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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

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The Power of AI in Biotechnology: Revolutionizing Innovation

Artificial intelligence in biotechnology can speed up drug discovery, deliver analytics, accurately diagnose medical conditions, edit gene structures, develop personalized medicine, and do much more to help mankind. Here, we’ll discuss the role and importance of AI in biotech industries.  The role of AI in biotechnology is gaining momentum in recent times. The biotech industry is redefining its processes using AL and ML technology to get better, faster, and more accurate results. Be it the pharma industry, healthcare, animal husbandry, or agriculture, AI and biotech are setting the stage for greater advancements and innovations.  Biotech or biotechnology is the use of technology in biology. Pharmaceutical companies are the major players in this industry, though others are catching up quickly. Statistics show that the pharma industry will spend around $3 billion on AI in drug discovery by 2025. 82% of industry experts opine that the industry will continue with the digitalization of its operations during post Covid-19 pandemic.  The power of big data and data analytics can revolutionize the biotech industry on various fronts. From using machine learning algorithms to natural language processing (NLP), neural networks, and advanced robotics, AI pushes the boundaries and creates more possibilities to improve the quality of life. New biotech companies are entering the global market, using artificial intelligence as an integral part of their business. Reports show that the biotech industry received $2.4 billion in venture funding by December 2022. Many rely on AI Biotech companies and solution providers to integrate the systems with advanced technology and use AI in all verticals.  In this blog, we’ll read about the role of AI in biotechnology and how applied AI is helping biotech companies to drive innovation and be ready for future developments.  Can AI and Biotech Save Lives? Technically, yes, artificial intelligence and biotech can save lives. This is done by helping researchers create better quality drugs (with fewer or no side effects), correctly diagnose complex ailments in the healthcare industry, identify and change gene patterns to prevent life-altering diseases, etc.  Most importantly, combining AI and biotech allows different players in the industry to share information and work together irrespective of geographical restrictions. They can create AI-based pharma (medical) tools and find solutions to save lives.  How is Artificial Intelligence Transforming the Biotechnology Industry? The advancement in the biotech industry now relies on big data, AI, and ML technologies. Many experts and top-level executives of leading biotech and pharma companies have said that 2023 is when AI in biotechnology will bring revolution and value and also fit for the purpose it is being used for.  AI’s role in biotechnology is not limited to automating repetitive tasks or structuring data flow. Artificial intelligence can help in research, day-to-day work, data analytics, drug manufacturing, and much more. It speeds up the entire process by assisting human researchers to process large datasets accurately in a short time. Diagnosing and Treating Mental Illness  The growing cases of mental illness have been a cause of concern in the last few years. Mental illness affects around 13% of the world’s population. The Covid-19 pandemic has led to an alarming increase in depression and anxiety. People with depressive symptoms went from 193 million to 246 million (28% increase), while anxiety disorders showed a 25% increase (298 million to 374 million).  Artificial intelligence can help doctors and scientists detect early signs of mental illness and use preventive medication or treatment to help patients feel better. Treating mental illness is hard because the symptoms exhibited by patients are varied and not universally applicable. However, advanced AI programs can observe the brain waves of patients to recommend the best antidepressant therapies for each patient.  AI algorithms are being used to study and identify the behavioral loops in addicts to determine the chances of relapse so that physicians can take the necessary action. Furthermore, AI can help create personalized medicine that will be more effective in treating the patient. However, there is a lot more research to be done in this field to help people deal with mental illness and lead better lives.  Creating 3D Protein Structures  Proteins are one of the four most important macromolecules essential to building life. The scientific community has been using X-ray crystallography and nuclear magnetic resonance to identify protein structures. They have added 187,000 identified structures to the database. However, the process is slow and laborious. Many more proteins are yet to be identified.  A UK-based team of AI researchers created AlphaFold, a machine-learning platform that uses the existing database to predict protein structures and build 3D models. The tool takes only a small fraction of the time takes originally to complete the job, that too with greater accuracy. This can help biotech companies identify more proteins quickly and use the vital ones in developing effective drugs. Diseases like cystic fibrosis and muscular dystrophy can be treated with the help of this technology.  Gene Coding Identification  Machine learning in Biotech can be researchers build powerful models to study and understand human genomics. The next-gen technology used by biotech companies helps sequence a gene in less time and creates an alternative method to homology-based sequence analysis.  CRISPR (clustered regularly interspaced short palindromic repeats) can electively modify and edit the DNA or genes of living organisms. This allows scientists to identify and destroy the DNS from bacteriophages that cause infections and diseases. The technology can also help create personalized medicine based on the genome of the patient. This can be a game-changer in treating hereditary diseases and identifying the probability of a patient inheriting a family disease or illness.  Lab Assistants  AI in biotech is also being used to simplify tedious tasks. Artificial intelligence programs are acting as lab assistants and managing backend work as well as complex tasks like data analytics. H2O.ai is a fast, accurate, and open-source big data analytics platform that allows people to use its statistical models to identify patterns in datasets. Even though the company caters to many industries, many other AI platforms cater exclusively to the biotech, health, and pharma industries.  Another role of AI as a lab assistant is the use of robotic devices in research centers, healthcare

