Page 35 – DataToBiz

Yashica Chopra
October 01, 2021
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Machine Learning is Right for Your Business: Is it True?

Machine learning can make an enterprise an industry leader by streamlining the processes and increasing returns. ML is right for every organization, and here’s why. Have you heard of the terms artificial intelligence and machine learning? Or, the correct question would be how many of you have not heard of them. Isn’t it? The interest in AI and ML has been steadily growing over the years. Many organizations have already started investing in ML technologies to improve their business operations. However, it is vital to understand what ML is, why your business needs machine learning, and what kind of technologies you should adopt. To make things easy for you, we’ll explain the various facets of ML in this blog. In today’s world, there is no shortage of data. The world has generated around 2.5 quintillion data bytes every day in 2020. Every user created 1.7 MB of data per second. Data is available in real-time for processing and analysis. This helps organizations around the world to derive accurate insights and use them to make decisions. But how is this data processed? Manually collecting, cleaning, and analyzing big data is not really possible and takes too much time and effort. Artificial intelligence tools and machine learning algorithms help replace the manual processing of data. Machine learning models can process data in real-time, identify patterns, and share valuable insights that help enterprises streamline their day-to-day business operations. In recent years, machine learning adoption rates have improved in all parts of the world. The machine learning global adoption rate is 45%, with another 21% exploring ML methods to get started soon. There are several practical applications and benefits of using machine learning in the business. It has become necessary to invest in artificial intelligence and machine learning tools to revamp the business process and compete in the market. Both SMEs and large-scale enterprises need to understand what machine learning is and how it can change their business for the better. But first, let’s start with the basics. In the first section, we’ll read more about machine learning, the definition, model, purpose, and more. Overview of Machine Learning Machine learning is an AI application that helps systems read and understand vast amounts of data. Machine learning is used to develop programs that can process and analyze data to deliver predictions and insights. The main aim of using machine learning is to empower a computer to learn and understand data in various forms. In short, machine learning helps computers process data the way a human mind does. Enterprises develop machine learning models customized to work with the type of data collected to make the business successful. Is machine learning AI or is machine learning the same as Artificial Intelligence? Machine learning is a subset of AI. The concept of machine learning is to empower machines to learn and adapt through self-training. It deals with the statistical methods and algorithms that teach a computer to understand and process data. This means that while machine learning can be called AI, the reverse is not true. It is an application of artificial intelligence where a machine is trained to think and make decisions like a human. AI has more components than machine learning. It is a broader subject. Artificial intelligence aims to make computers smart enough to solve complex problems without human intervention. In other words, Artificial intelligence creates intelligent machines, while machine learning trains these machines to become efficient and accurate. Machine Learning Models The machine learning algorithms are widely classified into supervised and unsupervised learning, semi-supervised learning, and reinforcement machine learning. 1. Supervised Learning This model uses the learning of the past and implements it on new data through labeled examples. It is used to predict future outcomes and trends. The input is mapped to output based on a given example of input-output pair. Regression Model: A regression model is where the output is continuous and is further subcategorized into the following- 1. Linear Regression This is where you find a line that best fits that data when you draw a graph with the X and Y-axis. Multiple Linear Regression (where a plane of best fit is determined) and Polynomial Regression (where a curve of best fit is found) are an extension of this model. 2. Neural Network We hear about neural networks more often than other models. It is a series of algorithms to recognize the several relationships and underlying connections between datasets. Neural networks are vital for deep learning. Each node is connected to the other to send data across the network. The network is trained using huge data sets and becomes accurate over time. Google’s search algorithm is the best example of a neural network. This model has been developed to resemble the operations and thought processes of the human mind. 3. Decision Tree This is a popular model where every question/ assumption is a node. The probable answers to each question will lead to more questions (nodes), and you finally have the answers (called leaves). This method considers all possible conditions before coming up with the final answer. The more nodes you have, the better will be your decision. However, this method is not always accurate due to the various factors involved. 4. Random Forest This is a collection of decision trees. However, random subsets of variables are selected at each step of the decision tree. Then the mean of predictions for each tree is selected. The final decision is selected depending on how many predictions are similar (same). This is a majority-based model that tries to eliminate the risk of individual error. Classification Model: A classification model is where the output is not continuous but discrete. It uses logistic regression the probability of outcomes in two. The output value should either be 0 or 1. Decision trees, random forests, and neural networks are a part of the classification model as well. The only difference is the change in output. 2. Unsupervised Learning The information used to train the algorithm is neither labeled

Kavika Roy
September 27, 2021
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8 Tips for Making AI Adoption Easier

