Page 11 – DataToBiz

Yashica Chopra
April 10, 2024
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Guide 101- Choosing the Right eCommerce Analytics Partner

The rapidly evolving eCommerce landscape demands businesses to be proactive in adopting the latest developments and technologies to become successful players in the competitive market. Here, we’ll discuss how to choose the right eCommerce analytics partner. The eCommerce industry has seen tremendous growth over the years. It is a fast-paced sector with fluctuating demands and customer preferences. Many people today like to place an order online rather than go out and buy the product in person. According to Statista, the average number of products per order has increased to 4.95. Moreover, the eMarketer platform predicts that groceries will become the largest eCommerce category in the US by 2026. The Asian countries are in no way lagging. The total revenue of the eCommerce and online retail sector in Asia was $1.7 trillion in 2023. With such clear statistics, it’s evident that eCommerce businesses should stay alert 24*7 to grab market opportunities and make proactive decisions. That’s where eCommerce analytics can help. But what is it, and how can you choose the right eCommerce analytics partner to help your business adopt data-driven technologies? Let’s find out below! What is the Analysis of eCommerce? eCommerce analytics refers to the process of collecting data from multiple internal and external sources to determine the trends, patterns, customer behavior, preferences, market opportunities, risks, future demands, etc., to plan your inventory and marketing strategies. It includes metrics like every stage of the customer journey, your business KPIs, competitor analysis, and many more. So, how to do eCommerce data analysis? eCommerce analytics requires large datasets, analytical models, and data visualization tools like Power BI, Tableau, artificial intelligence/ machine learning technologies, etc. It is a comprehensive and continuous process that streamlines workflows and delivers actionable insights in real-time to decision-makers. You can set up data analytics for eCommerce by partnering with a reliable service provider who manages the processes from end to end. What is an eCommerce Data Analyst? An eCommerce data analyst is a professional with expertise in data analytics, statistics, mathematics, and technology (computer skills). The person has many roles and responsibilities, such as the following: With such varied expectations, it is easier to work with an eCommerce analytics partner company that has an experienced team of data analysts and AI engineers to manage all tasks effectively and deliver the desired results. How to Choose the Right eCommerce Analytics Partner In today’s digital era, every retail and eCommerce business faces intense competition from others. You are at risk of being outdated if you don’t quickly adapt to the changing conditions and present the best side to your customers. But how will you know what’s trending and what will trend in the coming months? How can you track the fluctuating demands and changing customer requirements? eCommerce analytics is the key. By hiring a worthy partner, you can use accurate data-driven insights to make decisions and boost your business. From supplier identification to inventory management, marketing, distribution, and transportation, you can streamline every aspect of your business with the help of an eCommerce analytics partner. Here’s how to find the right solution partner for your eCommerce business. Strategic Planning and Consulting Strategy is the core of any business. To be a successful eCommerce business, you should have a robust strategy for adopting analytical models, marketing and promoting your business, increasing customer satisfaction, achieving your goals, etc. Your eCommerce analytics partner should have in-depth knowledge and know-how to create a feasible and future-proof strategy to digitally transform your business and streamline it at all levels. Industry-Wide Experience Experience is definitely an important factor as it hints at the company’s ability to understand the nuances of the industry. The trends, developments, requirements, market conditions, etc., of every sector are different. The eCommerce industry is complex due to the extensive number of parties involved. Working with an eCommerce analytics partner who has previously delivered projects from businesses from the same industry provides an assurance that it can handle your specifications and show the results. Technology and Innovation Data analytics involves various technologies and tools. The same can be said for eCommerce. In such instances, you need a partner with access to the best and latest eCommerce analytics tools. The service provider should know which tools are most suitable for your long-term goals and how to integrate them with your systems. Similarly, the company should give prominence to innovation and stay up to date about the new developments. This will give your eCommerce business an edge over competitors and help you enter new markets with greater confidence. Flexible Managed Services The size of your eCommerce business doesn’t matter, but the products and services you provide will definitely impact your brand reputation and customer experience. To run a digital business successfully, you need technology that works 24*7*365. While glitches and downtime cannot be avoided, you can minimize them by having a trusted partner to provide managed IT services. Your eCommerce analytics partner can be your managed service provider, thus ensuring everything goes on smoothly. Credentials and Certifications Certifications denote expertise. An eCommerce analytics company should have proficiency in handling large volumes of data (on-premises and cloud), building data pipelines, and choosing the best third-party app integrations. A company that is a certified partner of Microsoft, Google, AWS, etc., will have the technological know-how and direct access to advanced tools provided by the industry giants. This will make things easier for your business. Diverse Service Capabilities eCommerce analytics trends show that customer-centric businesses have greater revenue and better reputation. A modern-day customer uses different channels to discover your business. A website is no longer enough. You will also need to manage mobile apps, social media sales, upload your products on third-party platforms, and so on. But how do you track your inventory and sales? This can be streamlined by an efficient eCommerce analytics provider while providing real-time reports about the best ways to enhance your customer journey. Data Security and Support When adopting a data-driven model, you should also make sure that all the necessary security measures are followed to ensure that datasets

Yashica Chopra
April 09, 2024
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Traditional Analytics vs. Manufacturing Analytics – Which One to Go for in Industry 4.0