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Top 5 Manufacturing Analytics Companies Leading the Charge in 2025

Manufacturing analytics or Industry 4.0 is here. It is the use of data analytics to make effective decisions based on real-time insights. Here, we’ll discuss the need for manufacturing analytics solutions and the top companies that offer these services.  Industry 4.0 is here to revamp the manufacturing sector and promote greater productivity. Statistics show that the global smart manufacturing market will grow at a CAGR (Compound Annual Growth Rate) of 13.1% between 2022 and 2029 to reach $658.41 billion. A decade ago, in 2013, McKinsey&Company predicted that IoT (Internet of Things) would generate around $3 to $6 trillion in economic growth by 2025.  We can see this happening through Industry 4.0 or manufacturing analytics. Using data analytics in manufacturing to collect, store, process, and analyze large datasets will enhance the overall performance and growth of the enterprise. From streamlining OEE in production to increasing profits, manufacturing analytics can benefit an enterprise in many ways.  In this blog, we’ll understand how data analytics can revolutionize your factory and look at the top companies offering tailor-made manufacturing analytical services. We’ll also get a brief insight into the benefits of using data analytics in the manufacturing sector.  What is Manufacturing Analytics? Manufacturing analytics is the use of managed analytics, data warehousing, and other data-driven algorithms to improve various functions of the factory. From enhancing product quality to reducing costs, speeding up transportation, and optimizing the supply chain, manufacturing analytics solutions can help your business make effective decisions throughout.  Historical and real-time data (collected through IoT devices) are analyzed using powerful machine-learning algorithms to derive actionable insights. Using business intelligence tools like Power BI can increase the visibility of KPIs and ensure that they are aligned with the business goals.  How to Improve OEE?  OEE stands for Overall Equipment Efficiency. Measuring OEE in manufacturing enterprises is one of the best practices followed in the industry to understand the cause of losses and ways to minimize them. OEE tracking will help you identify areas for improvement and implement the right strategies to increase factory production (without compromising quality).  Quality, performance, and availability are the three main components of OEE. You can improve OEE by implementing the following steps:  This is possible by using manufacturing analytics and tracking the developments through the OEE dashboard. Partnering with an offshore Power BI company offering manufacturing analytics will make it easier to adopt data-driven models and use actionable insights to improve OEE and make better decisions for your business.  Top Manufacturing Analytics Companies 1. DataToBiz  DataToBiz is an award-winning manufacturing analytics company offering customized Power BI services for small, medium, and large enterprises from various niches. It works with businesses from different regions like South Asia, the Middle East, North America, Australia, Africa, etc. The company offers a range of services to digitally transform a manufacturing enterprise and give it an edge over competitors.  A few services by the company are as follows: DataToBiz has helped many multinational FMCG companies improve their manufacturing efficiency and establish their brand reputation in the global market. It follows a systematic process to understand the enterprise’s current situation, challenges, and goals to create a comprehensive strategy.  Then, the service provider develops and deploys the necessary tools to collect, store, transform, and analyze data. The insights are delivered to employees through a customized data visualization dashboard. DataToBiz offers end-to-end solutions to support manufacturers achieve their goals.  2. NVIDIA Corporation  NVIDIA is a popular company known for manufacturing some of the best computer accessories like CPUs, GPUs, and more. However, it also offers a range of services to businesses from other industries by leveraging artificial intelligence and machine learning algorithms. The company works with manufacturing enterprises to transform them completely by harnessing the power of AI. Some of the services offered by NVIDIA are listed below: NVIDIA uses its AI systems and the latest GPUs to automate recurring factory work. From developing robots to handle complex machinery in enterprises to allowing supervisors to work with virtual factories (and control the machines remotely), the company provides several solutions to use technology in the manufacturing industry.  Predictive maintenance analytics and workplace safety (to take care of employee health) solutions by the company help the enterprise to efficiently manage production, equipment, and workforce. NVIDIA also offers accelerated edge computing solutions to leading manufacturers in the industry.  3. Rockwell Automation  Rockwell Automation is a global leader in industrial automation and believes in three major elements- innovation, productivity, and sustainability. It works with enterprises from diverse industries and helps them effectively streamline their manufacturing units and factories. The company has helped many leading businesses, ranging from automotive manufacturers to FMCG enterprises, textile industries, chemical manufacturers, and more.  The company provides lifecycleIQ services, such as:  Rockwell Automation’s AllenBradley® and FactoryTalk® product brands are popular around the world. The company promises technological innovation, expertise, and corporate responsibility to all its clients. Manufacturers can implement automation and smart technologies at different stages of production (right from procurement to quality testing and packaging).  The company is also an OEM (Original Equipment Manufacturing) Partner. It also offers strategic and consulting services for specific requirements. Since Rockwell Automation focuses equally on sustainability, manufacturing enterprises looking to develop a transparent supply chain can benefit from its expertise.  4. Amazon Web Services Amazon Web Services (AWS) offers comprehensive IT and AI solutions to businesses from around the globe. AWS helps simplify digital transformation in the manufacturing industry by helping enterprises adopt smart manufacturing to increase productivity, quality, and availability of machines. The company helps manufacturers extract valuable insights from machine data using its plethora of services.  The company provides extensive services for the following: AWS offers pay-as-you-go microservices and cloud solutions to support manufacturers lower the cost of building IT and OT infrastructure. It helps enterprises develop new products and speed up innovation by using advanced manufacturing technologies. The analytics derived from manufacturing data are provided in real-time to help mid and top-level management make effective decisions.  The company also focuses on providing tailor-made strategies to improve operational efficiency in factories and boost OEE scores. From workplace safety to data security and

