Page 23 – DataToBiz

Yashica Chopra
July 24, 2023
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9 Things Start-up Leaders Should Know About Generative AI

Generative AI uses deep learning algorithms and other technologies to generate output based on the user input. It is an advanced form of general AI capable of handling versatile tasks. Here, we’ll discuss the various aspects startup leaders should know about generative AI to implement the technology in their businesses. Artificial intelligence has been revolutionizing various industries in recent times. Statistics show that the annual AI adoption rate will grow by 37.3% from 2023 to 2030. Artificial intelligence is integral in our personal, social, and professional lives. However, we know that AI is still in the initial stages of exploration. Generative AI is a prime example of this. ChatGPT has changed the way we look at AI. It has created many discussions across the globe. The top companies have been working hard to release their versions of generative AI tools. Other SMBs and multinational organizations are hiring AI consulting firms to understand the importance of generative AI and adopt relevant tools to boost their business. But what is generative AI? What does it do in today’s scenario? How can CEOs and entrepreneurs use generative AI in their businesses? Let’s find the answers to these questions. What is Generative AI, and How Can it Help Your Business? Generative AI is a set of algorithms that can generate new content (text, image, video, audio, etc.) from the training data. The generative AI models are built on models trained using large datasets with unlabeled data. The algorithms learn to self-supervise themselves and identify the underlying patterns. It uses deep learning to complete tasks effectively compared to other AI models. The generative AI landscape covers a range of applications that optimize business processes, create content, brainstorm ideas, write and debug codes, etc., to reduce pressure on employees and complete tasks effectively. Enterprises can use generative AI in various ways to revamp their business model, reduce costs, increase productivity, and generate higher ROI. Generative AI is a subset of machine learning and is different from discriminative AI, which deals with data categorization. Generative AI models help businesses in the following ways: What are Various Techniques Used for Generative AI? Generative AI is not based on any single technique but is a combination of several techniques, such as: These techniques allow generative AI to process input data and generate output as required by the user. This is why generative AI can create versatile types of texts, images, videos, 3D designs, etc. It is highly useful in the medical and healthcare industry as it generates 3D visualizations of DNA structures, proteins, crystals, drug combinations, and so on. However, all of them are not in every generative AI model. AI leaders choose the necessary techniques based on the type of model they want to create and its purpose. Should Start-Ups Explore Generative AI? The answer is yes. Startups, small businesses, and growing companies should explore generative AI to survive in competitive markets. Using the latest technology to streamline internal processes and increase ROI will strengthen the foundation and allow startups to stand alongside established enterprises. Furthermore, startups can directly develop agile and scalable IT infrastructure and save money on replacing legacy systems. However, it’s vital to make informed decisions when bringing major changes to the business model. AI consultants recommend that CEOs understand the following aspects to know how generative AI can benefit their business. 1. Fast Developments in Generative AI Generative AI is evolving at a rapid pace for the last few years. From OpenAI’s ChatGPT to Google’s Med-PaLM, Meta’s LLaMA, Amazon Bedrock, Google Bard, and Microsoft’s integration of ChatGPT 4 with Office 365, the developments are occurring frequently. Entrepreneurs should understand that generative AI and the future are not fixed but constantly evolve. They need to create an infrastructure and work culture where technology and talent can keep up with the developments and adapt to the changes quickly time. Constant investment is necessary to stay up to date. Generative AI is not a one-time investment. 2. It’s More Than a Chatbot Even though we know of generative AI as an advanced chatbot, it is a lot more complex and capable. However, generative AI takes things ahead. It can classify, edit, and summarize almost any type of content. It can answer questions, draft letters, code, outline, strategy, and more. As the models evolve, generative AI can be integrated with enterprise workflows to automate and perform specific tasks. Some AI tools are already providing these services. 3. Scalability and Competitive Advantage Generative AI is versatile and scalable on various levels. Firstly, it is different from other deep learning models that can perform only one specific task. Generative AI can handle multiple types of tasks, resulting in greater efficiency and productivity. This makes it easier to use a single model to perform all the necessary tasks at work and gain a competitive advantage in the market. It can also be scaled to suit the growing business needs. 4. Identifying the Right Use Cases The success of any technology or development can be seen when it is used for the right purpose at the right time and in the right manner. CEOs should ensure that they know why, how, where, and when they want to use the tools in their startups. The budget of the project will also depend on these aspects. For example, generative AI in the customer service department can assist representatives in quickly solving customers’ complaints and speeding up ticket closing. Similarly, the sales and marketing team can brainstorm strategies and use generative AI to create a foolproof promotional campaign and relevant content without spending too much time and resources. CEOs have to identify where to use generative so that they can decide on the next step. 5. Reworking Existing Model vs. Training New Model Is generative AI rewriting your business model? The answer depends on the use cases you finalize and how you want to achieve the results. If you already have accurate ML models, you can customize them and set up integrations with generative AI. For example, using generative AI as a SaaS (software as a service) tool is cost-effective compared to

Yashica Chopra
July 18, 2023
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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

Purnima Chauhan
July 04, 2023
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8 Top AI Companies in the United Kingdom (Updated for 2025)