Industry 4.0 involves the digital transformation of enterprises and promotes data-driven decision-making in the manufacturing sector. Here, we’ll discuss the differences between traditional analytics and manufacturing analytics and which is the best choice in Industry 4.0. Industry 4.0, or the fourth industrial revolution, is a hot topic in the sector. Many SMEs and large enterprises have been exploring the advantages of adopting data-driven models, big data analytics, and AI technologies to streamline manufacturing, distribution, marketing, and customer service processes. According to a report by Emergen Research, the global Industry 4.0 market is estimated to touch $279.75 billion by 2028 at a CAGR of 16.3%. Manufacturers from different industries, like automotive, pharma, electronics, FMCG, etc., are investing in IoT (Internet of Things) devices, AI manufacturing analytics solutions, robotics, and other advanced technologies. But what exactly is manufacturing analytics? How different is it from traditional analytics? Which one should you choose when digitally transforming your manufacturing business? What are the benefits of manufacturing analytics? Let’s find out in this blog! How Analytics is Used in the Manufacturing Industry? Before we determine whether traditional analytics or manufacturing analytics is right for your enterprise, let’s first understand which analytics is used in the manufacturing industry. A large amount of data is generated every day across different departments in a manufacturing business. This data can be used to determine historical patterns and future trends to make better and faster decisions. Data analytics in manufacturing helps streamline various internal processes to reduce costs, decrease wastage, increase revenue, and amplify profits. What is Traditional Manufacturing Analytics? Analytics has long been a part of the industry. However, as the name suggests, traditional analytics was done with paper ledgers, abacus, etc. As technology developed, calculators, Excel, Lotus, etc., were used. Statistical models like regression analysis, time series analysis, hypothesis testing, and many more helped businesses analyze historical data to identify patterns and trends. The process, though simple, was lengthy, time-consuming, and had a high margin of human error. What is Manufacturing Analytics? Manufacturing analytics, on the other hand, is a new process that uses the latest data analytical models to deliver accurate, faster, and real-time insights for decision-making. The manufacturing analytics market size was $12.5 billion in 2023 and is expected to reach $29.42 billion by 2030 with a CAGR (compound annual growth rate) of 17.14%. Manufacturing analytics models can be built on-premises or hosted on the cloud through SaaS (Software as a Service) solutions. Manufacturing analytics companies build the necessary data pipelines and third-party connections to streamline workflow in the enterprise. This allows employees to access data and insights in real time and make efficient decisions. It increases transparency, visibility, and flexibility throughout the manufacturing unit. What is the Difference Between Traditional Analytics and AI Analytics? The development of artificial intelligence and machine learning models has brought many changes in the global market. Data analytics is no longer limited to Excel or pivot tables. Today’s manufacturing analytics solutions include powerful AI models that can process large amounts of data in real time and provide near-immediate insights in the form of graphical reports and data visualizations. Let’s consider traditional analytics vs. manufacturing analytics to determine which is the best choice for Industry 4.0. Static vs. Dynamic Traditional analytics is static, while modern AI manufacturing analytics is dynamic. That’s because traditional analytics rely on existing visualizations and predefined questions. If you enter a new question, it requires additional time and resources to find the answer. Moreover, the traditional analytics dashboards don’t automatically update themselves when new data is added. AI manufacturing analytics is dynamic as it comes with a conversational interface. When a query is provided by the employee, the model uses some computing power to provide the answer in a few minutes. Additionally, the dashboards can read and provide the answers in natural language using NLP (natural language processing) technologies. It is similar to using a voice assistant like Siri or Alexa. Efficiency Efficiency is another major differentiating factor between traditional and AI manufacturing analytics solutions. While the former typically provides answers to ‘what’, the latter can also offer insights about ‘why’ and ‘how’. For example, you can not only ask what the sales volume is but also find out why it is less and how it can be increased to achieve your targets. Furthermore, the process involved in traditional analytics makes it difficult for employees when a deadline is looming. It doesn’t offer the required flexibility or scalability to handle varying data volumes. This is not a concern with AI analytics. Manufacturing analytics set up using AI models work seamlessly with any amount of data and can be scaled to suit your business needs. Accuracy The accuracy of the derived insights can have a huge impact on your decisions. That’s why manufacturers who rely on traditional analytics go with a gut feeling or opt for guesswork. The margin of error is high (even a minute mistake in data entry can lead to a different result), which makes them wary of trusting the insights completely. However, manufacturing data analytics is solely driven by data. There’s no need for a hypothesis, influenced by the individual’s viewpoint and perspective. Artificial intelligence doesn’t ignore the elements a human analyst may miss. It can also delve deeper into the datasets to identify patterns that cannot be seen by humans. As long as your data is clean, the insights will be accurate and reliable. Moreover, manufacturing analytics service providers offer data engineering solutions to collect, clean, and store data, which reduces the risk of using low-quality data for analytics. Time There’s an old saying – ‘time is money’. This is applicable even more in today’s world, where everyone wants immediate results. Traditional data analytics is time-consuming and requires a lot of manual effort. A team of data entry employees, data analysts, etc., have to constantly work to add new data to the database, clean it, sort it, and then analyze it. Fortunately, manufacturing analytics doesn’t require as much effort, even though the core idea is the same. Data collection is automated and can be set to occur as frequently as you want. Modern AI analytical tools can process unstructured and semi-structured data with ease and

Kavika Roy
April 08, 2024
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AI Product Development For Emerging Startups