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DataToBiz’s Data Engineering is Helping Enterprises Achieve Better ROI

With an increasing amount of data being generated every day, DataToBiz, a global enterprise has been equipping businesses in the United States with custom data-centric solutions to drive business growth and build modern data pipelines. “Data Engineering acts as an infrastructural layer, fueling rapid data-driven growth within business organizations,” says Ankush Sharma, Co-Founder, and CEO, DataToBiz. DataToBiz, an acclaimed data transformation firm operating in the States, is helping enterprises make informed data strategies and achieve better ROIs. After recently completing a milestone of 5 years in the industry, DataToBiz has been dedicated to fulfilling the increasing needs for data-driven decision-making and is at the forefront of providing innovative and effective data engineering solutions to its acclaimed clients. Working together for Data Transformation! With the increasing amount of data being generated in today’s business landscape, it’s essential for companies to effectively manage and utilize their data to stay competitive. DataToBiz understands this need and has been providing customized data engineering solutions that help companies make sense of their data. The company has successfully served various enterprises within multiple sectors and is going ahead consistently. Major market players in the states including Tosoh Quartz, FTCSolar, Nordex, Dentsu, TransSIGHT, etc have been implementing DataToBiz’s advanced data analytics, data modeling, and data warehousing solutions. This enabled them to extract actionable insights from their unstructured and big data and make data-driven decisions that positively impact their bottom line and ultimately increase ROI. Under the expertise and partnership of DataToBiz, several companies under various niches were able to: –  Track sales performance across different stores and outlets in real-time. This helped increase their overall sales by 7%.–  Reduce operational costs by 8% within a year of implementation of their Data Warehousing systems, further supplemented by BI and AI solutions.–  Reduce the average lead time (from order to successful delivery) by a whopping 25%.–  Effectively monitor the competitors’ activities and track opportunities in neglected yet potential domains with a significant increase in market share by 7% by the end of FY. DataToBiz – A Hallmark of Trust DataToBiz has emerged as a reliable and valued ally for enterprises from diverse industries since its inception in 2018. By offering innovative solutions that convert complex data into meaningful insights, the company has cemented its position as a trusted partner for businesses worldwide. Being the official cloud partner for both Azure and AWS, DataToBiz has proudly held the distinction of being Clutch’s top-ranked artificial intelligence company in India, a testament to the company’s unwavering commitment to excellence. At the heart of DataToBiz’s success lies a highly skilled team of domain experts, data engineers, and cross-functional consultants. Together, they have helped clients define their data processes, process raw data more efficiently, streamline operations, automate workflows, optimize marketing strategies, and enhance customer engagement, among other digital transformation objectives. Through a personalized approach that is catered to the specific needs of each client, DataToBiz empowers businesses to harness the power of their data and stay ahead of the curve in a rapidly evolving business landscape. As a result, the company has gained a reputation as a go-to partner for enterprises seeking to unlock the full potential of their data and drive growth and success. Out-of-the-Box AI Offerings Apart from driving digital innovation through data engineering services, DataToBiz in-houses two live AI-based product lines including PrepAI, the most advanced AI-powered question-generation platform, and HirelakeAI, an AI-based intelligent hiring solution, along with other innovative automated solutions under development. “Data engineering is an essential component of any successful data strategy,” says P.S Dhillon, Co-founder, and COO, DataToBiz. “We are proud to have helped many companies achieve better ROI by unlocking the full potential of their data,” he added. Originally Published on Digital Journal

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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

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Data Engineering Services: A Modern Business Essential