Artificial Intelligence has become an inseparable part of the technology landscape as companies became aware of its benefits. Enterprises are focusing on automating their business processes to drive better results and ensure top-notch customer engagement. They need artificial intelligence solutions that are efficient enough to support digitization and deliver data-driven insights. With this, the demand for AI development and AI-based companies has grown in recent years. The UK is a financial hub and is among the top countries known for the best AI technology and services. It’s true that you will find the top artificial intelligence companies in the United Kingdom offering services at affordable prices. Another reason why people prefer AI-based companies in the UK as the country is actively investing in innovation and the government of the UK has also set an aim for Britain to lead towards innovation with the latest technologies. The country has invested billions into AI development and research projects to date and will continue to do so till they reach its goal. As an entrepreneur, if you are planning to invest in AI, you must consider AI-based companies in the UK before you make any final decision. Top AI companies in the United Kingdom 1. OneTrust OneTrust is a rapidly-growing cybersecurity company that is revolutionizing how people manage their privacy and third-party risk. As one of the UK’s leading unicorn companies, OneTrust has raised approx £699m across four megadeals. Their investors include Insight Partners, Technology Crossover Ventures, and Coatue Management. What sets OneTrust apart is its innovative use of artificial intelligence in the cyber security space. The OneTrust platform allows users to easily manage and secure their privacy and third-party risk. The AI company has acquired two other startups, DataGuidance and Optanon, to integrate their privacy and security compliance technology into the OneTrust platform. OneTrust is one of the top IT companies in the UK, with its secure and comprehensive platform, the software is set to continue making managing and securing privacy and third-party risk a much easier task. 2. DataToBiz Next on the list of top artificial intelligence companies serving the UK is DataToBiz. An AI and Big Data Analytics firm that helps organizations to manage their data assets and find the best ways to surface insights from the data to enable businesses to take data-centric decisions. DataToBiz specializes in data of all sorts and helps businesses understand the language of their data. The company is well-equipped with the latest tools and technologies, such as ML, AI, LLM, Deep Learning, Neural Networking, Natural Language Processing (NLP), Big Data Analytics, Power BI, Tableau, Qlik Sense, Kibana, and Grafana. Founded by a team of data scientists and top AI & Machine Learning experts, DataToBiz is one of the top choices among AI companies in UK. It provides businesses with custom-made solutions to solve their problems. The services offered by the company include data warehousing & strategy, data engineering & analytics, data science, data visualization, visual analytics, business intelligence, staff augmentation and more. 3. Halfspace Halfspace is an AI and advanced analytics company based in the UK. The company helps businesses to identify, and understand their data-driven opportunities by offering top-notch analytics and AI solutions for better decision-making. They have an efficient team that works across a variety of platforms and tools, including Amazon (AWS), Google Cloud, Power BI, Microsoft, Databricks, etc. to offer the best results. Halfspace also offers consulting services to help businesses understand their data and develop strategies to make the most of it. This includes training and guidance on data use and analytics, as well as helping businesses identify areas of improvement and opportunities for growth. Founded in 2015 and based in Kobenhavn, Denmark, the company brings together the expertise of engineers, researchers, data engineers, and mathematicians with PhDs in engineering, physics, computer science, and applied mathematics. 4. PatSnap PatSnap is an innovative, comprehensive platform that helps businesses make informed decisions based on their intellectual property (IP) and market analysis. The platform is built around a comprehensive analysis platform that enables users to track their competitors and gain insight into the global market. With the help of this platform, businesses can easily develop strategies and make informed decisions that drive their business success. They offer a range of tools that enable businesses to evaluate and manage their IP assets. In addition to providing its users with a comprehensive analysis platform, PatSnap also offers several other services. These include a patent search tool that allows users to quickly and easily search for patents, a patent tracking system that enables users to monitor the progress of their patent applications and a patent monitoring system that helps users keep tabs on competitor patents. The company is based in the UK and Singapore, with locations in China and the United States. 5. Wayve Wayve Technologies Ltd is an engineering and software development company that specializes in artificial intelligence-based software for self-driving cars. The company was founded in 2017 and has headquarters in London. The company is composed of experts from various fields, including engineering, software development, and artificial intelligence. Wayve is highly experienced in the development of autonomous driving software, and the company is dedicated to creating safe and reliable AV technology. It has implemented the first public trials of their AI-powered AV technology in the UK, giving users the opportunity to experience autonomous driving in real-world environments. This has allowed Wayve to gather valuable data and insights to further develop its technology and enhance the user experience. As Wayve continues to prove the worth of its AI-powered AV technology, major companies such as Microsoft and Virgin are recognizing its potential. This has resulted in partnerships and collaborations with Ocado Group, Asda, and DPD, in order to trial its technology on their delivery fleets. 6. iGenius iGenius is an augmented analytics company that works over reimagining data interaction for businesses. Their products add direction to data-driven decisions and make it easier for any team to extract value from their business data, regardless of their

Kavika Roy
July 02, 2023
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AI Revelation: Why Businesses Place Their Trust in These AI Powerhouses?

