AI Development: In-house vs Outsourcing (Complete Guide)

In this data driven world, businesses are turning to advanced tools such as Artificial Intelligence and Machine Learning to extract valuable insights and boost productivity. AI-powered solutions offer great potential for automation, and help in decision-making. However, many companies still need to know-how to implement these strategies properly. As businesses strive to stay competitive in the marketplace, many are turning to the use of AI to gain the competitive edge. But with a wide range of opportunities and methods of development, companies generally opt for outsourcing AI development.  In this blog, we will provide a comprehensive overview of the advantages of each approach and the key considerations to take into account while deploying in house vs Outsourced AI development. What is AI Development? AI development refers to the process of creating intelligent machines that can perform tasks without human intervention. It involves a combination of machine learning, natural language processing, robotics and other technologies that allow computers to learn from data and make decisions based on that information. The goal of AI development is to advance automation in various industries, including healthcare, finance, transportation and more. Developers use algorithms and models to create systems that can analyze vast amounts of data quickly and accurately. They also work on improving the ability of machines to understand human speech or written text through NLP technology. As AI technology continues to evolve rapidly, it presents new opportunities for businesses looking for ways to streamline processes or provide better customer service experiences. Also, AI is playing an increasingly important role in business, and its impact is only going to grow. By automating tasks and providing insights that would otherwise be hidden, AI is helping businesses boost efficiency, drive growth, and gain a competitive edge. While some businesses are still hesitant to embrace AI, the benefits are becoming too great to ignore. For businesses that are looking to stay ahead of the curve, investing in AI development is a smart move. Factors to Consider While Choosing In-House vs Outsourcing AI Development There are a number of factors to consider when deciding whether to develop AI in-house or outsource it to a third-party.  What is the Difference Between Outsourcing and In-House Development? In-house AI development Are you considering in-house development for your business? As a professional, you know that having your own team with the necessary IT skills and abilities can make all the difference. On the other hand, the management of such a team is a serious load on your budget. While in-house development may be more expensive in the short term, it becomes more cost-effective in the long run. Moreover, for small businesses, an in-house IT department can often handle both the support and development of internal information systems. In-house development also allows for better communication and collaboration between the development team and other departments within the company, leading to more efficient processes and improved project results. By understanding the advantages of in-house development, organizations can make informed decisions as to the best approach for their specific project needs. Advantages of In-house AI development  Customization  In today’s competitive business environment, having the ability to customize your Artificial Intelligence (AI) solutions is a must. With the ability to tailor the features and settings of your AI solution to meet the exact needs of your business, many companies are opting to develop their own in-house AI solutions.  In accordance with the culture  Collaborating with a local development team means engaging with individuals who understand the culture, values, and standards of your organization. This type of relationship ensures employees to seamlessly integrate into the organization and add value to their roles. By hiring locally, organizations can benefit from unique perspectives and skillsets that come from having a team that is more familiar with their environment. Outsourcing AI Development Outsourcing AI development can be defined as the process of contracting with an external organization or individual to create, train, and deploy AI models on behalf of another company. There are many reasons why companies might choose to outsource AI development, including lack of internal expertise, time constraints, and cost considerations. When done correctly, outsourcing AI development can be a highly effective way to quickly and efficiently deploy AI capabilities within an organization. By leveraging the expertise of a highly-skilled, external provider, you can streamline your product development while gaining access to specialized engineering & domain expertise. With the right outsourcing partner, you can take advantage of cost savings, faster time to market, and the flexibility to scale your operations as needed. Advantages of Outsourcing AI Development  Collaboration with AI Experts When it comes to developing AI products, it becomes  crucial to ensure that you have the right experts on board. However, due to the increasing demand for AI, it is difficult to secure full-time experts. Fortunately, outsourcing AI development can help you to bypass this issue. It enables you to access an experienced and professional team without the associated costs of hiring in-house. Cost-Efficient option  One of the most obvious advantages of outsourcing is the cost savings that can be realized by making use of the services of Asian countries, where the cost of labor is often much lower than in Western countries. Furthermore, these services are often of good quality, meaning that businesses are able to benefit from low pricing without compromising on the quality of the work produced. Difference Between in House vs Outsourcing AI Development Conclusion  When it comes to making a decision about in house vs outsourcing AI development, it is important to consider several factors. Cost is certainly one of the key components, however, it is also important to examine the pros and cons of each model to make the right choice. By understanding the differences between in-house AI development and outsourcing, you can make an informed decision and ensure the best outcome for your project. As artificial Intelligence services become more widely available, businesses are facing the dillema of whether to pursue in-house development or outsourcing for their AI project. While