Struggling to stand out in the crowded products and app market? AI product development can be your helping hand. This approach leverages artificial intelligence to create smarter, more engaging products that users love and emerge as market success. Emerging technologies like artificial intelligence (AI) are transforming how companies operate, and those who leverage AI first stand to gain a significant edge in the marketplace. But how can you leverage AI to create groundbreaking products? The answer lies in AI product development. This strategic approach involves integrating AI capabilities into your product offerings, creating intelligent solutions that address real-world problems. A study by McKinsey Global Institute revealed that AI could generate an additional $13 trillion in global economic activity by 2030. Don’t you think, your enterprise should be ready to be a part of this economic boom? Building AI-powered products may seem complex, but when experts jump in, it’s not that complex! AI product development agencies specialize in helping startups like yours navigate the AI Product development journey, phase by phase offering these: AI Consulting Services: Gain strategic guidance on how to best integrate AI into your product roadmap. AI Product Strategy: Craft a winning plan that outlines your AI product’s goals, functionalities, and target market. AI product development team: Access a team of skilled engineers and data engineers(on a contractual basis or full-time) to bring your vision to life. AI product development tools: Utilize top-of-the-edge platforms and resources to streamline your AI product strategy in real-time. The video below provides a better understanding of the AI product development process. It showcases the expertise of DataToBiz, a leader in affordable AI product development. In Conclusion With so many AI product development service providers available, choosing the right partner is crucial. Look for an agency with a strong track record in your specific industry and experience in tackling common AI product development challenges. They’ll help you navigate the entire process, from defining your AI product development plan to ensuring seamless integration and ongoing maintenance. So, to get the process right for your enterprise, get in touch with an AI development agency today and see how AI can revolutionize your product line and stage it up for successful market launch and sustenance. Fact checked by –Akansha Rani ~ Content Creator & Copy Writer

Manpreet Kaur
April 02, 2024
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10 Ways AI Consulting Can Future Proof Your US Enterprise

AI consulting is a game-changer for US businesses, with nearly 77% jumping on board around the world. It’s making things smoother, smarter, and safer, boosting creativity and keeping companies on the growth track. Did you know that a whopping 42% of companies are already dipping their toes into the waters of AI? And get this – over 50% are gearing up to dive right in and incorporate AI technologies in 2024! That means a staggering 77% of companies are either already using AI or seriously considering it. AI is not just the future, it’s happening now, and businesses are racing to stay ahead of the curve. The push for the AI implementation process to embrace digital change can’t be overstated. US enterprises must go beyond just thinking about this concept and start using new technologies and methods that meet the changing demands of customers. Taking an active approach to digital change, especially with the help of artificial intelligence consulting services, distinguishes progressive companies. This makes them more attractive to customers looking for fresh and tailored experiences. Why Does Your Business Need AI Integration? A business’s prosperity is deeply rooted in the satisfaction of its clients. This entails a profound understanding of the customer from the get-go, positioning oneself ahead of competitors all vying for the same lucrative opportunities where consumer spending is most generous. Amidst these considerations, the pivotal role of digital transformation becomes undeniable for businesses aiming to flourish and sustain their operations. This has led to a heightened demand for digital transformation consulting. Within this realm, artificial intelligence consulting services are broadening their impact, offering a plethora of remarkable solutions across various domains. 10 Ways Artificial Intelligence Consulting Services Can Enhance Digital Success Artificial intelligence consulting services provide numerous advantages for achieving digital success. By streamlining operations with automation and utilizing predictive analytics for well-informed decisions, businesses can gain a competitive edge, foster innovation, and ensure enduring growth in today’s dynamic market environment. Here are 10 ways through which artificial intelligence services and solutions can amplify digital success for your organization: 1. Better Customer Insights with Artificial Intelligence With AI, organizations delve deep into customer personas, going beyond surface-level similarities to truly understand who their customers are. This profound understanding enables the development of precise persona-based marketing strategies, ensuring customer loyalty and satisfaction from the outset of digital transformation projects. AI empowers companies to anticipate potential issues and barriers, allowing them to address challenges proactively. With the right applied intelligence solutions in place, organizations can navigate their digital transformation journey seamlessly, without encountering any obstacles. Artificial intelligence consulting services emphasize the importance of AI in ensuring a smooth transition, highlighting the role of tools like Power BI. By embracing AI-driven insights, businesses pave the way for enhanced customer experiences and sustained success in the competitive landscape. 2. Forecasting Consumer Trends and Behaviors AI-driven algorithms are now enabling the creation of models that can predict customer behavior and preferences. These models, known as propensity models, use predictive analytics and machine learning to forecast the likelihood that a person will buy certain products. This prediction is based on their past interactions with marketing efforts from various companies, guiding them towards actions like buying more products or different ones. Such models have been incredibly successful in keeping customers and reducing the number who leave because they not only help maintain existing customers but also attract new ones through improved targeting. By understanding each individual’s personality, communications can be more tailored and relevant. AI consulting companies can help enhance the overall effectiveness of marketing strategies. 3. Data-Driven Strategies for Marketing/Campaign Success AI implementation in business is a game changer for marketing in various ways. This approach helps in understanding what customers want and predicting what they will do next, all done by machines without needing people to step in. When a company that focuses on digital changes uses AI to dig deeper into data, it makes sure to find the most useful and precise information. This technology is a game-changer for companies like Facebook & YouTube, which depend on advertisements. It gives them better ways to reach the right people, leading to more efficient spending on things like buying ads or answering customer questions. These artificial intelligence implementation examples exhibit a shift to more efficiency, all thanks to AI. 4. Enhanced Unified Security Measures AI is transforming the way organizations enhance their security measures as part of their digital growth plans. This shift is crucial because the dangers to individual privacy and personal data are increasing. Most strategies for digital growth focus on improving software and workflows rather than prioritizing the safety of people’s information. This shift in focus is driven not just by the growing needs of businesses. It also stems from the heightened awareness and concern among individuals, shaken by numerous well-publicized data breaches. These incidents have exposed sensitive data on the internet, often without the victims being aware until they read about it in news articles. Here, an AI consulting company can help you establish more secure systems. 5. Integrated Customer Support The future of customer support is being driven by AI technology. According to a study by Gartner, businesses that incorporate AI into their support centers could see a 25% boost in efficiency. This improvement also transforms the way customers receive help. This is particularly relevant in situations where data might be scarce or not fully leveraged due to the ongoing shift towards digital methods in both online shops and physical stores. In such cases, people can receive assistance remotely, which enriches the understanding of their needs and allows for more personalized service. In major world economies like the USA, the UK, and India, artificial intelligence consulting services companies focusing on digital innovation are exploring how AI can enhance customer service across various sectors including AI implementation in healthcare and implementation of AI in agriculture. They are doing this by monitoring customer interests and employing chatbots for communication. 6. Improving Your Decision-Making Skills In many companies, choosing

