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

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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

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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,

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Top 5 Travel Analytics Solutions Companies to Boost Your Business

Data analytics has a crucial role in the ever-changing travel industry. It helps businesses stay at the top of the game while increasing ROI. Here, we’ll discuss the  top  travel analytics solutions provider to partner with in the US market.  The travel industry suffered during the Covid-19 pandemic but is quickly recovering from losses as people around the world resume travel  . According to Statista, the global revenue earned by the travel industry will touch $927.30 billion in 2024 and grow at a CAGR (compound annual growth rate) of 3.47% to reach $1,063 billion by 2028. Similarly, online sales in the industry are expected to be around 76% by 2028.  Travel companies, hotels, resorts, and other businesses in the travel and hospitality industry are revamping their processes to adopt data-driven technologies and provide quality services to customers. Data analytics has become a core differentiating factor that gives  businesses a competitive edge over others in the local and international markets. It’s no surprise that the big data market volume is to record $84 billion in 2024, with the big data analytics market projected to reach $349.56 billion by year-end.  Let’s understand the importance of data-driven decision-making in the tourism industry and learn more about the top five companies offering travel analytics solutions in the US and global markets.  How is Data Analytics Used in the Travel Industry? Data analytics allows businesses to process large amounts of data to derive meaningful insights for decision-making. Data analytics and business intelligence have helped many industries successfully initiate digital transformation. They have a similar role in the travel sector. Tourism data insights can be used to streamline internal processes, offer better and more personalized services, enhance customer experience, manage pricing and expenses, and increase ROI. From strategy formation to minimizing the risk of disruptions and losses, data analytics can help the travel industry in several ways. Many businesses are hiring travel analytics solutions providers to convert their data into actionable insights for more effective decision-making.  What is Travel Analytics? Travel analytics is data analytics and business intelligence in the travel industry. It involves statistics, analytical tools like Power BI, data visualizations, machine learning algorithms, artificial intelligence models, etc., to derive real-time insights.  Travel analytics helps identify the patterns and trends in historical data. It provides context and helps detect the root cause of different challenges faced by companies in the sector. Hospitality BI solutions help business organizations optimize their processes at different levels to provide high-quality services to customers.  Top 5 Travel Analytics Solutions Companies to Partner With  DataToBiz DataToBiz is a well-known service provider offering managed analytics for the travel industry. It works with startups and established travel businesses, chains of hotels, resorts, vacation property owners, and others to provide real-time analytical insights about customers, market trends, pricing, etc. The company also delivers advanced solutions for data warehousing for tourism businesses to collect and store their datasets in a central repository. The data warehouse is integrated with data sources and analytical tools to continuously provide actionable insights to decision-makers across all departments.  DataToBiz helps businesses overcome various challenges faced in the travel and hospitality industry. For example, it provides predictive analytics for demand forecasting, price optimization, and capacity planning. It promotes operational efficiency by streamlining the supply chain, optimizing the workforce, setting up triggers for fraud detection, etc. The company also takes care of risk management to create seamless workflows within the business. DataToBiz helps travel agents and hoteliers understand customer requirements and create personalized packages accordingly.  This award-winning company is a certified partner for Microsoft, Google, and AWS.  Amadeus  Amadeus is a travel analytics solutions provider in the global market. The company has clients in over 190 countries and is used by the top businesses across the travel and hospitality industry. It works with various businesses like airlines, hotels, travel agencies, cruise companies, airports, rail companies, etc. The company offers a range of products for different requirements. It provides end-to-end services for business management, analytics, operations, payments, and travel advertising. Be it process automation, API integration, or collaborations, the company delivers the necessary solutions to make it happen.  The solutions offered by Amadeus are categorized into products, services, content, and extensions. The company has 359 products, such as an A-CDM portal, booking analytics, productivity tracker, search analysis, dynamic pricing planner, and many more. It has developed an ecosystem where travel companies, payment providers, airlines, hotels, etc., can collaborate and work together to offer premium services to customers. It has won various awards and is also focused on promoting sustainable solutions.  TravelSpends  TravelSpends offers real-time analytics for businesses in the travel and aviation industry. It helps optimize travel expenses (T&E) by deriving in-depth analytics about business costs, etc. Tripnomics is a product developed by the company to capture travel patterns, optimize pricing, and increase transparency in the entire supply chain. The platform provides real-time validation for price changes and helps with continuous monitoring. It also ensures that the managers and decision-makers have complete control over the process. Furthermore, the platform is regularly updated to ensure it adheres to the rules and regulations in the industry.  TravelSpend assists businesses to adopt and effectively use technology for greater travel management reporting and decision-making. #Twitconnect, My Visa Track, and Trip Authorization Tool are a few other solutions offered by the company. It works with multinational organizations, Fortune 500 companies, government and public sector agencies, and other SMEs. The company offers consulting services for different requirements like strategy, technology adoption, solution implementation, and differentiation. It has helped many businesses save money by optimizing costs without compromising quality.  Accenture  Accenture is a popular multinational company with decades of experience in offering varied services to businesses around the globe. The company has a strong presence in the travel industry and works with businesses to help them regain their growth in the competitive market. It helps travel agencies reinvent their processes in the post-pandemic scenario and tackle the changing market conditions effectively. The company also focuses on enabling businesses to understand what the customers want by delivering tailored real-time travel analytics solutions.  Accenture emphasizes the importance of innovation and technological