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6 Top AI Companies in Finance Industry (Updated for 2025)

Artificial intelligence brings a modern approach to the traditional banking and finance sector. It plays a vital role to strengthen internal processes and increase customer experience. Here, we’ll discuss the role of AI in the finance industry and the leading AI companies offering advanced solutions in the global market.  Artificial intelligence has established its presence in many industries, including finance. In fact, around 50% of financial establishments have moderately incorporated AI in their process. According to Statista, 35% of them have fully functional AI systems, while only a 3% haven’t yet adopted artificial intelligence in the finance industry.  A limited survey conducted by NVIDIA showed that almost half the respondents were migrating their IT infrastructure to hybrid could to optimize the performance of their AI tools. The survey also reported that banks and trading firms are busily adopting advanced technologies like NLP (natural language processing), large language models, fraud detection software, portfolio optimization tools, etc., to enhance customer experience. Intelligent avatars and speech AI are also being used by finance and insurance companies.  Furthermore, the companies have stated that using artificial intelligence in corporate finance has increased customer experience by up to 45% and operating efficiency by up to 35%, alongside reducing the cost of ownership by up to 20%.  Moving from traditional banking methods to modern methods using AI is beneficial in many ways. Most banking and insurance firms partner with AI FinTech companies to speed up the adoption process and reduce the overall cost of revamping their systems. It also allows financial establishments to access expert talent without hiring them as in-house employees.  This blog is dedicated to the role of AI in the finance industry and the top companies that offer offshore services to banks, trading firms, insurance agencies, etc.  Uses of Artificial Intelligence in the Finance Industry  AI financial advisory startups offer strategic solutions and build artificial intelligence-based tools exclusively based on the bank’s requirements. AI companies also customize existing tools and dashboards to integrate them with banking software to streamline daily operations and facilitate effective decision-making. Let’s look at some popular uses of artificial intelligence in the finance sector.  Personalized Wealth Management  Banks and investment firms can offer personalized suggestions for wealth and portfolio management using NLP (natural language processing) and machine learning algorithms. You can set up chatbots that provide 24*7 support to customers and help them make the right investment decisions based on their personal data.  Fraud Detection and Cybersecurity  The increase in online fraud has forced finance establishments to use advanced software and applications to detect such cases in the early stages and prevent losses. Banks, insurance companies, etc., can rely on fraud detection software to flag users or transactions that raise red flags and alert the managers. Similarly, AI tools can also be used to increase data security and minimize the risk of data theft through security breaches. For example, a FinTech company in Sweden could reduce the rate of fraudulent transactions by 47%.  