10 Common Data Analytics Mistakes to Avoid 

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

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Top 6 Benefits of Data Visualization for Sales Teams

Data visualization is the process of presenting complex data in easy-to-understand graphical formats on an interactive dashboard. Here, we’ll discuss the significance of data visualization in sales and the range of benefits it offers to boost a business in competitive markets.  Business intelligence is the process of combining strategies and technologies to collect, analyze, and manage large amounts of data in an enterprise. The analytics are presented in neat and structured reports to help make effective decisions faster. Data visualization is used to convert analytics into graphical reports with interactive elements. It presents insights in different readable formats and allows you to make changes in real-time.  Statistics show that the data visualization market was valued at $5590.75 million in 2021 and is expected to touch $9464.0 million by 2027 with a CAGR (compound annual growth rate) of 9.17%. According to a report, data visualization can make decision-making 5x faster and execution of the decision 3x faster. A study shows that using visual data can shorten the overall time spent on business meetings by up to 24%.  Several organizations hire Power BI consultants to set up a data visualization dashboard in different departments. Microsoft Power BI is one of the leading visualization tools with many user-friendly features and third-party integrations.  In this blog, we’ll understand why data visualization is vital for your business and the benefits of data visualization in streamlining sales, the most important department in many enterprises.  What Is Data Visualization and Why is it Important?  Data visualization is the process of converting large datasets into graphical format to make actionable insights more accessible to users. It reduces the time taken to identify patterns and trends and highlights outliners through different visual formats(like graphs, charts, maps, etc.). Data visualization is sometimes called information visualization, information graphics, and statistical graphics. It belongs to the data science processes and data presentation architecture (DPA).  For example, the sales analytics dashboard used by the sales and marketing teams uses data visualization to present advanced analytics in easy-to-understand reports. This helps monitor KPIs (key performance indicators) and ensure that your strategies are always aligned with the business goals. From streamlining data collection to hastening the entire process and reducing mistakes, data visualization has a crucial role in helping your business become successful.  Benefits of Data Visualization for Sales So what are the benefits of visualization in the sales and marketing department? Let’s find out below.  1. Better and Effective Decisions  The sales teams have to make quick decisions and be proactive to make the most of market opportunities. Data visualization tools provide graphical reports that help make decisions using accurate and reliable information. Since visual data is easy to comprehend, it allows teams to discuss more aspects in detail and finalize their decisions in less time.  The visualizations also allow you to understand how the reports change when you consider different variable parameters. In short, data visualization is an effective way to help businesses adopt data-driven decision-making processes.  2. Gather Data from Multiple Sources  Sales teams have to process data from various channels and applications. For example, you need customer data from CRM systems, brand mentions from the internet, lead generation data from multiple channels (email, website, social media, offline, etc.), and competitors’ market share to create and implement a robust sales strategy to boost your business. Data visualization tools can be integrated with several data sources to create a unified interface. Your sales team can directly access the data they want by using the same tool without going through a long-winded process of asking their superiors to share the necessary information.  3. Gain New Perspective  Data visualization reports are dynamic and provide different insights as you change the parameters. You can gain new perspectives as you analyze more visual reports. For example, the sales graph for the current month may show that you achieved more than the target. However, when the parameter is changed to six months, you may notice that the overall sales have been declining or not up to the mark.  Instead of going through pages of reports and guessing the overview picture, you can use data visualizations to present the final outcome in a clear format. Your sales team will find it convenient to use visualizations for correlations, which provide more perspectives for the given data.  4. Bring Context to Content  The numbers in a report make no sense when taken out of context. They could mean anything, and trying to identify the relationship between the numbers and the data in the corresponding column and row headers can be stressful.  Data visualization provides an effective solution to this problem. For example, a bar graph report for sales performance will show the exact situation for the given period. The bars are not just decorations but indicators of the performance of the teams, products, and the overall business.  5. Save Time  When you consider the above-listed benefits of data visualization, you can notice one common factor in all of them. Data visualizations save time and energy for the sales team. Sales reps don’t have to feel burdened by pages of paperwork and sheets with never ending data.  Everything is automated in the setup. The data visualization tools are connected to data sources and other applications that continuously collect, clean, store, and analyze data. You just have to run a query for the tool to generate the report in real-time.  6. Tell a Story  One of the major applications of data visualization tools is to make data more interesting. Instead of sharing boring and lengthy reports, you can convert the whole thing into an interactive presentation to tell a story. A report by Accenture shows that more than 80% of the respondents thought data visualization tools were an effective means for storytelling in the business context.  Data visualizations are easy to generate and can make meetings more interesting. Some businesses are combining visualizations with VR tools to create 3D images and graphs. This increases the collaborative nature of the tool and helps the sales team present its points most engagingly.  More About the Importance of Data Visualization in Sales Sales data visualization can help you

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7 Top AI Companies in Finance Industry in 2023

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. A few of them are listed below.  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

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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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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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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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5 Top Manufacturing Analytics Companies Driving Industry Transformation in 2023

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 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 machine learning algorithms and operational data to improve various functions of the factory. From enhancing product quality to reducing costs, speeding up transportation, and optimizing the supply chain, manufacturing analytics can help your business in making 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 identifies 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 supply chain optimization,

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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. It is also termed 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 company more employee-centric.  12. Better

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Why Do Modern Businesses Need Data Engineering Services?

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 that 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. Data engineering 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  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.  Data Integration  Data integration 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 removes errors and duplicate data to increase the accuracy of the derived insights.  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 solutions to ensure that the business can make better decisions and become proactive. Enterprises should look to partner with companies that provide complete services using advanced data engineering technologies.  Why Businesses Need Data Engineering Services Many BI and AI companies offer data engineering system design and engineering as a service (EaaS) models to empower modern businesses to use datasets effectively by optimizing resources and reducing costs.  Here are some reasons for business enterprises to invest in data engineering services.  1. Manage IT Systems 

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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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