Page 20 – DataToBiz

Yashica Chopra
September 07, 2023
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Leveraging Power BI in FMCG

Insights derived from retail and eCommerce data can help the FMCG industry in various ways. Business intelligence tools like Power BI are useful in making faster business decisions based on real-time data. Here, we’ll discuss the role of BI in FMCG markets. FMCG (Fast-Moving Consumer Goods) is also known as Consumer-Packaged Goods and deals with a large variety of perishable and durable goods from different industries. From food items to cosmetics, stationery, and groceries, many products are categorized as FMCG. According to Cognitive Market Research, the global FMCG market is estimated to reach $17,939.41 billion by 2030, with a CAGR (compound annual growth rate) of 6.11%. In other statistics, The FMCG’s share is expected to touch 18% this year in Africa. The FMCG market is always bustling with activity online and offline. Countless sales happen each day around the globe. Needless to say, large volumes of data are generated by businesses, vendors, and customers. This data can be analyzed to make better decisions and streamline internal operations. FMCG Business Intelligence is in high demand in the market to derive data-based analytics and insights. Business intelligence is a part of many industries. The global BI software market is expected to touch US$25.73 billion in 2023. In this blog, we’ll read more about the use of analytics and reporting in the FMCG industry and the role of Power BI in delivering insights in real-time. What is the Role of Analytics in FMCG? Digitization The FMCG industry is going digital by adopting the latest technologies and tools to collect, store, and analyze data generated by businesses. This allows for seamless connectivity, greater flexibility, agility, and scalability. Inventory Management Business intelligence companies create exclusive dashboards to provide data visualizations about inventory held by the business at the manufacturing unit, warehouses, stores, and the ones in transit. This enables brands to cater to the changing market demands without running out of stock. Supply Chain The supply chain is vital for the FMCG industry to thrive. We have seen how disruption affected businesses during Covid lockdowns. However, analytics can help businesses prepare for such adverse conditions and minimize losses. Warehouse Operations A smarter warehouse is more effective and can reduce the risk of damage to the stored items. Using warehouse management software and integrating it with data analytics tools will provide real-time insights about stock levels, shipment movement, etc. Logistics and Transport FMCG analytics and reporting dashboards can help businesses plan their deliveries carefully to avoid delays and increase customer satisfaction. Geo-analytics makes it easier to plan the distribution and delivery routes to minimize cost, distance, and time taken for the shipment to reach from one point to another. Personalization Customers demand personalization in today’s world. Brands can offer personalized products, deals, discounts, and loyalty programs by analyzing customer data to understand their preferences. This will increase customer loyalty and attract more consumers to the business. Pricing and Sales The prices of FMCG products are flexible and change based on market demand, quality, availability, etc. Businesses can adopt a proactive pricing model to automatically adjust the prices based on the insights derived from BI tools. How is Power BI Used in the FMCG industry? Microsoft Power BI is a popular business intelligence tool used by many leading businesses from different industries. Companies hire BI consultants to set up the Power BI dashboards and customize them based on business requirements, goals, and metrics. In this section, we’ll see how the tool can be used in the FMCG industry to deliver real-time insights for decision-making. Unified Analytics The FMCG data comes from various sources like legacy systems, IoT (Internet of Things) devices, packaging units, distribution units, warehouses, factories, stores, etc. This data has to be combined and presented in a single dashboard to streamline the business operations. Power BI is used to create a unified dashboard with real-time data updates from several sources. Decision makers can get a macro and micro view of the data from each source and organize the dashboard layout to suit their needs. Monitor KPIs KPIs or Key Performance Indicators are essential for every business and department to track their progress and ensure they are moving in the right direction. Businesses can build Power BI KPI dashboards to track each indicator/ metric with the goals, resources, and other influencing factors. It allows employees to make proactive decisions to achieve weekly/ monthly/ quarterly/ yearly goals based on real-time data. Sales Dashboard How many sales have each product recorded for the defined duration? What is the overall sales value? How is each store performing in reaching the sales targets? The Microsoft Power BI sales dashboard provides answers to many such questions. It keeps the sales and finance departments up to date with the latest information about the stock. This allows employees to plan and implement marketing campaigns at the right time to boost sales. It also shows the weak areas where the business needs to improve to sell more products. Supply Chain Dashboard The FMCG industry cannot afford supply chain disruptions. Reactionary decisions can help with damage control but don’t minimize risk. However, with a supply chain Power BI dashboard, businesses can track the internal and external factors that can cause trouble. The dashboard provides warning signs and alerts to be proactive in avoiding risk and minimizing losses. It also helps choose the best path to optimize resources and maximize returns. From comparing vendors to speeding up deliveries, the dashboard is helpful in many ways. Campaign Analysis Dashboard Power BI in the FMCG industry also helps in streamlining marketing campaigns and analyzing their performance. Many brands in the industry rely on omnichannel marketing to reach out to a wide range of target audiences. This data from multiple sources can be easily collected and analyzed to provide clear reports using the Power BI tool. It is a great way to work on the Marketing Mix Model, where multiple factors, such as price, promotion, market demand, product placement, etc., are used to determine if the campaign is converting into revenue. Demand Forecasting Dashboard Business intelligence is combined