Loan and Risk Assessment (Corporate)  AI has different roles in the corporate banking sector. From determining the risk of approving loan applications to improving underwriting, artificial intelligence is used in different ways to mitigate risk and increase ROI. For example, US Bank uses AI to analyze customer data and identify risky members. This is done to prevent money laundering through their bank accounts.  Another example is where an African-based company streamlined data sources for credit risk assessment by integrating its systems with an NLP engine and using a data warehouse to collect data from multiple credible sources.  Top AI Companies in Finance Industry Statistics show that more than 75% of millennials would never go to a bank branch if they can complete transactions online. They prefer mobile banking services with chatbots, personalized recommendations, and more.  The finance industry can cater to customers’ requirements by partnering with the best artificial intelligence companies. 1. DataToBiz  DataToBiz is among the leading companies in artificial intelligence from India. It has clients from different countries like the US, UK, Australia, the Middle East, India, South Africa, and more. The company offers complete AI and ML services to small, medium, and large organizations from different industries. It has offered custom FinTech solutions to IDBI Bank, ICICI Bank, NPCI, etc. The company’s AI services include strategic planning, pilot implementation, platform customization, big data analytics, digital transformation, IoT (Internet of Things), and many more. DataToBiz has won several awards in the artificial intelligence industry. It has a team of expert AI and ML engineers who work in tandem with the clients to set up and streamline internal processes.  It also offers the following services: Banks and other financial establishments can rely on the company to adopt advanced and emerging AI tools to create innovative financial products, enhance customer satisfaction, mitigate risk, improve data security, and provide intuitive dashboards to employees for real-time decision-making.  2. HighRadius HighRadius is one of the best AI companies in the global market. It offers automation software for CFOs and has helped 700+ clients from around the world. The company specializes in building robust and out-of-the-box AI solutions in the accounting and finance sector. It also works with organizations from other industries like retail, insurance, pharma, life sciences, etc.s The automation solutions by the company are designed to help with DSO (Days Sales Outstanding) reduction, working capital optimization, and bad debt reduction and to increase overall productivity in less than six months. HighRadius offers services such as: The company has developed autonomous tools to manage receivables, treasury, and reporting in detail. The various products of the company are used to process more than $4.7 trillion in transactions per day. It has clients like Unilever, Hershey’s, Kellogg Company, etc.  3. Darktrace Darktrace is one the top AI companies in finance, offering a range of products to enhance cybersecurity for its clients from 110 countries around the globe. The company has developed self-learning AI that offers powerful solutions to help businesses understand their processes and customers to the minute detail. It emphasizes the need for advanced cybersecurity applications to protect the bottom line.  The company’s Darktrace Cyber AI Loop uses continuous feedback