How can organizations streamline AI adoption? Learn a few tips from artificial intelligence consulting experts on integrating AI in an organization and ensuring the adoption is successful. It’s not news that companies have been implementing Artificial Intelligence in their business process to optimize time, costs, and the staffing involved. AI solutions are taking centre stage for conferences and showing their vast potential across various industries, including retail and manufacturing. The processes in industrial facilities have been embedded with virtual assistants, while chatbots are installed to answer all the customer questions on e-commerce or other websites in general. According to a recent report, the global AI market is expected to grow by $76.44bn, progressing at a CAGR of 21% during the financial years 2021-25. Despite AI’s popularity in organizations and businesses being aware of its importance to remain competitive, AI’s adoption is not easy. Despite many AI services available to choose from and implement, companies find the process comprehensive and challenging. Right from data ingestion to model monitoring, each step of AI has its own advantage. However, it needs to be implemented in the right way. Tips for Making AI Adoption Easier Here are a few tips we gathered from artificial intelligence consulting experts on integrating AI in an organization and ensuring the adoption is successful. 1. Familiarity with the technology An organization has people employed from different backgrounds and work in various job profiles. Everyone must understand the need and importance of implementing a technology meant to improve growth and provide expansion for the firm. Experts suggest that it is essential for the firm and teams to become familiar with what modern AI is capable of. There’s an ocean of information available online. It is advisable for the team leads to familiarize themselves and share knowledge about predictive analytics and machine learning with others. 2. Identification of problems Once everyone is through with the basics, identify the issues that AI is expected to eliminate. Exploring the problems with different solutions helps in integrating the existing process with new ones. That way, the organization gets to keep the originality of the process in place while adopting AI and its capabilities to solve business problems. However, there’s one thing that MUST be considered to provide demonstrable value to the solution- AI implementation examples or use cases in the same industry or similar business. For example, suppose a retail shop owner wishes to use AI for his store. In that case, he may look for use cases of retailers or supermarket franchises that have implemented computer vision analytics for the growth of their business. 3. Find the internal capability gap There is a lot that goes into the process of AI adoption in the firm. To ensure everyone is on the same level, it is important to focus on what are the organization’s business goals that need to be achieved through AI implementation and the tools that you have to identify and analyze it. Identifying and addressing your internal capability gap would mean for you to evolve as a team. Depending on the business, there may be existing projects or teams that can organically support it. However, if there are multiple requirements and the team is short of skills or the knowledge to handle AI adoption single-handedly, AI consulting firms can provide an implementation plan. 4. ROI Yes, like every business deal, you need to assess the potential of the technology and the financial value of its implementation. Even successful AI projects take a long time to generate ROI. In fact, pilot projects may not yield any at all. However, pilot projects don’t cost much and can always be outsourced to AI and ML consulting firms. They can help you determine what type of data should be collected at higher volumes and identify the current gaps. The more you analyze, the more knowledge you tend to gain out of it, opening doors to a greater ROI in the long term. 5. Build an in-house team or partner with vendors While it is suggested for a company to build AI internally in the long run, there might be several limitations to these as well. For example, if a business falls under the shopping retail, SME, or manufacturing industry, hiring a whole team to look into the AI requirements doesn’t sound like the best decision. Not only will it require a lot of effort to onboard the suitable candidates, but also it will be financially draining. Partnering with an AI vendor to build customized AI tools is also the best option for a tailored solution. To cite an example, HSBC partnered with an AI vendor to develop an anti-money laundering tool even though they had an internal AI team available. Connecting with leading AI consulting services like DataToBiz can be the key to the right implementation of artificial intelligence technology in your processes. Talk to the AI engineer today. 6. Integrate data-driven decision making at all levels AI adoption in a firm is expected to improve daily operations by empowering people with data insights. Since people carry out daily operations, the firm must adopt a culture of data-driven decision-making, where people, right from those in the C-suite to those at the bottom, are made a part of it. When AI is adopted, the right way employees can augment their skills and judgment with algorithmic recommendations to achieve a better outcome than humans when doing it themselves. This can only happen if employees trust their AI tools and feel empowered to make decisions. Take, for example, a national supermarket chain where the manager needs to optimize floor space and product placement using existing data. The local managers using an AI tool can track real-time in-store customer behavior and decide where to place high-demand products during a particular time of the year. 7. Break down data silos AI requires a lot of data from many parts of the organization. The corporate departments store data in silos that don’t interfere and can only be accessed by specific teams. This has been a barrier

Yashica Chopra
September 15, 2021
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7 Ways of Using AI Marketing to Improve Customer LTV in Retail