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Why Leading US Tourism Brands Are Investing in Data Warehousing (And You Should Too)

Data warehouses store large amounts of structured and unstructured data from different sources such as travel agencies, customers, and vendors. Let’s explore how travel analytics services help to deliver optimal customer experience, enhance operational efficiency, and grow the travel business. Data analytics enable travel businesses to explore huge datasets spanning customer preferences, feedback, and bookings to optimize their operations and services. Data warehouses play an important role in supporting data analytics by giving a unified view of data facilitating easy and reliable analysis. According to GlobeNewswire, the DWaaS (Data Warehouse as a Service) market is projected to reach USD 7.69 billion, at a compound annual growth rate of 24.5% by the year 2028. In this blog, we will find out why investing in data warehousing is important for tourism brands to stay ahead in the game. Types of Data in the Tourism Industry Benefits of Managed Analytics for the Travel Industry Now let’s discuss the benefits of managed analytics in the travel and tourism industry: Better Customer Experience: With advanced data analytics, travel companies get relevant information about customers regarding their preferences, buying patterns, and behaviors. The companies analyze this information to predict insights and personalize their service offerings to deliver exceptional experiences. Enhances Operational Efficiency: Using data analytics, travel companies run smoothly by recognizing patterns and ways to improve them. For example, airline operations analytics enable airlines to pick the best routes to save fuel. Competitive Pricing: The pricing in the tourism industry often fluctuates depending on peak and off-peak seasons, demand, special events, and competitor pricing. Travel companies can analyze these factors to optimize their prices and remain profitable at the same time. High ROI: Analysts use data analytics (obtained by tracking metrics such as conversion rates, and click-through rates) to assess how well the marketing campaigns are performing, comprehending which channels and strategies yield the highest value. Data-related Challenges in the Tourism Industry Some of the common challenges faced by travel agencies are: Privacy and Security: The tourism regulatory frameworks in the US such as the US Travel and Tourism Advisory Board (TTAB) enforce that all travel companies must comply with the standards of collection, storage, and usage of customer data. Any breaches or infringements are liable to penalties. Processing and Interpretation in Real-time: Elements such as cost, customer preferences, and availability keep changing frequently. Gathering and processing this data becomes difficult. Even if travel companies manage to collect and analyze data, interpreting it to obtain meaningful results needs the expertise of analysts and data scientists, resulting in a skill gap. Data Quality: There are possibilities of duplication and errors in the recorded data which may lead to wrong insights and decision-making. Since travel analytics solutions get data inputs from disparate sources (CRMs, booking platforms, and social media channels), integrating these huge data sets is a tough task. Ethical Handling of Data: Apart from abiding by regulatory compliance, travel analytics solutions must implement ethical considerations to handle data by preserving customer privacy. How can Data Warehouses help overcome the above Challenges? Data warehouses play an important role in overcoming the challenges by offering a centralized and organized repository to store, arrange, and analyze huge data volumes. Since travel companies collect data from diverse sources, data warehouses make it easy to consolidate information. It allows users to access data stored in a single repository from anywhere, saving time and facilitating quick decision-making for predictions. Data warehouses ensure uniformity and reliability of information across all levels, allowing seamless processing. They aid in integrating data from multiple sources, hence minimizing the time needed for comprehensive reporting and analysis. Important Factors to Consider when Choosing a Data Warehouse for your Travel Business Choosing the data warehouse for your business requires these considerations beforehand: Data types: Determine the kind of data that you want to store in your data warehouse. Performance:  Find out the query processing speed for easy data retrieval. Size: Asses the size of the datasets you need to store in the data warehouse. Maintenance:  Evaluate the efforts to oversee and control data warehousing processes. Cost: Estimate the budget that you can spend on data warehouse deployment. Community:  Find out which tools and resources are required for integration after the data warehouse has been deployed. How Does the Travel Industry Use Data Analytics? Data analytics allows travel businesses to conclude meaningful insights and make concrete decisions by assessing market trends, customer feedback, and real-time data. Let’s explore how: Forecasting Travel Demand: Travel companies can predict the demand for travel services by analyzing existing trends and analyzing historical data. For example, an increase in flight reservations indicates growing interest, allowing the companies to advertise related services and launch targeted marketing campaigns. Customizing Customer Experiences: In response to the ever-evolving customer needs, the travel industry is moving towards delivering personalized experiences, supported by data analytics. By analyzing customer data, companies can cater to individual preferences. Also, businesses assess customer feedback and complaints to find out well-performing areas and areas that need improvement. Sentiment analysis classifies emotions as positive, negative, or neutral. The travel companies review their services accordingly, enhancing customer loyalty and satisfaction. Strategic Planning: It becomes easy for travel companies to identify evolving trends such as increasing preference for eco-tourism. These insights allow the marketing teams to plan strategically, launch marketing campaigns, and tap new opportunities. Managing Risks: Unforeseeable situations such as adverse weather conditions or epidemics may bring paramount havoc to the travel industry. With the help of real-time data analytics, travel companies can handle associated risks and send alerts for disruptions. This enables them to make arrangements to reduce adverse effects on the travel plans of customers. Conclusion: Travel and tourism companies can grow by leaps and bounds by implementing data warehouses. The travel analytics solutions provide meaningful insights to enhance operational efficiency, lower risks, assess marketing campaigns, forecast trends, and deliver incredible customer experiences. These platforms also aid in overcoming the challenges that pop up when dealing with large volumes of data with the help of reliable data governance practices. Data is an integral force and