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How AI and Metaverse Have Changed Our View of the World 

Metaverse is a virtual world personalized to provide various services to each user. Artificial intelligence is an integral part of Metaverse and can determine the extent of its role in our lives. We’ll discuss how AI and Metaverse have changed our future.  Metaverse is the buzzword in today’s market. It is a combination of various advanced technologies like AR, VR, AI, etc. Metaverse allows users to engage with 3D objects in a virtual space. AI and Metaverse are set to create a new reality for mankind.  Statistics show that the Metaverse market is likely to reach $783.30 billion by 2030. Many leading multinational giants, like Facebook, Google, etc., are working on Metaverse.  But what exactly is Metaverse? What is the role of AI in developing Metaverse, and how will it impact the world? Let’s find out. But before we delve into Metaverse, let’s start with the basics and understand what artificial intelligence is.  What is Artificial Intelligence? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI enables machines to mimic the human mind for decision-making. Artificial intelligence helps computers process vast amounts of information to identify hidden patterns, detect trends, and process information similar to a human brain.  AI is categorized into the following:  Deep learning and machine learning are subsets of AI and are used in several applications. Artificial intelligence has been adopted by various businesses in recent years. A survey report shows that more than 86% of CEOs consider AI the mainstream technology in their organizations. Close to 85% of enterprises have increased their budget for AI compared to 2019. The global AI market was valued at $93.5 billion in 2021. It is likely to grow at a CAGR (compound annual growth rate) of 38.1% from 2022 to 2030.  AI is used by small, medium, and large enterprises from various industries. From Healthcare to manufacturing, every business needs AI to streamline its process. Artificial intelligence is used in the following ways:  What is Metaverse?  Metaverse is a blend of multiple technologies. It is not a single entity and requires a range of technologies to work together. Metaverse is a collected virtual space that merges the virtual and real worlds. In fact, meta refers to ‘virtual’, and verse is short for ‘universe’. So, Metaverse is a virtual universe. Virtual reality, augmented reality, artificial intelligence, mixed reality, etc., are used to create Metaverse.  The virtual worlds can be accessed from computers, smartphones, consoles, VR devices, and more. Metaverse can allow people to interact with the virtual world without moving in the real world. However, it’s important to note that Metaverse is still in the developing stages.  Top companies are investing in bringing Metaverse closer to common people. Some smaller organizations are updating their IT infrastructure to be ready for Metaverse by creating virtual worlds that closely resemble real life. That said, a few VR-based apps are also being termed Metaverse by businesses (especially in the gaming industry). These are hybrid models at best and not Metaverse.  Role of AI in Metaverse  With so many technologies required to develop Metaverse, what role does AI have in it? How important is AI to create Metaverse?  The role of artificial intelligence is to support the extensive features of Metaverse. AI will enhance user experience. It will make user interaction with Metaverse a seamless and worthy experience.  In short, AI will aid creation, collaboration, interaction, and personalization in Metaverse. It can make the virtual world a delightful experience for users.  The Risk of Using AI in Metaverse  While AI is great for personalization, it isn’t without risks for users. Micro-targeting can go either way if it becomes manipulative or is missed by people in power.  It can be hard to separate fact from fiction if one is not alert. Data manipulation is another risk, which can be controlled through in-built safety/ regulatory algorithms. One way to minimize this risk is by setting up strict regulations about data collection and the role of AI agents in Metaverse. If we can control the misuse of technology, there’s no denying the plethora of advantages it offers to the public.  How AI and Metaverse have changed the Future  Metaverse will use AI algorithms the way social media platforms use AI for newsfeed personalization, ad targeting, etc. NLP (natural language processing), facial recognition, voice recognition, and other actions are also executed by AI.  A Harvard University report shows how AI will affect eight areas of human activities by 2030. The areas include:  The report highlights the need for reliable hardware to handle the developments and create a safe space for the world at large to use Metaverse in daily life. Sentiment analysis, emotional recognition, and biometrics (physical + behavioral) will play a vital role in the personalization of the virtual world for a user. Mark Zuckerberg, CEO of Meta, says that it could another five or ten years to make Metaverse a mainstream technology. Right now, it is limited to interactive headsets, AR/VR games, and mixed-reality applications.  Conclusion  Metaverse will open up new possibilities in technologies and blur the lines between real and virtual. You can create a virtual avatar of yourself to interact with others in the 3D virtual world. It erases boundaries and provides opportunities for businesses to market their products to the right audiences.  Metaverse also enhances learning and can provide a user-centric experience. Artificial intelligence plays a crucial role in making this happen by bringing together other technologies to make Metaverse engaging. 

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How to Make Stock Price Predictions Using Reinforcement Learning?