Can you run a business without promoting it? No, right? You need to have a detailed marketing plan to attract new customers and retain the existing ones. Marketing is essential for a retail business to flourish in competitive conditions. When we talk about marketing and customers, things get rather tricky. Who is your target audience? What metrics should you consider when reaching out to the target audiences? Why is customer LTV important and yet undervalued until recently? Marketing relies on too many factors, and not understanding these factors can result in an unsuccessful or semi-successful marketing plan. Though several companies have understood the importance of data and are making an effort to collect it in real-time, the results are varied. Why? It’s because collecting data doesn’t automatically translate to understanding it. Unless the enterprise adopts the data-driven model by investing in artificial intelligence and machine learning, it is not possible to fully tap into the benefits of processing real-time data. AI solutions can help you understand your target audiences and customers. You can improve customer lifetime value using AI and ML technology and generate more returns for the retail business. How? When we talk about marketing and customers, things get rather tricky. What metrics should you consider when reaching out to the target audiences? Why is customer lifetime value important and yet undervalued until recently? How can AI solutions help you improve customer LTV and bring more returns? We’ll find the answers to these questions and more in this blog. Our focus will be in terms of the retail sector since customers are an integral part of the industry. Without customers, there is no retail sector, isn’t it? What is Customer Lifetime Value? Whether you call it CLTV or customer LTV, the term represents the amount of money a customer would bring to the brand during their entire time as a paying customer of the business. It tells us the value of a customer and helps decide how much you can invest to retain them with the business. CLTV shows us if a customer will repeat their purchases or not. A customer with a higher value is more likely to be a fan of your brand and will continue to make purchases (unless you do something to annoy them). Does it mean retailers do not have to focus on customers with lower value? Of course not. Marketing is sophisticated, and hence the need for artificial intelligence in retail. Understand CLTV and how it can impact your marketing plan, customer satisfaction, and profits. How to Calculate Customer LTV? Now that we know what customer LTV is, it’s time to see how this value is calculated. How do you decide whether a customer is valuable or not? How do we know if the value is high or low? CLTV is calculated based on four KPIs (Key Performance Indicators)- CLTV = AOV * PF * GM * (1/CR) One aspect you should remember is that the customer LTV should be higher than the customer acquisition cost (CAS). There’s no point in spending $100 if the customer’s LTV ranges around $50 or $70, isn’t it? Why Does Customer LTV Matter in Retail? Now, this is a question many retailers might have in mind, especially if you haven’t bothered about CLTV earlier. Your retail business might be booming, and in such instances, you might wonder if there’s a need to put so much effort into something like this. The answer is yes. Customer LTV matters to every retailer. Whether you know it or not, whether your business is highly successful or struggling to take over, CLTV should be one of the primary factors to consider when deciding a marketing plan for your business. Traditionally, retailers went with instinct and experience in assessing customers and their LTV. While we cannot completely ignore the business instinct, relying on it is not enough in this competitive market. That’s where AI in the retail industry is changing the way retailers make decisions. Artificial intelligence makes it easy for you to identify valuable customers and devise a marketing plan to retain them. AI helps in increasing customer value. The approach you follow can convince a customer to spend more and stay with your business for the long term. Customer LTV is necessary for retail for the following reasons- Speeds up Customer Retention CLTV helps you identify customers who are easy to retain and find a way to speed up the process. Your marketing strategy will be more effective when you know how much you should invest in acquiring a customer and the best way to go about it. Helps Incentivize Repeating Customers In the eCommerce market, the chances of selling to a repeat customer are as high as 60%-70%. However, the likelihood of selling to a new customer is around 5% to 20%. CLTV helps you understand your repeat customers and increase their value. AI empowers you to nudge these customers to spend more money, either by buying frequently or buying more (or both). Generates More Profits Most retailers follow the Pareto Principle. It means that 20% of customers bring 80% of the business. This principle helps in categorizing customers with higher LTV and spending more money on retaining them. Your marketing plan should have different strategies to acquire and retain customers from different categories. Increases Customer Loyalty Do you know that just a 5% increase in customer loyalty can lead to anything between a 25% to 95% increase in the average profits? This goes to show how vital customer loyalty is for every business, especially a retailer. Customer LTV helps you assess which customers can bring such profits. You can create a marketing plan to increase their loyalty towards the business. Remember that customer LTV doesn’t mean spending more on customer acquisition. It means spending carefully on each customer to increase the retention rate and purchasing frequency. AI services help you achieve this by targeting CLTV. How to Improve Customer Lifetime Value using AI Marketing in Retail The following are seven ways to use AI marketing to improve customer LTV and increase

Yashica Chopra
September 14, 2021
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How to Use Power BI to Extract the True Value of Your Data?

Processing data sets to identify patterns and trends to draw meaningful conclusions is called data analytics. It helps enterprises use real-time and historical data to derive insights that help in decision-making. Data analytics requires the use of modern technology and tools. Business intelligence, reporting, and data visualization are components of data analytics. These together help organizations adopt the data-driven model and streamline the business processes. One of the notable business intelligence tools in the market is Power BI by Microsoft. Several SMEs and large enterprises worldwide use Power BI to connect, model, visualize and share data across the business. You can either hire Power BI professionals or rely on offshore Power BI services provided by consulting companies. Microsoft calls Power BI a powerful to bridge the gap between data and decision-making. It has several features that help you extract the true value of data and understand it to make the apt decisions for your business. Types of Power BI Power BI is a package of several products and services. These can be used individually and together to attain the expected results for the organization. You can understand how Power BI works by reading the beginner guides. Power BI Desktop This is a free self-servicing tool that can connect to 70+ in-house and cloud data sources. It helps analyze data and generate reports. Power BI Service This service is a cloud-based service hosted on Microsoft’s cloud platform to allow employees to access dashboards and reports through different devices (browser, mobile, etc.). Power BI Pro This is a paid cloud-based service with additional features that facilitate collaboration between teams. It allows easy sharing of data reports and can embed the data visuals into Power BI apps. Power BI Premium This is not an application. In fact, it is a service that allows you to share the reports in the cloud with people from outside the enterprise. It brings flexibility and scalability to the app, making it possible for an organization of any volume to adopt Power BI with ease. Power BI Mobile This can be used on smartphones with Android, iOS, and Windows 10 operating systems. Power BI Report Server This is used to access the reports created using the desktop or the Pro version. It is an on-premises report server that allows your employees to access reports from any device and location. It is beneficial if you have limited storage capacity on the cloud or external data centers. Power BI Embedded This is for developers to embed Power BI to other software and application used within the enterprise. Advantages of Using Power BI Power BI is undoubtedly one of the best tools to make the most of your business intelligence capabilities and gain an edge over competitors in the industry. The following are some of the advantages of using Power BI- Features Power BI has rich and user-friendly features that help the process, analyze, and present data in attractive and easy-to-understand formats. Data visuals can be generated independently or in collaborative environments. System Integrations Power BI can be integrated into different apps to produce and share reports throughout the enterprise. The features can be customized to suit the varying needs of each business. Easy Accessibility Since Microsoft Power BI can be used on multiple operating systems and devices, it increases accessibility to data and data analytics. The apps are user-friendly and can be used by employees with little or no training in Power BI. That said, this aspect is limited only to the end-user features. Regular Updates From Microsoft Microsoft doesn’t just release products into the market and leave them. The company constantly improves the features, adds new ones, and makes Power BI even more powerful. Multiple Data Sources Collecting real-time data is now easy with Power BI. Whether it is IoT sensors, social media, media, or any other source, collecting raw data in real-time and using it for analytics is a quick and easy task. The changes in data will be automatically reflected in the reports and on the dashboard. Seamless Integration Power BI consultants can integrate the tool into your business environment with apparent ease. Whether you want to use it through cloud services or on-premises servers, experts will ensure that the process is streamlined and simplified. This allows end-users (employees from different departments) to use Power BI for data and report sharing with any team member. Personalized Dashboards The dashboard is one of the greatest features of Power BI. Apart from allowing employees to get real-time reports and edit them using several features like drag-and-drop, the dashboard can be personalized by each employee. The sales team will need reports of one kind while the accounts team needs another. The elements in the dashboard can be customized (added and removed) to suit the team’s requirements. Secure Data Processing and Reporting Microsoft knows the importance of data security and has provided enough options to set up security levels for data analytics and sharing reports. Employees who generate the reports can set up Row-Level Access filters that allow only certain teams to edit or modify a section of the report. This brings a balance between data sharing and data security. Employees can view the reports, but only those with permission can edit them. Ease of Setup and Use Setting up Power BI is as easy as it can get. There is no need to put the team under extensive training to use the tool. Experts from Power BI consulting companies will handle the initial process, and employees can use the dashboards to use the reports in their day-to-day work. Customization of Power BI App The app can also be customized to make navigation simple, fast, and effective. If your employee has to spend precious time searching for the required data, it doesn’t make sense to use such a powerful tool, right? By customizing the navigation, employees can find data and reports in less time and complete their work. Breakdown of Information The main aim of using Power BI is to understand data. If the data visuals are complex and confusing, how

