Page 9 – DataToBiz

Kavika Roy
March 04, 2024
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Data Warehousing in Finance – 5 Ways a Data Warehouse Can Improve Your Financial Forecasting

This blog covers basic information about what exactly data warehousing in finance is and the top 5 ways a data warehouse/storage can improve your financial forecasting and reporting! A data warehouse is a centralized storage that allows you to collect, track, and analyze critical data points over time to run your financial operations efficiently and make sound decisions. When you have a large amount of financial data to manage, process, and analyze, it requires effective and fluid processes that use dependable data transfer services to help you enhance your operations. Data warehousing can be extremely helpful in situations like these. What is a Data Warehouse? A Data warehouse is a data management system that stores huge amounts of data for future processing and analysis. Consider it a big storage where trucks (i.e., source data) release their data. The data is then sorted into rows and rows of well-organized shelves that make it easy to find the exact information you’re looking for later. One of the primary advantages of data warehousing is that it provides a comprehensive view of an organization’s financial health. Businesses can make better decisions about where to allocate their resources if they have access to all essential data in one place. The main aim of data warehousing is to create a collection of historical data. This data can be utilized to discover patterns and trends, allowing organizations to make more informed decisions about future operations. This makes obtaining data for powerful insights and other Business Intelligence (BI) uses simple. A data warehouse is a critical component of business intelligence. That broad word refers to the information architecture that modern businesses utilize to track past successes and failures and impact future decisions. Key Takeaways: Why is Data Warehousing Important in Finance? Data warehousing is vital in the financial sector due to the vast volume of data being handled and analyzed. Financial institutions create a large amount of data from their day-to-day operations, which can be tremendously valuable if used properly. Data warehousing can help businesses in the financial sector in several ways: Data Warehouse Use Cases in Finance Listed below are some of the applications for employing Data warehousing in finance industry: Customer Data Management: Be it manufacturing, retail, or other industries, companies must evaluate consumer data to improve service quality and develop long-term client relationships. A data warehouse assists them in understanding customer behavior and developing personas by collecting data from various sources and facilitating relevant analytics. It enables them to better manage sales and marketing efforts and find cross-sell opportunities. Pattern Discovery: To gain accurate insights, financial services companies must constantly analyze crucial success and failure indicators. To understand how well the business’s financial health is, it must track customer deposits, loans, needed reserves, and other factors. They also need to evaluate patterns over time, and a data warehouse makes both current and historical data available in one location. Accurate data analysis can also help uncover fraud. Minimize risks: Data warehousing in Finance for eCommerce, retail, travel, or other industries automates the risk management process while monitoring the risks that various entities, such as investors and competitors, may pose. It also protects data by not disclosing comprehensive information and limiting employee access to specific roles. 5 Ways a Data Warehouse Can Improve Your Financial Forecasting 1. Improved overall efficiency Several businesses collect huge volumes of data from various sources (including price, customer information, purchase trends, vendor information, and so on). The enormous amount of information might be overpowering, rendering it all useless. Storing this data across numerous platforms is costly. A data warehouse solves this problem by bringing order to chaos. It saves and organizes data in a centralized repository, allowing businesses to identify and extract possible benefits using appropriate business intelligence tools. Data warehousing in Finance makes it easier for business users to: Financial big data warehouses also help businesses save money on data management by consolidating data into a single location rather than maintaining it across various platforms and paying multiple vendors. 2. Time management and prompt decision-making Effective decision-making frequently requires input from a variety of people within an organization, such as financial analytics, marketing teams, management, and others, as well as data from many sources. Previously, decision-makers had to cross-check numerous data banks across multiple platforms, which frequently required assistance from technical support. Simply obtaining this information would be a time-consuming, labor-intensive procedure. However, data warehouses combine data from many sources into a single, easily accessible repository, making business-critical information available. When finance data companies need to make quick, effective choices, all relevant parties may quickly access the data in one location. Numerous businesses may easily and quickly detect significant business trends and intelligence using AI-powered algorithms, allowing them to make more profitable decisions. 3. Data quality management Nowadays, businesses generate data at a quick pace. Data must be managed to ensure that it is current, accurate, and in the format needed by analytics platforms. Managing data quality can be time-consuming, especially when information is scattered across multiple platforms. However, merging data into a data warehouse system makes data cleansing simple and cost-effective. You may compare data from numerous sources, eliminate errors or duplicates, and guarantee that your data is reliable and consistent. Furthermore, most data warehouse technologies feature sanitization algorithms that automate the process, eliminating the need for costly data quality management programs. 4. Improved data security In the face of cybersecurity threats, securing business-critical data has become a demanding task for all modern enterprises. This is especially true for firms that handle sensitive or proprietary data, such as banks and pharmaceutical industries. Storing data in a single, centralized location makes it easier to protect than monitoring and managing various data banks and platforms. Many data warehouse technologies incorporate capabilities that improve data security both inside and outside of the business. Users can build user groups to control permissions and data access for specific individuals/departments within an organization. Data warehousing in Finance frequently utilizes encryption techniques to secure data at

Kavika Roy
February 29, 2024
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The Strategic Role of Hotel Data Analytics in the Hospitality Industry