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6 Top UK Real Estate Analytics Firms Advancing into the Data Age

The real estate sector in the United Kingdom is going through a transformational phase in recent times. Here, we’ll discuss the top real estate analytics consulting firms in UK and their role in promoting technological adoption in the industry.   The real estate market constantly faces challenges, be it inflation or socioeconomic conditions. According to CBRE, the UK real estate market had been an uphill task as the interest rates hit a fifteen-year high. The asset values declined, making it hard for investors to buy a property and maintain it. With the inflation falling in at the end of 2023, real estate companies in the UK are hopeful.  Moreover, with the elections scheduled, many investors and brokers feel things will get better in the second half of 2024. The report shows that commercial real estate will be more attractive this year since the yields are likely to stabilise.  In such conditions, it’s even more crucial for real estate agencies to stay one step ahead of the market changes to provide investors with worthy properties. Competition is also high, with around 146,100 private real estate companies in the UK. These businesses had a yearly turnover of 72.81 GBP in 2023. If the conditions become favorable, the market share will increase by a greater margin. The agency can achieve its goals by investing in real estate business intelligence to derive actionable and real-time property analytics for effective decision-making.  From identifying potential properties for sale to being the first broker to reach out to clients, real estate analytics services can help you streamline and accelerate your goals. Let’s find out more about consulting firms offering real estate analytics in the UK.  What is a Real Estate Consulting Firm? A real estate consulting firm offers managed analytics for real estate agencies and brokers. The consulting firms have diverse responsibilities and help with digital transformation, data governance, data warehousing and engineering, business intelligence, etc., to unlock the power of real estate data. The service providers provide the necessary know-how, technology, tools, and expertise for strategic planning, implementation, and maintenance of data analytical tools.  The primary purpose of a real estate analytics firm is to promote data-driven decision-making in real estate agencies, business owners, and investors. Conducting market surveys, identifying patterns and trends, managing portfolios, lease advisory, due diligence, etc., are some services offered by these firms. In short, partnering with a real estate analytics consulting company is the best way to digitise the business and gain a competitive edge.  Top Real Estate Analytics Consulting Firms in UK  DataToBiz DataToBiz is among the top real estate analytics consulting firms in the UK, offering end-to-end services to residential and commercial agencies, brokers, and corporate companies in the industry. It provides managed data warehousing for property development solutions to stay updated about the properties in the regions the business operates. The company uses business intelligence tools like Power BI to build customised dashboards for property management, investor analysis, market forecasting, etc.  DataToBiz also builds tailor-made apps for property portfolio analytics. It enables real estate agencies to effectively use the right BI solutions for property investment and decision-making. The company is a certified partner of Microsoft, Google, and AWS. It follows a client-centric model to accelerate success and help clients achieve their goals. It has worked with over 300 firms from various markets and has more than 97% project satisfaction rate. DataToBiz combines technology, domain experience, and local expertise to empower real estate companies to build sustainable businesses and proactively handle market volatility.  Deloitte  Deloitte is a popular multinational company with a presence in many countries. Deloitte Real Estate combines property services with financial reporting and business advisory solutions to provide complete support to real estate agencies and corporate companies. It uses years of expertise to build and implement property financial reporting using BI tools and derive real-time construction project analytics. It helps address the various challenges faced when handling high-capital projects. The company also has a team to specifically handle real estate and tax concerns for private individuals/ residential clients.   Deloitte gives importance to sustainability and climate concerns. It uses generative AI design to enhance urban development and planning without adversely affecting nature. As a real estate market intelligence service provider, the company understands the importance of data and its role in helping agencies, brokers, and investors make the right decision in a quick time. Deloitte has a solution called Real Estate Climate Value at Risk (RECVR) in partnership with Longevity to enable decision-making based on climate-risk information. This is a great tool for corporate businesses to choose commercial property.  KPMG KPMG has completed over 150 years in the UK and is a well-known company offering auditing and tax services for businesses from various industries, including real estate. It provides customised services to effectively manage tax, assurance, and deal advisory, along with ESG (environment, sustainability, and governance) solutions. The company handles global asset management analytics for real estate agencies and corporate businesses with offices around the world. It supports businesses in adopting the latest technology to grab market opportunities and attract more clients.  KPMG also provides consulting services for strategic formation and technology implementation in the industry. It assists businesses manage risks and challenges like taxation, regulation, sociopolitical influences, etc., to minimise losses. The company’s real estate BI solutions are aimed at providing the much-required foundation for agencies and businesses to navigate the changing market landscape and uncertainties. KPMG has been contributing to support Britain and its businesses to promote faster growth and development.  Bain & Company Bain & Company is a global consultancy with a presence in 65 cities across 40 countries. The company offers tailored and integrated experiences to businesses to help them achieve better and quicker outcomes. It has a platinum rating from EcoVadis and is among the top 1% of companies that focus on high environmental, social, and ethical performance. The company has worked on over 580 service cases in the real estate industry by offering property data insights, building the necessary tools, and providing due diligence based on the client’s requirements.  Bain & Company works with agencies, property owners, REITs, and others to assist them in handling various requirements and challenges. It provides several services,