From cutting-edge technology to unparalleled expertise, discover how a few AI powerhouses are shaping the future of AI and earning the trust of enterprises worldwide. As enterprises are increasingly turning to AI to help them solve complex problems and make better decisions, a few key players have emerged as leaders in the field. These AI companies are trusted by businesses across multiple industries to provide cutting-edge AI solutions that drive growth and innovation. Accenture As quoted by Paul Daugherty, Accenture’s Chief T&I Officer, “AI is poised to transform every aspect of our lives and every sector of the economy.” Accenture is a multinational professional services company, founded in 1989, that offers a wide range of services to help businesses and organizations increase efficiency, streamline operations, and drive innovation. The company is headquartered in Dublin, Ireland, and provides operations in more than 51 countries. With a global network of AI experts, Accenture provides various AI services, including data management, machine learning, and cognitive computing. DataToBiz One such industry leader is DataToBiz. The organization has recently celebrated its 5th year of operation in the industry. Throughout its journey of existence, DataToBiz is helping businesses to grow by delivering insights and implementing inventive solutions. DataToBiz has been in the limelight for delivering high-impact, customized solutions that meet the unique needs of each client. From predictive modeling to natural language processing solutions, they leverage AI to create a strong future for their corporate partners. More info: https://www.datatobiz.com/hire-ai-engineers Along with featuring on Govt of India and MeitY, DataToBiz has proudly held the distinction of being Clutch’s top-ranked artificial intelligence company in India. The company has successfully served various enterprises within multiple sectors and is going ahead consistently. Major market players including Tosoh Quartz, FTCSolar, Nordex, Dentsu, TransSIGHT, etc. have been implementing DataToBiz’s advanced AI solutions. DataToBiz also has two AI-based SaaS products: PrepAI, an advanced online test maker, and HirelakeAI, an AI-driven hiring solution, along with other innovations in the pipeline. “From predicting the future to automating mundane tasks, AI is opening up a world of possibilities, and we would love to try them all!” says Ankush Sharma, Co-Founder, and CEO, DataToBiz. IBM Watson Last, but not least, IBM Watson is another AI hub that businesses trust. With a powerful cognitive computing platform, the company helps businesses make sense of vast amounts of data with meaningful insights. Its AI solutions have been used in a variety of industries, including healthcare, finance, and retail. Founded in the year 2010, with headquarters located in New York, USA, the company’s vision is to leverage the power of AI to help businesses solve complex problems, with their range of AI solutions, including NLP, and computer vision, among others. IBM Watson provides services in more than 175 countries across the globe with the key objective of making AI accessible for all businesses. AI & The Future As AI continues to revolutionize industries and drive innovation, these AI Powerhouses are inspiring businesses to harness the power of AI and embrace the endless possibilities of the future. With AI as their guiding force, businesses are paving the way for a paradigm shift in the way industries operate and thrive. Originally Published on Digital Journal

Yashica Chopra
June 30, 2023
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Top 6 Benefits of Data Visualization for Sales Teams

Data visualization is the process of presenting complex data in easy-to-understand graphical formats on an interactive dashboard. Here, we’ll discuss the significance of data visualization in sales and the range of benefits it offers to boost a business in competitive markets. Business intelligence is the process of combining strategies and technologies to collect, analyze, and manage large amounts of data in an enterprise. The analytics are presented in neat and structured reports to help make effective decisions faster. Data visualization is used to convert analytics into graphical reports with interactive elements. It presents insights in different readable formats and allows you to make changes in real-time. Statistics show that the data visualization market was valued at $5590.75 million in 2021 and is expected to touch $9464.0 million by 2027 with a CAGR (compound annual growth rate) of 9.17%. According to a report, data visualization can make decision-making 5x faster and execution of the decision 3x faster. A study shows that using visual data can shorten the overall time spent on business meetings by up to 24%. Several organizations hire Power BI consultants to set up a data visualization dashboard in different departments. Microsoft Power BI is one of the leading visualization tools with many user-friendly features and third-party integrations. In this blog, we’ll understand why data visualization is vital for your business and the benefits of data visualization in streamlining sales, the most important department in many enterprises. What Is Data Visualization and Why is it Important? Data visualization is the process of converting large datasets into graphical format to make actionable insights more accessible to users. It reduces the time taken to identify patterns and trends and highlights outliners through different visual formats(like graphs, charts, maps, etc.). Data visualization is sometimes called information visualization, information graphics, and statistical graphics. It belongs to the data science processes and data presentation architecture (DPA). For example, the sales analytics dashboard used by the sales and marketing teams uses data visualization to present advanced analytics in easy-to-understand reports. This helps monitor KPIs (key performance indicators) and ensure that your strategies are always aligned with the business goals. From streamlining data collection to hastening the entire process and reducing mistakes, data visualization has a crucial role in helping your business become successful. Benefits of Data Visualization for Sales So what are the benefits of visualization in the sales and marketing department? Let’s find out below. 1. Better and Effective Decisions The sales teams have to make quick decisions and be proactive to make the most of market opportunities. Data visualization tools provide graphical reports that help make decisions using accurate and reliable information. Since visual data is easy to comprehend, it allows teams to discuss more aspects in detail and finalize their decisions in less time. The visualizations also allow you to understand how the reports change when you consider different variable parameters. In short, data visualization is an effective way to help businesses adopt data-driven decision-making processes. 2. Gather Data from Multiple Sources Sales teams have to process data from various channels and applications. For example, you need customer data from CRM systems, brand mentions from the internet, lead generation data from multiple channels (email, website, social media, offline, etc.), and competitors’ market share to create and implement a robust sales strategy to boost your business. Data visualization tools can be integrated with several data sources to create a unified interface. Your sales team can directly access the data they want by using the same tool without going through a long-winded process of asking their superiors to share the necessary information. 3. Gain New Perspective Data visualization reports are dynamic and provide different insights as you change the parameters. You can gain new perspectives as you analyze more visual reports. For example, the sales graph for the current month may show that you achieved more than the target. However, when the parameter is changed to six months, you may notice that the overall sales have been declining or not up to the mark. Instead of going through pages of reports and guessing the overview picture, you can use data visualizations to present the final outcome in a clear format. Your sales team will find it convenient to use visualizations for correlations, which provide more perspectives for the given data. 4. Bring Context to Content The numbers in a report make no sense when taken out of context. They could mean anything, and trying to identify the relationship between the numbers and the data in the corresponding column and row headers can be stressful. Data visualization provides an effective solution to this problem. For example, a bar graph report for sales performance will show the exact situation for the given period. The bars are not just decorations but indicators of the performance of the teams, products, and the overall business. 5. Save Time When you consider the above-listed benefits of data visualization, you can notice one common factor in all of them. Data visualizations save time and energy for the sales team. Sales reps don’t have to feel burdened by pages of paperwork and sheets with never ending data. Everything is automated in the setup. The data visualization tools are connected to data sources and other applications that continuously collect, clean, store, and analyze data. You just have to run a query for the tool to generate the report in real-time. 6. Tell a Story One of the major applications of data visualization tools is to make data more interesting. Instead of sharing boring and lengthy reports, you can convert the whole thing into an interactive presentation to tell a story. A report by Accenture shows that more than 80% of the respondents thought data visualization tools were an effective means for storytelling in the business context. Data visualizations are easy to generate and can make meetings more interesting. Some businesses are combining visualizations with VR tools to create 3D images and graphs. This increases the collaborative nature of the tool and helps the sales team present its points most engagingly. More About the Importance of Data Visualization in Sales Sales data visualization can help you

