Top 5 Power BI Dashboard Examples to Get You Started

When it comes to using data to its fullest, one thing that comes to everyone’s mind is data visualization. After all, data visualization is what helps display and understand the data better and use the same to make informed decisions. That’s where Power BI comes in. Power BI is an impressive tool using which you can create an interactive, visually immersive, and dynamic dashboard and make the most of your data. Want to learn more about what Power BI actually is and how you can use it to unleash the power of data visualization? Read along. This blog discusses what Power BI is and lists Power BI dashboard examples that you can use to visualize data in different domains within your business. Also, you’ll learn some tips to create your custom dashboard using Power BI dashboard examples. What is Power BI? Power BI is a Business Intelligence tool from Microsoft similar to Tableau, using which you can create interactive and actionable reports and dashboards. With Power BI, you can pinpoint issues and tell a story using the dashboards and understand data effectively. Also, Power BI allows you to source data from sources that aren’t a part of the Microsoft ecosystem. That means you can use any data source to input data into Power BI and get a coherent and interactive dashboard ready. What is Power BI Dashboard? You can think of Power BI dashboards as visualizations or templates that update on their own when the data supplied changes/updates. Because of development functionalities, you can drag and drop visualization widgets on the canvas and create a custom dashboard without writing a single line of code. Common Power BI dashboard examples include the finance dashboard, sales dashboard, cost optimization dashboard, and a lot more. More on this later in the blog. What is the Difference Between a Dashboard and a Report? Dashboards help visualize specific aspects of an organization. While you can include any number of data sets, dashboards work best when you focus on a particular area. Also, using dashboards, you can visualize and organize the data in real-time. On the other hand, reports have a broader scope because of which they offer a high-level view of the entire organization. However, reports are static and represent a snapshot of a particular area or the entire organization. What’s Better? Well, dashboards are optimal for BI and analytics needs, especially when you want to focus on different business aspects such as customer segmentation, finance, sales, etc. You can check in real-time how your products are performing and draw an action plan accordingly. Top 5 Power BI Dashboard Examples Here are some of the best power bi dashboard examples: 1. Finance Dashboard Who is the Dashboard For? The finance dashboard is for executive-level employees who want insights into an organization’s financial data/performance. As an executive-level employee, you can find high-level insights that make scanning the entire dashboard pretty easy. What’s more, the finance dashboard allows you to dig deep into granular metrics if you find an insight worth exploring.   What’s the Purpose of This Dashboard? The purpose of the finance dashboard is to offer the users a high-level view of your company’s financial performance. Users can check out the financial performance over time by product category and location.  What are the Key Insights of This Dashboard? The finance dashboard is one of the best power BI dashboard examples that offers the following key insights: 2. Sales Dashboard Who is the Dashboard For? The sales dashboard is meant for sales managers to help them read sales data in an interactive manner. What’s the Purpose of This Dashboard? The purpose of the sales dashboard is to help sales managers understand how their business is performing. You can get an overview of the sales along with all the granular details corresponding to each opportunity. As a sales manager, you can determine the aspects where you need to focus to meet your sales targets. What are the Key Insights of This Dashboard? 3. HR Dashboard Who is the Dashboard For? As the name tells, the ideal audience for this Power BI dashboard is HR managers who want to keep track of the demographics of the employees. What’s the Purpose of This Dashboard? The HR dashboard offers insights such as average age, duration, and salary. And the purpose is to help HR managers learn more about their employees and understand them better. It’s also meant to assist the entire HR team in ensuring that the company has the employees it needs. What are the Key Insights of This Dashboard? 4. Marketing Campaigns Dashboard Who is the Dashboard For? Yet another important power BI dashboard example is the marketing dashboard. It’s meant for marketing managers who regularly launch campaigns and want to track how the campaign performed. What’s the Purpose of This Dashboard? The purpose of the Marketing campaigns dashboard is to determine the effectiveness of marketing campaigns across different product segments & channels. And using these insights, marketing managers can tweak their campaigns to improve their performance.   What are the Key Insights of This Dashboard?  5. Customer Segmentation Dashboard Who is the Dashboard For? The customer Segmentation dashboard is meant for companies dealing in the B2B domain. It’s meant for senior managers who want to analyze the customers closely. What’s the Purpose of This Dashboard? The customer Segmentation dashboard is one of the best Power BI dashboard examples meant for senior managers. It’s because by using this dashboard, senior managers can visualize revenue coming from every individual product. They can also visualize & determine which product is doing the best and which isn’t. What are the Key Insights of This Dashboard? Now that you know about some of the most popular Power BI dashboard examples let’s learn how you can create a similar Power BI dashboard for your business. Tips for Designing a Power BI Dashboard 1. Consider Your Audience Always consider keeping your ideal audience in mind while designing a Power BI dashboard: For instance, if it’s a