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Why is Big Data important in the Telecom industry?

Big data in telecom industry enables companies to extract valuable insights, enhance customer experiences, plan investments, and monetize data.  In today’s digital age, the telecom industry has become a crucial pillar for global connectivity and communication. With millions of users and devices generating massive amounts of data every day, the telecom industry has recognized the importance of harnessing this data to drive growth, improve operational efficiency, and enhance customer experiences. This is where big data comes into play. What is Big Data? Big data refers to the large volumes of complex and diverse data sets that cannot be easily managed or analyzed using traditional data processing techniques. Big data in the telecom industry encompasses a wide range of information, including customer profiles, call records, network logs, location data, social media interactions, and more. Analyzing this vast amount of data allows telecom companies to gain valuable insights and make informed decisions. Types of data in telecommunications One crucial aspect to consider before embarking on big data analytics is a comprehensive understanding of the data itself. In the telecom industry, data can be categorized into three main types: network data, call-detail records, and customer data. Each type provides valuable insights and plays a significant role in the data analytics process. Network data This includes data on network performance, traffic volume, bandwidth utilization, latency, and network faults. Analyzing network data helps optimize network infrastructure, plan for capacity upgrades, and improve overall network performance. Call detail records (CDR) CDRs contain information about each call, such as the caller and recipient’s phone numbers, call duration, time of call, and location data. Analyzing CDRs provides insights into call patterns, network usage, and customer behavior. Customer data This includes demographic information, customer profiles, preferences, purchase history, and interactions with customer support. It helps telecom companies understand their customer base and deliver personalized experiences. Benefits of using Big Data in the telecom industry Big Data offers numerous benefits to the telecom industry, enabling companies to derive valuable insights and develop effective solutions. Here are some key benefits: 1. Enhancing Customer Experience Big Data allows telecom companies to gather and categorize vast amounts of user information, enabling them to personalize customer experiences. By analyzing customer data, telecom companies can provide proactive assistance, resolve issues quickly, and offer tailored services.  ● Automated chatbots and self-help options empower customers to find solutions independently. ● Customer interactions can be recorded and used for employee training, leading to better service and profitability. ● Additionally, companies can identify network connectivity or internet speed issues in specific areas and take corrective measures to retain customers. 2. Targeted Marketing By leveraging customer behavior patterns, billing information, and issue resolution data, telecom companies can not only resolve customer problems and improve services but also target customers with relevant offerings. For example, based on previous purchases, companies can offer personalized data packs, additional benefits, or promotional offers. Real-time information on pack expiry and data consumption can help companies provide customers with timely offers. By combining demographics, purchasing behavior, and preferences, telecom companies can send targeted push notifications and marketing campaigns to attract and retain customers. 3. Predicting Churn Churn, the number of customers leaving a service, poses a significant challenge for telecom companies. Big Data analysis allows companies to understand the reasons behind churn, such as service quality, network issues, social media trends, price changes, and unresolved queries. By proactively addressing these issues, telecom companies can reduce churn. They can reach out to dissatisfied customers, offer discounts or service credits, and monitor social media sentiments to prevent customer defection. By employing these analytics techniques, telecom operators have the opportunity to predict and reduce customer churn by an impressive 15%. 4. Investment Planning Big Data assists telecom companies in strategic investment planning for network expansion and modernization. By analyzing network usage, identifying congestion during peak hours, and forecasting traffic, companies can make informed decisions about infrastructure upgrades and investments. Strategic planning helps optimize services, maintain service quality, and compete effectively. Companies can align investments with projected returns on investment (ROI), customer experience goals, and market trends, leading to efficient resource allocation. 5. Data Monetization Telecom companies accumulate vast amounts of valuable customer data, including demographics, location, network usage, device details, and application usage. This data can provide insights that are valuable to other businesses. Telecom companies can offer data analysis services to industries such as retail, financial services, advertising, healthcare, and public services while complying with privacy regulations. Data monetization allows telecom companies to leverage their data assets and generate additional revenue streams. 6. Price Optimization Setting optimal prices for telecom products and services is essential in a competitive market. Data analytics allows telecom operators to analyze customer reactions to different pricing strategies, evaluate purchase history, and monitor competitor pricing. Optimizing pricing based on data insights can maximize ROI, improve sales effectiveness, and retain loyal customers. 7. Product Development and Innovation Integrating data analytics into the product development process allows telecom companies to ensure high-quality performance and meet customer requirements. Real-time data from multiple sources can be used to develop innovative products and services that cater to user needs, such as offering Wi-Fi services across various locations. 8. Performing Preventive Diagnostics Data analytics helps telecom operators identify patterns and behaviors that precede system failures. Operators can perform preventive maintenance, replace and repair equipment, and minimize service disruptions. Predictive analytics based on big data also enables operators to analyze customer intentions and identify influencers among their customer base. 9. Recommendation Engines Utilizing recommendation engines powered by smart algorithms, telecom companies can predict and meet customer needs. By analyzing customer profiles, preferences, and behavior, recommendation engines provide personalized recommendations for products and services, enhancing customer satisfaction and driving engagement. Big Data triumphs in the telecom industry The following are some real-life examples of companies that have successfully applied big data in telecom strategies.  Reliance Jio Reliance Jio, the telecom giant in India, has witnessed incredible success in a short span of time, thanks to its effective utilization of big data. In just 24

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Overcoming Procurement Challenges: Strategies and Solutions