Yashica Chopra
September 03, 2021
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How to Get Started with the Modern Data Management Approach

Data is priceless in today’s world. Never before was it considered as essential to a business as it is in recent times. Whether it is easy to access real-time data or the availability of advanced technology to process and analyze this data, enterprises are trying to make the most of this precious raw material. Data management in today’s world needs a modern approach to effectively handle the complex problems of dealing with multiple data formats. Data in its original form is raw, unstructured, and comes in multiple formats. Data management is the process of collecting, sorting, arranging, and managing vast amounts of data to help businesses derive valuable insights from it. From data governance to data architecture and security, modern data management is a multifaceted discipline. But following the modern data management approach is not easy, especially for enterprises that are used to traditional business methods. That’s where consulting companies take care of data management services and help SMEs unlock the full potential of data on their own. In this blog, we’ll see how to adopt the modern data management approach in an enterprise. But before that, let’s read a little about data management, why it is necessary for every business, and what benefits an enterprise can get by investing in modern data management. What is Data Management? Data management is a combination of various functionalities that help enterprises use accurate and reliable data to derive insights that help in decision-making. An enterprise needs a comprehensive data management strategy if it wants to adopt the data-driven approach and rely on business intelligence tools to generate valuable reports. The enterprise needs to establish policies and best practices to ensure that the data management strategy is aligned with the goals. The data management system includes a wide range of processes like- Types of Data Management Data management can be classified into the following types- Master Data Management (MDM) This is a process where all data sources lead to the same data center. A master database is updated and used to make all business-related decisions. A single, latest version of the database is used as a reliable source of data. Data Security Security filters and applications are used at every level to ensure that the data in the database cannot be easily accessed or hacked by people without permission. It also safeguards the data from being deleted or modified by mistake. Data Quality Management Identifying and removing duplicates, eliminating inconsistencies, etc., are a part of data quality management. This is done when data is collected from all sources and stored in the database. Data Stewardship A data steward is a tool that is used to supervise and implement data policies across the enterprise. A data steward is also a person responsible for the same. Data Governance These are the set of laws framed by the top management to ensure proper use of data. It is a framework of rules and regulations which the data stewards implement in the business. A data governor is a person who supervises the data steward. Data Warehousing The process of storing vast amounts of data and using it for analytics is known as data warehousing. It can either be managed on-premises or on the cloud. Choosing the right data warehouse is crucial for a business to successfully utilize its data. Big Data Management Collecting, cleaning, and processing huge amounts of data is known as big data. Managing raw, semi-structured, and structured data collected from multiple sources is called big data management. Traditional systems are not enough for big data management. What is Modern Data Management? So how is modern data management different? It is nothing but an advanced approach where data is connected with the latest technology like artificial intelligence to get predictive insights, identify challenges and opportunities in the market, and help SMEs make faster and better decisions. Why Do You Need Data Management? There are quite a few reasons for a business to invest in data management and ensure there are no glitches in the system. Something as basic as feeding wrong information can lead to disastrous results for the business. Enterprises hire big data consulting services to streamline their business processes and migrate from the traditional approach to the modern one. It makes the transitions easier and amplifies the results. In short, the success of a modern enterprise depends on how effectively it can manage the huge amounts of data in this system. How to Build a Modern Data Architecture? Following a comprehensive step-by-step is essential if you want to build a modern data architecture in your business. Benefits of Modern Data Management Whether it is cloud data analytics or on-premises services, the following are some of the benefits of following the modern data management approach in an enterprise. Data Security and Data Privacy Compliance The increasing cyberattacks have resulted in new and stringent data security policies. Unless businesses adhere to these regulations, they cannot be eligible to bid for government projects. Modern data management services include high-level data security and enable enterprises to use automated tools for data protection. Speeding up Modernization Digital transformation is a continuous process where the systems, tools, employees, and work environment has to be aligned to achieve the common goals. Modern data management makes it simpler for the management and employees to accept and adapt to the changes. This increases business efficiency and productivity. Optimization of Resources There is no point if data scientists spend more than half of their time collecting and cleaning data. When will this data be analyzed? What about real-time data? Data strategy consulting services are the answer. Automating recurring tasks, using AI-based technology, and investing in advanced software for data analytics will help enterprises optimize resources and increase returns. Enhancing Decision-Making Abilities Isn’t this what every enterprise wants? Imagine getting in-depth reports that are easy to understand. It will help employees and management choose the best way forward for the business. Being armed with accurate insights can make an enterprise a leader in the industry. Modern Data Management Approach: Things to