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How to Export Power BI Dashboards using Power Automate in 7 Steps

Microsoft Power Automate formerly Microsoft Flow, is a tool for automating processes and tasks by connecting different applications and platforms. It is a low code or no-code application where flows can be built quickly and easily from scratch or pre-built templates. Power Automate integration with Power BI will allow exporting the dashboards or reports in various formats (PDF, PowerPoint, Excel) with ease.  In the organization Power BI reports are shared by the Power BI service (Cloud environment) to various users in the organization and it is mandatory that everyone should hold the license of service to view the content published and shared with them. Using Power Automate the exported reports can be shared with both internal and external users on a set schedule time in a certain format. What is Power BI Power BI helps to create real-time high-level analytical dashboards when compared with excel in terms of easy data modeling, handling huge data sets, ETL, logical functions, and a wide range of visuals with better UI/UX. Power BI uses powerful compression algorithms to import and store the data, which permits you to analyze, and visualize large datasets that can’t be viewed in Microsoft Excel. Excel is not designed to work with big data and has a more adjusted amount of data. We don’t have to reduce or downsize the size of the dataset to perform the analysis, the entire detailed report and summarisation can exist in a single file. Integration with multiple data sources is one of the key features of Power BI. Data can be imported from multiple sources to power BI and all the ETL operations, data modeling can be performed in Power BI itself using Power Query. This will reduce the pain to create any kind of model in the native systems before the integration is made. What is Power Automate You might be wondering what is Power Automate and who can use it. Let me put it in simple terms with certain examples to make it better. Power Automate is an automation tool developed by Microsoft with the intention to allow citizen developers to access the capability of automation in day-to-day tasks. Whether you are an IT, Marketing, Finance, or HR professional, you will be able to use Power Automate. It Empowers everyone to build automated processes with flows in Power Automate. Use low-code, drag-and-drop tools and hundreds of pre-built connectors that automate repetitive, mundane tasks with ease. It boosts efficiency by recording and visualizing your end-to-end processes with a process advisor, providing guided recommendations for creating flows and deep insights that take the guesswork out of what to automate. Ex: With the help of Power Automate one can create a flow to send an alert email whenever a new row is created in a SharePoint list (a SharePoint list is connected to any ticketing tool and it creates a new row whenever a new ticket is raised). Save all your mail attachments into a drive-by using Power Automate and the list of capabilities are endless. Power Automate Connectors It has hundreds of pre-built connectors and it’s updated very frequently with more and more connectors. Features of Power Automate ·        No Code or low code automation tool ·        Save and secure Cloud-based tool ·        Pre-built connectors ·        Readily use Template flows ·        Peek Code feature ·        Dynamic content expression ·        RPA (UI Flows) ·        AI Builder Now it’s time to see some action points. Let’s hit the example to export the BI reports to various users through email How to Export Power BI Dashboards using Power Automate Step 1: Create your report on the Power BI desktop and import Power Automate visual onto the page Step 2: Click on the Power Automate visual ellipses to edit the visual Step 3: Click on New (left top corner) to create an instant cloud flow Step 4: Search for Power BI connector and then choose trigger as Power BI button clicked Step 5: Click on New Step and search for Power BI as a connector and Export to file for Power BI reports as an action Step 6: Fill in the details in the actions Workspace – where the report will be hosted in service Report – Report Name Export Format – PDF Step 7: Create a new step and search for Gmail or Outlook and select Send email as an action Fill TO Subject Body Click on Advanced options and fill the attachments using dynamic content Save the flow and Test it. Place the visual according to your choice and run the flow to trigger the action and send a mail to defined users. Licenses required This will work on Power BI PPU Conclusion We have demonstrated how to export and share Power BI reports with internal and external users (which does not require Active Directory accounts) using Power Automate inside Power BI using a button to trigger the flow, the same can be automated using Power Automate by scheduling the flow. By adding more steps you can create a better creative flow to meet your business needs as we discussed formerly in the article.