Procurement can be a real nightmare for businesses, from navigating complex regulations to managing multiple suppliers across the globe. One misstep can lead to missed deadlines, poor-quality products, hefty fines, and global supply chain disruption. For instance, The World Economic Forum estimates that disruptions in global supply chains can lead to a loss of $4.4 trillion in global GDP. But before that, let’s first understand what procurement means. What is Procurement? Procurement, the process of acquiring goods and services, is a crucial aspect of any business. It involves selecting suppliers, negotiating contracts, and managing relationships with them to ensure the timely delivery of quality products or services.  In today’s business landscape, organizations face a range of procurement challenges that can impact their operations, reputation, and bottom line. In this blog, we will discuss the top procurement challenges and strategies to overcome them. Effective procurement is critical for organizations as it enables them to control costs, manage risks, and maintain a competitive edge. It also plays a vital role in ensuring that organizations comply with regulatory requirements and ethical standards. As such, procurement must be managed efficiently to achieve business goals and objectives. Top Procurement Challenges As organizations strive to stay competitive in today’s ever-changing business landscape. Procurement has become a critical function that can make or break their success. However, there are numerous challenges, that professionals must stay informed about and adapt accordingly, to stay ahead of the game. Supply Chain Transparency Lack of transparency in the supply chain can lead to various issues, including poor-quality products, delivery delays, and reputational damage. It can be challenging to track the movement of goods and services across the supply chain, particularly if there are multiple suppliers involved.  Data Management Managing procurement-related data can be complex, particularly if it is spread across different systems or departments. Inaccurate or incomplete data can lead to errors in forecasting, budgeting, and supplier selection. Without accurate and complete data, organizations may struggle to identify cost-saving opportunities, negotiate favorable contracts, and assess supplier performance effectively. Supplier Relationship Management Establishing and maintaining effective supplier relationships is essential for procurement success. However, managing multiple supplier relationships can be challenging, particularly if they are spread across different regions and time zones. This can result in ineffective communication, resulting in process delays and breakdowns.  Compliance and Regulatory Issues Organizations must comply with a range of regulatory requirements, such as labor laws, environmental regulations, and data privacy regulations. Non-compliance can lead to hefty fines, reputational damage, and legal issues. Non-compliance with regulatory requirements can result in hefty fines. For instance, in 2020, the US Department of Justice imposed $2.8 billion in fines on companies for violations of the Foreign Corrupt Practices Act. Cost Management Managing costs is a top priority for procurement professionals. However, it can be challenging to balance cost-saving measures with the need to maintain quality and meet delivery deadlines. According to a 2021 Deloitte Global Chief Procurement Officer survey, the top three procurement priorities for businesses are cost optimization, supply chain resilience, and improving operational agility. Risk Management Procurement involves various risks, such as supplier bankruptcy, delivery delays, and quality issues. Failing to manage these risks can lead to operational disruptions and financial