Yashica Chopra
March 30, 2024
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Guide 101- Choosing the Right Supply Chain Analytics Partner

The increasing complexity of supply chains in today’s world makes it even more necessary to adopt data-driven models for efficient decision-making. Here, we’ll discuss the role of a supply chain analytics partner and how to choose the right one for your business. The supply chain is one of the top priorities for a business. Disruptions can not only affect your supply chain but also lead to loss of revenue and diminished customer satisfaction. It’s 2024, and the right time to revamp your supply chain using data analytics, artificial intelligence, and automation. According to a PwC survey in 2023, almost 86% of businesses agreed that investing in technology is necessary to handle supply chain risks. Moreover, a report by Deloitte shows that around 70% of emissions come from the supply chain. This can be tackled by making your supply chain sustainable and transparent through digitalization and technology adoption. Using analytics can strengthen your supply chain management processes. Businesses can work with a reliable supply chain analytics partner to overcome the challenges and achieve long-term goals. But how would you identify and select the right supply chain partner? What sets the best apart from the rest? How should you choose the right partner to digitally transform your supply chain? Let’s find out in this blog. What are the 4 Types of Supply Chain Analytics? Supply chain analytics is the process of collecting, storing, and analyzing large datasets to derive meaningful and actionable insights. These insights are presented as data visualizations through graphical reports and used to make faster and better decisions. When you choose a supply chain analytics partner, make sure they offer the following analytical solutions. Descriptive Analytics Descriptive analytics involves KPIs (key performance indicators) and historical data to describe the various supply chain events of the past. Predictive Analytics Predictive analytics for the supply chain is where real-time data is collected from different sources and analyzed to determine the possible future outcomes, trends, customer demands, etc., that will affect the business. Prescriptive Analytics Prescriptive analytics plays a vital role in decision-making as it provides insights into the areas for improvement and the best way forward to tackle a situation while minimizing risk. Cognitive Analytics Cognitive analytics and diagnostic analytics are similar in a way. Both deal with determining the root cause of a problem and finding a comprehensive and long-term solution for it. It helps take appropriate steps to resolve supply chain challenges. What to Look for in a Supply Chain Analytics Partner The right supply chain analytics partner can handle the various processes from end to end and offer continuous support to enhance your supply chain across all levels. The service provider should take care of everything from strategy formation to deploying analytical models and creating adequate supply chain risk management policies. So, what are the criteria for a supply chain partner? How do you identify the best analytical company to become your long-term partner? Here’s how. Types of Services A supply chain analytics partner has to take up many roles and responsibilities. It has to provide a robust strategy to digitally transform the supply chain. It should help identify issues in planning and designing a reliable analytics model to integrate with the existing business systems. The partner should also set up custom supply chain dashboards to deliver on-demand analytics and insights in real-time. Similarly, it should offer embedded analytics and business intelligence visualizations based on your requirements. Most importantly, you should have the flexibility to choose the services you want – consulting, end-to-end, customized, or rapid analytics as per your needs. Access to Technology Technology is another essential aspect to consider when choosing a supply chain analytics partner. For example, IoT (Internet of Things) devices and RFID tags can make your supply chain more efficient. Does the company offer artificial intelligence and machine learning-based analytical models? Can the service provider use AI to automate your supply chain tasks? Does it have the necessary tools to deploy transportation management systems (TMS) for real-time tracking and alerts? Can you invest in robotics for warehouse management? Domain and Industry Expertise An increase in demand for supply chain analytics has led to more companies offering similar services. However, not every company has a team of expert analytics, AI engineers, and data scientists to cater to your diverse and complex requirements. The supply chain analytics partner should have the necessary expertise in using the latest technology as well as understanding the minute aspects of the supply chain industry. Additionally, the company should have good knowledge about your niche. This will ensure that the company knows your competitors, challenges, market trends, opportunities, risks, customers, etc., to create solutions that align these aspects with your business goals. Experience and Project Portfolio The supply chain analytics partner should have the necessary experience in delivering complex supply chain projects to different clients. Ask for their project portfolio to determine if the company can handle your specifications. For example, the analytics partner should help you achieve your business goals like supply chain cost reduction, inventory optimization, increased sales and revenue, minimized losses, and enhanced customer satisfaction. An ABI research study predicts that over 4 million robots will be used in around 50,000 warehouses around the globe by 2025. So, if you want to use robots in your warehouse, your supply chain analytics partner should help you set up the necessary automation and workflows. Communication and Transparency Another essential aspect to consider is how well the supply chain analytics company can communicate and interact with your teams. Is it easy to approach the analytics working on your project? Do they send regular updates? Do they explain the concepts in simple terms and answer your questions satisfactorily? Are they transparent about what they offer and what they don’t? Do you have a clear idea about the pricing? Have you had to pay for hidden charges at the last minute? A worthy supply chain partner will keep things simple and uncomplicated. You will know the pricing and services offered in advance. There will be little to no confusion. Maintenance and Support Your supply chain already has numerous vendors,