Data engineering focuses on the practical applications required to establish a flexible, scalable, and agile data infrastructure in an enterprise. It is the most vital part of adopting the data-driven model to make business decisions. Here, we’ll discuss data engineering services and their role in modern businesses.  Data is an integral part of the IT industry. Over the years, users have generated large volumes of data, which is being collected by businesses to fine-tune their products/ services and enhance customer experience. Statistics show that an average user generates 1.7 MB of data per second.  Around 97.2% of businesses are investing in artificial intelligence and big data, though a study shows that many companies analyze only up to 40% of the data they collect from all sources. Another interesting observation is that companies that actively use big data analytics increase their profits by around 8%. Wouldn’t it be beneficial for businesses to further utilize data to amplify their profits?  So, how can it be done?  The answer is big data engineering services.  In simple terms, data engineering is the process of streamlining data collection, storage, and analytics to get more insights from datasets. However, it is an elaborate process that requires IT infrastructure and expert skills. Data engineering is the foundation of building the data analytical model in an enterprise. Businesses partner with data engineering companies to adopt the data-driven model for effective and faster decision-making. Enterprises find it a cost-effective solution to rely on offshore data engineering service providers to derive actionable insights using AI and big data analytics.  In this blog, we understand the process in detail and explain why data engineering is needed for every modern business around the world.  What is Data Engineering? Data engineering is the process of collecting and validating data to ensure high-quality datasets are available for data scientists. Data engineering is a vast domain that includes a range of skills, tools, and applications. It is a combination of several modules like data infrastructure, data mining, data crunching, data acquisition, data modeling, and data management.  A data engineer should maintain the data infrastructure that supports business intelligence solutions. They should work with programming languages, database software, machine learning, and artificial intelligence algorithms. They can work in small teams that focus only on ingesting data into systems or be a part of large teams offering data engineering services & solutions and collaborate with data scientists and database administrators to streamline the data pipeline in mid and large-sized enterprises.  What are Data Engineering Services? Data engineering services are varied and versatile. The top data engineering services companies offer end-to-end solutions to design, build, deploy, and maintain a seamless system that collects, cleans, stores, processes, analyzes, and visualizes data through BI tools. The following are some important services offered by the companies:  Data Ingestion  Data ingestion is the process of moving or replicating data from sources to the cloud storage platform. It is a prominent step in the modern data stack. It determines the quality and type of data a business uses for analytics. Data engineers have to determine if this process will take place in batch mode or in real-time. Factors like cost and resource allocation play a vital role in finalizing the time frame for data ingestion.  Data Storage  Data storage management is another crucial part of data engineering services. The data collected from multiple internal and external sources has to be stored in a central database for further processing and analysis. Data engineers have to design the best data storage method that allows employees to access datasets in real-time. Data storage solutions can be on-premises or on the cloud. Businesses can even use a combination of both. Data warehousing and data lakes are two popular methods used to store vast amounts of data. Businesses offer Azure data engineering and AWS data engineering services to build and customize cloud data storage centers.  Data Integration  Data integration is a key data engineering service as it sets up the necessary connections between different systems, apps, and databases. It is the process of setting up the connection between the central database with the input and output channels. For example, the sources have to be connected with the data warehouse to collect data. Similarly, the data warehouse has to be connected with ERP systems and BI tools to run analytics and share data visualizations with the end user.  Data Processing Data processing is the process where large datasets are cleaned and manipulated to derive useful information. Data from the data warehouse or data lakes are retrieved, classified, cleaned, and formatted to make it ready for analysis. This stage helps remove errors and duplicate data to increase the accuracy of the derived insights.  It is yet another important part of data engineering services as low-quality data can result in incorrect insights which can lead to wrong business decisions.  Business Intelligence  Business intelligence is a vital part of the process. This is where data is converted into meaningful information and presented in graphical reports. Data engineers have the responsibility to identify the right BI tool based on business requirements and customize it accordingly. The dashboards also have to be set up and integrated with the rest of the infrastructure to provide data visualizations in real-time to employees across all departments.  How Does Data Engineering Help a Business? Data engineering or information engineering is the foundation for adopting and using the data-driven model in an enterprise. Data engineering and analytics go hand in hand and have to be aligned at all times to ensure that the top management and employees can access actionable insights at any given point in time. This allows them to make faster decisions based on reliable reports rather than guesswork.  Once data engineers set up the data architecture (systems and connections), data scientists can perform the analytics and share reports. Artificial intelligence tools and machine learning algorithms are used in the process to ensure the seamless and real-time flow of data from one system to another.  Typically, data and engineering services help businesses in the following ways: Data engineering companies also offer data analytical

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Six Big Losses in OEE and Four Tips to Improve Them: From Inefficiency to Excellence