Recently, as more sectors have come to understand the value of data analytics, we have seen an increase in its adoption. In this regard, the hotel industry is not excluded. Through hotel data analytics, hotels and hotels, and hospitality businesses can gain knowledge of client behavior, preferences, and patterns that allow them to personalize their offerings, improve service quality, increase operational efficiency, and optimize income. According to the HotelTech Report, 77% of hoteliers view data analytics as a priority. Increasing guest loyalty and enhancing guest experience are the main objectives for hotel data analytics. Other tech-enabled requirements involve data security, multichannel reservation support, and physical safety development. Data Analytics in the Hospitality Industry Data Analytics is the process of gathering, evaluating, and applying data to make decisions that will benefit the operations and financial success of hotels, restaurants, and other hospitality-related businesses. A large amount of data is produced by the hotel sector from a variety of sources, such as reservation and transaction histories, social media, loyalty programs, and customer reviews. Predictive modeling, machine learning, artificial intelligence, and other data analytics approaches can assist companies in generating insightful conclusions from this data for better operations and customer satisfaction. For instance, hotel data analytics can be used, to spot trends in bookings and to maximize personnel, inventory, and room pricing. Restaurants may increase menu options and customize patron experiences by analyzing data on client preferences, dining behaviors, and comments. Key performance indicators including revenue per available room (RevPAR), customer satisfaction, and loyalty can all be tracked and measured by businesses leveraging hotel data analytics. Through this research, companies may learn more about the demographics that make up their target market and customize offerings that will ultimately increase customer retention and brand loyalty. They can also find areas for cost savings and measure their performance in comparison to peers by spotting patterns and trends in massive data sets. Advantages of Hotel Data Analytics in the Hospitality Industry Utilizing data has become increasingly valuable for numerous businesses as an outcome of technological advancements. This digital treasure trove can also help the hospitality industry. A few of the advantages of managed analytics in the hospitality industry are listed below. The following key performance indicators (KPIs) must be used to assess the efficacy of hotel revenue management analytics: Hotel Revenue Management Hotel Revenue management experts find opportunities to market their services to the right customer through the right marketing channel at a reasonable price by utilizing a variety of hotel revenue management tools and approaches. Experts keep an eagle eye on several metrics to evaluate a property’s competitiveness in comparison to its competitors. Furthermore, many data types, including historical occupancy rates, current reservations, and key performance indicators, can help boost revenue management. One of the hotel departments that uses the most data, aside from accounting, is hotel revenue management. Revenue managers use internal data, such as historical performance and business on the books, and data from the outside, such as consumer demand and competitor activity, to inform choices about pricing, stock controls, advertisements, and projections of demand. Customer Opinions Managing a successful hotel successfully requires prioritizing the needs of your customers while deciding which services to provide. Check visitor information to learn about their preferences, habits, and characteristics. By using this data, you may improve customer experience, personalize your products and services, and develop more focused marketing campaigns. The more information you can use to explain guest profiles, the more likely you are to be successful in the long run. Optimization of Marketing Campaigns With the correct customer data at your fingertips, you can now focus your marketing efforts on reaching every stage of the buyer’s journey. A better understanding of the most effective channels and methods allows you to modify spending in a way that maximizes return on marketing investment. Identification of Fraud Hoteliers need to be mindful of the risks related to fraud and data breaches because they operate in a sector that handles vast volumes of sensitive data. You can review transactions and identify any acts of fraud using hotel data analytics. Enhance security and reduce the possibility of fraud safeguards both your visitors and your business’s image. When it comes to spotting odd spending habits and mismatches in financial transactions, data can be a big help in recognizing these situations appropriately. Analysis of Customer Feedback Customer feedback and reputation management provide insight into your hotel’s performance. Utilize them to identify service gaps and, if you can, identify areas for improvement. Customer feedback analysis can assist in addressing issues and providing appropriate responses. You can ensure a consistent visitor experience and prioritize changes with the help of the data produced by your reputation management. Estimating the Need for Maintenance When it comes to predicting equipment lifespan and doing routine maintenance, data analytics is surprisingly useful. Preventive repairs reduce downtime for every machine or facility that may encounter problems. The likelihood of receiving complaints from guests decreases with increased hotel maintenance frequency. For example, Sensors, indicators, and meters that record data on equipment temperature, vibrations, noises, and other elements important for predictive maintenance analysis. Automation of the Supply Chain The supply of products for restaurants, laundry services, cleaning supplies, bedding, towels, and sheets is important for the hotel industry. Controlling distribution procedures, supplier relationships, and inventory levels are necessary. You can now prevent running out of supplies, particularly during times when demand is high. From making data-driven ordering decisions to providing real-time inventory reporting, everything is achievable. Regardless of the number of customers in the hotel, you can make sure everything is operating smoothly. Predicting Another area where hotel data analytics is beneficial to hoteliers is forecasting. Make use of analytical reports to learn more about inventory management, personnel requirements, and demand. It can assist cut expenses and maximize planning when it comes to long-term strategic decisions, which can ultimately improve your hotel’s bottom line. Your revenue manager will have the necessary resources to provide the appropriate services at the right moment

Manpreet Kaur
February 28, 2024
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How to setup a data warehouse for manufacturing data?