Yashica Chopra
September 05, 2023
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Top 13 Data Engineering Trends and Predictions 2025

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 2025. 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 2025 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. 13 Top Data Engineering Trends and Predictions in 2025 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 data architecture will be bi-directional and allow data

Yashica Chopra
August 25, 2023
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7 Top Digital Transformation Companies in the United Kingdom

Digital transformation is a continuous process of adopting new technologies and data-driven models to gain a competitive edge and increase business ROI. Here, we’ll discuss its importance and the top digital transformation companies in the UK. Technology is essential for a business to survive in today’s world. From a startup to a multinational enterprise, every business organization needs to invest in relevant technologies to manage resources, speed up daily processes, enhance customer experience, and increase revenue. This indicates a strong need for digital transformation. B2B and B2C organizations should revamp their outdated systems and digitalize their processes. However, this is a continuous process and involves various elements. From experts to analysts to top management, mid-level decision-makers, employees, and stakeholders, many people are a part of digital transformation. Digital transformation companies have a prominent position because of their extensive range of services. In this blog, let’s find out more about digital transformation, its importance, and the top service providers in the UK. What is Digital Transformation? Digital transformation is using digital technologies to revamp existing systems or create new systems, business processes, work culture, and customer experiences in an enterprise. Data is stored digitally in a centralized database (data warehouse or data lake) and is connected to different applications to allow data analytics in real-time. Digital transformation brings agility, scalability, and flexibility to a business. It provides a competitive edge and allows businesses to make proactive decisions. What is Digital Transformation Consulting? Statistics show that 89% of large and global enterprises have invested in digital transformation and artificial intelligence. However, only 31% of these companies have achieved the expected results and revenue. It’s a clear indication that spending money on digital transformation without a comprehensive strategy and clear guidance will not be beneficial to the business. That’s where consulting companies play a vital role. Digital transformation consulting is the process of hiring an offshore service provider or a third-party consultant to provide end-to-end services to digitally transform the business process and achieve the expected results. Digital transformation experts from consulting companies understand your business vision, mission, internal systems, end goals, etc., to create a foolproof plan for digitalizing your business at all levels. The consulting company will also upgrade the existing systems, build new ones, and strengthen your IT infrastructure. The consultants will work with your employees to streamline data flow between different applications. Furthermore, they offer long-term maintenance and support services to ensure the systems are working without glitches. Top Digital Solution Providers in the UK 1. DataToBiz DataToBiz is one of the best digital strategy agencies in the UK, with clients from different continents around the globe. The company offers end-to-end solutions to help SMBs, MSMEs, and large enterprises adopt the latest technology like artificial intelligence, machine