OEE is Overall Equipment Effectiveness, a standard measurement to determine the efficiency and productivity of a manufacturing industry. Here, we’ll discuss ways to resolve the six big losses in OEE and the need for Power BI services to achieve the results.  The manufacturing industry has been growing despite various concerns due to supply chain disruptions, labor shortages, inflation, and restrictions that affected the global market in the last couple of years. According to Statista, global industry production (excluding the US) increased by 1.53% in December 2022. The emerging markets registered an increase of 3.3%, while US industrial production increased by 2.07% for the same period.  Deloitte released a report detailing the top five trends to watch out for in the manufacturing industry. Investing in technology to mitigate risk is the first and most important trend for 2023. This is an important factor as many manufacturers and working towards OEE improvement in production.  The six big losses in OEE (Overall Equipment Effectiveness) can severely affect the revenue and growth of a manufacturing enterprise. Technology plays a vital role in minimizing OEE loss and increasing the overall quality, production, and profits of the industry. Manufacturing companies partner with business intelligence service providers to digitally transform their processes and improve OEE effectively. In this blog, we’ll read more about what OEE is, the losses of OEE, and ways to resolve them in a business.  What is an OEE Breakdown?           OEE (Overall Equipment Effectiveness) is considered the gold standard to measure manufacturing productivity. A business with higher OEE implies being more productive and optimally using resources, while reducing defects or wastage. Quality, performance, and time are the three major parameters used to measure OEE.  Measuring OEE in manufacturing helps the industry improve its processes, benchmark progress, and increase the productivity of equipment by eliminating waste. Typically, a business with 100% OEE is termed perfect, while a business with 85% OEE (or more) falls into the world-class category. A typical industry will have about 60% OEE, and anything close to 40% or less is considered low OEE. This denotes that the manufacturer has a greater share of losses and should address the issues to get better results.  OEE = (Good Count × Ideal Cycle Time) / Planned Production Time Availability Planned Stops Unplanned Stops  Performance Small Stops Slow Cycles  Quality Production Rejects Startup Rejects  Overall Equipment Effectiveness (OEE) Fully Productive Time  What are the Six Big Losses in OEE? Before we learn how to improve OEE, let’s first understand the six big losses that affect the manufacturing industry.  1. Equipment Breakdowns  Equipment breakdowns and failure account for significant loss of time and productivity in an enterprise. This is usually categorized as availability loss as it is unplanned downtime. The equipment is not running when it is expected to run leads to wastage of time (puts everything behind schedule) and productivity (the factory produces a lesser quantity of goods due to equipment failure or breakdown).  This loss can also include relevant aspects such as tooling failure, unplanned maintenance, lack of material or operator (worker), or due to a blockage on either side of the manufacturing process. For example, if the production of a certain product involves eight steps, a disruption at the third step will affect the fourth step just as the disruption at the fifth step.  2. Machine Setup and Adjustment  The second loss of OEE in production deals with the lack of availability of the equipment due to setup, installation, customization, or other adjustment requirements. This is the duration where the machine has to be running but is instead being adjusted (due to various reasons).  Though it is also considered availability loss, it comes under planned stops (as the workers or technicians stop work because they need to adjust the equipment before it starts running again). Cleaning, periodic maintenance, warmup time, cool down time, quality inspections, etc., are some examples of this.  3. Minor Stops or Breaks  Minor stops or idling time is the short duration when the machine’s operator stops its usage to resolve an issue. This is usually a minute or two (for each stop). It is categorized as performance loss because the equipment is available and running but is not performing (or producing) during that short period.  Minor stops can include machine idling due to a slight delay in feeding input material, incorrect settings, misaligned sensors, material jams, design complexities, and periodic mandatory cleaning sessions. All kinds of stops or breaks (in production) that are for less than five minutes for each period come under this type of OEE loss. These can be hard to track and can lead to major breakdowns if ignored.  4. Slower or Reduced Speeds This is the duration when the equipment is slower than the Ideal Cycle Time, thus resulting in the production of fewer goods than expected. It is categorized as performance loss as the equipment is not producing as many goods as it usually does. The Ideal Cycle Time is the fastest possible time taken to manufacture a product. It is a theoretical value calculated based on the given specifications of the machine.  Many reasons can lead to reduced speeds. A few common causes are improper equipment maintenance, poor lubrication, jamming, worn-out spare parts, bad working environment (unsuitable for production), poor quality raw or input materials, inexperienced operator, sudden shutdowns, etc.  5. Process Defects  Every batch produced may have some defective parts or goods despite the production process being stable throughout. Both scrapped products and the ones that can be reworked or reused are totaled in this loss. OEE measures quality based on the First-Pass Yield, which makes process defects a quality loss issue. If the quality of the products is good, they wouldn’t be marked defective.  Process defects can be due to various reasons like wrong or incorrect settings, providing wrong raw materials to the equipment, the inefficiency of the operator, or the expiration of materials (commonly found in pharmaceutical and food industries).  6. Lesser Yield or Higher Scrap The last OEE loss is reduced or lesser