Data warehouses store data and facilitate quick analysis and reporting for actionable insights. With effective data warehouses, you can gather data from different data sources. In this blog, we’ll find out how manufacturing analytics companies can build a data warehouse for manufacturing data and gain relevant insights. Manufacturing organizations are undergoing transformational changes owing to the exponential growth of data. According to the IDC forecast, the global data sphere is expected to expand by 175 zettabytes by 2025. This massive growth indicates a data-driven world characterized by constant tracking and monitoring. Data plays an important role in highlighting areas for improvement, whether it’s inventory management, production, logistics, and warehouses. The challenge lies in collecting data in real-time and using it efficiently. By leveraging a data warehouse for manufacturing data, companies can store and process vast amounts of data with the help of manufacturing analytics solutions. What is data warehouse for manufacturing ? A data warehouse for a manufacturing company is a digital repository of disparate data sets. It gives a consolidated view of data from different systems such as operational and transactional data management. Manufacturing organizations collect information across different stages of their processes, such as product and process design, assembly, maintenance, and recycling. A data warehouse aggregates structured data from multiple sources, giving accurate data analysis. How to create a data warehouse setup? Here is a quick overview of the steps of building a data warehouse for manufacturing companies Step 1: Understand business requirements Note down the functional and non-functional requirements of your business according to their priority. For example, if your business will expand and grow in the immediate future, scalability must be your top priority. Figure out departmental goals and align them with the project. Assess the existing tech stack and data to get an idea of the current and future needs. Step 2. Investigate source data Define all the data sources and identify the primary sources of record to prevent unnecessary data loading, since specific datasets might be present across multiple storage systems. For example, you can transfer the sales order information from the order management system to logistics software. However, the OMS serves as the single data source, since the logistics software may alter data, compromising the quality of insight. Step 3. Develop conceptual, logical, and physical data models Once you have delineated all the business requirements, you need to create a preliminary enterprise data warehouse model to visualize and represent key business processes and their interrelationships. Make sure you build these models in collaboration with the domain experts to account for industry-specific subtleties. Conceptual data models help to set up relationships among core business entities and outline the information needs of an organization. For instance, a supply chain company might identify entities such as products, customers, shippers, carriers, suppliers, orders, and manufacturers. Logical data models have more elaborate details such as attributes (columns) associated with each business entity. For example, the product’s price Physical data models include primary and foreign keys. A primary key works as a unique identifier within a table, while a foreign key is inserted from one table to another to establish a relationship between tables. Since business operations evolve continuously, it’s imperative to ensure data models remain adaptable. Step 4. Define and create a data warehouse schema Now you need to structure the final version of data modeling into a data warehousing schema. Select the most suitable schema from different schema types, consulting a software architect. Step 5. Deploy a data warehouse architecture gradually When you have a data warehouse schema in place, create a data warehouse architecture. Focus on factors such as cost, security, performance, and scalability to choose a flexible architecture according to business requirements. What are the applications of data warehouses in manufacturing? Manufacturing production and distribution organizations centralize their data using a data warehouse, giving a comprehensive analysis to determine existing patterns and trends, forecast market shifts, pinpoint growth opportunities, identify areas for development, and facilitate strategic decision-making. They face critical decisions regarding in-house production and outsourcing that impact the industry. By using OLAP (Online Analytical Processing) tools within data warehouses, businesses can analyze trends, detect early indicators of potential challenges, and enhance decision-making. Data warehouses monitor product shipments and portfolios, allowing companies to identify product lines and evaluate underperforming ones depending on customer feedback and historical performance metrics. Characteristics of a Data Warehouse The main characteristics of data warehousing in the manufacturing industry typically include: Subject-oriented In a data warehouse, decision-makers (stakeholders, executives, and leaders) analyze data by focusing on specific subject areas, by narrowing relevant data sets. This ensures a clear understanding and streamlined analysis by limiting unnecessary information. Data warehouses are organized on specific subject areas such as customer data and inventory to facilitate analysis. Integrated Data warehouses from disparate sources within an organization are consolidated and standardized in a data warehouse to ensure consistency and coherence across complete datasets. Time-variant Data warehouses store historical data over time, including a temporal element and spanning an extensive time horizon. The immutability of time elements is a crucial aspect of time variance and record key displays time variance. Non-volatile Once data is uploaded in the data warehouse, data is updated to protect it from temporary changes. The data is in read-only form and allows only access and loading functions. What are the four phases of data warehouse design? Manufacturing analytics companies implement the below phases to design data warehouses to ensure the effectiveness of infrastructure. Offline operational database: In this first stage, data is transferred from operating systems to servers. This separation prevents any impact on the performance of the OS, enabling easy data loading, processing, and reporting. Offline data warehouse: During this stage, data is updated periodically since the data is refreshed from the operational database. Real-time data warehouse: At this stage, data warehouses are updated in real-time as transactions occur in the operational database. It involves event-based triggers that send notifications to update records accordingly. Integrated data warehouse: All the transactions are updated

Yashica Chopra
February 23, 2024
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The Role of Manufacturing Analytics Solutions in Pharma