learning, data science, business intelligence, etc. It provides comprehensive data engineering and management solutions to streamline data flow and use real-time data for advanced analytics. PrepAI, HirelakeAI, Virtual Try-On Platform, etc., are some products developed by the company. A few services offered by the company are as follows: The company promotes digital transformation to speed up decision-making and provide a competitive edge to businesses from different industries. Whether manufacturing, healthcare, supply chain, EdTech, FinTech, surveillance, or travel, the company knows how to help clients succeed. From maximizing efficiency to boosting revenue, DataToBiz assists enterprises in effectively overcoming business challenges and achieving their goals. It has a transparent and flexible pricing system and is a certified cloud partner with AWS, Microsoft Azure, and Google. 2. Cognizant Cognizant is a global digital transformation company with clients from different industries and sectors. It helps enterprises transform their business experiences by reimagining the processes and modernizing technology. The company provides services to unlock business potential and stay up-to-date in today’s fast-changing world. It develops responsive technologies without ignoring social and environmental responsibility. It ranked 194 among the Fortune 500 companies in 2022. The services offered by the company are listed below: Cognizant also offers research services in areas like Metaverse, future-ready cities, deep green, future-ready business benchmarking, and more. It integrates data and analytical tools with various enterprise systems to provide real-time insights for effective decision-making. The company believes in engineering excellence through high-end technological solutions. Furthermore, the company’s digital strategy speeds up digital adoption and streamlines change management in clients’ businesses. 3. HCL HCL Technologies was founded in 1976 and is one of the leading IT companies in the world. It has over 2000 patents and 10,000 clients, with a presence in many countries. The company’s products and services are used by large enterprises to revamp their internal processes and stay ahead in competitive markets. The company works with establishments from various industries and promises world-class innovation. It has many top service providers in its ecosystem (AWS, RedHat, Dell, Google, Intel, Microsoft, etc.). The company has categorized its services into four areas of expertise, such as: The company is also focused on sustainability and aims to provide long-lasting results to its clients and stakeholders. HCL Technologies has divided its ecosystem to cater separately to start-ups and large enterprises. With a tagline of Supercharging Process, the company promises to offer industry-leading capabilities to help clients transform their businesses across all verticals. 4. Boston Consulting Group Boston Consulting Group (BCG) is a global consulting firm with 100+ offices in 50+ countries worldwide. It works with many Fortune 500 companies and has an annual revenue of $11.7 billion. The company provides various services to help businesses unlock the power of technology and expand their reach in global markets. It has expertise in many fields, ranging from finance to marketing, manufacturing, strategy management, people management, etc. The company has a unique collaborative model that enables it to revamp the processes at all levels and departments in the client’s enterprise. It offers the following services: BCG also offers services to tackle climate change and promote sustainability in an enterprise. It helps organizations build technologies and IT systems for the future. The company offers scalable services for artificial intelligence adoption in several industries, such as aerospace, education,