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What Is Computer Vision as a Service? (Only Guide You Need)

Have you ever wondered how an iPhone instantly identifies your face and unlocks the device? Or better, how do the uber-cool Tik-Tok filters work? Today, there are numerous instances where smart devices seem to have farfetched applications, but in most cases, these applications are powered by a common tech, computer vision.  Advanced computing capabilities and recent developments in computer vision have propelled the domain far enough. Such is the advancements in the field that several agencies providing Computer vision as a Service have popped up worldwide. Machine vision as a service refers to outsourcing third-party computer vision algorithms instead of developing and constantly upgrading in-house.  With that basic idea about computer vision as a service, here is a detailed approach to the entire scheme. This will also aid you better before rounding off to a service provider.   So How Does Computer Vision Work? Computer vision is part of the umbrella term Artificial Intelligence and is neatly implemented using programming tools like Python and OpenCV. Here are the following steps in a computer application that works uninterruptedly in the making of computer-vision-enabled software.  Acquiring Image Datasets The first step in functioning a computer vision algorithm is determining the acquisition strategy of the image datasets. Do you want your users to upload pictures, or is the camera self-serving? Is there a possibility of having both of these features simultaneously? The options are endless; hence, the software architecture must be designed to accommodate the best possibilities.  Labeling Datasets When it comes to a machine learning algorithm, there are numerous data points to be fed to the system. The labels instruct the machine about the job. Further, labeling the dataset makes it more search-friendly, with attributes like color, intensity, and size used for the same.  Processing the Data Now that you have labeled data, it is time to undergo a meticulous quality check by testing it against a training dataset. Here, the images undergo a series of automated processes that enhance the images. This involves adding or removing pixels, sorting misclassified data, or removing noise.  Data Augmentation The images are further modified with various operations such as cropping, compressing, flipping horizontally or vertically, and blurring, among others. The following exercise trains the system for better image recognition capabilities. This model serves as the base of other processes, including but not limited to,  testing and optimization, training models, and others. Understanding the Visuals Your model is now prepared to work autonomously with various visuals in form of images or videos. The system continues to improve when used regularly.  Common Computer Vision Solutions Most advanced computer vision service providers are well equipped with various solutions. The algorithms for each service are personalized for every client, which helps attain goals in record time. On the other hand, the hassles involved in the R&D of the algorithms are done by agency experts for better results. Some of the common services include: Benefits of Getting Computer Vision as a Service Solution While vision analytics is still in its infancy, numerous companies worldwide have rolled out remarkable solutions. Computer vision and other advanced biosecurity solutions are the next waves in technology. Here are some definite reasons why computer vision as a service should not be ignored at any cost. Challenges with In-house Setups When it comes to in-house setups, there are a ton of factors that prohibit companies from doing so. These are as follows: Hardware Requirements While investing in an internal setup sounds good, an in-house setup can prove costly. Here are the two sides of the coin. Data Collection and Processing When working in-house, companies face a major problem: the lack of training data sets. In such scenarios, the company must generate a huge amount of data. However, the biggest concern is not deploying a team on the streets or forming partnerships. It all boils down to privacy and the use of the data.   Use Cases of Computer Vision Services in Business While there are numerous instances where computer vision plays a vital role, here are a few examples that highlight the effect.  Amazon Go Imagine a situation where you walk in, pull out all the things you need, and walk out without bothering the cashier! No, this is not part of science fiction. Amazon is set out to build thousands of such structures powered by machine learning and machine vision. The store runs with minimal user interference, and with computer vision, it keeps track of every individual’s stock, maintenance, and billing.  Google Lens The industry leader in innovation, Google is known for its audacious dreams. However, one such project that failed badly was Google Glass. Aimed to bring the power of computer vision to everyone, the device was way ahead of its time and hence succumbed to death. However, the company gracefully introduced most of its features in an app called Google Lens. Google Lens can solve complex mathematical problems, identify plants, and even translate languages in real-time, making it one of the most productive apps on the internet.  Tesla Cars The leading electric car manufacturer Tesla is known for its self-driving feature. The car collects information about the roads and uses it meticulously to self-drive even in the worst working conditions and hence could not be ignored at all costs.  Conclusion While there is a range of opportunities that could be done with computer vision, it is best to outsource solutions unless you work on a large scale. Companies with decades-old experts in the industry understand the requirement and help both the production and enhancement of these A.I.-powered solutions. 