Yashica Chopra
March 29, 2024
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Guide 101 – Choosing the Right Manufacturing Analytics Solutions Partner

Every manufacturer can harness the full potential of business data by implementing data-driven models for decision-making. Here, we’ll discuss the need for data analytics in manufacturing and the factors to consider when choosing the right manufacturing analytics solutions partner. Manufacturing analytics is the process of using operational and supply chain data, advanced technologies, and statistics to identify patterns and trends, forecast future demand, and derive meaningful insights for day-to-day decision-making. According to GII Global Information, the big data analytics in the manufacturing industry market size is expected to be $8.93 billion in 2024 and predicted to grow at a CAGR (compound annual growth rate) of 21.60% to touch $23.72 billion by 2029. The same report mentions a McKinsey study, which showed that a data-driven business is twenty-three times more likely to attract clients and nineteen times more likely to be profitable than its competitors that don’t use analytics. A recent report by Deloitte suggested that 86% of the respondents (manufacturing executives) believe that smart solutions will drive competitiveness in the coming five years. In such instances, an enterprise from the sector needs to invest in manufacturing analytics solutions and participate in Industry 4.0. This is best achieved by partnering with an analytical solution provider and digitally transforming the business processes under expert guidance. But how would you identify and select the right supply chain partner? In this blog, we’ll discuss the guide to choosing the right manufacturing analytics solutions partner and the role of analytics in the industry. Choosing the Right Manufacturing Analytics Solutions Partner Digital transformation in manufacturing is a continuous and long-term process that requires regular monitoring to adopt the latest technologies and achieve the expected results. A manufacturing analytics solutions partner provides end-to-end services to build, develop, implement, customize, integrate, and maintain the data models. However, not all service providers are suitable for your business. Choosing the right analytics partner is crucial to be successful in your business. Here are a few factors to consider when hiring a manufacturing analytics solutions partner in the industry. Experience in Data Analytics and AI The increasing demand for data analytics has resulted in many new service providers entering the market. However, individual experience is necessary when building AI and ML analytical models for a manufacturing enterprise. Enquire the details about the team members’ experience in the field before hiring a manufacturing analytics solutions partner. Knowledge and Expertise in Industrial Processes Another important aspect to consider is whether the company has knowledge of your industry (manufacturing) and niche based on the products you produce. This is to ensure that the manufacturing analytics solutions partner understands the industry-wide practices, international regulations, etc. when customizing the analytical models and setting up the dashboards. Timely Delivery and Project Completion Manufacturing analytics implementation is a time-consuming process. The service provider has to build data pipelines, streamline workflows, automate processes, customize dashboards, and set up third-party integrations. However, you want a partner who can complete the project as per the agreed-upon schedule and deliver the insights to your employees. Compatibility in Vision and Execution Compatibility in vision is another important factor. The service provider may have the necessary experience and technology but if they cannot understand your business vision or don’t align with your processes, it can create clashes and discord between teams. Choose a manufacturing analytics solutions partner who is flexible enough to accommodate your requirements and encourage better collaborations. End-to-End Tailor-made Solutions Does the company offer a complete scale of solutions to tackle end-to-end changes in your business processes? Partnering with an end-to-end service provider will help ensure that the entire project will be handled by a single company. You don’t have to work simultaneously with different analytical and AI teams to transform your manufacturing processes digitally. Furthermore, you can have your own manufacturing analytics platform developed by the company. Agility, Flexibility, and Scalability Flexibility and scalability are necessary for enterprises to thrive in the ever-changing market conditions. That means your service-providing partner should be equally flexible and scalable to accommodate your dynamic requirements. This will empower you to take proactive measures to grab more opportunities. Meaningful and Accurate Real-Time Insights Real-time insights help make quick decisions. However, the insights have to be accurate to minimize the risk of incorrect decisions. The analytics service provider should prepare your data and improve its quality to provide meaningful insights. Partner with a manufacturing analytics solutions partner who also takes care of data engineering and management. Long-Term Maintenance and Support Cloud-based manufacturing analytics are a part of digitally transforming your enterprise. The insights and data visualizations can be accessed by your employees through the personalized dashboards. However, the systems need regular monitoring and periodic updates to work without glitches. Choose a service provider who can become a long-term maintenance and support partner. Pricing and Transparency Finally, the pricing models offered by the company should also be considered. A reliable manufacturing analytics solutions partner will be transparent about the prices and state the additional costs upfront. Moreover, you should have the freedom to choose a pricing model based on your project specifications. How Data Analytics is Used in the Manufacturing Industry Data analytics in the manufacturing industry combines smart manufacturing solutions like data engineering, IoT (Internet of Things) devices, predictive analytics, big data analytics, data visualization, machine learning, edge computing, automation, etc., to enable smarter, agile, and scalable factories with greater productivity. So, what insights you can provide with analytics to the manufacturing industry? Here are a few: Production Management The insights from manufacturing analytics can help your factory teams adjust production quantities in real time based on the stock left in the warehouse, the stock at the factory unit, and the market demand for the product. Additionally, you can identify the root cause for low quality in products, product failures, etc. Industrial IoT (IIoT) analytics helps with production and quality management. Supply Chain Management The supply chain is crucial for a manufacturing company. In times when disruptions can lead to major losses, it is essential to streamline the various elements of the supply chain and eliminate unwanted steps. Be it order management, supplier management, demand forecasting, setting up early warning