The adoption of advanced data analytics in pharmaceutical manufacturing analytics solutions in pharma manufacturing has made it easier to analyze simplified the process of analyzing enormous datasets and produce valuable insights. As a result, the pharma companies are ahead of the game, by managing their operational efficiency and cutting cost costs related to raw material, energy, and much more. Pharmaceutical manufacturing has been impacted by global trends such as variable-cost increases, high capital expenditure requirements, increasing operational complexity, and improving savings. Also, they need to manage quality assistance, optimize their processes, and remain future-proof by minimizing their vulnerabilities. With the help of pharmaceutical manufacturing software, pharma companies can achieve these goals by continuously analyzing data and deriving meaningful outcomes. According to a report by MarketsandMarkets, the advanced analytics market will increase from USD 64.3 billion in 2023 to USD 226.2 billion by 2028, at a CAGR of 28.5%. Increasing adoption of PAT tools and other technologies, along with the urgency to address issues such as high costs, sustainability mandates, and changing demands, prompts the need for advanced manufacturing analytics solutions analytics in pharma industry. How do pharmaceutical companies use data analytics? With the help of data analytics, pharmaceutical companies analyze data from disparate sources to predict future outcomes. For example, they can estimate the possibility of drug failure during research and development, identify patient traits prone to reactions to certain medicines, and find optimal medication production timelines to prevent expiration. Discovering and developing drugs: Advanced analytics enable pharma companies and their partners to find and select effective therapeutic compounds for specific diseases. By analyzing diverse data points related to each target such as historical behavior with other proteins, past experimental outcomes, and efficacy of the previously tested drugs. Pharma Supply Chain: The pharma manufacturing companies need to ensure the timely delivery of medicines to the patients. Insights from analytics enable them to understand the fluctuations in demand concerning economic conditions or regional demographics. By leveraging these insights, drug companies can optimize resource allocation and make medicines easily accessible. Ensuring drug safety and pharmacovigilance: Drug safety and pharmacovigilance are critical for pharmaceutical companies, owing to the legal liabilities and medication recalls. Adverse drug reactions could lead to severe consequences for the patient and the pharma company. Therefore, advanced analytics serve as a valuable valuable to identifying valuable tool to identifying patient demographics prone to intolerance of specific medicines and those with high susceptibility to ADRs. Real-time PAT Tool Data Monitoring: Advanced analytics tools can process data generated from PAT instruments in real time, providing immediate insights into the manufacturing process. This allows for quick identification and response to deviations, ensuring product quality and consistency. Drug Distribution and Sales: Using manufacturing analytics solutions in pharma, the manufacturers can predict drug sales precisely. Next, they can optimize inventory management for a more streamlined distribution process, thereby lowering risks related to inadequate supply or overstocking. Comprehending historical data points such as previous sales trends in different regions and customer segments makes it easy to forecast future sales. Pharmaceutical companies can further identify and tap sales opportunities, enabling originations to assign resources strategically and improve return on investment. Marketing: Analytics enable the pharma companies to find out the patient demographics who are most likely to benefit from a specific drug throughout the drug life cycle. Customer Relationship Management makes it easy to nurture customer relations and gives a transparent view of customer interactions across different aspects of business touch points throughout sales. Also, predictive modeling enables tracking of customer experiences across channels such as online ads and email companies, allowing pharma companies to market their products effectively. Clinical Trials: Time and patient enrolment expenses are big hurdles during clinical trials. Pharma companies use data analytics to identify patients with specific traits for particular clinical trials. This approach streamlines patient selection, ensuring engagement till the trial ends. Further, the predictive models optimize research outcomes by minimizing the need for excessive test subjects, and resources, and speeding up the drug development process. What are the benefits of implementing manufacturing analytics solutions in pharma plants? By adopting manufacturing analytics solutions in pharma, pharma plants can reap the following benefits: Predictive maintenance: With the help of predictive maintenance technology, it is easy to detect glitches that signal budding issues and allow engineers to resolve them before they aggravate. Further, the plant managers can optimize maintenance scheduling, and plan downtime strategically to lower disruption. Risk mitigation: Pharma plants are risky environments. Advanced analytics tools enhance safety levels within the plant and surrounding area by ensuring smooth operations and early detection of issues to prevent health hazards. Quality control: The pharma industry needs impeccable quality control with strict vigilance. Any compromise in the quality standards poses huge risks to the patients, along with severe consequences for the pharma companies. Predictive monitoring analytics track the product quality throughout production and give warnings about minor quality variations that can be resolved immediately to prevent substantial product loss and maintain regulatory compliance. Optimize processes: Pharmaceutical companies analyze data from different production phases, giving a concrete understanding of how to improve resource utilization for better efficiency. The analytics give early notifications about any potential loopholes, enabling engineers to address them, without impacting the processing cost and product quality. Supply chain management: Manufacturing data analytics companies leverage analytics to enhance the supply chain and prevent supply shocks. The plant owners can proactively plan and procure the necessary resources, get visibility into the stock levels, ensure timely availability, and reduce potential disruptions. What measures do you need to take while using manufacturing analytics solutions in pharma plants? When using manufacturing analytics solutions in pharma, pay attention to the following factors: Challenges for pharma companies in the implementation of manufacturing analytics solutions Conclusion With insights from manufacturing analytics solutions in pharma, pharma manufacturing companies can improve their product quality, optimize resource allocation, and reduce health, environmental, and safety risks. Visibility into plant processes and operations makes it easy to detect bottlenecks, failures, and inefficiencies and reduce waste,

Yashica Chopra
February 22, 2024
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5 Hidden Costs in Manufacturing Exposed by Managed Analytics