Purnima Chauhan
June 28, 2023
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AI Development: In-house vs Outsourcing (Complete Guide)

In this data driven world, businesses are turning to advanced tools such as Artificial Intelligence and Machine Learning to extract valuable insights and boost productivity. AI-powered solutions offer great potential for automation, and help in decision-making. However, many companies still need to know-how to implement these strategies properly. As businesses strive to stay competitive in the marketplace, many are turning to the use of AI to gain the competitive edge. But with a wide range of opportunities and methods of development, companies generally opt for outsourcing AI development. In this blog, we will provide a comprehensive overview of the advantages of each approach and the key considerations to take into account while deploying in house vs Outsourced AI development. What is AI Development? AI development refers to the process of creating intelligent machines that can perform tasks without human intervention. It involves a combination of machine learning, natural language processing, robotics and other technologies that allow computers to learn from data and make decisions based on that information. The goal of AI development is to advance automation in various industries, including healthcare, finance, transportation and more. Developers use algorithms and models to create systems that can analyze vast amounts of data quickly and accurately. They also work on improving the ability of machines to understand human speech or written text through NLP technology. As AI technology continues to evolve rapidly, it presents new opportunities for businesses looking for ways to streamline processes or provide better customer service experiences. Also, AI is playing an increasingly important role in business, and its impact is only going to grow. By automating tasks and providing insights that would otherwise be hidden, AI is helping businesses boost efficiency, drive growth, and gain a competitive edge. While some businesses are still hesitant to embrace AI, the benefits are becoming too great to ignore. For businesses that are looking to stay ahead of the curve, investing in AI development is a smart move. Factors to Consider While Choosing In-House vs Outsourcing AI Development There are a number of factors to consider when deciding whether to develop AI in-house or outsource it to a third-party. What is the Difference Between Outsourcing and In-House Development? In-house AI development Are you considering in-house development for your business? As a professional, you know that having your own team with the necessary IT skills and abilities can make all the difference. On the other hand, the management of such a team is a serious load on your budget. While in-house development may be more expensive in the short term, it becomes more cost-effective in the long run. Moreover, for small businesses, an in-house IT department can often handle both the support and development of internal information systems. In-house development also allows for better communication and collaboration between the development team and other departments within the company, leading to more efficient processes and improved project results. By understanding the advantages of in-house development, organizations can make informed decisions as to the best approach for their specific project needs. Advantages of In-house AI development Customization In today’s competitive business environment, having the ability to customize your Artificial Intelligence (AI) solutions is a must. With the ability to tailor the features and settings of your AI solution to meet the exact needs of your business, many companies are opting to develop their own in-house AI solutions. In accordance with the culture Collaborating with a local development team means engaging with individuals who understand the culture, values, and standards of your organization. This type of relationship ensures employees to seamlessly integrate into the organization and add value to their roles. By hiring locally, organizations can benefit from unique perspectives and skillsets that come from having a team that is more familiar with their environment. Outsourcing AI Development Outsourcing AI development can be defined as the process of contracting with an external organization or individual to create, train, and deploy AI models on behalf of another company. There are many reasons why companies might choose to outsource AI development, including lack of internal expertise, time constraints, and cost considerations. When done correctly, outsourcing AI development can be a highly effective way to quickly and efficiently deploy AI capabilities within an organization. By leveraging the expertise of a highly-skilled, external provider, you can streamline your product development while gaining access to specialized engineering & domain expertise. With the right outsourcing partner, you can take advantage of cost savings, faster time to market, and the flexibility to scale your operations as needed. Advantages of Outsourcing AI Development Collaboration with AI Experts When it comes to developing AI products, it becomes crucial to ensure that you have the right experts on board. However, due to the increasing demand for AI, it is difficult to secure full-time experts. Fortunately, outsourcing AI development can help you to bypass this issue. It enables you to access an experienced and professional team without the associated costs of hiring in-house. Cost-Efficient option One of the most obvious advantages of outsourcing is the cost savings that can be realized by making use of the services of Asian countries, where the cost of labor is often much lower than in Western countries. Furthermore, these services are often of good quality, meaning that businesses are able to benefit from low pricing without compromising on the quality of the work produced. Difference Between in House vs Outsourcing AI Development Conclusion When it comes to making a decision about in house vs outsourcing AI development, it is important to consider several factors. Cost is certainly one of the key components, however, it is also important to examine the pros and cons of each model to make the right choice. By understanding the differences between in-house AI development and outsourcing, you can make an informed decision and ensure the best outcome for your project. As artificial Intelligence services become more widely available, businesses are facing the dillema of whether to pursue in-house development or outsourcing for their AI project. While