Yashica Chopra
August 24, 2023
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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

Yashica Chopra
August 22, 2023
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7 Top Artificial Intelligence Companies in UAE

Artificial intelligence has gained prominence in Middle East countries. Many businesses in UAE are revamping their existing processes to achieve greater success through AI. Here, we’ll discuss the top AI companies in the UAE and the benefits of partnering with them. UAE has been a hub for the oil and gas industry for decades. However, the region is aiming to become a leading center for ICT (information and communication technology) industry for international trade. Middle East countries are setting up free trade zones to promote the use of software products and services in all industries. Artificial intelligence, cloud computing, cybersecurity, the Internet of Things (IoT), etc., are gaining popularity in the UAE market. In this blog, we’ll read more about AI and the list of artificial intelligence companies in the UAE that offer tailor-made services to various enterprises. What is Artificial Intelligence? Artificial intelligence is an umbrella term encompassing various techniques, technologies, processes, applications, and models. It includes fields like machine learning, deep learning, computer vision, etc., which are used for an array of purposes by different businesses. Every industry needs AI in today’s world to adopt the latest technologies and survive market competition. The global AI market is expected to reach $407 billion by 2027, according to a report by MarketsandMarkets. As per Forbes, 60% of businesses say AI will enhance customer relationships and productivity. Most SMBs and large enterprises partner with AI consulting firms to digitally transform their business processes and adopt AI to achieve the desired results. This helps them save time, money, and resources while reducing the risk of losses. AI experts from offshore consulting companies create the necessary strategies to implement, integrate, and maintain AI applications in an organization. Is Artificial Intelligence in Demand in UAE? Yes. The UAE government has ambitious plans to promote the adoption of artificial intelligence in the country. It has adopted the National Artificial Intelligence Strategy 2031 and established Many companies that are entering the market regularly to accelerate the use of AI in the region. Apart from these, local businesses and enterprises work with several offshore AI firms to increase their productivity, ROI, and revenue. Top Artificial Intelligence Companies in UAE 1. DataToBiz It offers AI platform customization, pilot implementation, and full-scale digital transformation to adopt the data-driven model in a business. The company takes care of all the steps from start to finish and provides complete assistance and expert support at every stage. From strengthening the supply chain to enhancing customer experience, increasing productivity, quality, and business efficiency. DataToBiz offers various services, such as: The company has clients from the Middle East, North America, South America, Asia, Australia, and Africa. It is a certified partner of Microsoft, Google, and AWS (Amazon Warehouse Services) with over 97% client satisfaction rate. Businesses can use the real-time actionable insights provided by DataToBiz to make effective decisions, create innovative products, and amplify revenue. 2. Accubits Technologies Accubits is an AI company with clients from the Fortune 500 list from around the world. It works with tech startups to provide comprehensive software product development services and technological solutions. The company is known for building enterprise-grade blockchain platforms with greater transparency, security, and efficiency. It combines the world of blockchain and web3 using advanced technologies. The company’s AI services include generative AI development and integration, automation, computer vision, and consultancy services to help various organizations benefit from using artificial intelligence for day-to-day work. Accubits also provides business intelligence solutions to drive accurate insights for decision-making. The company offers the following services: Accubits has been named the most promising brand in 2021 by the Economic Times. It promises ROI plus (Return of Innovation Positive), a new metric developed by the company to determine how their knowledge is helping clients achieve success. The company has an active presence in UAE and the Middle East. 3. Silwana Infotech Silwana Infotech is a software development and AI company in UAE offering transformative and technology-driven solutions to clients from several industries. It has more than two decades of experience in the IT industry and follows a client-first approach to provide scalable, flexible, and result-oriented solutions to each business. The company primarily works with enterprises from the MENA region (Middle East and African Countries). It uses cutting-edge technologies to cater to client’s requirements and help them achieve the desired results. The company has offices in nine locations and has worked on 700+ projects in more than ten industries. Silwana Infotech offers the below services: The company starts by analyzing the feasibility of the project before developing and implementing the right technology and tools in a business. It also offers support and maintenance services to ensure the business doesn’t suffer from disruptions due to technical glitches. It offers onshore and offshore services, along with a hybrid model that combines both business models. 4. Citrus Consulting Services Citrus Consulting is a technology firm that offers several IT-based services to clients in the UAE. It provides AWS hyperscaling services to help businesses expand and develop their IT infrastructure on the cloud. It believes in innovation to make systems and processes more agile in an enterprise. The company uses modular solutions to provide clients with a choice to select their technology stack instead of relying on a single tool. The company is a hybrid cloud expert and also specializes in information and cybersecurity system implementation and development. It works with businesses from the Middle East and African regions and India in assisting enterprises through their digital transformation journey. Citrus Consulting Company provides the following services: The company has a robust ecosystem and belongs to the Redington Group, known for its presence in the IT and mobile industry for many decades. It promotes the use of new-age technologies like AI, IoT, robotics, big data analytics, and 5G to help businesses overcome challenges, automate processes, and optimize workflow to increase ROI. 5. EORA.ai EORA is a group of companies that uses machine learning, natural language processing, computer vision, and data science to provide unique solutions to businesses. With offices in Moscow and Innopolis,