Manufacturing is the most challenging aspect of any business. Several factors affect manufacturing such as accuracy, focus on details, and economical use of resources. But, there is more hidden and challenging information to identify significant components. Metrics like quality, dependability, and flexibility are included in this. To find the exact metrics and make informed decisions every business requires managed analytics services. Finding this true cost of the product involves identifying hidden costs and analyzing the total cost of Ownership (TCO). TCO includes all direct and indirect expenses such as manufacturing, buying, shipping, design, inventory control, labor inefficiencies, and even refunds. However, it also provides the most exact statistics to compare different production technologies, such as 3D printing and injection molding. What is Manufacturing Analytics? Manufacturing analytics solutions are used by manufacturers to track KPIs, decrease unplanned downtime, increase customer satisfaction, and optimize production efficiency. Industry 4.0, or smart manufacturing, is the term used to describe the larger trend. To do this, data gathered from industrial machinery and traditional IT systems must be combined, and analytics software must be used to make better decisions. Additionally, by predicting bottlenecks managed analytics for the supply chain in manufacturing processes that could interfere with order fulfillment, producers can determine the underlying reasons for production mistakes. 5 Hidden Costs in Manufacturing exposed by Managed Analytics With the help of managed analytics as a service, and using managed BI for smart factories, businesses can now automate the process of converting data into insights, which will help them further accomplish objectives like successful customer retention and the ability to forecast customer behavior through predictive modeling and 360 Degree Customer View. We have covered several important subjects in our blog during the past year. This has involved deciding what has to be improved; that is, figuring out all the possible areas to enhance, picking the crucial ones, and seeing those through to the end. For your improvement activities to produce any meaningful results, this is vital. The key features that set apart businesses that truly improve are knowing what to do and having the discipline to follow through without spreading your available resources. No amount of skill, managerial ability, or procedural expertise can bring about meaningful change in the absence of these qualities. Numerous hidden expenses exist. While some are particularly detrimental in high-product-mix processes like customized fabrication, others are inherent in practically any value-producing operation. Furthermore, if these expenses are not identified and addressed, they frequently complicate other improvement initiatives. Let us now discuss the ones that are most relevant to high-product-mix operations. Information Mistakes or Errors It might be very overwhelming to have all the information you need on what to construct when to build it, what to order when to order it, dates and quantity changes, revision levels, acceptable quality, and a lot more. Many things can go wrong. The more information there is, the more varied it is, and the more likely it is that something will go wrong if it is not expressed effectively. The information problem causes numerous forms of waste. That might be a monster. The quantity and diversity of information in a high-product-mix store are unlikely to alter. You have no control over that. However, you can make improvements by converting the input data into a standard output format that is highly accurate, understandable, and actionable. The idea is to create a system that feeds accurate data to the real processing operations, particularly those that take place on the factory floor. Correctness, clarity, timeliness, understandability for all participants, and identical actionability — the ability to produce the same action independently of the person performing it — are all requirements for the information. This instruction seems quite lofty. However, upon performing a root cause analysis to determine what went wrong and what causes needless expenses, you will nearly always discover that information errors are at the top of the list. It’s also possible to overcome the majority of these problems with the help of manufacturing data warehouse services or managed analytics. Here’s a basic yet typical example: A new machine that you recently purchased can process parts 50% faster than the previous one. Isn’t it an improvement? Not if you build to the incorrect revision, overbuild, or build the incorrect thing at the incorrect moment, that is. The costs of correcting the situation will outweigh the throughput gain. And those expenses will be forgotten. These overhead costs, however, hidden, are actual, cumulative, and unavoidable if nothing is done about them. Searching Because they follow the routine as usual, search costs are hidden. We are used to watching ourselves and other people engage in the common conversation that begins with the phrase “Let’s see… where can that be?” It’s entirely avoidable, fully typical, and also quite expensive. The one benefit of search waste, which is a type of downtime and information waste, is that it is comparatively simple to improve. Eighty percent of it can be resolved with any long-term 5S/visual workplace strategy. It is one of the few enhancements that is theoretically simple to start. The execution of the sustaining plan is the sole area in which knowledge is needed. It’s difficult to discover good reasons to tolerate search waste and its associated expenses. Because search wastes time, they typically manifest as variations in efficiency and collectively cause schedule problems and delayed orders. Overexposure to Material and Labor Movement It is rather typical to move materials around in a plant. Moving people around is also common. However, moving objects and people excessively has an impact that is almost identical to searching excessively. It’s one more traditional waste that lean concepts have found. It can need changes to the structure of plants and equipment, strict adherence to quantity and timing guidelines, and an emphasis on part flow, which makes it more difficult to eliminate. However, much like search waste, it can be greatly improved. To find the best answer, experience is undoubtedly necessary. However, progress can be accomplished gradually. Two excellent examples are

Yashica Chopra
February 13, 2024
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Transforming Hotel Data Analytics : Resilient Data Warehouse