Analyzing the stock market using artificial intelligence has been a work in progress recently. Here, we’ll discuss how you can develop an AI stock prediction model using reinforcement learning.  Analyzing the behavior of the stock market has been a subject of interest and challenge in the AI industry. Data scientists, market analysts, and financial experts have been curious to determine whether it is possible to overcome these challenges. The biggest concern is the need for an extra large dataset to build a predictive system based on supervised learning algorithms.  Furthermore, even the most advanced technologies seem to be inadequate to accurately predict the changing prices in the stock market. Yet, accurate AI stock price prediction could be possible without relying on large datasets.  In this blog, we’ll try to identify the challenges of stock market prediction and understand if we can use reinforcement learning for stock prediction and data analysis in Python, that too, using limited or no data to train the algorithm.  Before we proceed to read more about stock price prediction using machine learning, let’s understand more about the data analysis methods used to process stock market data.  Types of Data Analysis Techniques Used on Share Market Data  The stock market data is analyzed in different techniques. These are categorized as – Time Series Analysis and Statistical Data Analysis. 1. Time Series Analysis  A time series is defined as a sequence of data points that appear/ occur in successive order in a given period. It is the opposite of cross-sectional data, where the events that occur at a specific time point are captured.  The time series analysis tracks the movement of the chosen data points over the specified period. The data points are usually the price of the stock/ share/ security. The prices are collected at regular intervals to analyze the patterns.  There are various techniques to perform the time series analysis on stock market data. Let’s check them out in brief.  a. Moving Averages:  The moving average of a stock is calculated to smooth the price data and constantly update the average price. In finance, the MA (moving average) is considered a stock indicator and is used in technical analysis. The short-term price fluctuations are mitigated in this process. The MA is further divided into the following:  i. Simple Moving Average (SMA)  SMA is calculated using the arithmetic mean for a given set of values over a specific period. Here, the set of values is the stock prices. These are then added and divided by the number of prices in the set.  Formula: A1+ A2+ A3+… Ann Here, A= average in the period; nn= number of periods; SMA= n   ii. Exponential Moving Average (EMA)  The EMA gives more importance to recent prices to make the average price more relevant based on the new information. The SMA is calculated first to use in the EMA formula.  The smoothing factor is calculated next to determine the weighting of EMA- 2/(selected period+1). Formula: EMAt= [Vt×(1+ds)]+EMAy×[1−(1+ds)] Here, EMAt= today’s EMA; Vt= today’s value; EMAy= yesterday’s EMA; ds= smoothing (number of days) Some other types of moving averages are:  b. ARIMA:  It is another approach to time series forecasting. ARIMA and exponential smoothing are widely used methods as they offer a complementary approach to the problem. ARIMA describes the auto-correlations in data, while exponential smoothing relies on seasonality in data and trend description.  c. Box Jenkins Model:  This model can analyze different types of time series data for forecasting. It is a mathematical model that uses inputs from specified time series to forecast data ranges. The Box Jenkins model determines the outcomes based on the differences between data points. It identifies trends for forecasting stock prices using autoregression, moving averages, and seasonal differences.  d. Rescaled Range Analysis:  It is a statistical technique developed to assess the magnitude and nature of data variability over a certain period. The rescaled range analysis method is used to identify and evaluate persistence, randomness, and mean reversion based on the time series data from the stock markets. This insight is used to make proper investment strategies.  2. Statistical Data Analysis  It is the common value that occurs in the dataset.  It is the middle number in the dataset. For example, in 4, 6, 7, 9, and 11, the median is 7.  It is the average value of the dataset.  It is also called standard normal distribution or Gaussian distribution model. It is charted along the horizontal axis, representing the total value spectrum in the dataset. The values of half the dataset will be higher than the mean, while the other half will be longer than the mean. And the other data points will be around the mean, with a few lying on extreme/ tail ends on both sides.  It measures the asymmetry/ symmetry of the price/ data point distribution. The skewness will be zero in a standard normal distribution. A negative skewness will lead to a distorted bell curve on the left, while positive skewness will cause a distorted bell curve on the right side.  What is Reinforcement Learning? It is an area of machine learning that takes the appropriate action to maximize returns for a given situation. Many software applications and machines use reinforcement learning (RL) to identify the best behavior/ path to arrive at the desired result for a specific situation.  Reinforcement learning is different from supervised learning. In the latter, the training data is the answer key to training the model with the correct answer. However, in RL, the reinforcement agent decides which task to perform, as there is no specific answer used for training. It allows machine learning developers to train the algorithm without using a dataset. The algorithm will learn from experience and improve itself over time.  What are the Different Datasets Available for Stock Market Predictions? Fortunately, there are a few datasets available to train the algorithms. Developers can access the datasets from the following:  NIFTY-50 Stock Market Data  The data is available from 1st January 2000 to 31st April 2021. It provides

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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

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11 Key HR Metrics to Track in 2023: 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 2023. 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 2023. 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 in 2023: 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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