Yashica Chopra
June 26, 2023
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6 Top AI Companies in Finance Industry (Updated for 2025)

Artificial intelligence brings a modern approach to the traditional banking and finance sector. It plays a vital role to strengthen internal processes and increase customer experience. Here, we’ll discuss the role of AI in the finance industry and the leading AI companies offering advanced solutions in the global market. Artificial intelligence has established its presence in many industries, including finance. In fact, around 50% of financial establishments have moderately incorporated AI in their process. According to Statista, 35% of them have fully functional AI systems, while only a 3% haven’t yet adopted artificial intelligence in the finance industry. A limited survey conducted by NVIDIA showed that almost half the respondents were migrating their IT infrastructure to hybrid could to optimize the performance of their AI tools. The survey also reported that banks and trading firms are busily adopting advanced technologies like NLP (natural language processing), large language models, fraud detection software, portfolio optimization tools, etc., to enhance customer experience. Intelligent avatars and speech AI are also being used by finance and insurance companies. Furthermore, the companies have stated that using artificial intelligence in corporate finance has increased customer experience by up to 45% and operating efficiency by up to 35%, alongside reducing the cost of ownership by up to 20%. Moving from traditional banking methods to modern methods using AI is beneficial in many ways. Most banking and insurance firms partner with AI FinTech companies to speed up the adoption process and reduce the overall cost of revamping their systems. It also allows financial establishments to access expert talent without hiring them as in-house employees. This blog is dedicated to the role of AI in the finance industry and the top companies that offer offshore services to banks, trading firms, insurance agencies, etc. Uses of Artificial Intelligence in the Finance Industry AI financial advisory startups offer strategic solutions and build artificial intelligence-based tools exclusively based on the bank’s requirements. AI companies also customize existing tools and dashboards to integrate them with banking software to streamline daily operations and facilitate effective decision-making. Let’s look at some popular uses of artificial intelligence in the finance sector. Personalized Wealth Management Banks and investment firms can offer personalized suggestions for wealth and portfolio management using NLP (natural language processing) and machine learning algorithms. You can set up chatbots that provide 24*7 support to customers and help them make the right investment decisions based on their personal data. Fraud Detection and Cybersecurity The increase in online fraud has forced finance establishments to use advanced software and applications to detect such cases in the early stages and prevent losses. Banks, insurance companies, etc., can rely on fraud detection software to flag users or transactions that raise red flags and alert the managers. Similarly, AI tools can also be used to increase data security and minimize the risk of data theft through security breaches. For example, a FinTech company in Sweden could reduce the rate of fraudulent transactions by 47%. Loan and Risk Assessment (Corporate) AI has different roles in the corporate banking sector. From determining the risk of approving loan applications to improving underwriting, artificial intelligence is used in different ways to mitigate risk and increase ROI. For example, US Bank uses AI to analyze customer data and identify risky members. This is done to prevent money laundering through their bank accounts. Another example is where an African-based company streamlined data sources for credit risk assessment by integrating its systems with an NLP engine and using a data warehouse to collect data from multiple credible sources. Top AI Companies in Finance Industry Statistics show that more than 75% of millennials would never go to a bank branch if they can complete transactions online. They prefer mobile banking services with chatbots, personalized recommendations, and more. The finance industry can cater to customers’ requirements by partnering with the best artificial intelligence companies. 1. DataToBiz DataToBiz is among the leading companies in artificial intelligence from India. It has clients from different countries like the US, UK, Australia, the Middle East, India, South Africa, and more. The company offers complete AI and ML services to small, medium, and large organizations from different industries. It has offered custom FinTech solutions to IDBI Bank, ICICI Bank, NPCI, etc. The company’s AI services include strategic planning, pilot implementation, platform customization, big data analytics, digital transformation, IoT (Internet of Things), and many more. DataToBiz has won several awards in the artificial intelligence industry. It has a team of expert AI and ML engineers who work in tandem with the clients to set up and streamline internal processes. It also offers the following services: Banks and other financial establishments can rely on the company to adopt advanced and emerging AI tools to create innovative financial products, enhance customer satisfaction, mitigate risk, improve data security, and provide intuitive dashboards to employees for real-time decision-making. 2. HighRadius HighRadius is one of the best AI companies in the global market. It offers automation software for CFOs and has helped 700+ clients from around the world. The company specializes in building robust and out-of-the-box AI solutions in the accounting and finance sector. It also works with organizations from other industries like retail, insurance, pharma, life sciences, etc.s The automation solutions by the company are designed to help with DSO (Days Sales Outstanding) reduction, working capital optimization, and bad debt reduction and to increase overall productivity in less than six months. HighRadius offers services such as: The company has developed autonomous tools to manage receivables, treasury, and reporting in detail. The various products of the company are used to process more than $4.7 trillion in transactions per day. It has clients like Unilever, Hershey’s, Kellogg Company, etc. 3. Darktrace Darktrace is one the top AI companies in finance, offering a range of products to enhance cybersecurity for its clients from 110 countries around the globe. The company has developed self-learning AI that offers powerful solutions to help businesses understand their processes and customers to the minute detail. It emphasizes the need for advanced cybersecurity applications to protect the bottom line. The company’s Darktrace Cyber AI Loop uses continuous feedback