The travel and hospitality industry is evolving through the adoption of data analytics and BI solutions. This is done by modernizing the hotel data analytics infrastructure. Here, we’ll discuss the ways to build a secure and scalable data warehouse and the role of analytics in the industry. The hospitality industry is investing in data analytics and business intelligence to effectively manage the increasing demand from customers. Data analytics helps unlock the hidden trends and patterns in large amounts of data to understand customer behavior, preferences, likes, dislikes, etc. It enables hotels and similar service providers to streamline operations and personalize offerings based on customer requirements. The global hospitality market is valued at $3.95 million in 2024 and is expected to touch $7.239 million by 2027 at a CAGR (compound annual growth rate) of 10.62%. Hotels can adopt data-driven models to derive real-time hospitality business intelligence insights and reports to make faster and better decisions. For this, you need to invest in a reliable, secure, and scalable data warehouse. In this blog, we’ll read more about the role of data analytics in the industry and the best practices to follow when building a data warehouse for hotel data analytics. What is Hospitality Data Analysis? Hospitality data analytics is the use of analytical tools to process historical and real-time data from the hospitality industry. It is a powerful tool that can positively impact various aspects of the business, such as customer experience, marketing, pricing, food and beverage sales, occupancy rates, etc. Hotels prefer to partner with a reliable hospitality data analytics company to set up the necessary IT infrastructure for implementing data analytics and business intelligence. The service provider will build a data warehouse and integrate it with BI tools like Tableau, Power BI, etc., and create custom dashboards for employees to access the insights in real time. Best Practices for Building a Secure and Scalable Data Warehouse The global active data warehousing market touched $10.8 billion in 2023 and is expected to reach $21.5 billion by 2032 at a CAGR (compound annual growth rate) of 7.68%. The data warehouse is a central repository storing massive amounts of data collected from multiple sources. It can be integrated with numerous third-party applications to run real-time analytics and derive business intelligence reports. However, building a data warehouse requires planning and expertise. You should ensure it is secure, scalable, and built using the best tools and technologies in the market. Typically, a data warehouse contains three main components Each layer is equally important and has definite purposes. These have to be aligned with your business requirements and long-term goals. The data warehouse is not limited to the present but is a tool for the future. That’s why data warehouse developers and service providers follow the below-listed best practices to deliver the best travel analytics solutions to businesses. Choosing the Technology A data warehouse can be built using various databases like traditional relational databases, open-source solutions, cloud-based databases, columnar databases, etc. Here, you choose a type based on your business volume and future plans. Since it gets expensive to build multiple data warehouses, you need a model that can be easily scaled and expanded as your business grows. That way, you add more layers to the existing model without starting from scratch every time. Moreover, the data warehouse should continue to deliver efficient results without lags and breakdowns. Open-source and cloud-based data warehousing models are preferred in today’s world due to the flexibility and customizability they offer. Designing the Data Warehouse Model The data modeling method you select affects the analytics and insights you derive by processing the datasets in the data warehouse. Go for reliable data modeling techniques like snowflake schema or start schema as they allow optimization of data retrieval. This leads to efficient query processing without consuming too many resources. You should also consider the types of queries you will use during day-to-day work. For example, a hotel employee has to constantly track the number of guests, advance booking, available free rooms, etc. Streamlining the ETL Process ETL stands for Extract, Transform, and Load. This stage focuses on extracting data from multiple sources and transforming it into structured formats to eliminate redundancy and then loading it into the data storage systems. With the continuous generation of data in the hospitality industry, the derived hotel data analytics can be accurate when the ETL process is efficient and free of errors. Techniques like parallel processing, data validation, etc., can enhance the ETL pipeline and create seamless data flow in the establishment. Ensuring Data Integrity and Consistency Data is the core of data-driven decision-making models. Insights derived from low-quality data can be unreliable and incorrect, leading to wrong decisions. This can be very costly, especially in the hospitality industry, where customer experience is a priority. For a hotel to derive accurate and actionable insights, the input data used for hotel data analytics has to be of top quality and free of mistakes and duplication. The data warehouse should have the means to implement data checks at various stages to increase overall consistency and quality. Data profiling techniques have to be implemented to detect anomalies in data sets, tags, etc., and highlight missing or incorrect values before the data is used for business intelligence reporting. Focusing on Scalability and Performance With new data being created every minute, you should inevitably prepare to scale the data warehouse periodically. There are different ways to scale, such as horizontal scaling, vertical scaling, data compression, indexing, partitioning, etc., that allow the central database to accommodate more data for storage and analytics. At the same time, weighing down the data warehouse with massive amounts of datasets can result in lags and delays. This has to be countered to ensure the efficient performance of the data analytics tools. Hospitality data analytics company like DataToBiz helps businesses find the best solutions to ensure scalability and performance in the present and future. The Need for Data Backup and Recovery Planning Data backup is a must for every business. Data loss is one of the biggest concerns and

Kavika Roy
February 09, 2024
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10 Ways Analytics Managed Services Will Change Your Business in the Next 5 Years

The increasing complexity of data management and the constant advancements in technology, managing and extracting insights from data may seem like a daunting task. However, analytics managed services can step in to meet the growing demands of data management. Regardless of your role as a CEO, IT head, or CXO, you’re keenly aware of the vital role data-driven decision-making plays in today’s business landscape. So here, we will delve into some transformative ways in which Analytics Managed Services are meant to reshape your business in the coming five years. Enhanced Data Governance Effective data governance is the foundation of successful data analytics. Analytics Managed Services provide robust data governance, ensuring that your data is secure, compliant, and readily available. This guarantees data integrity and mitigates risks, allowing you to focus on strategic objectives. Streamlined Data Architecture Data architecture can be complex and overwhelming, especially as your business grows. Managed Analytics Services offer expertise in data architecture, helping you design and maintain a robust infrastructure. This ensures that your data is organized, accessible, and scalable as your business expands. Establishment of a Data Warehouse As data volumes grow, traditional data warehousing may become inadequate. Managed Analytics Services can help you migrate to modern, cloud-based data warehousing solutions. This transition not only saves costs but also offers the flexibility needed for agility and growth. What’s a Data Lake? A Data Lake can be a treasure trove of insights when managed effectively. Managed Analytics Services help you set up and maintain a Data Lake, making it a valuable resource for your data analytics initiatives. This keyword-influenced strategy will be a game-changer in data management. Data Science Analytics Managed Services Data science is at the heart of making sense of data. Managed Analytics Services provide access to a pool of data scientists who can develop advanced machine learning models and algorithms, helping you extract valuable insights and predictions from your data. Big Data Implementation Big data is no longer a buzzword but a reality for many businesses. Managed Analytics Services assist in harnessing the power of big data by offering the expertise required to handle vast datasets and extract actionable intelligence from them. The Right Partner Choosing the right partner for Analytics Managed Services isn’t just about immediate benefits; it’s a strategic move that can take your business five years ahead of the competition. With the right consultant by your side, you’re not just keeping pace with current data trends; you’re anticipating and adapting to what’s coming shortly. By collaborating with companies that understand your long-term vision, you can leverage their expertise to unlock growth and innovation. This allows you to harness the power of data today while staying well-prepared for the data landscape of tomorrow. In essence, the right managed analytics service provider becomes your bridge to a data-driven future, ensuring that your business is not just competitive but leading the way in the next five years and beyond. The Next 5 Years… As we look ahead to the next five years, Managed Analytics Services will be one of the driving forces behind business transformation. For enterprises like yours, embracing these services will not only enhance your data management capabilities but also open up new opportunities for growth and innovation. In a world where data is king, Managed Analytics Services are your crown jewels, ensuring that your business stays at the forefront of the data-driven revolution. But what about the next 10 years? How will these services continue to evolve and change the way we do business? It’s a question worth brainstorming! The pace of technological advancement is relentless, and the data landscape is continually shifting. Will your business remain adaptive and competitive in this ever-changing environment? With Managed Analytics Services by your side, you’re equipping your business to thrive in the future. So, start exploring these services and consider how they will help your business. Originally Published on DataToBiz

Kavika Roy
February 07, 2024
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How Managed Analytics Services Enhance Customer Experience?