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9 Top Business Intelligence Trends to Follow (Updated)

The business intelligence landscape has been completely revolutionized over the past ten years, and access to the cloud has become a common phenomenon. Interactive business dashboards and insightful data visualizations replaced plain spreadsheets, and the use of data exploded. The past few years saw a massive boom in the business intelligence industry. The data product chain became democratized due to the rise of self-service analytics. Data began to be handled not just by analysts. Some of the trends continue to be a part of the business intelligence industry. But in the current year, the strategies and tools have become even more personalized, and some new trends have emerged too. Businesses- small or big, are no longer contemplating the need for business intelligence analytics but are rather concerned about which BI solution fits the best within their business needs. Every business is now aware that they need data visualizations for better analysis. So they are busy figuring out how to leverage modern BI dashboard software to present data stories in the best possible manner. The theme of business intelligence this year will revolve around data discovery and data security: clean and secure data presented but powerfully. Collaborations will be made between artificial intelligence and business intelligence.  Top Business Intelligence Trends To Follow Some of the biggest business intelligence trends are a no-brainer. Artificial intelligence leads the race with workforce automation and digital transformation sectors seeing massive improvements. Read on to know why. Artificial Intelligence Artificial intelligence aims to make machines perform those complex tasks on their own that can only be executed through human intelligence. Our interactions with analytics and data management are getting revolutionized through artificial intelligence. According to the Strategic Technology Trends report, the trend will combine engineering and hyper-automation with AI with a high focus on possible security risks and vulnerable attack points. Businesses can enjoy real-time alerts about what is happening every second and get immediately notified about unexpected events. Integrating AI in BI solutions will assist in automatic and comprehensive analysis of the full dataset from any data source without human effort. You can instantly access business reports on growth or trends or forecast, anomalies, what-if analysis, value drivers, key segments correlations, etc. AI can also be utilized for online verification processes, like CAPTCHA technology, with the help of generative adversarial networks (GANs). Data Discovery Data discovery means discovering patterns and discrepancies in data. It is the process of using advanced analytics and visualizations to present all the data collected from different internal and external sources. It has great benefits in keeping relevant stakeholders involved with the data since it allows them to extract actionable insights and intuitively manipulate and analyze the data. The demand for data discovery tools across businesses of all sizes has boomed due to the increasing need for data usage and insights. Generating insights that add value to the business requires a deep understanding of the relationship between data in the form of guided, advanced analytics, visual analysis, and data preparation.  Online data visualization and discovery tools are helping businesses create a sustainable decision-making process. The detailed and interactive reports or sales charts presented with several graphs will help teams spot crucial outliers and trends within minutes. Since it is a fact that humans process visual data better, in 2022, the usage of the dashboard as a visual communication and collaboration tool will increase.  In-depth data analysis through interactivity and augmented analytics will replace simple KPI monitoring. KPI dashboards will have other interactive features, too, such as real-time data and AI-based alarms. Business users need software for this purpose that is: Data Literacy The ability of businesses to use data analytics and insights in their decision-making has become a core factor in determining the business’s success. From goal setting to strategizing to taking action, businesses require data at every step. No wonder data literacy is of utmost significance for every business. It is the reading, writing, analyzing, and communicating data in a particular context. Data literacy requires a deep understanding of all the tools and technologies adopted and techniques and methods implemented for data analysis.  Business leaders must equip all the organization members with the training and tools required for working with data and analytics. Managers need to assess the skill sets of employees, and managers need to identify gaps and weak spots. Team members fluent in data can be appointed as mediators for non-skilled groups. With the right tools and quality training, all the members will acquire enough data literacy to use data as the key language and perform advanced analysis. By 2025, prediction says that data literacy will be so widespread that businesses will no longer require data scientists to progress technologically. Data Quality Management Currently, there is abundant data in every business flowing in from literally everywhere, and it has become crucial to assess data quality before using it. Poor quality data can cost businesses around $9.7 and $14.2 million per year. No wonder data quality management is an increasingly significant trend. Poor data quality can lead to a poor understanding of consumer behavior, wrong estimation of conversion rates, poorly generated marketing budgets, incorrect resource allocation, bad investments, and other errors that can harm businesses significantly.  Data quality management is the solution to all these problems. It ensures that businesses only use the correct data for analytical purposes to arrive at the right data-driven decisions. Data quality depends upon how complete, timely, accurate, consistent, and compliant it is. There can be no outdated data that does not fit within the timeline or duplicate or missing values. Companies are collecting complex data from several sources regularly, and managing these data using the right tools and processes has become critical. Predictive & Prescriptive Analytics Tools Predictive analysis means forecasting future possibilities by extracting information from existing data sets. It is data mining of past data. Companies get an insight into their future along with alternative scenarios and risk assessments that are reliable enough. It helps companies better understand their customers,