Yashica Chopra
June 24, 2023
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How AI and Metaverse Have Changed Our View of the World

Metaverse is a virtual world personalized to provide various services to each user. Artificial intelligence is an integral part of Metaverse and can determine the extent of its role in our lives. We’ll discuss how AI and Metaverse have changed our future. Metaverse is the buzzword in today’s market. It is a combination of various advanced technologies like AR, VR, AI, etc. Metaverse allows users to engage with 3D objects in a virtual space. AI and Metaverse are set to create a new reality for mankind. Statistics show that the Metaverse market is likely to reach $783.30 billion by 2030. Many leading multinational giants, like Facebook, Google, etc., are working on Metaverse. But what exactly is Metaverse? What is the role of AI in developing Metaverse, and how will it impact the world? Let’s find out. But before we delve into Metaverse, let’s start with the basics and understand what artificial intelligence is. What is Artificial Intelligence? Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. AI enables machines to mimic the human mind for decision-making. Artificial intelligence helps computers process vast amounts of information to identify hidden patterns, detect trends, and process information similar to a human brain. AI is categorized into the following: Deep learning and machine learning are subsets of AI and are used in several applications. Artificial intelligence has been adopted by various businesses in recent years. A survey report shows that more than 86% of CEOs consider AI the mainstream technology in their organizations. Close to 85% of enterprises have increased their budget for AI compared to 2019. The global AI market was valued at $93.5 billion in 2021. It is likely to grow at a CAGR (compound annual growth rate) of 38.1% from 2022 to 2030. AI is used by small, medium, and large enterprises from various industries. From Healthcare to manufacturing, every business needs AI to streamline its process. Artificial intelligence is used in the following ways: What is Metaverse? Metaverse is a blend of multiple technologies. It is not a single entity and requires a range of technologies to work together. Metaverse is a collected virtual space that merges the virtual and real worlds. In fact, meta refers to ‘virtual’, and verse is short for ‘universe’. So, Metaverse is a virtual universe. Virtual reality, augmented reality, artificial intelligence, mixed reality, etc., are used to create Metaverse. The virtual worlds can be accessed from computers, smartphones, consoles, VR devices, and more. Metaverse can allow people to interact with the virtual world without moving in the real world. However, it’s important to note that Metaverse is still in the developing stages. Top companies are investing in bringing Metaverse closer to common people. Some smaller organizations are updating their IT infrastructure to be ready for Metaverse by creating virtual worlds that closely resemble real life. That said, a few VR-based apps are also being termed Metaverse by businesses (especially in the gaming industry). These are hybrid models at best and not Metaverse. Role of AI in Metaverse With so many technologies required to develop Metaverse, what role does AI have in it? How important is AI to create Metaverse? The role of artificial intelligence is to support the extensive features of Metaverse. AI will enhance user experience. It will make user interaction with Metaverse a seamless and worthy experience. In short, AI will aid creation, collaboration, interaction, and personalization in Metaverse. It can make the virtual world a delightful experience for users. The Risk of Using AI in Metaverse While AI is great for personalization, it isn’t without risks for users. Micro-targeting can go either way if it becomes manipulative or is missed by people in power. It can be hard to separate fact from fiction if one is not alert. Data manipulation is another risk, which can be controlled through in-built safety/ regulatory algorithms. One way to minimize this risk is by setting up strict regulations about data collection and the role of AI agents in Metaverse. If we can control the misuse of technology, there’s no denying the plethora of advantages it offers to the public. How AI and Metaverse have changed the Future Metaverse will use AI algorithms the way social media platforms use AI for newsfeed personalization, ad targeting, etc. NLP (natural language processing), facial recognition, voice recognition, and other actions are also executed by AI. A Harvard University report shows how AI will affect eight areas of human activities by 2030. The areas include: The report highlights the need for reliable hardware to handle the developments and create a safe space for the world at large to use Metaverse in daily life. Sentiment analysis, emotional recognition, and biometrics (physical + behavioral) will play a vital role in the personalization of the virtual world for a user. Mark Zuckerberg, CEO of Meta, says that it could another five or ten years to make Metaverse a mainstream technology. Right now, it is limited to interactive headsets, AR/VR games, and mixed-reality applications. Conclusion Metaverse will open up new possibilities in technologies and blur the lines between real and virtual. You can create a virtual avatar of yourself to interact with others in the 3D virtual world. It erases boundaries and provides opportunities for businesses to market their products to the right audiences. Metaverse also enhances learning and can provide a user-centric experience. Artificial intelligence plays a crucial role in making this happen by bringing together other technologies to make Metaverse engaging.