Prasansa Singh
August 27, 2021
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How Can Big Data Help in Augmenting Cybersecurity?

With the ever-evolving cybercrimes, data handling, and security challenges, users must remain informed with ways to tackle them. The good news is that various enterprises are coming up with multiple solutions to combat the nuanced tools and mechanisms being used for data intrusions. The most prominent of them being Big Data Services. Big Data analytics in cybersecurity industry has various applications if enforced properly. If we’re to believe the reports, the incidents of data breaches continue to rise every single year which is why we must make cybersecurity an absolute priority. The world faces more than 10,000 malicious files and 100,000 malicious websites on a daily basis. Phishing attacks account for over 80% of the reported security incidents. Since the pandemic outbreak, remote workers have also been the target of alarming cyberattacks. Despite knowing about the risks related to cyberthreats, most of the companies were still not cautious and ended up being a victim to hackers and cybercriminals. What is Cybersecurity? Cybersecurity can basically be defined as a mechanism meant to protect your devices and the data contained in them. Its application also expands to networks and software. It’s not just a department that’s responsible for protecting your data, rather every step you take towards creating a safer environment for your system contributes to an overall secure user experience. A cyber threat comprises any malicious attempt at unauthorized access to a network for stealing private data or making changes to it. Phishing attacks, ransomware, malware, MITM, SQL Injection, Emotet, etc are just a few types of cyber threats. Why is Cybersecurity Important? Due to various vulnerabilities in the system, a company may end up becoming a victim of a cyberattack. There have been multiple instances of really dangerous cyberattacks in the past, for e.g the SamSam ransomware attack that took place in Atlanta, the Stuxnet worm that attacked the nuclear facilities in Iran in 2010, and one of the most recent and global data theft account- Pegasus, the spyware. Overall cyberattacks cost the world economy more than $1 trillion according to McAfee- a leading cybersecurity firm. Apart from the financial point of view, there are many other benefits to cybersecurity like- managing and protecting big data thereby improving confidence in a company’s reputation and hence customer loyalty with a company. There are various global laws like HIPAA, GDPR, DCC, etc that are enforced globally to protect citizen data and to regulate security standards. Application of Cybersecurity Some steps that can be taken for securing your browser and system experience: Apart from these small steps, there are dedicated cybersecurity professionals and companies like Intruder, Cipher CIS, Symantec, Cisco, etc who are responsible for defending and even predicting data breaches on a much larger scale. Their services include vulnerability assessment, external network security audit, cloud security, network security, endpoint security, etc. There are a few added benefits to cybersecurity like productivity enhancement, gaining client confidence in terms of data security, and improved system performance. What is Big Data? Big Data comprises a huge amount of data, structured or semi-structured. Data is generated in a high volume, at a great velocity, and in a huge variety. Did you know that Social Media is the highest contributor towards the generation of data? Important tools like Apache Hadoop, HPCC, Statwing, Qubole, etc can be used to analyze and interpret a huge amount of data accurately and efficiently. The analysis and storage involve Artificial Intelligence and Machine learning as well. Big Data can in fact be converted into Actionable Intelligence to get an added advantage over one’s competitor. Data Analytics can also act as a catalyst to achieve an organization’s goals if applied properly. Big Data is creating a revolution due to its multifold usage throughout the world. The retail industry benefits from an accurate representation of customer behaviors and patterns thereby contributing towards achieving higher sales. And the healthcare industry benefits by harnessing data pertaining to past records and thereby providing more personalized medications and even developing more effective treatments. Cybersecurity companies have been applying big data technologies for cybersecurity in recent times and the results have been promising. Big Data’s role in mitigating cyber-threats Considering the rate of both data production and cyberattacks, it’s not surprising that Big Data seems like the perfect solution to fight and prevent cybercrimes. Cyber professionals can comb through the huge amount of data in a time-efficient manner and thus detect and resolve the issues, enabling them to even prevent and predict any future possibilities of data invasion and data theft. Big Data analytics insights are crucial for the detection and prevention of cyber threats like ransomware, malware, phishing attacks, etc. Using Big Data Analytics in cybersecurity comprises analytical tools, data management, and examination techniques that integrate data from various hosts, devices, and networks and then providing the information to sensors installed within, to detect and prevent any upcoming attacks. A Few Big Data Technologies for Cybersecurity Predictive Nature: Big Data analytics make use of past records, algorithms, and statistics to look out for certain patterns which might’ve caused an issue in the past. Thereby, predicting future attacks while also providing information about previous attacks that might have occurred in a system. Constant Monitoring: Data validates access to the systems while tracking logged-in activities thereby preventing a breach as well as managing passwords efficiently. Complete Automation: Professional Big Data analysts can actually automate the entire detection and prevention of threats process, saving time and human efforts. Actionable Intelligence-Driven Risk-Management: With constant monitoring and processing of datasets, a foundation for actionable intelligence is formed which further plays a crucial role in detecting and predicting any cyberthreats Now, Big Data may apply any past or current insights pertaining to cybersecurity, collected from various sources to solve any issue. It, therefore, becomes rather convenient for the analysts to address and resolve any threats really fast. Threat Forecast: With the help of AI algorithms, companies can even predict any upcoming threats based on past and present records and identification of any patterns after

Yashica Chopra
August 26, 2021
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How Demand Forecasting Is Helping the Retail Industry?