losses. Strategies for Overcoming Procurement Challenges Procurement is a critical function in any organization, responsible for sourcing and acquiring the goods and services needed to support the business. Despite these obstacles, procurement teams can overcome them and emerge stronger by adopting effective strategies. Leveraging Technology Solutions A study by the Hackett Group found that procurement organizations that leverage technology to automate processes and improve data quality can achieve up to 25% savings in procurement costs. Technology solutions such as procurement software, e-sourcing platforms, and data analytics tools can help organizations manage procurement more efficiently. These solutions can automate manual processes, streamline workflows, and provide real-time insights into procurement data. Investing in Supplier Relationship Management Programs Developing robust supplier relationship management programs can help organizations build strong and long-lasting relationships with their suppliers. This involves regular communication, performance evaluations, and incentivizing suppliers to meet key performance indicators. Risk Management: Assessment and Planning Regular risk assessments help organizations identify and mitigate procurement-related risks. This involves evaluating the likelihood and potential impact of various risks and developing risk management plans to mitigate them. Improving Compliance Training & Monitoring Organizations should develop comprehensive compliance monitoring and training programs to ensure that employees and suppliers comply with regulatory requirements and ethical standards. This includes regular audits, training sessions, and communication about the importance of compliance. Implementing Cost-Saving Measures Organizations can implement various cost-saving measures, such as consolidating suppliers, negotiating better contracts, and reducing waste. However, it is essential to balance cost-saving measures to maintain quality and meet delivery deadlines. Implementing Solutions for Procurement Challenges Implementing the above strategies can help organizations streamline their operations & gain more visibility above the procurement challenges. However, overcoming these challenges may require assistance from technology and industry experts. It requires more understanding of the business data, risks associated, and regulatory updates. Data Analytics Data analytics companies and business intelligence consultancy services provide organizations with the expertise and tools needed to manage procurement-related data more efficiently. These services help organizations identify patterns, trends, and insights from their procurement data, enabling them to make better-informed decisions. Benefits of Data Analytics Improved Data Quality: Data analytics consultancy services help organizations improve the quality of their procurement-related data. By standardizing and cleansing data, organizations ensure that it is accurate, complete, and up to date. This, in turn, enables organizations to make better-informed decisions, reduce errors, and improve supplier performance. Real-time Insights: Data analytics consultancy services provide organizations with real-time insights into their procurement data. By using dashboards, reports, and other visualization tools, organizations can quickly identify trends, patterns, and anomalies in their procurement data, enabling them to take corrective actions promptly. Cost Savings: Data analytics consultancy services help organizations identify cost-saving opportunities. By analyzing procurement data, organizations can identify areas where they can reduce costs, such as by consolidating

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Top 10 Benefits of Procurement Analytics 2023 – Why Is it Important?