Yashica Chopra
August 19, 2023
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15 Common AI Mistakes to Avoid: Ensure a Smooth AI Journey

Artificial intelligence offers endless benefits to a business but can be overwhelming to implement without proper guidance and support. Here, we’ll discuss the common errors enterprises make in AI implementation along with the ways to avoid them. Artificial intelligence has been a part of the global market for years. While it has several definitions, AI is mainly the science and engineering of making machines intelligent. It combines computer science and datasets to enable quicker problem-solving. Many SMBs, MSMEs, and large enterprises are adopting AI technology and tools for various purposes. A research report shows 44% of companies in the private sector will adopt AI in 2023. According to Statista, AI is mainly used for managing data as a business asset, establishing data culture, driving innovation, performing data analytics, and building a data-driven enterprise. However, several business organizations have suffered from artificial intelligence failures. In fact, various studies show that 70% of companies have minimal or no impact of AI, and a whopping 87% of data science projects don’t make it to production. In a way, AI mishaps are one of the reasons for organizations to be wary of adopting advanced technology for digital transformation. For example, the automated hiring algorithm used by Amazon turned out to be biased against women and hired only white men (due to biased training data). A real estate tycoon in Hong Kong filed a lawsuit against a business that sold him an AI robot to manage his funds. The robot lost around $20 million every day instead of increasing the funds by making the right investment decisions. It’s crucial to understand where things go wrong and avoid such mistakes to successfully implement AI technology in a business. In this blog, we’ll read about the common AI mistakes every business should avoid and ways to achieve the desired results. What are the Most Common AI Mistakes to Avoid? 1. Half-Hearted Attempts Artificial intelligence can offer a multitude of benefits to a business. However, it requires heavy investment in building a strong IT infrastructure and training employees to work with the latest tools. Making half-hearted attempts or investing in AI for the sake of it is a sure way to generate losses. AI adoption requires planning, implementation, and continuous tracking to deliver results. Haphazard processes will only waste business resources and lead to additional complications. To prevent such issues, businesses should take the time to develop a strategy for AI adoption and follow it. 2. Lack of Data Quality Is your data large enough to make AI effective? This is one question many people ask since AI is associated with large datasets. However, businesses forget to consider data quality and its impact on artificial intelligence. The AI model is as good as the data used to train it. If businesses don’t provide high-quality data input, the model will not deliver accurate results. Lack of data quality is another major reason for AI failure in enterprises. This can be avoided by investing in proper data systems and ETL (extract, transform, load) models to collect, clean, format, and process the data before it is fed into AI models for training. Data management is necessary to avoid skewed or incorrect models. 3. Unclear Business Goals Why should a business invest in AI? What problems does the top management wish to solve using artificial intelligence? Which business goals can be achieved through AI adoption? Every enterprise should have clear answers to such questions before investing in AI. Simply copying a competitor is likely to result in failure. Establish definite and measurable business goals. Align these goals with the business vision and mission. Then create an AI adoption strategy that supports the business goals and objectives. Evaluate the impact the AI model would have on the establishment and calculate the expected ROI. The trick to avoiding AI failure is to spend more time strengthening the foundation instead of building AI on weak bases. 4. Not Focusing on Change Management AI adoption requires many changes across the organization. The internal processes, IT systems, employee working methodologies, and organizational culture have to be changed and revamped to align with how AI systems work. Quite a few businesses don’t pay enough attention to these changes. They don’t have a comprehensive plan to implement the changes cohesively at each level. This results in disruptions, miscommunication, delays, and unexpected losses. Companies offering AI consulting services insist on developing a change management strategy to ensure employees, management, systems, and processes are in sync and work towards the same goals. It’s vital to address the cultural and behavioral aspects of AI adoption to overcome roadblocks and ensure a smoother transition from outdated methods to the latest ones. 5. Relying on Black Box Models AI models are complex and hard to understand. There’s no denying this statement. However, when a business relies on such black box models (ones that offer almost no explanation of how the algorithms work), it creates opacity in the enterprise. Lack of transparency and accountability can lead to many issues for the business. Who will be responsible for the wrong insights? Avoid using black-box AI models in the organization. Work towards building transparent models and maintaining clear documentation to understand how things are done. Businesses that cannot afford to build AI models from scratch can partner with AI companies to customize existing tools and increase transparency. Put in extra effort to explain the process to employees and establish procedures that make people accountable. 6. Not Enough Expertise Introducing artificial intelligence in a business is no small task. AI models are best handled by experts with the necessary domain knowledge. Asking the existing talent pool to use AI technology without proper training is likely to cause errors and additional problems. When planning the digital transformation of a business, it’s important to identify the talent gap and find ways to fill it. Organizations can hire an in-house team of AI experts to initiate the process or rely on offshore service providers. Building a team from scratch is cost-intensive and time-consuming. However, working with AI companies is quicker, cheaper, and more effective.

Yashica Chopra
August 17, 2023
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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

Kavika Roy
August 15, 2023
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Artificial Intelligence vs. Machine Learning: Key Differences