Forecasting is the process of analyzing existing data to determine future events. In the retail sector, forecasting is used to understand the customers’ purchase behavior. Actual data that is in existence and its related market research might differ by the kinds of products a retailer usually intends to trade for, but the basic denotation of forecasting in retail accompanies similar patterns, even across different product lines. Demand forecasting is one of the major aspects of running a successful retail business. A retailer cannot accurately predict and prepare for the changing market conditions and customers’ requirements without demand forecasting. And one such indispensable component is demand forecasting. Put separately, demand describes the eagerness of consumers to purchase a specific product, whereas, forecasting predicts future performances by utilizing statistics of the pre-existing data. Hence, demand forecasting in retail is the insightful prediction of consumers’ purchase actions regarding a specific product, at a specific price and in a specific time area. Simply, it is the demand forecasting of future bids that lead the businesses to create a win-win situation for consumers and themselves. Many small and big companies implement demand forecasting for supply chain control, financial planning, and customer success management. This strategy allows businesses to prevent any sort of loss and ensures client retention while making any necessary and sudden changes. Without proper implementation of demand forecasting techniques, businesses might find it hard to possess an adequate amount of stocks in hand. Especially, in the COVID times where physical interaction has come to a halt and companies are making rapid changes to their working models. It plays a significant role in the growth and survival of the retail market. Importance of Demand Forecasting in Retail Here are the two major points that define the importance of demand forecasting: 1) Cost-efficient One of the simplest ways to maximize profit in the retail business is to cut down costs. Initially, you can diminish the amount of money you have invested in unnecessary inventory. That will result in lower carrying costs since you have lesser stock in hand. Plus, this also ensures that you don’t go out-of-stock and capitalize on every sale you make. While the above ways do work, a systematic approach towards the need for demand forecasting ensures to beef up the profits and business structure. Once you forecast the demand, you can easily have a look over the time period and check if you are close to hitting the anticipated sales. If you were left behind on your goals, you can always use marketing and advertising techniques. And, in case you underestimated, you can reorder the stuff and cross-promote a similar product. 2) Customer Experience Suppose you visited an e-commerce site to purchase groceries and essentials for your home. Unfortunately, the brand you prefer is currently out of stock. What will be your next move? Will you wait for the brand to restock them and then proceed to make a purchase? Or, will you look for another brand that offers similar items? The latter, right? Enhancing the customer experience is the quickest way to improve profits. Neglecting the “out-of-stock” from your business can retain your consumers and will stop them to entertain your competitors. Plus, aiming towards the end-users might lead to more referrals and loyalty, rather than focusing on raising the prices, altogether. What Are the Advantages of Demand Forecasting in Retail? It is necessary to perform demand forecasting in a proper way to stay in the retail industry game. Forecasting may not be easier for both brick and mortar and giant companies, however, the results-driven by it can make a huge impact. Here are the advantages of demand forecasting: 1) Better Financial Planning The demand forecasting formula is used to help you achieve better and smarter financial planning for your retail business. Detailed and accurate information obtained from the forecasting lets retail companies build better financial plans. Accurate predictions can easily highlight the peak intervals, demand trends followed by months, dates, or even a specific time of the day, and seasonality. Hence, making it easier for companies to look upon the cash flow and safeguard themselves from peaks and unexpected expenses. 2) Eliminates Staffing Problems A well-structured demand forecasting process can result in preventing staffing problems in companies. These issues generally arise during the peak season. It can be easily avoided by anticipating the hike and fall in the demand during different time intervals or days. This way, companies can eliminate the guessing and come up with a more structured schedule list. Moreover, managers can also plan the shifts accordingly for the workers. 3) Enriches the Marketing Plans Forecasting also plays a vital role in developing effective marketing campaigns. For instance, if a company experiences a loss in sales, it is advisable to put effort into its marketing strategies. With proper demand forecasting solutions, marketing executives can develop properly tailored marketing promotions that can take on the loss. Moreover, making modifications to the pricing index according to the customers can also make an impression. 4) Advanced Inventory Management Not only the inventory but forecasting also helps the companies willing to enhance their production management. Companies strive for inventory overflow whenever their product goes out of stock or sales fall down in peak times. Precise forecasting allows them to anticipate and adapt the situation to meet the required demands accordingly. In addition, it also ensures that the company holds the required material, shipping, and labor. What Are the Reasons to Use Demand Forecasting in Retail? The biggest benefit reason to use demand forecasting is to reduce uncertainty in retail operations. Demand forecasting kills uncertainty, substantially, with its predicted calculations and thus, allows retailers to order, allocate, and refill accordingly. Additionally, it is also beneficial in terms of workforce scheduling and optimizing capacity management systems. Benefits of Demand Forecasting Here is the list of benefits driven by demand forecasting: How Is Demand Forecasting Performed? Demand forecasting relies on 3 main models that are used in the retail industry. However, each model