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Breaking Down Language Barriers: How AI is Revolutionizing Translation

In today’s world, the ability to communicate effectively across multiple languages has become more important than ever. As businesses continue to expand their global reach, the demand for high-quality translation services has increased significantly. Fortunately, advancements in artificial intelligence (AI) have led to the development of sophisticated translation technologies that are transforming how we communicate across different languages. Here, we will explore how AI is revolutionizing translation, the benefits of using the latest AI powered translation technologies, and the potential impact on the future of communication. The Evolution of Translation Technology Translation technology has come a long way. Initially, translation was done manually, with human translators spending hours poring over texts and dictionaries to translate them from one language to another. However, this method was slow and expensive and often resulted in inaccuracies due to the human element involved. With the advent of computers, machine translation was introduced in the 1950s. Machine translation uses software to translate text automatically without human intervention. However, the quality of the translations produced by machine translation was often poor due to the limitations of the software at the time. In recent years, advancements in Artificial Intelligence and machine learning have led to the development of sophisticated translation technologies that are capable of producing high-quality translations in real-time. These technologies are changing how we communicate across different languages, making it easier than ever to break down language barriers. How AI Translates Languages AI powered translation tools use machine learning algorithms to analyze vast amounts of language data and learn how to translate different languages accurately. These tools can process natural language and identify patterns and relationships between different words and phrases. By doing so, they can translate text from one language to another with impressive accuracy. Types of AI Powered Translation Tools There are several types of AI powered translation tools, each with strengths and weaknesses. Some of the most common types include: Rule-Based Machine Translation Rule-based machine translation involves translating text based on a set of predefined rules. These rules are created by human linguists and programmers, and the translation process involves matching words and phrases in the source language to their corresponding words and phrases in the target language. Statistical Machine Translation Statistical machine translation involves analyzing vast amounts of language data to identify patterns and relationships between different words and phrases. By doing so, statistical machine translation tools can translate text from one language to another with high levels of accuracy. Neural Machine Translation Neural machine translation involves using deep learning algorithms to process natural language and translate text from one language to another. These algorithms can learn from vast amounts of language data, making them highly accurate and efficient. The Benefits of AI-Powered Translation AI powered translation offers several benefits over traditional translation services, including: Cost-Effectiveness AI powered translation tools are often more cost-effective than human translators, making them an excellent option for individuals and businesses on a budget. Efficiency AI powered translation tools can translate vast amounts of text quickly, making them an excellent option for individuals and businesses that need to translate large volumes of text regularly. Accuracy AI powered translation tools can translate text with impressive accuracy, making them an excellent option for individuals and businesses that require high levels of accuracy in their translations. Implications for Businesses and Individuals The rise of AI powered translation has significant implications for businesses and individuals alike. For businesses, AI translation tools offer a cost-effective and efficient way to communicate with customers, employees, and partners in different countries and linguistic regions. This can help to expand the reach of businesses and increase their global competitiveness. For individuals, AI translation tools can make it easier to navigate daily life in a new country, communicate with locals, and access information in a different language. Challenges and Limitations of AI Translation While AI powered translation tools offer many benefits, there are also several challenges and limitations to consider. First and foremost, these tools may struggle with idiomatic expressions, cultural nuances, and other subtleties of language that human translators are more adept at handling. Additionally, AI translation tools may not be suitable for translating highly technical or specialized content, as they may not have the required expertise or knowledge. Finally, there is a risk that reliance on AI translation tools could lead to a loss of human language skills and cultural knowledge over time. The Future of Communication The potential impact of AI powered translation technologies on the future of communication is enormous. As these technologies continue to improve, they will make it easier than ever for people to communicate across different languages, breaking down language barriers and promoting cultural exchange. In the future, we may see AI powered translation technologies integrated into everyday devices like smartphones and laptops. This would make it possible for people to communicate with each other in real time, regardless of the language they speak. The impact of AI powered translation on businesses is also significant. As businesses continue to expand globally, the ability to communicate effectively with customers in different languages will become increasingly important. By using AI powered translation technologies, businesses can communicate with their customers in real-time, regardless of the language they speak. Conclusion In conclusion, advancements in artificial intelligence have revolutionized translation technology, making it easier to communicate effectively across multiple languages. AI powered translation tools use machine learning algorithms to analyze language data and identify patterns, resulting in accurate translations. There are several types of AI powered translation tools, each with strengths and weaknesses. They offer cost-effectiveness, efficiency, and accuracy over traditional translation services. As AI powered translation technology continues to improve, it has the potential to break down language barriers and promote cultural exchange, making it easier for people to communicate across different languages in real time. This technology also has significant implications for businesses looking to expand globally and communicate with customers in different languages. Originally Published on The Tech Trend