Often, individuals in the technology world use the terms Artificial Intelligence and Machine Learning interchangeably. However, while AI and ML are closely related, they’re different in almost every aspect. Want to know the difference between AI and Machine Learning or how they’re different? Read along. The internet is full of definitions that fail to define AI and ML properly, which is why the confusions arise. However, in this blog, you’ll find a detailed differentiation of AI vs. Machine Learning. So much so that even a non-tech-savvy person will be able to differentiate these terms. In addition, you’ll also find the advantages or use cases/applications of AI and ML in different business domains, which will further help deepen your understanding. So, read in full. AI Vs. Machine Learning What is AI or Artificial Intelligence? Some individuals think of AI as creating a conscious sentient being via programming, which is often termed artificial general Intelligence. On the other hand, data scientists use AI at a practical scale for solving real-world problems such as customer service, data analysis, manufacturing, etc. So, we can conclude that Artificial Intelligence is an umbrella term for technology that helps individuals autonomously solve problems by simulating human Intelligence. And this is done using algorithms that analyze input data and produce outputs. Take a chatbot, for example. Whenever a customer types in his/her query, the bot comes up with a suitable answer (output) based on the question (input). What’s more amazing is that AI algorithms can react to input data in different forms. Take virtual assistants or personal assistants like Google or Siri, for example. Whether you say, “What’s the weather like” or “What’s the temperature” or maybe “How’s the weather” the assistant is going to notice the keyword and provide you with the correct answer. Note. AI has numerous subsets such as Machine Learning, deep learning (common application: virtual assistants), neural networks (common application: facial recognition), computer vision (common application: image recognition or object recognition), and natural language processing. What is Machine Learning? Machine Learning, as stated above, is one of the subsets of Artificial Intelligence. However, unlike AI, Machine Learning as a term doesn’t mean creating smart machines or computer programs that can simulate human behavior to solve problems. It means creating computer systems that learn and improve on their own, using experiences (huge volumes of data) without human intervention. We can cite the example of voice assistants to understand Machine Learning. Earlier, the assistants used only to recognize specific statements like “What is the weather outside” and give an output. If anything else was said, the assistant wasn’t able to produce the desired output. However, because of Machine Learning, assistants can now recognize and respond to a variety of different statements having the same meaning. Another example of ML is predictive analysis. Several companies, such as Netflix and Amazon, use predictive analysis to predict user behavior and deliver a better user experience. What’s more, banks use predictive analysis to predict fraudulent patterns and stop illicit transactions from happening. In a nutshell, if AI is the grand vision of creating intelligent computer programs or machines, ML consists of the models, technology, and processes that are being used by experts to reach there. How are Machine Learning and Artificial Intelligence Different? Machine Learning and Artificial Intelligence can be differentiated based on several factors such as their scope, primary goal, application, the data type they deal with, etc. Here’s a table that can help you differentiate Machine Learning and Artificial Intelligence better: Criteria Artificial Intelligence Machine Learning Primary Goal To create computer systems that simulate human intelligence for solving problems. To enable machines to learn automatically from data and improve the accuracy of outputs Hierarchy AI sits at the top with Machine Learning, deep learning, computer vision, neural networks, and natural language processing being its subsets. ML is a subset of AI and deep learning is the subset of Machine Learning that involves deep learning algorithms, vigorous training data, and multilayered neural networks. Scope AI has a pretty broad scope. Limited scope, as the focus is on making machines more accurate. Applications Chatbots, voice assistants, humanoid robots, etc. Google search algorithms, recommendation engines, etc. Types Weak AI, Strong AI, and General AI. Supervised learning, Unsupervised learning, and Reinforcement learning Use Cases of AI And Machine Learning? 1. Fraud Detection (Anomaly Detection) AI and ML are revolutionizing the finance industry because of predictive analysis. ML can help finance companies, especially banks, with fraud pattern recognition and raise the alarm whenever there’s something fishy, thus preventing fraud from happening. Please Note. While there’s already a rule-based system that’s used by different banks, it’s accused of causing false positives. And this costs the banks a lot of time as the employees have to mitigate the issue only to find it was a false alarm. However, fortunately, as an ML system learns from its experiences, the chances of false positives reduce which helps save time and resources. 2. Automation With AI in place, companies can easily automate redundant tasks and improve efficiency. And this is true for every domain across the globe. Here are some examples: Education Sector Teachers have to manually process attendance and create questions or exam papers redundantly, which requires a lot of time and effort. However, with AI, teachers can use attendance tools or biometric systems to mark attendance and use tools such as PrepAI for generating intuitive question papers within minutes. Manufacturing Manufacturing is highly labor intensive and has a lot of moving parts which increase the likelihood of making mistakes. However, AI makes it easy. Using automation tools or robots at a manufacturing facility, companies are drastically improving their delivery potential and reducing the error margin while ensuring worker safety and innovation. 3. Customer Support Machine Learning algorithms work behind the voice assistants such as Google’s Google Assistant, Apple’s Siri, or Microsoft’s Cortana for speech recognition using NLP or Natural Language Processing to provide you with an answer to your query. And several companies integrate voice assistants or chatbots on their

Yashica Chopra
August 13, 2023
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7 Top Procurement Analytics Companies to Improve Your Supply Chain