Rohit Narain
June 22, 2023
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How to Make Stock Price Predictions Using Reinforcement Learning?

Analyzing the stock market using artificial intelligence has been a work in progress recently. Here, we’ll discuss how you can develop an AI stock prediction model using reinforcement learning. Analyzing the behavior of the stock market has been a subject of interest and challenge in the AI industry. Data scientists, market analysts, and financial experts have been curious to determine whether it is possible to overcome these challenges. The biggest concern is the need for an extra large dataset to build a predictive system based on supervised learning algorithms. Furthermore, even the most advanced technologies seem to be inadequate to accurately predict the changing prices in the stock market. Yet, accurate AI stock price prediction could be possible without relying on large datasets. In this blog, we’ll try to identify the challenges of stock market prediction and understand if we can use reinforcement learning for stock prediction and data analysis in Python, that too, using limited or no data to train the algorithm. Before we proceed to read more about stock price prediction using machine learning, let’s understand more about the data analysis methods used to process stock market data. Types of Data Analysis Techniques Used on Share Market Data The stock market data is analyzed in different techniques. These are categorized as – Time Series Analysis and Statistical Data Analysis. 1. Time Series Analysis A time series is defined as a sequence of data points that appear/ occur in successive order in a given period. It is the opposite of cross-sectional data, where the events that occur at a specific time point are captured. The time series analysis tracks the movement of the chosen data points over the specified period. The data points are usually the price of the stock/ share/ security. The prices are collected at regular intervals to analyze the patterns. There are various techniques to perform the time series analysis on stock market data. Let’s check them out in brief. a. Moving Averages: The moving average of a stock is calculated to smooth the price data and constantly update the average price. In finance, the MA (moving average) is considered a stock indicator and is used in technical analysis. The short-term price fluctuations are mitigated in this process. The MA is further divided into the following: i. Simple Moving Average (SMA) SMA is calculated using the arithmetic mean for a given set of values over a specific period. Here, the set of values is the stock prices. These are then added and divided by the number of prices in the set. Formula: A1+ A2+ A3+… Ann Here, A= average in the period; nn= number of periods; SMA= n ii. Exponential Moving Average (EMA) The EMA gives more importance to recent prices to make the average price more relevant based on the new information. The SMA is calculated first to use in the EMA formula. The smoothing factor is calculated next to determine the weighting of EMA- 2/(selected period+1). Formula: EMAt= [Vt×(1+ds)]+EMAy×[1−(1+ds)] Here, EMAt= today’s EMA; Vt= today’s value; EMAy= yesterday’s EMA; ds= smoothing (number of days) Some other types of moving averages are: b. ARIMA: It is another approach to time series forecasting. ARIMA and exponential smoothing are widely used methods as they offer a complementary approach to the problem. ARIMA describes the auto-correlations in data, while exponential smoothing relies on seasonality in data and trend description. c. Box Jenkins Model: This model can analyze different types of time series data for forecasting. It is a mathematical model that uses inputs from specified time series to forecast data ranges. The Box Jenkins model determines the outcomes based on the differences between data points. It identifies trends for forecasting stock prices using autoregression, moving averages, and seasonal differences. d. Rescaled Range Analysis: It is a statistical technique developed to assess the magnitude and nature of data variability over a certain period. The rescaled range analysis method is used to identify and evaluate persistence, randomness, and mean reversion based on the time series data from the stock markets. This insight is used to make proper investment strategies. 2. Statistical Data Analysis It is the common value that occurs in the dataset. It is the middle number in the dataset. For example, in 4, 6, 7, 9, and 11, the median is 7. It is the average value of the dataset. It is also called standard normal distribution or Gaussian distribution model. It is charted along the horizontal axis, representing the total value spectrum in the dataset. The values of half the dataset will be higher than the mean, while the other half will be longer than the mean. And the other data points will be around the mean, with a few lying on extreme/ tail ends on both sides. It measures the asymmetry/ symmetry of the price/ data point distribution. The skewness will be zero in a standard normal distribution. A negative skewness will lead to a distorted bell curve on the left, while positive skewness will cause a distorted bell curve on the right side. What is Reinforcement Learning? It is an area of machine learning that takes the appropriate action to maximize returns for a given situation. Many software applications and machines use reinforcement learning (RL) to identify the best behavior/ path to arrive at the desired result for a specific situation. Reinforcement learning is different from supervised learning. In the latter, the training data is the answer key to training the model with the correct answer. However, in RL, the reinforcement agent decides which task to perform, as there is no specific answer used for training. It allows machine learning developers to train the algorithm without using a dataset. The algorithm will learn from experience and improve itself over time. What are the Different Datasets Available for Stock Market Predictions? Fortunately, there are a few datasets available to train the algorithms. Developers can access the datasets from the following: NIFTY-50 Stock Market Data The data is available from 1st January 2000 to 31st April 2021. It provides