Procurement analytics is much more than just spend analysis. It can help all departments in the business to correct and strengthen their strategies and increase overall efficiency. Here, we’ll discuss the benefits of procurement analytics for an enterprise. Procurement is crucial for a business. It influences the various elements of the supply chain, right from production to quality control to distribution of the goods. With such an important role, it’s no surprise that businesses need to adopt the latest technologies to stay up to date in the dynamic global market and survive the competition.  In many enterprises, the procurement teams are already handling cross-functional activities and working with teams from other departments. In fact, according to a survey by Procurious, more than 81% of procurement teams feel pressurized to be more effective when responding to supply chain challenges. While this is alarming, the good news is that close to 80% of CFOs intend to maintain or increase enterprise digital investment in 2023. Procurement analytics is the process of using data and technology to help procurement teams make better, faster, and more efficient decisions. Instead of relying on guesswork or outdated reports, the teams can use real-time analytics to choose the raw materials, vendors, procurement methods, transportation routes, and so on. As per the report by Verified Market Research, the global procurement software market will touch $9.5 billion by 2028 with a CAGR (compound annual growth rate) of 7.6%.  In this blog, we’ll read more about the benefits of procurement data analysis and how businesses can overcome challenges to fill the gaps in skills and technology.  Why is Data Analysis Important in Procurement? Procurement analytics involves data collection from multiple sources (and ERP systems) and the analysis of this data to derive actionable insights for faster decision-making. The collected data is categorized into different segments based on the business KPIs. The analytical reports are shared using business intelligence or data visualization tools.  However, procurement and spend analysis are much more than data visualization reports. It helps business enterprises make use of historical and current data to determine the best way forward in managing vendors, suppliers, and other members of the supply chain. The ultimate aim of procurement analytics is to optimize resources and increase ROI while enhancing customer satisfaction.  Types of Procurement Analytics How can Procurement Analytics Benefit Your Business? CPOs (Chief Procurement Officers) have a great responsibility on their shoulders. They need to ensure the cost of procurement is within the budget without compromising quality and also maintain good partnerships with vendors. At the same time, CPOs have to consider various business goals and align the department goals accordingly.  Fortunately, procurement analytics can provide a comprehensive solution to all these aspects and more. Let’s look at the various benefits of using purchasing analytics in the business.  1. Resource Allocation How to manage resources for the entire business? Are there areas in a business that is overspending? Can the organization reduce expenses without cutting corners? The answers to these questions can be found by relying on procurement analytics. That’s because the various activities of procurement analytics are interdependent and influence one another.  For example, by monitoring inventory, the teams can determine the usage of different materials within the businesses. It helps track which items are necessary and which can be procured in fewer quantities to prevent overspending. By optimizing resource allocation, the business can improve the spending management indicators and increase opportunities to save costs.  2. Risk Management Countless things could go wrong during the procurement process. External factors can lead to delays, damages, and other issues, resulting in losses for the business. Even if an enterprise has been following the same procurement process for years, the element of risk will always remain.  However, procurement analysis can help in managing this risk efficiently. It empowers the teams to identify bottlenecks and challenges that can and cannot be controlled by the business. Then, these issues are addressed individually to establish a risk management policy and minimize losses.  For example, an organization with vendors from other countries has to create a risk policy to handle geographical border restrictions, weather-related delays, socio-political factors like wars, etc., to ensure that the supply keeps coming despite the adversities.  3. Data-Driven Decisions Data is the key to making informed decisions in today’s world. Data analytics help combine past and present data to derive meaningful insights for identifying trends, understanding the challenges, and becoming proactive to make the most of market opportunities.   Procurement analytics will help the enterprise track the demand and supply in the market and adjust the orders accordingly. Demand forecasting reports help stay one step ahead of competitors and meet customer demands before other businesses can take advantage.  4. Supply Chain Transparency The supply chain network of a business can be lengthy and complex. Maintaining transparency throughout the process can be difficult without using the right technology. That’s where using analytics in procurement helps organizations. Data is collected from different sources and made available to teams from various departments across the business. This allows the finance, IT, marketing, and customer service teams to use the data to improve sales and customer experience.  For example, customers around the world are becoming more aware of sustainability and eco-friendly practices. Many customers want to know where and how a business procures its materials. A transparent supply chain makes it easy to share this information with customers. In fact, some brands use it as a marketing strategy to attract customers.  5. Category Management Category management is a part of spend analysis and vice versa. The spend categories are created by grouping similar types of purchases under individual heads. Spend analysis gives the procurement teams the necessary data to segment the purchases and expenses by creating a basis for category structure. It is the starting point of category management and allows businesses to have a clear picture of how much they spend on each category and ways to optimize the expenses.  Not only do procurement and spend analysis help with category management but also provide businesses with the necessary insights to link

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10 Steps to Create a Data-Driven Culture