Ishan Vyas
March 27, 2024
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Power of AI and ML Helps in Elevating Data Quality

AI and ML redefine data quality by dynamically synergizing through automation, predictive analysis & seamless integration. Companies adopting these advancements benefit from informed decisions & competitive advantage in today’s data-driven landscape. Here, we’ll discuss how AI & ML helps in Elevating Data Quality. Introduction In today’s data-driven landscape, the quality of information is critical to determining your business success. The bigger your data set is, the more complex they are to utilize. Here comes the importance of ensuring data accuracy, consistency, and reliability. With advancements in science and technology, Artificial Intelligence (AI) and Machine Learning (ML) have become pivotal allies in the pursuit of data excellence. Using ML & AI applications, an organization’s data science and analytics capabilities are improved to make well-informed decisions faster involving complex facts and figures. According to Gartner, enterprises on average lose 12.9 million $ annually due to poor data quality. This shows the significance of empowering data excellence. This blog explores how AI and ML are revolutionizing data quality, to enable companies to stay adaptable and perform predictive analysis to formulate intelligent decisions. Listed below are a few ways to understand how these two immersive technologies can maintain data hygiene to avail purposeful and profitable results. Automated Data Cleansing and Validation for Unrivaled Accuracy AI and ML have revolutionized data cleansing and validation processes via automated tools and intelligent technologies. Gone are those days when decision-makers suffered due to process delays and human-induced errors. With the advent of smart algorithms identifying and rectifying data discrepancies, data cleaning has become more efficient. However, for specific use cases, traditional data cleaning methods might not be sufficient, especially when dealing with limited datasets. This is where Generative AI comes into play. Generative AI techniques can be used to artificially generate synthetic data that resembles real data, addressing the limitations of small datasets. This can be particularly beneficial for training machine learning models that require a significant amount of data to perform optimally. By leveraging Generative AI for data augmentation, organizations can enhance the effectiveness of their data cleansing and validation processes. Predictive Data Quality Analysis to Anticipate Challenges Before They Arise Advanced ML models forecast potential data quality issues and suggest preemptive actions. According to Gartner, 70% of organizations by 2025 will focus on small and wide data making analytics more significant and reducing data feed for AI. Predictive analytics can detect and prevent defects early, reduce rework and technical debt, automate testing, prioritize tasks, minimize testing cycles, optimize test coverage, enable faster feedback loops, and enable data-driven decision-making. It also has many benefits in facilitating agile development and DevOps practices, The rising impact of AI in Software Development extends beyond automation. In the current competitive business environment, analyzing predictive data quality has become a game-changer. Autonomous Data Monitoring As a Proactive Approach to Data Quality A perfect blend of AI and ML ensures autonomous data quality monitoring to eliminate unexpected data issues. AI-driven tools continuously analyze data streams and flag anomalies as they occur. Maintaining data quality improves decision-making. Considering this in mind, companies are focusing on applying AI and ML techniques to establish data excellence uninterruptedly. Seamless Data Linkage and Integration to Unlock Insights from Silos Data integration has long been a challenge, leading to fragmented information and suboptimal insights. In the current era of data science and business intelligence, AI and ML technologies facilitate seamless data linkage across various sources. With advanced and improved data integration practices, key stakeholders can avail effective insights to devise futuristic strategies. Eradicating Bias For Ethical and Fair Decision-making AI and ML have made significant strides in addressing bias in data. Algorithms presently are designed to detect and mitigate bias from data sources. Market studies have shown that investments in AI and ML techniques have increased and are enabling enterprises to develop ethical decisions. Improving data quality for fair decision-making is crucial in an era where 70% of IT decision-makers do not rely on the validity of the available data. Conclusion As we embrace the potential of AI and ML in 2024, the impact on data quality is undeniable. Automated data entry and ingestion powered by AI facilitates identifying missing fields for necessary data inputs. With intelligent data capture techniques, maintaining validity is ensured. That’s exactly where you might need a reliable AI & ML development company to make sure requirements are met perfectly. Gartner indicates the transformative effects of AI-powered data cleansing, predictive analysis, real-time monitoring, data integration, and bias eradication. With these technologies at the helm, businesses can make better-informed decisions, uncover hidden opportunities, and unlock the true potential of their data. Fact checked by –Akansha Rani ~ Content Creator & Copy Writer

Pawan Chabra
March 26, 2024
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AI for Travel Industry: Key Aspects to Consider

Explore the transformative impact of AI on the travel industry, from personalized recommendations to enhanced customer service. Uncover key trends, challenges, and opportunities as travel companies embrace AI to create more efficient and sustainable experiences for travelers worldwide. The impact of Artificial Intelligence (AI) has been felt far and wide, reshaping how businesses operate and interact with customers. In this era of technological advancement, the travel industry stands at the forefront of AI adoption, leveraging its capabilities to enhance customer experiences and streamline operations. As per Statista, the worldwide market for AI in travel reached a valuation of $81.3 billion in 2022. It is projected to expand significantly, with estimations indicating a growth of $423.7 billion by 2027. This growth trajectory suggests a Compound Annual Growth Rate (CAGR) of 35% over the period from 2022 to 2027. This exponential growth highlights the increasing recognition of AI’s potential to revolutionize the travel sector. The travel industry, characterized by its dynamic nature and constant evolution, has swiftly used AI technologies to stay ahead. From personalized recommendations to seamless customer service, AI for travel industry is reshaping every aspect of the trips and journeys around the globe. Because an increasing number of travelers are utilizing AI for travel recommendations and booking, there is growing acceptance and demand for AI-driven solutions within the travel sector. AI-powered price optimization strategies have been shown to potentially boost travel companies’ revenue, highlighting the tangible benefits of integrating AI into business operations. Companies like Google, with its AI-powered travel services such as Google Flights and Google Maps, has introduced innovative solutions for itinerary planning and navigation. Travel agencies like Expedia and Booking.com have invested heavily in AI-driven recommendation engines and customer service platforms to enhance user experiences. Additionally, startups specializing in AI for travel industry, such as Hopper and Lola.com, have disrupted traditional travel models with predictive pricing algorithms and personalized booking assistance.