Procurement analytics is the collection and analysis of manufacturing data in an enterprise. It helps manufacturers make data-driven decisions in real time. Here, we’ll discuss the top procurement analytical companies in detail. Data and artificial intelligence help many manufacturers make informed decisions based on actionable insights. Enterprises spend heavy amounts on production and supply chain management to deliver the end product to customers. However, many external and internal factors can lead to delays, shortages, greater expenditures, and losses. Such adverse situations can be avoided by adopting the data-driven model. Procurement and spend analysis provides decision-makers with the necessary information to minimize risk and maximize returns. It helps supervisors and managers make smarter decisions based on insights derived by processing manufacturing data. Analytics for procurement is not a new concept. Several enterprises are already using business intelligence consultancy services to get analytical and data visualization reports in real time. In this blog, we’ll understand what procurement analytics is and the top data analytical companies that help manufacturers improve their supply chain. What is Procurement Data Analytics? Procurement analytics is the process of collecting, cleaning, and analyzing manufacturing data to derive actionable insights. The insights help make decisions about vendors, purchases, invoice payments, credit, overhead expenses, equipment maintenance, workplace safety, production cycle, quality control, supply chain management, and much more. According to Research and Markets, the global procurement analytics market is valued at $4.88 billion in 2023 and is expected to touch $10.26 billion at a CAGR (Compound Annual Growth Rate) of 20.4%. Many MSMEs and large enterprises from the manufacturing industry are investing in procurement data analysis to gain a competitive edge in the market by enhancing customer experience. Why is data analysis important in procurement? Analytics is the most vital component that helps manufacturers leverage data to streamline operations, production, maintenance, supply chain, and all aspects of the businesses to save money and resources and increase ROI. A few reasons to use analytics in procurement are as follows: Top Procurement Analytics Companies 1. DataToBiz DataToBiz is an award-winning data analytics company that works with MSMEs, SMBs, and large enterprises from different continents. The company helps enterprises unlock the true potential of data to facilitate business expansion and growth in competitive markets. It offers end-to-end services for: DataToBiz has earned a reputation for being a reliable Power BI consultancy provider in various industries like manufacturing, transportation, healthcare, FinTech, EdTech, etc. The company is a certified partner of AWS, Google, and Microsoft and has worked with 300+ firms. It has a satisfaction rate of more than 97% for offering tailor-made solutions to each client and helping them generate higher profits. 2. Everstream Analytics Everstream Analytics offers risk management and predictive analytics services to help improve global supply chain standards. The company converts uni-dimensional data into multi-dimensional to provide complete information and sharper analytics to clients. The company helps enterprises from many industries like manufacturing, automotive, heavy equipment, chemical, food and beverages, energy, etc. It offers the following services: Everstream Analytics has helped many enterprises reduce the time to identify and assess disruptions by around 50% to 70%. The company works with leading supply chain providers to strengthen their services and save costs. Data, procurement, logistics, supply chain, and weather are the primary focus areas of the company. It also helps manufacturers create sustainable and lean supply chains. 3. Sievo Sievo is a procurement analytics firm that helps data-driven enterprises to make transparent sourcing decisions. The company deals with extracting, classifying, and enriching data from various ERP systems and external data sources. It aims to save time for enterprises by implementing advanced technologies to derive analytics. The company offers services such as: Sievo assists several manufacturers to find the right suppliers and meet compliance standards with ease. From material forecasting to savings lifecycle management and reducing scope 3 CO2 emissions, the company provides the necessary software and analytics for business to achieve their goals. With a head office in Finland, Sievo has clients from around the globe and works mainly with large enterprises. 4. AnyDATA AnyData offers ready-to-go data-driven software products to help businesses use advanced technology to digitally transform internal processes. The company has developed a Modern Data Platform with all the vital components required to collect and analyze large datasets. It helps clients spend less time on data analytics and more time on expanding the business. The company’s services include: AnyData provides analytical solutions to enterprises and consultants. The company calls itself a one-stop shop for advanced analytics, dashboard customizations, integrated security, artificial intelligence, contract management, and much more. The products developed by the company can be run on the cloud, on-premises servers, desktops, and mobile devices. From supplier management to procurement analytics, it delivers complete services to enterprises from various countries. 5. Robobai Robobai is a global FinTech leveraging AI company that helps businesses transform their supply chains. The company emphasizes creating ethical supply chains through 360° visibility. It focuses on providing accurate reports about sanctions, indigenous suppliers, ESG initiatives, etc., to help clients make better decisions. The company’s services are as follows: Robobai empowers treasury and procurement teams to use data wisely, make better decisions in choosing suppliers, and make the supply chain more transparent. The company provides AI tools and analytics to automate processes and save time and resources for enterprises. Businesses from the manufacturing industry and other niches can use RobobAI to automatically improve data quality and derive insights. 6. Simfoni Simfoni is a digital transformation company, revolutionizing how businesses spend money. The company eliminates the hurdles and hassles from procurement to control and save business spend. It also helps automate procurement and supply chain processes to increase sustainability and achieve business objectives. The company offers an array of services, such as: Simfoni helps enterprises understand the benefits of procurement and purchase analytics by providing modular, purpose-driven solutions customized for each business. It uses AI technology to enable deep automation and streamline procurement to reduce unwanted expenses. The company supports manufacturers to benchmark performance, enhance spend visibility, and track the KPIs

Kavika Roy
August 11, 2023
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Everything You Need to Know About Large Language Models