With proper planning and implementation, enterprises can effectively use data to make business decisions. However, a strong data culture is still a theory for many business organizations. Here, we’ll discuss the steps to create a data-driven culture in an enterprise.   Businesses need to work with quality data to make effective business decisions. While we cannot ignore the importance of human expertise, combining both is the best way to boost a business in a competitive market. This requires using data and data analytics to make decisions.  Statista reports that the global big data analytics market will reach $655 billion by 2029, while the predictive analytics market is estimated to touch $41.52 billion by 2028. A business organization can enjoy the benefits of data analytics and business intelligence by adopting a data-driven culture. Another report shows that 57% of leading brands are already using data to drive innovation.  So how do you create an effective data-driven culture in your enterprise? Let’s find out in this blog. But first, let’s dive into the basics. What is a Data-Driven Culture? Is data the key to a data-driven culture? Absolutely! A data-driven culture is where the workforce uses statistics, analytics, facts, insights, predictions, etc., to make everyday business decisions and optimize their tasks. Team leaders, managers, and C-level executives use insights to understand various elements of work and how these affect business performance.  Many components contribute to creating a data-driven culture in an organization. However, the major aspects are as follows:  1. Data Maturity Data maturity refers to the process of storing and retrieving data over time. It depends on your data governance policies and how well you manage and maintain the datasets with accurate tags, metadata, etc.  2. Data Leadership Data leadership defines the role of leaders and decision-makers in managing business data. These people understand the importance of quality data and help maintain a work culture where decisions are made based on data analytical reports.  3. Data Literacy Data literacy is the act of ensuring business data is structured, accessible, reliable, and accurate. It also involves training employees to understand and use datasets effectively for day-to-day work.   Investing in a data-driven culture requires expert guidance and support. SMBs and large organizations partner with a reputed data analytics company to revamp their internal processes and work culture the right way.  What are the Steps in Data Management? The data management framework has to be structured and aligned with the business process. Here’s how to implement successful data management in your enterprise and create a strong foundation for data-driven culture.  Steps to Create a Data-Driven Culture Understand that creating a data-driven culture is not limited to technological investments. The focus is equally on changing the existing work culture to help employees use new technology and tools at work.  The following steps will help you build an effective data-driven culture in your enterprise.  1. Start with the Top Management The top management and C-level executives play a vital role in influencing other employees. They need to understand the importance of data-driven solutions and establish it in the organization. When the management makes it a norm to use data and evidence for decision-making, employees will follow it over time.  For example, the team leader or manager can allocate a few minutes at the beginning of a meeting to go through the analytical reports and observe whether the proposals are backed by data. Then, they can discuss the proposals and the reports to help other employees realize why they should work with data. When the top management sets an example, it becomes easier for employees to adapt to the changes.  2. Choose Metrics Carefully How do you analyze the business performance? It can get complicated and confusing without metrics. Every enterprise has its own metrics for analytics. However, there’s no guarantee that the existing metrics are correct or suitable for accurately measuring the business.  For example, many businesses use competitor analysis because they need to keep track of what other brands in their industry are up to. An FMCG company will have to decide its pricing and marketing strategy based on customer behavior, market trends, and competitor’s offerings. Not factoring in either factor can result in skewed analytics, which invariably leads to wrong decisions.  Define metrics carefully and make sure they are always aligned with the business vision, goals, and industry standards.  3. Don’t Isolate Data Scientists One major mistake many organizations make is to keep the data scientists and business leaders in separate units. While the analytical reports are shared between the teams, the people responsible are not always collaborating and usually work in isolated teams. This can affect the quality of analytics and will soon widen the gap between reality and analysis.  Leading brands have managed to avoid this issue by eliminating the boundaries between data scientists and business leaders. The staff (team members) is rotated between different departments to keep communication flowing. Another method is to ensure that the top management has the necessary technical know-how to directly interact with data scientists and work with them. While it is not necessary to replace existing executives with AI and ML engineers, it is important to train them in the basics.  4. Provide Access to Data A common complaint from employees is that there don’t have access to data or analytics at work. It takes too much time and energy to obtain even the basic data, and this isn’t enough to make data-driven decisions. Despite democratizing the storage systems, analysts aren’t given access to information from other departments due to different constraints.  This challenge can be handled by identifying the data related to the KPIs for the project/ quarter and providing enterprise-wide access to this information. For example, if the sales analyst has to create a demand forecasting report, they should have access to information about past sales, customer feedback, inventory, etc. While data security is a concern, it can be handled through effective data governance and by setting up authorized access employees based on their project requirements.  5. Assess Uncertain Aspects To build an

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