Have you ever wondered how businesses magically seem to know what you want, sometimes even before you do? Well, it’s not magic; it’s the power of data analytics. Do you want to know how Managed Analytics Services help you redefine the customer experiences? From interpreting large amounts of data to understanding customer needs, this blog focuses on how businesses seemingly anticipate your needs and deliver personalized meaningful experiences. In this blog, I’m going to take you on a journey to explore how managed analytics services can transform the way businesses serve you, and ultimately, how you experience their products or services. So, grab a coffee, get comfy, and let’s dive in! What Are Managed Analytics Services, Anyway? Let’s start with the basics. Managed analytics services are like having a team of data masters at your disposal but for businesses. They help companies collect, analyze, and make sense of the massive amounts of data they gather from various sources. Think of it as a behind-the-scenes superpower that organizations use to enhance their operations, and it indirectly affects you — the customer. The Heart of Customer-Centric Business In a customer-centric business, it’s not just about selling products or services; it’s about delivering a memorable and delightful experience that keeps customers coming back for more. Managed analytics solutions play a pivotal role in achieving this goal. Personalization Imagine strolling into your favorite online store, and boom! Right on the homepage, you’re greeted with a list of products that feel like they were handpicked just for you. This isn’t luck; it’s the result of analytics services at work. They use your past behavior, preferences, and even demographic data to create a personalized shopping experience. When you find what you want quickly, you’re a happy customer, right? Think about e-commerce giants like Amazon. They’re masters at suggesting products you’re likely to buy based on your past purchases and browsing history. But how do they do it? Managed analytics services are the engine behind these personalized recommendations. They analyze your data to understand your interests and behaviors, creating a tailor-made shopping experience just for you. This level of personalization goes beyond just e-commerce. Music services like Spotify and even news websites use similar techniques to keep you engaged. It’s like having your shopper, DJ, or news editor. Predicting Your Needs Before You Do Ever had Netflix recommend a show you end up binge-watching all weekend? Managed analytics services help businesses predict their future needs. Hear me out on this one! They analyze your past behavior to suggest products, services, or content that you’re likely to be interested in. It’s all about saving you time and effort by offering solutions before you even realize you need them. Predictive Analytics Let’s take the example of a food delivery app. It can use your previous orders, the time of day, and even the weather to predict what you might want to eat. So, when you open the app, your favorite cuisine is right there, ready to be ordered. It’s like they’ve read your mind, right? But it’s not just about food. Airlines use predictive analytics to anticipate delays and manage flights more efficiently. Banks use it to prevent fraudulent transactions. It’s everywhere, making our lives smoother and more convenient. Smooth Customer Support, Thanks to Analytics Imagine having an issue with a product or service and reaching out to customer support. With managed analytics services in play, the support team can access a wealth of information about your history with the company. They know what products you’ve bought, and issues you’ve faced before, and can provide tailored solutions. No more explaining your situation repeatedly; they already know you! Multi-channel Support Whether you contact customer support through chat, email, or a phone call, businesses aim to offer a seamless experience. With the help of analytics, they can track your interactions across different channels. If you start a conversation on chat and switch to a phone call, they pick up right where you left off. It’s like talking to a friend who remembers everything you’ve discussed. Moreover, analytics can identify trends in customer support queries. If multiple customers face the same issue, businesses can address it proactively, preventing further inconvenience. This means that issues get resolved faster, and you don’t have to deal with recurring problems. Streamlined User Experience When you visit a website or use an app, you want it to be smooth sailing, right? Managed analytics services play a crucial role in optimizing user experiences. They track how users interact with a website or app, identify pain points, and suggest improvements. The result? You enjoy a seamless, error-free experience. The User Journey Map Creating a user-friendly website or app is no longer guesswork. Managed analytics services help businesses understand the user journey. They analyze where users drop off or get stuck, and this insight guides improvements. For instance, e-commerce websites use analytics to enhance the checkout process. If they notice that customers abandon their carts at the payment stage, they can investigate why. Maybe it’s a complicated form or a lack of payment options. Analytics provides the data needed to identify these issues and make the necessary changes. But user experience doesn’t stop at websites and apps. Think about self-checkout kiosks at grocery stores or touch-screen menus at restaurants. Analytics ensures that these interfaces are user-friendly and efficient. Improved Product Development Businesses want to create products or services that you’ll love. Managed analytics services help in this process too. By analyzing customer feedback, usage patterns, and market trends, they can guide businesses in refining existing products or creating new ones. This means more of what you like and less of what you don’t. The Voice of the Customer Customer feedback is a goldmine for businesses. It tells them what’s working and what’s not. Managed analytics services can aggregate and analyze this feedback to extract valuable insights. For example, car manufacturers use customer feedback to design safer and more comfortable vehicles. Tech companies use it to refine software features. Even restaurants use it to tweak their