Large language models use NLP and deep learning technologies to deliver personalized and contextually relevant output for the given input. LLMs are powerful, robust, and useful to an enterprise. Here, we’ll discuss the benefits, challenges, applications, and examples of LLMs. Artificial intelligence is growing leaps and bounds in recent times. Generative AI brought a revolution and disrupted the global industry. The top brands are following suit by investing heavily in AI and large language models to develop customized applications like ChatGPT. According to a Verta, Inc. survey, 63% of business organizations plan to continue or increase their budgets for AI adoption. Based on a report by Juniper Research, ML spending has increased by 230% between 2019 and 2023. Large Language models are being extensively researched and developed by universities and leading multinational brands in the international world. While there’s no denying the heavy expenses required to implement LLMs in an enterprise, it cannot be ignored either. LLMs are proving to be beneficial on many fronts. From R&D to customer service, large language models can be used for a variety of tasks. In this blog, we’ll find out everything we know to know about AI large language models. What is Meant by Large Language Models? A large language model is typically an AI algorithm that uses artificial neural networks to process and understand inputs provided in the human language or text. The algorithms use self-supervised learning techniques by analyzing massive data in various formats and understanding the patterns, context, etc., to provide a relevant output as the answer. LLMs can perform tasks like text generation, image generation (from text), audio-visual media generation, translating text, summarizing input, identifying errors in code, etc., depending on how and why it has been developed. The models can converse with humans and provide human-like answers. Large language models essentially use deep learning and natural language processing technologies to understand complex entity relationships and generate output that is semantically and contextually correct. However, developing an LLM from scratch is cost-intensive and time-consuming. Large Language Model consulting companies work with open-source LLMs and train them with the client’s proprietary data to fine-tune the algorithm as per the business requirements. Enterprises can adopt LLM applications in a quick time and gain a competitive advantage. What are the Parts of a Large Language Model? An LLM has a highly complicated architecture with various components, technologies, and connections. However, the following parts are important in building a large language model for a transformer-based architecture: 1. Input Embeddings The input text is transformed into individual words and sub-words in a process called tokenization. These tokens are embedded in a continuous vector representation. The semantic and syntactic information of the input is captured here. 2. Positional Encoding This part deals with providing the position of each token based on the input. This ensures that the model understands the input in sequential order to retain its meaning and intent. 3. Encoder Encoder analysis is based on the neural network technique. An LLM will have multiple encoder layers. These are the core of the transformer architecture and have two stages – self-attention mechanism (identifying the importance of tokens based on attention scores) and feed-forward neural network (capturing interactions between the tokens). 4. Decoder Layers Not all LLMs have a decoder layer. However, the decoder enables autoregressive generation for the model to generate the output based on the tokens. 5. Multi-Head Attention Multi-head attention is where several self-attention mechanisms are run simultaneously to understand all possible relationships between the tokens. This allows the model to interpret the input text in multiple ways (useful when the text is vague). 6. Layer Normalization Applied to each layer in the LLM, this part stabilizes the learning process of the algorithm and makes the model more effective in generating a more generalized output for various inputs. 7. Output Layers The output layers change from one LLM to another as they depend on the type of application you want to build. Benefits of Using Large Language Models Now that you know how large language models work, let’s look at the advantages of implementing LLMs in an enterprise. 1. Adaptability Large language models can be used in different departments for varied tasks. You can fine-tune the model with different datasets and change the output layers to deliver the expected results. LLMs can be used for numerous use cases. Businesses can develop more LLMs based on the core model and add more layers to expand it and use the applications across the enterprise. Over time, the LLMs can be adopted throughout the organization and integrated with the existing systems. 2. Flexibility Even though LLMs are yet to reach their full potential, they are already flexible and versatile. You can use an LLM application for content generation, media generation (image, audio, video, etc.), classification, recognition, innovation, and many other tasks. Furthermore, the models can process input of any size (from a single line to hundreds of pages of text). You can deploy the models in each department and assign different tasks to save time for your employees. 3. Scalability Large language models can be expanded as the business grows. You don’t have to limit the role of LLMs in your enterprise as the business volume increases. The applications can be scaled to accommodate the changing requirements. They also can be upgraded with the latest technologies and datasets to continue providing accurate and relevant results. LLMs are easy to train as they can read and process unstructured and unlabeled data. There’s no need to spend additional resources on labeling data. However, low data quality can lead to inaccurate output and inefficient applications. 4. Performance LLMs are robust, powerful, and highly efficient. They can generate responses in near-real-time and have low latency. Using an LLM application saves time for employees. It allows them to use the results right away and complete their tasks. For example, an employee doesn’t have to read dozens of pages to understand the content. They can use LLMs to summarize the information and read only the important points that