Ankush Sharma
August 25, 2021
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8 Tips for Using AI to Improve Customer Insight Strategy

Artificial Intelligence or AI applied appropriately, can help deliver customer insights that deliver an in-depth understanding of purchase behavior, needs, likes, dislikes, and preferences. Do you know your customers? Do you understand what makes them buy your products or avail of your services? Do you know how many customers are unhappy with your business and looking at competitors? How do you plan to retain them? To successfully answer these questions requires customer insight. Customers are the heart of every business. In order to succeed, you need a loyal customer base to buy, promote, and recommend your brand. The demand and supply ratio in the modern market is constantly changing. People today have more options to choose from. While this is wonderful for consumers, it puts responsibility and pressure on businesses to ensure that their customers are satisfied and keep them happy. Whether it is product quality, variety, price, or post-purchase services, several factors play a role in influencing a customer. Sometimes, all it takes is one special offer by a competitor to lose a customer. In such instances, having a comprehensive customer insight strategy can help you retain your customers and attract new ones. However, traditional processes aren’t enough anymore. You need artificial intelligence solutions to use your customer insight strategy and obtain the expected results. In this blog, we’ll learn more about customer insight strategy, why it is necessary, and how AI can effectively improve the strategy and your business. What is the customer insight strategy? Customer insight means having an in-depth understanding of customers’ purchase behavior, needs, likes, dislikes, and preferences. It is to know whether a product will be loved by your customers or not. The insights are a combination of multiple factors like inquiries, feedback, customer analytics, market intelligence, research, segmentation, loyalty analysis, and more. Consumer insights play a critical role in marketing strategy development for your business. You need to use those insights to create a strategy that delivers results and puts your brand at the top. To survive the competitive market and set your business apart from your peers, you should compulsorily gather customer insights and create a strategy to achieve your goals. Ways to gather customer insights Customer insights aren’t generated of their own accord. You need to collect them using different methods. Each method has its pros and cons and focuses on revealing a new aspect about the customers. The following are customer insight techniques used by businesses around the world- Trend analysis This is classified into three types based on geographic location, time frame, and behavioral patterns. The current trends are analyzed to predict the future ones. Custom surveys These are conducted to get a general idea of how satisfied/ dissatisfied customers are with your brand. Market analysis It is a quantitative and qualitative assessment of the market to understand customer segmentation and purchase patterns. Interviews These are used to gather the voice of the customer to see if a particular product/ service is worth pursuing. Online research Using the internet to conduct surveys, polls, share questionnaires, and gather feedback is known as online research. Social listening This is a consumer AI technique where you monitor social media for mentions, tags, etc. You track your business as well as your competitors. Predictive analytics Using ML algorithms and AI to predict future trends based on historical and real-time data is known as predictive analytics. Focus groups You collect data by interacting with a selected group of customers/ target audiences. The need for customer insight strategy for businesses Before we talk more about using AI services for customer insights strategy, let’s see why you need to consider customer insights for your business. Customer insights and product strategy: Customer insight strategy provides you with relevant information about how customers will receive a product from your brand. You might be sure that customers will like a product you plan to release in the coming days. But what if they don’t like it? How will you know with certainty that the new product will be successful in the market? Customer insight strategy is the answer. You can collect firsthand information from the target audiences and use that to develop a proper product strategy. Customer insights and marketing: Customer insights analytics are used to help in planning your marketing strategy for existing and new products. How to take a product into the market and attract people? You use customer feedback to create a strategy that will highlight the products and the brand to show customers that you can provide them with what they are looking for. It also helps in segmenting customers into exact categories and creating targeted promotional campaigns for each segment. Customer feedback and customer service: Customer feedback and service go hand in hand. It is a part of the customer insight strategy to gather feedback from customers and make the necessary changes to the processes so that customers are happy with your business. You can collect feedback immediately after providing a service, contact them after a while, or wait for them to review your business. Find answers to the following questions If you want to use artificial intelligence to improve the customer insight strategy for your business, you need to first answer a few questions. These will help in providing you with a clear picture of how to implement AI in the business. What do you want for business? Unless you have a concrete goal, you cannot define the parameters to collect customer insights and use them to develop a strategy. You need to consider the short-term, mid-term, and long-term visions of your brand. Where and how to gather data? Which customer insight tools should you use to gather data? How do you plan to approach your customers? We’ve already mentioned different methods to collect customer insights. Besides the above, you can also use Google Analytics, social media analytics, social mentions, Google Trends, and other data collection tools to gather insights. Which data storage and data analytics tools do you need? The collected data requires to be stored and processed to derive insights. Do you want to

Abhishek Singh
August 23, 2021
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PrepAI – Most Advanced Question Generator

PrepAI is the smartest AI-Powered question generator and a solution to create question-answer pairs for exams and online test papers. It can generate questions from text in minutes. It turns up as a solution in the form of a question answer generator that helps teachers, exam administrators and content leaders save time and reduce human errors. PrepAI is the world’s most powerful solution for question generation using NLP. Watch the video to learn how to generate questions from text online using PrepAI – PrepAI Question Generator Demo PrepAI leverages the power of Artificial Intelligence and Machine Learning algorithms to create questions from text of varying difficulty. Besides being a smart and random question generator for test authors, it also serves as a one-stop solution for all content needs of an organization. Developed by EdTech professionals and innovative engineers, it uses Natural Language Processing technique for text analysis so that you can generate questions out of any text you feed, PDF/Docx file, Video and Wiki search. A Quick Walkthrough to Get You Started 1. First, add your content… Login and enter the title of the question paper. Select ‘Type or Paste Text’ as Content Source for now. Copy and paste the content for which you want to generate questions. When you are done, click on the ‘Generate Questions’ button. 💡 There are other Content Source options available, such as: Enter Topic Name, Video and Upload PDF/Docx. 2. Check the generated questions and options PrepAI takes a few moments to process and generate questions and answers. Go through the results. If needed, you can edit the questions or options too. Select the questions you want to include in the question paper. Click on ‘Preview Question Paper’. 3. Review the question paper and download Check the question paper review. If you are satisfied with it, then click on ‘Finalize Questions’. You have an option to download the question paper in PDF, DOC or EXCEL. Hope that helps! Resources:

Kavika Roy
August 20, 2021
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Importance of Building AI and Data Science Capability For Strategic Growth

Businesses across the globe have started adopting artificial intelligence and data analytics to streamline their operations. Investing in the latest technology helps enterprises optimize resources, increase productivity, improve quality and get a faster return on investment. From following the traditional approach of decision-making, SMEs are slowly turning into data-driven businesses. Big data is being stored, processed, and analyzed to gain actionable insights. These insights are gathered from historical and real-time data to help the management make faster and better decisions. However, doubts and confusion about AI, ML, and data science still persist. The concepts overlap and coexist. That makes things a little hard to understand. Artificial intelligence consulting companies have been helping SMEs understand how AI, ML, and data science are different from each other. The companies provide continuous assistance to revamp the business processes and build an efficient operational system in the enterprise. The aim is to strengthen the business in all aspects to achieve its long-term goals. What is Artificial Intelligence? One thing most of us do know is the generic definition of artificial intelligence. Minsky and McCarthy, in the 1950s, defined AI as a task performed by machines that would usually be handled by humans. But artificial intelligence is much more than a machine taking over the tasks of a human. AI is a collection of algorithms that help computers understand the relationship between various entities, gather meaningful insights, or plan a course of action for the future. AI is all about actions. Machine learning and deep learning are a part of artificial intelligence. The machine-learning algorithm can learn and improve itself without the intervention of humans. Have you read about AI solutions like Chatbots and virtual assistants providing personalized assistance to employees depending on their responses to the questions? It means that the machine learning algorithms use data in the system and the response of the employees. The algorithms learn from the feedback and deliver better accurate services. Types of AI Artificial intelligence is classified into different types based on functionality and technology. Based on Functionality: Reactive Machines These are the oldest AI type of AI systems with limited capabilities. They do not execute memory-based functions but are used to respond quickly to typical input datasets. Theory of Mind These AI systems are supposed to understand the human mind, emotions, feelings, thoughts, etc., and identify the factors that influence the human thinking process. Limited Memory These AI systems learn from previous data and are the ones we commonly see in today’s world. A vast amount of data is fed to train these systems. Self-Aware These AI systems are yet to be fully used in the market. Some of these are still in the developing stages. These systems are meant to have the self-awareness and consciousness of a normal human being. Based on Technology: Artificial Narrow Intelligence (ANI) Also known as weak AI, this is the common type of AI technology we see in the industry. The systems use a predefined set of constraints to process data and deliver results. Artificial General Intelligence (AGI) This technology is connected to the Theory of Mind and has been fully developed as yet. The aim of this technology is to develop machines that create independent connections across various domains. Artificial Super Intelligence (ASI) This technology is still in the early stages of development and can be linked to Self-aware AI systems. Why Use AI for Future Growth According to Fortune Business Insights, the global AI market value is expected to touch $267 billion by 2027. AI-powered robots and machines are going to become an integral part of most industries, be it the manufacturing or service and hospitality sectors. Investing in AI at this stage will ensure that the organization is ready to adapt to the changes in the future market and grab more opportunities. How Do You Develop an AI Strategy? (AI Strategy Framework) Developing an AI strategy is a task, right? Why not take the help of our AI services for easy implementation and customized solution for your business problems? Our AI developers have the skill and expertise required to transform your organization into a dominant force in your niche. What is Data Science? Data science is a broad field of science that deals with collecting, cleaning, storing, processing, and analyzing data to derive meaningful insights. The techniques of mathematics and statistics are used along with advanced technology and tools to derive useful information and knowledge from vast amounts of data. Data analytics, data mining, and data visualization are a part of data science. AI and ML are used in the data science field to produce insights that help in better decision-making. Hiring the leading data analytics company in India provides the necessary skills and technology required to gather raw data and process it to detect hidden patterns, identify market trends, and predict future outcomes for the enterprise. Data scientists and analysts use AI and ML tools to work on chunks of data (historical and real-time) to draw inferences and generate reports to explain the insights in a simple and easy-to-understand format. How Do You Develop a Data Science Strategy? (Data Science Strategy Framework) Similar to the AI strategy framework, the data science framework also needs to follow a systematic process to yield the expected results. Relationship Between Data Science and Artificial Intelligence Human intervention is required in data science to process and analyze huge amounts of data and derive actionable insights. Data scientists build ML models that can be implemented in the business to make the most of the data that’s available in the enterprise to forecast business. The following are the two popular ways in which consulting companies use data science in business development. Predictive Analytics: Data from the past and present are used to analyze trends and patterns to predict the future outcomes for the business and in the market. This helps enterprises be ready to grab new opportunities in the market and make the most of the latest trends. Organizations can also be better prepared to deal with market fluctuations. Predictive analytics is