Data Engineering

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,

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

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,

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

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

Data Engineering Trends for 2026: 15 Developments CTOs Should Know

Data engineering is the process of building, deploying, and integrating data pipelines to streamline data flow within an enterprise. It is the foundation for business intelligence processes to run and deliver actionable insights. Here, we’ll discuss the top data engineering trends and predictions for 2026 every technolohy head, CTO needs to know! Data engineering is a growing discipline in the global market. It involves the process of designing and building data pipelines to collect, transform, and transport data to end users (data analysts and data scientists) to derive actionable insights. The data pipelines have to connect all data sources to the central data warehouse or data lake. The success and accuracy of data analytics depend on how well data engineers set up the foundation. This requires high-level data literacy skills. Unfortunately, there is a gap between the demand and supply of qualified and experienced data engineers in the market. It’s one of the primary reasons many SMBs and large enterprises partner with offshore data engineering companies to adopt advanced data-driven technologies and processes for effective decision-making. Many experts feel that 2026 will be a vital year for data engineering. In this blog, we’ll take a detailed look at the various big data engineering trends and predictions that will transform the industry at different levels. 15 Top Data Engineering Trends and Predictions in 2026 1. Increase in Cloud Management Cloud has become a favorite for many businesses around the world. Small, medium, and multinational companies are moving their data and IT infrastructure from on-premises to cloud servers. Data Engineering in AWS (Amazon Warehouse Services), Microsoft Azure, Red Hat, etc., are in high demand. While some companies are directly building data pipelines on the cloud, others are migrating their existing systems to cloud servers. 2. Greater Budget Allocation for FinOps Another trend is the need for data cloud cost optimization. Top vendors like BigQuery and Snowflake are already talking about ways to optimize the data cloud cost and make cloud services more cost-effective to businesses from various industries and markets. Financial managers are becoming a part of data teams to ensure that their data engineering strategies and processes will deliver the necessary returns. While there aren’t enough best practices in the industry (data engineering is still in its early stages), data teams are finding ways to overcome the challenges and make their cloud-based data architecture more agile, flexible, scalable, and future-proof. The cost of ownership is also a crucial topic of discussion. 3. Usage-Based Data Workload Segmentation In the current scenario, companies are focusing on using a unified cloud-based data warehouse. For example, AWS data engineering is popular for offering data warehousing services to several business enterprises. However, the same type of database cannot be suitable for all kinds of data workloads. Experts predict that organizations will shift from data warehouses to data lakes where different databases and tools are individually organized and grouped into a unified setup. This can make the data architecture cost-effective and increase its performance. 4. Data Teams with Higher Specializations Though data engineers are in short supply due to the complexity of the job, data teams will continue to expand and include professionals with more specializations. For example, the data teams will have data engineers, data analysts, data scientists, analytical engineers, etc., to handle different aspects of establishing and using the data architecture in an enterprise. DevOps managers, finance managers, data reliability engineers, data architects, data product managers, etc., are other specializations we will see in future data teams. 5. Metrics Layers in Data Architecture In traditional data pipelines, the metrics layer (also called the semantics layer) is in the middle, between the ETL (extract, transform, load) layer and the cloud data warehouse. It defines the metrics for the values in the data tables and ensures consistency to eliminate errors during business analytics. Experts predict that the metrics layer will have an addition of a machine learning stack that has its own infrastructure. The ETL layer will continue to do its job, but the data will flow through the machine learning stack, which will help data scientists choose the right metrics for the given data. One day, the metrics layer and the ML stack will be combined to work as a single automated unit. 6. Data Mesh The concept of data mesh is one of the emerging DE trends discussed by many top companies. This new architectural model is said to help organizations overcome the limitations of traditional data warehouses and centralized data lakes. Date mesh is the decentralization of data governance and ownership. As discussed in the previous trends, domain-specific data platforms, tools, and databases will be established for greater efficiency. The idea is to build resilient, dynamic, and agile data pipelines that offer more autonomy, interoperability, and control to every member of the data team. However, establishing a data mesh requires more skills and tools. However, centralized data warehouses will continue to exist until enterprises can successfully build and deploy data mesh architecture. 7. Increase in Success of Machine Learning Models In 2020, a report by Gartner shows that ML models had only a 53% success rate. That too when they were built by companies with strong AI foundations and prior experience. It means even three years ago, only half the machine learning models could be deployed accurately and effectively. However, the success rate has been increasing over time. Soon, a greater percentage of the ML models can be successfully deployed by organizations. Of course, this will be possible when businesses overcome challenges such as misalignment of needs and objectives, overgeneralization, testing, validating issues, etc. 8. Changes in Cloud-Premises Architecture The architecture for data flow within an enterprise usually combines three different software applications. Databases from different departments (CRM, CDP, etc.) are connected to the data warehouse. The business intelligence and data visualization tools are connected to the other end of the data warehouse. Data flow occurs only in one direction. However, in modern data engineering, the data flow will occur both ways. The next-gen cloud

Top 10 Benefits of Procurement Analytics – 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 pressured 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. 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 the strategies

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

10 Common Data Analytics Mistakes to Avoid

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