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Good life, Great life Podcast: Ankush Sharma on DataToBiz and Entrepreneurship

🎧 Episode Overview Ankush Sharma joined Brian Highfield in the 24th episode of Good Life, Great Life. Ankush shared his experiences starting his first entrepreneurship venture, DataToBiz, and how it changed him. A data evangelist by profession, Ankush, believes that data science is the answer to most complex business problems. Self-motivation defines Ankush’s career and is the foundation for DataToBiz.  Listen to Ankush Sharma talk about how DataToBiz came into existence and the efforts he and his co-founder Parindsheel S Dhillon put in during the initial phase. Find out what the COVID-19 pandemic brought to the table and where DataToBiz stands today.  💎 Key Takeaways:  💬 Key Quotes: “Hire people who are smarter than you so that you can delegate work and be confident that they’ll take care of it.”  “The work culture in a startup should be a reflection of the founder, and for me, people come first.” To Sum Up… This episode is about an IT employee-turned-entrepreneur’s journey in the competitive world. It’s not just about the first step, but the subsequent steps Ankush has to take to establish and successfully run DataToBiz, an AI & BI consulting company.  Attitude matters and Ankush Sharma shows why. 

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Top 6 Leading Machine Learning Companies in India in 2023

Machine Learning is a branch of Artificial Intelligence. It’s based on the idea that machines can learn from the data they’re fed and can identify patterns and make data-based decisions without much human intervention. Several businesses across the globe are using ML for predictive analysis, fraud detection, customer segmentation, understanding market trends, reducing customer churn, and growing their businesses exponentially. Are you too planning to use or integrate machine learning into your business? If yes, you’ve come to the right place. This blog talks about the top 6 machine learning companies in India that can help you use Machine Learning in your favor. You can go through the list, compare the companies, and choose the best based on your requirements. And to make the decision-making process easy, we’ve also listed some tips after the list ends. Top 6 Machine Learning Companies in India 1. TRIGMA TRIGMA is one of the leading IT companies that deals with consultancy services and software development, majorly. The company has its offices in India, The USA, and Canada and has over 200+ employees deployed in different departments. What services do they offer? As far as AI and Machine Learning is concerned, TRIGMA helps businesses with: However, AI and ML are only a small part of the services TRIGMA offers. Here are other services you can avail of at TRIGMA: How long have they been in business? TRIGMA has been in business for over 12 years. Unique Features 2. DataToBiz DataToBiz is one of the top companies for machine learning that is headquartered in Mohali, Punjab, India. This organization comprises talented ML and AI experts who help businesses of all sizes solve complex business problems and automate redundant tasks. Also, DataToBiz was recently recognized as the Top Artificial Intelligence Company in India by Clutch. Whether you need help with fraud detection, risk management, surveillance, predictive analysis, and anything related, DataToBiz is all you need. The company has helped hundreds of organizations adopt and implement AI solutions and can help you too. What services do they offer? DataToBiz offers a range of Machine Learning services to businesses of all sizes, such as: How long have they been in business? Even though DataToBiz has been in the industry for 4 years, they’ve achieved commendable feats in the AI and ML industry on a global scale. Over the course of its inception, DataToBiz has worked with huge banking organizations and multiple industries  Unique Features 3. Talentica Software Talentica Software specializes in helping startups turn their ideas into products using the best possible technology. With Talentica on your side, you can choose the right tools & technology, and architecture, lower your operational costs, expand your customer base and adopt better technologies. What services do they offer? Talentica Software, as a company, is pretty diverse and offers numerous services using the below technologies: How long have they been in business? Talentica Software has been in business for over 18 years. Unique Features 4. QBurst QBurst is artificial intelligence consulting and software development company that has worked on 2500+ projects and has 150+ active clients. It helps companies with their consultation needs and also offers end-to-end software development services. QBurst, unlike tech giants, caters to the needs of businesses of all sizes, be it small,  medium, or large. What services do they offer? When it comes to AI and Machine Learning, QBurst can help you with: Apart from Machine Learning, here are the other services the company offers: How long have they been in business? QBurst has been in business for over 17 long years. Unique Features 5. ValueCoders Next on our list of top machine learning companies is ValueCoders. ValueCoders, known majorly for outsourcing software development and software consulting, deals with clients across the globe and boasts a customer satisfaction rate of 97% percent. What services do they offer? As far as Machine Learning is concerned, ValueCoders can help you with: Apart from Machine Learning, here are the other services offered by ValueCoders: How long have they been in business? ValueCoders has been in the market for about 18 years. Unique Features 6. PixelCrayons PixelCrayons is one of the most affordable machine learning companies in India. It offers Machine Leering solutions to businesses of all sizes at 60% less cost. PixelCrayons, since its inception in 2004, PixelCrayons has launched over 11500+ projects. What services do they offer? PixelCrayons offers the following services in the Machine Learning domain: Other services include How long have they been in business? PixelCrayons has been serving its clients for over 18 years. Unique Features This was the list of the best machine learning companies here in India. But which one is the best artificial intelligence consulting firm or the best machine learning company? If you aren’t able to decide, read along. The below section will offer you the information you need to make an informed decision. How to Choose the Right Machine Learning Company? Here’s how: 1. Check the Services  The Machine Learning domain is pretty vast. If a company’s services page says “Machine Learning”, that never means they offer everything from business analytics and fraud detection to ML integration. So, check the services page for the exact service that you want. For instance, if you want to integrate AI and Machine Learning, the services section should say that the company helps with integration. Otherwise, you might just waste your and the company’s time. 2. Look for Customer Reviews/Testimonials/Case Studies This is a no-brainer; still, most people ignore it.  Look for customer reviews, case studies, or testimonials left by your ML company’s previous clients. This is just a way to determine whether or not the previous clients were satisfied with the company’s services. One way to check client testimonials/customer reviews is by visiting the machine learning company’s website. However, this isn’t effective as companies can easily fabricate customer reviews. So, a more effective and genuine way would be going for Google reviews/ratings. Just type the company’s name followed by “Google reviews,” and you’ll get the true picture of the organization. For instance,

