for investment decision-making. Data and analytics are the key drivers of success when it comes to tech startups and organizations having their roots or branches in IT. These companies are spurring worldwide disruptions and giving birth to innovative and digital business models. Some common examples include Rocket Loans and Quicken Loans, where approvals for mortgages and loans are given based on simple and automated processes. These companies are more relevant for data analytics-based investment decisions, as data is readily available in the ready-to-process form. However, even brick-and-mortar establishments can respond to technology-enabled disruptors. Such is the power of data analytics and automation! Key Benefits Offered In Decision-Making For Private Equity Investments Automation and data analytics can offer three major benefits for the digital transformation of private equity investments (PEIs), which are discussed in detail in this section of the article. 1. Tracking Project Profitability Investors can establish a preferred vendor approach by adopting a hybrid model for investment decision-making. This hybrid model can help investors develop better governance policies and performance expectations and calculate better scale efficiencies. Hence, the investors can see the projects running behind schedule, or the companies running over budget, or identify the other key problems that are leading to unprofitable businesses. Thus, the investors can change, manage, or increase investments in their portfolio companies in a better and data-driven manner. 2. Ensuring Transparency Of Cash Flows Volatile cash flows can not only amount to outstanding loans or debts, but they can also drag massive investments down as they crash or become a steady drain of resources. Using predictive analytics in association with quantitative and qualitative studies done specifically on portfolio companies can lead to highly consequential results or data insights. Investors can see how well their investments are going to be used or are going to spur results in the long run. They can conduct these studies again after some time and compare the results with the previous analysis to identify any hidden patterns or discrepancies in the reports. Such comparative studies will also help them in understanding the various points of concern that arise over time and whether they can escalate into business risks or not. 3. Comparative Analysis Almost all PE investors are prone to the common occurrence, where the same brand is yielding different returns in different locations. While some branches tend to bring higher revenues, others fail to even stay afloat. In such cases, data analytics can be of immense importance as it can help in collecting, processing, and evaluating multiple types of data, such as: Thus, data analytics is of extreme importance and can have many amazing and crucial applications in PEI decision-making for better returns. Up next, we discuss the three major trends in PEIs that are all set to transform the sector and reinforce the hold and relevance of data analytics in the sector. Private Equity Investment Decision-Making: Why Data Analytics? Below, we discuss the various reasons PE stakeholders, right from PE firms to investors, are looking for data analytics-based solutions for key decision-making and strategizing. 1. The Role Of PE CFOs Is Evolving Given the massive change spurred by the COVID-induced meltdown, the roles and responsibilities of PE CFOs have seen a major revision and evolution. CFOs now need to be more tactical, technical, strategic, and empowered with insights stemming from data and analytics rather than hunches or business acumen. Recent studies done by E&Y suggest that 78% of CFOs are looking for raising larger investments that have led to a serious race for assets among investors. So, PE firms are looking at ways to make more strategic decisions, in terms of capturing investor allocations for alternative investments, such as: All this requires a heavily data-driven mindset and resourcefulness that screams for data analytics-based offerings such as software tools, analytics apps, or custom SaaS offerings. 2. The Talent Profile Of PE Firms Is Also Evolving Amid the increasing skill gap and the Great Resignation, and COVID-wave scares, companies are also focusing on increasing workplace engagement and employee motivation to retain and enhance their talent profiles. Studies reveal that 73% of PE managers focus on employee productivity and engagement for talent management. Talent management activities and attitude require an immersive understanding of the various factors that influence the entry and exit of an employee, such as: Such specific decision-making further creates the grounds for embracing data analytics to gain visibility into the granular level of organizational operations and processes. 3. Process Improvement And Innovation Within a short span of just four or five years, the digital transformation and innovation vision of

Yashica Chopra
September 23, 2022
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A Step-by-Step Roadmap to Big Data Implementation (Infographic)

Big data projects start by defining business needs. A step-by-step roadmap gives a clear picture of what results to expect from the project. The success of the establishment depends on how well the big data analytics model is integrated with the existing applications to provide seamless and real-time insights. Consolidate Data Sources Finalize and build a big data solution for the business. Choose between Data Warehouse or Data Lake to collect data from multiple sources and build a data flow within the enterprise. Data Storage The master data storage sends historical and real-time data for analytics. Choose technologies to build the data architecture and leverage big data solutions. Prepare and Train Data The quality of data determines the accuracy of the analytics. Clean, format, prepare, and train data to deliver actionable insights for better decisions. Data Governance Manage big data flow in the business and set up employee access to master data storage. Ensure consistency in data quality while optimizing cost and resources spent on the project. Data Visualization Establish a data-driven model and build self-service analytics at different verticals in the organization. Invest in data visualization tools to generate in-depth graphical reports at any time.