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How to Choose the Right Affiliate Program: Partner Up For Success

Affiliate programs let you monetize your web traffic and earn a commission for promoting a product or brand. It offers flexible and versatile earning options for website owners, entrepreneurs, and freelancers. Here, we’ll discuss the ways to choose the right affiliate program to generate more revenue.  Affiliate programs have grown by leaps and bounds over the years. It started as a side hustle and has become a prominent income source for millions of people from around the globe. Brands have expanded into the affiliate market so much that 81% of businesses use these programs to enter new markets and increase their customer base. Global affiliate program is valued at more than $17 billion. In fact, it is a major income generation source for 31% of publishers (website owners).  But what is an affiliate program?  An affiliate program is a mutual exchange program between a brand and a publisher, where the website owner (publisher) gets rewards for posting about a brand, referring and providing links to its products or services. It is a performance-based marketing channel that provides returns based on website traffic, click rate, subscription and purchase frequency.  For example, DataToBiz’s affiliate program offers exciting opportunities like an instant credit of $10 for generating a qualified lead and a flat 10% commission for each sales conversion. The company also offers recurring commissions and other incentives.  How Do Affiliate Programs Work? An affiliate program has four major parties involved in the process: The working of an affiliate business program can be complex for newbies. However, it is a structured and well-established process followed by several businesses. Some variations occur depending on how the businesses plan the commission model, but the overall process is pretty much the same.  This commission amount will not be collected from the customer. It will be paid for by the company. Brands follow different payment models or structures based on various factors like the type of affiliate network, the number of affiliates, industry regulations, etc. However, the following are the three commonly used payment structures:  Over the years, affiliate companies have developed unique and attractive commission schemes to add more affiliates to their network and promote the brand. These include: How to Find Affiliate Products? Google Trends shows that the search for ‘affiliate program’ doubled between 2015 and 2021. Moreover, 56% of advertisers and agencies have earned more revenue from affiliate programs since the Covid-19 lockdown in March 2020.  But how to find the right affiliate products to start growing your career as an affiliate marketer? Let’s find out.  Google Search  Do a quick search on Google to see a list of the top products trending in the searches. Get data from Google Search Console and analyze it to shortlist products.  Competitor Analysis How are your competitors in your niche? How many of them are affiliated with different businesses? Make a list of such publishers and the brands they promote. This will give you an idea about which products have more affiliates. However, remember that their products may not always be suitable for your audience.  Social Media Trends  First, identify the channel where your target audience is active. Then go through the ads, posts, and hashtags on the platform to identify which products are currently hot sellers. Also, gather information about products they are struggling with so that you can provide a solution o an alternative.  Google Trends  Google Trends is a free tool that helps you collect data about the latest search trends on the internet. You can get data based on geographical locations, volume, duration, etc. Make note of recurring products and add them to your shortlist.  Audience Feedback  Conduct polls and surveys on your website and social media accounts to ask for the opinion of your audiences. You will have to analyze the feedback to gather the necessary insights. Ask genuine and open-ended questions to get a better response.  Research Online Categories  The product categories listed on marketplaces like Amazon, eBay, etc., can provide an idea about the range of options available in your niche. You can go through blogs and articles that list online tools if you belong to the IT industry.  Products You Used  The easiest way to finalize products for an affiliate program is to choose from the ones you used. When you promote products you like, it becomes easier to convince others with your posts and experiences.  Affiliate Directories Check out the leading affiliate directories and networks in the market. Sort the products based on categories, commission rates, brand image, etc. It is not always a good idea to choose affiliate products with a higher commission. Other factors should also be considered before you make the final decision.  How to Choose the Right Affiliate Program Choosing the right affiliate program requires time, energy, and research. You need to be 100% sure of the brand and products you want to promote as an affiliate.  Niche Compatibility  The most important aspect is the niche and industry you belong to. It makes sense to choose businesses from your industry or relevant ones instead of something totally different. For example, a food blog specializing in baking and cake decorations should choose an affiliate program to sell baking equipment, electronics, gadgets, and items used to decorate baked goods.  Similarly, a website that explains artificial intelligence-based tools used in EdTech and training will find it beneficial to become an affiliate partner to an AI company from the niche. You can also promote other tools by the company as they are likely to be AI-based.  Another example is where a teacher can become an affiliate to sell online assessment tools like PrepAI, which allows students and their parents to create their own tests for practice.  Products Offered  Go through the lists of products available in your niche and the adjacent ones. You can start with a wider net and narrow it down as you get closer to finalizing the affiliate program. Here, many factors like product type, price, usage, limitations, etc., should be considered.  While some affiliates prefer to promote high-priced products, others opt for affordable products and work on attracting

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