Yashica Chopra
February 06, 2024
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5 Top Managed Analytics Companies for Manufacturing Startups in USA

The manufacturing industry doesn’t have to buckle under the pressure of increasing costs and demands. Data analytics empowers enterprises to make the right decisions. Here, we’ll discuss manufacturing analytics solutions providers for startups in the industry. With manufacturing enterprises moving from traditional production methods to smart factories and digitalization, managed analytics has gained prominence among established and new businesses. Gartner Digital Markets surveyed with over 3400 participants to arrive at some interesting insights. The survey report shows that 54% of manufacturers plan to spend 10% or more on software compared to 2023. Most responders said business intelligence and analytical solutions were a top priority. However, 47% agreed that identifying the right software is the biggest challenge as it directly impacts their ROI and success. The best way to overcome the challenge is to partner with a managed analytics service provider. Manufacturing startups need much more than access to technology to compete with global enterprises. You should also have the required skills and talent pool to effectively use the technology and align the processes with business goals. From data management to decision-making, the data-driven model is a collection of various tools, technologies, talents, and domain expertise. Here, we’ll understand how analytics are helpful and why manufacturing analytics services are a must for startups in the industry in the US and around the world. How Can Data Analytics be Used in Manufacturing? Data analytics is where large amounts of data are collected from multiple sources, cleaned, formatted, and stored in a central database to derive meaningful, accurate, and real-time insights. The insights are used by employees and top management to make important business decisions. Data analytics for manufacturing firms has gained prominence, with technology disrupting the industry. The data analytics provided from this sector are called manufacturing analytics and can be used to streamline a variety of factory, production, supply chain, logistics, marketing, and customer-related activities in the enterprise. According to the Business Research Company, the market size of manufacturing analytics has grown from $11.75 billion in 2023 to $14.26 billion in 2024 at a CAGR (compound annual growth rate) of 21.4%. It is expected to reach $32.39 billion by 2028 at a CAGR of 22.8%. Data analytics can be used in manufacturing in the following ways: Third-party consulting companies offer end-to-end manufacturing KPIs managed services to help enterprises identify, track, and assess their key performance indicators to determine if the business is going as per the plan. It helps survive market competition, increase return on investment, and generate more profits. 5 Top Managed Analytics Companies for Manufacturing Startups DataToBiz DataToBiz is among the top manufacturing data analytics companies in the US, with startups and established enterprises as clients. It empowers manufacturers to streamline their operational processes by unlocking the full potential of data and analytics. The company offers end-to-end services to derive actionable insights in real time using various tools and technologies like AI, ML, NLP, generative AI, LLMs, etc. It also provides customized manufacturing BI solutions by building dashboards for different parameters. The teams work with business intelligence tools like Power BI, Tableau, etc. DataToBiz is an award-winning company with a presence in different continents, including North America. The data governance solutions offered by digital transformation companies ensure manufacturers set up the necessary security systems to collect, manage, store, and use large amounts of data. The company is a certified partner of AWS, Microsoft, and Google and is trusted by many manufacturers. It provides assistance at every stage, from identifying KPIs and competitors to determining the best marketing strategy to promote the products. The company also helps with AI-based automation, IoT (Internet of Things) device implementation, robotics, OEE, supplier analytics, quality control, resource optimization, customer behavior analytics, and so on. Seeq Seeq is a global firm offering advanced analytics for manufacturing companies, petrochemicals, oil & gas industry, life sciences, pharma sector, etc., from different countries. With a headquarters in Seattle, empowers manufacturers to get better business outcomes by generating faster insights. The company has developed solutions like Seeq Workbench, Organizer, Data Lab, etc., to help enterprises convert raw data into actionable insights. The AI and ML platforms can be deployed quickly and customized to suit the client’s needs. Seeq focuses on maximizing production runs and helping businesses achieve their sustainability goals. The company assists manufacturers in streamlining operations to improve throughput, reduce production costs, and increase quality. It also empowers the workforce to use the analytics for day-to-day decision-making. The company has announced its partnership with Databricks to promote IT-OT convergence in enterprises. Furthermore, Seeq’s platforms can be integrated with AWS and Microsoft Azure. Cognex Cognex is a global service provider of software, sensors, vision analytics, and industrial barcode readers in the manufacturing sector. The company works with enterprises from various subsectors of the industry like automotive, semiconductors, electronics, machine building, vision-guided robotics, solar, etc. It assists clients in improving product quality by identifying and eliminating errors, reducing costs, monitoring production lines, etc. The company believes in intelligent automation for smart production. It helps enterprises build manufacturing data systems and integrate the latest technologies with their processes. Cognex offers a plethora of products, such as 3D vision systems, barcode readers, edge intelligence, OEM products, etc. It highlights the importance of artificial intelligence in promoting innovation and automation. Technologies like edge computing and deep learning help enterprises make data-driven decisions. The company also provides training through its classroom facilities in the US, Asia, and Europe. The training programs by Cognex are conducted online and offline and cover a wide range of courses (basic to advanced) for the manufacturing workforce. Tulip Tulip is an IoT (Internet of Things) software provider that helps manufacturers democratize technology to streamline business operations and overcome challenges. It transforms the production floor by setting up digital connections to create seamless workflows throughout the enterprise. The applications offered by the company are cloud-based and ready to be implemented in enterprises. Derive predictive analytics in manufacturing, build robust and customized solutions for specific concerns, and boost productivity. Tulip’s platform has generated 448% ROI, according to the Forrester Consulting Total Economic Impact™ study commissioned by the company. The

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