Pawan Chabra
March 22, 2024
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6 Top Myths of Artificial Intelligence Consulting Services (Debunked)

Uncover the truth behind myths surrounding Artificial Intelligence Consulting Services, showcasing their accessibility, affordability, and potential benefits for businesses. From dispelling notions of exclusivity to highlighting the transformative role of AI in product development, businesses are encouraged to embrace AI opportunities for future success. Defined as the simulation of human intelligence processes by machines, Artificial Intelligence (AI) holds the promise of unlocking unprecedented efficiencies, insights, and innovations across diverse sectors. According to a report, 59% of enterprises are currently engaged with AI and plan to expedite and enhance their investments in this technology. In this era of digital transformation, the role of AI consulting services is very critical. These services encompass a spectrum of solutions tailored to harness the power of AI for business growth and optimization. Contrary to popular misconceptions, AI consulting is not limited to tech giants but extends its benefits to organizations of all sizes. A study found that AI improves employee productivity by an impressive 66%, highlighting its accessibility and efficacy across industries. As businesses strive to innovate and stay competitive, integrating AI into product development processes becomes imperative. From enhancing user experiences to optimizing operations, AI product development holds the potential to drive sustainable growth and customer satisfaction. Thus, understanding the interplay between Artificial intelligence consulting services and product development is essential for navigating the evolving business landscape effectively. 6 Top Myths of Artificial Intelligence Consulting Services Several myths often cloud perceptions and hinder businesses from fully embracing the transformative potential of AI. Here are the top 6 myths related to Artificial Intelligence Consulting Services: Myth 1: AI Consulting Services are Only for Tech Giants The myth that AI consulting services cater exclusively to tech giants arises from a common misconception that only large corporations possess the resources and infrastructure to leverage AI effectively. This notion often leads smaller enterprises to believe that AI solutions are beyond their reach, assuming that they lack the financial means and technical expertise required for implementation. Consequently, many businesses, particularly small and medium enterprises (SMEs), may hesitate to explore Artificial Intelligence Consulting Services, fearing that such investments are reserved for industry giants. According to a recent survey by PwC, 72% of executives are looking at AI as the business advantage of the future, indicating a growing recognition of AI’s value among businesses of various sizes. Many AI consultancies offer tiered packages specifically designed to meet the needs and budgets of mid-sized companies, debunking the notion that artificial intelligence consulting services are only accessible to tech giants. For example, several AI consulting companies offer affordable AI solutions specifically designed for smaller companies, enabling them to leverage AI technology to drive innovation and competitiveness within their respective industries. Myth 2: AI Consulting Services are Expensive and Only for High-budget Projects A common misconception surrounding artificial intelligence consulting services is the belief that they are prohibitively expensive and exclusive to high-budget projects. However, this myth fails to consider the evolving AI technology and consulting practices ecosystem, which have become increasingly accessible and cost-effective. The SBEC survey reveals that approximately 48% of small businesses adopted AI tools within the last year, showcasing the widespread adoption of AI solutions across businesses of varying sizes and budgets. Additionally, the availability of affordable AI consulting companies further debunks this myth. AI consulting companies recognize the diverse financial capabilities of their clients and offer flexible pricing models to accommodate different budget levels. This flexibility ensures that businesses can access the expertise and support they need to implement AI solutions effectively, regardless of their budget size Myth 3: AI Consulting Services Replace Human Labor Entirely The myth that AI consulting solutions aim to replace human labor entirely arises from a fear of automation and job displacement. Some believe that AI technologies will make human workers obsolete, leading to mass unemployment and economic disruption. This misconception stems from a narrow understanding of AI as a substitute for human labor rather than a tool for augmenting and enhancing human capabilities. AI technologies are designed to complement and amplify human capabilities, enabling more efficient decision-making and problem-solving processes. For example, in healthcare, AI-powered diagnostic tools assist medical professionals in analyzing patient data and identifying potential health issues, but the final diagnosis and treatment decisions remain in the hands of trained physicians. By integrating AI systems with human expertise, businesses can leverage the strengths of both to achieve optimal outcomes. Studies indicate that automation and AI technologies have the potential to create more jobs than they replace. For instance, a McKinsey report estimates that only less than 5% of occupations can be fully automated, suggesting that most tasks will still require human input and oversight. Additionally, a World Economic Forum report predicts that while 85 million jobs may be lost by 2025 due to automation, 97 million new jobs will be created. Myth 4: Artificial Intelligence Consulting Services Guarantee Immediate Results Some businesses may believe that implementing AI solutions will instantly solve all their problems and lead to immediate improvements in productivity, efficiency, and profitability. This myth is perpetuated by vendors and consultants who may oversell the capabilities of AI without adequately addressing the complexities and challenges of implementation. As a result, businesses may expect quick and effortless outcomes from Artificial Intelligence Consulting Services, only to be disappointed when results fall short of expectations. Businesses must understand that AI implementation is a journey that requires careful planning, testing, and iteration to achieve meaningful outcomes. Studies show that AI projects often fail to deliver the expected value, with a study by Deloitte finding that AI projects can deliver the anticipated benefits in for around three years. Setting unrealistic expectations for immediate results can lead to disappointment and disillusionment with AI technology. Successful AI implementation requires a holistic approach that encompasses not only the initial deployment but also ongoing monitoring, optimization, and adaptation to evolving business needs. By prioritizing long-term strategy and continuous improvement, businesses can maximize the value derived from artificial intelligence consulting services and ensure sustained success. Myth 5: AI Consulting