Yashica Chopra
June 05, 2023
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10 Ways Data Analytics is Revolutionizing Restaurant Industry

Data analytics in the food and beverage industry is revamping how restaurants, diners, food courts, etc., provide services to their customers. Many problems in the sector can be solved using AI and ML technology. Here, we’ll discuss how data analytics and advanced technologies are revolutionizing the restaurant industry. The restaurant industry is among the most competitive markets around the world. Food is a part of our lives, and so is community living. Restaurants bring both aspects together with ease. Despite the competition, the restaurant industry is not with its issues. Like every other niche, it had to adapt and change to stay relevant in the global market. For example, the lockdowns during the Covid-19 pandemic resulted in a multitude of problems for restaurants. According to National Restaurant Association, 92% of restaurant operators said that the increasing cost of food is a major issue. However, many operators are optimistic and aim to establish positive growth in 2023. The food industry is expected to reach $997 billion, mainly due to the increase in food prices. Furthermore, close to 50% of restaurant owners predict fierce competition in 2023 and the coming year. Technology plays a major role in giving restaurants a definite edge over competitors. The global restaurant POS market size is estimated to grow at a CAGR (compound annual growth rate) of 6.4% from 2021 to 2028. Many small and large restaurants rely on data analytics service providers to plan and implement a data-driven model to make the most of analytics and insights to increase profits and customer base. In this blog, we’ll read about the role of data analytics for restaurants and how it can solve various problems in the food industry. How Can Data Science Help Restaurants? Data science can help restaurants make use of their raw data and derive actionable insights. These insights can be used to make faster and more effective decisions at different levels. Many restaurant operators hesitate to invest in data science as they don’t know where to start. Data science can help with the following: Ways Restaurant Analytics is Transforming the Industry 1. Greater Efficiency The AI and ML tools used to derive analytics for restaurants help the operators to increase the overall efficiency of the place. From identifying the right sources of ingredients to increasing transparency in the supply chain and identifying ways to minimize costs without compromising quality, data analytics can help restaurants in becoming more efficient and thus enhance customer experience. For example, automating reservations allows the staff to pay more attention to improving the diner’s service. Using AI devices to take orders will minimize the risk of human error and even help the staff recommend dishes based on what the customers order. 2. Streamline Marketing Restaurant data analytics is incomplete without focusing on marketing strategies. Every business needs a strong marketing plan to attract new customers and retain existing ones. A restaurant business has to understand what the customers want and establish communication channels to reach out to a wider audience. Special offers, discounts, suggestions based on previous orders, etc., can be set up based on insights provided by data analytics. 3. Quality Control Big data analytics helps restaurants ensure that they maintain quality standards at all stages. This includes the procurement of raw ingredients, kitchen cleanliness and maintenance, cooking, packaging, delivery, dining services in the restaurant, staff behavior, etc. Food has a short shelf life which affects the quality. This can be prevented or minimized by monitoring the supply chain movement, storage, and production quantity. 4. Supply Chain Management More customers are paying attention to how and where the ingredients are being sourced by restaurants. This is especially true for vegan consumers who prefer a transparent supply chain and want the restaurant to explicitly mention the sources. Similarly, diners and food courts that rely on local and international products can use data analytics for supply chain and inventory management to ensure there are no delays or issues with stock quantities. 5. Customer Sentiment Analysis Sentiment analysis is the process of identifying the various emotions customers feel when they interact with the restaurant business. From the ‘like’ on a social media post to online reservations, dining experience, communicating with the staff, response to food served, reactions about pricing, etc., are all factored in this analysis. It helps restaurants understand the strengths and weaknesses of the business from the customer’s point of view. The best way to derive customer sentiment analytical reports would be to hire a company offering customized data analytics solutions in the industry. 6. Demand Forecasting Restaurant predictive analytics helps the chefs finalize the menu based on customer preferences. If a restaurant gets more customers in the evening and has a maximum number of orders for pasta, it’s an indication that the dish is popular and should be definitely available during rush hour. This will help chefs plan the day’s work and adjust the meals cooked for other times. Even the portion of each serving can be decided based on this. It also minimizes food wastage by the restaurant. 7. Predicting Product Shelf Life Food products are perishables. While some taste good only for a few hours, other items can last a week or more. For example, cookies last longer than cooked risotto. Data analytics assists restaurants in correctly determining the shelf life of each product and also lists the guidelines for consumers to prevent health issues (or lawsuits). 8. Personalized Customer Experience Every customer wants a personalized experience from the businesses they interact with. From food portions to special requests to multiple payment options and so on, it’s vital to meet the customers’ expectations to ensure loyalty. This is possible by processing their information (previous orders, payments, recurring visits, feedback, etc.) to derive actionable insights. Customer data and feedback are a goldmine for restaurants. 9. Restaurant Layout Optimization How can the restaurant’s dining room be organized to increase capacity but also ensure privacy and prevent the room from appearing stuffed or suffocating? This is one of the biggest concerns for many restaurants, especially the ones with limited space availability. How many