Yashica Chopra
March 20, 2024
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Managing Supply Chain- In-house Team v/s Analytics Platform

Data is the cornerstone of every business and an important decision-making tool, especially to streamline the supply chain. Here, we’ll discuss the need for supply chain analytics and whether you should opt for an in-house team or pay for an analytical platform. The supply chain is vital for a manufacturing business and affects brand visibility and customer experience. Managing the supply chain is essential to streamlining your business operations and taking the products to customers at the right time. However, enterprises can no longer rely on outdated technologies and methods to handle modern-day problems. For example that’s well known, the COVID-19 pandemic led to severe supply chain disruptions around the globe. Such challenges can be overcome by adopting data-driven strategies and solutions to effectively manage the supply chain. Many businesses ask the same question – is business analytics good for supply chain management? Yes! Absolutely. In fact, supply chain analytics is a perfect solution to clear the various roadblocks that affect your success in competitive markets. A McKinsey report shows that an average supply chain has been digitalized by 43%. It indicates that many enterprises are quickly adopting analytics and business intelligence solutions to digitally transform their supply chains and make data-driven decisions. Similarly, the global supply chain analytics market is expected to touch $9925.25 million by 2027. So, how should you implement supply chain analytics in your business? What is the best way to increase supply chain efficiency? Does the in-house analytics team have an advantage over using an analytical platform? Let’s find out in this blog. What is the Role of Data Analytics in Supply Chain Management? Data analytics is a crucial part of supply chain management and has a diverse role. It helps optimize procurement, inventory, production, logistics, and costs. Supply chain analytics unlocks the potential of your raw business data to seamlessly navigate the complexities of supply chains. By analyzing the supply chain data, you can predict future demand, determine production quantities, and proactively address disruptions to minimize losses. It is helpful for the following: You can achieve your goals by building an in-house supply chain management team that uses data analytics and business intelligence to make data-driven decisions and contingency plans. How to Improve Supply Chain Efficiency – In-house Team v/s Analytics Platform When following proven strategies to improve supply chain efficiency using analytics, you should choose between an analytics platform and an in-house team. While the cost of a supply chain analytics platform is convenient, you will get higher ROI in the long term by working with expert partners. For example, when you invest in an in-house team, you can work with experienced service providers with access to the latest technologies. You can benefit more from the end-to-end solutions offered by the staffing and managed service providers. They take care of building the model, deploying it, and integrating it with your existing systems, along with long-term maintenance, support, and upgrades. Create a Supply Chain Strategy Have a strategy in place before you make changes to your business processes. Create a comprehensive supply chain management strategy to determine the core areas for improvement. This gives you a better understanding to choose the analytics team and platform and the best method to adopt the data-driven models and digitalization tools in your business. Invest in Supply Chain Analytics Team Experts are in high demand because they know how to get the job done and deliver the expected results despite the challenges. Even if you plan to customize a supply chain analytics platform and embed the dashboards, you need an in-house team to streamline the process and train your employees to use the insights. It is easier when you partner with a service provider to hire a team that will work with your business for as long as you want. Build Good Relationships with Suppliers Suppliers are a key element of your supply chain. Maintaining a good relationship with them will ensure better prices, fewer delays, and exceptional service. The supplier will be willing to the extra mile when you have a strong relationship with them. However, it is equally important to choose the right supplier before you strengthen your relationship. Analytics help in identifying worthy suppliers to add to your network. Streamline and Automate the Process An advantage of in-house analytics is the ease of customizing the systems and automating the processes. The experts can make the necessary changes to accommodate third-party integrations that can increase efficiency, reduce the risk of errors, and handle supply chain velocity at any given time. Be it invoicing, labeling, shipping, or order processing, various tasks can be automated using artificial intelligence tools. Expand the Supply Chain Visibility Increasing transparency and visibility can make your supply chains more efficient. It allows you to make accurate and proactive decisions based on reliable and real-time data insights. For example, you can ask the production team to increase or decrease production based on the inventory levels and market demand. This helps avoid stockouts and eliminates the chances of paying additional carrying costs due to logistics issues. Adopt the Right Technology Technology is necessary to simultaneously handle numerous processes. However, choosing the right technology is essential to achieve your goals. What kind of inventory management software do you need? What features should the supply chain management software provide? Are the various tools compatible with your databases? Do you want to invest in multiple tools from different vendors or rely on an in-house team that builds the necessary data pipelines to streamline the workflows? Implement Sustainable Practices Sustainability is the theme of the season. Governments and customers are looking at promoting businesses with sustainable and green practices. How can you minimize your carbon footprint and implement sustainable supply chain practices in your business? Here, you will benefit from the expertise of an in-house team that works on your project and understands your business requirements. Continuous Monitoring and Improvements Improving your supply chain efficiency is not a one-time process. It requires continuous monitoring to identify weak areas and make the necessary changes. The expert team can set up automated monitoring to