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Why Do You Need an AI Developer for Your Business?

Artificial intelligence can change a business in many ways. With more and more organizations investing in AI technology, it is vital to hire an experienced AI developer to handle the task from start to finish. We’ll discuss the need for artificial intelligence in enterprises and the role of an AI engineer in the same. Robotics, chatbots, virtual voice assistants, automation, algorithms, etc., are hot keywords in recent times. What is common here is the use of artificial intelligence technology to digitally transform a business. Many small, medium and large enterprises are adopting artificial intelligence in various verticals within their business.   Statistics show that the global AI market will touch $60 billion by 2025. Accenture’s study report (from 12 developed countries) shows that using AI can double economic growth and increase productivity by 40%. Close to 85% of businesses feel that using artificial intelligence will give them an edge over competitors.  In such instances, it has become a necessity for business organizations to adopt AI and go for full-scale digital transformation. Artificial intelligence solutions allow automation and customization of systems to streamline processes and increase profits for a business. But how does a business integrate AI technology with its existing systems? How should an enterprise invest in artificial intelligence to get the desired results?  You hire an AI developer to provide end-to-end services and make the necessary changes in your enterprise. Many artificial intelligence companies offer offshore AI development services for businesses from different industries. This allows you to use an expert team to build an artificial intelligence system (or customize an existing one) for your business needs.  Neither hiring nor training is required. The AI engineer comes with experience, does the job, and hands over the systems to you. The offshore company will also train your employees to use AI tools for day-to-day work. Continue reading to know more about why AI is crucial for your business and how hiring an AI developer can take your business to new heights.   What is Artificial Intelligence? A simple definition of artificial intelligence is a technology to prepare a system or machine that can mimic human intelligence to complete tasks that are usually performed by humans. Artificial intelligence focuses on processes and abilities rather than a function.  Machine learning, deep learning, artificial neural networks, etc., come under the umbrella term artificial intelligence. Data science is a separate field that goes hand in hand with AI to build a data-driven model in an enterprise. Artificial intelligence is a separate domain in the IT industry. IT professionals who learn AI technology and work with it are called AI developers or engineers. It includes building an artificial intelligence model from scratch, customizing existing models/ software, feeding quality data to the systems, and training the algorithms to deliver accurate insights. AI developers should be proficient in mathematics, statistics, and algorithms.  Though you can train the existing talent in your enterprise to work with AI technology, hiring an AI engineer will be an effective choice in many ways.  Why Do You Need an AI Developer for Your Business? Each enterprise has a different idea about artificial intelligence and ways to adopt it within the business. However, there are some basic aspects you need to consider before deciding whether you want an in-house team or an offshore AI engineer.  Qualification and Experience  AI developers need to have domain-specific skills and qualifications to build AI software/apps for your business. With an increasing demand for artificial intelligence engineers and developers, many students and professionals are joining AI courses. But can you reach out to the right talent to recruit for your organization?  Many businesses start by hiring offshore AI experts because of easy access to talent and experience. Recruiting campaigns don’t need to be expensive and time-consuming. There’s no need to train the new candidates to work with the existing systems.   AI service provider companies already work with different organizations from various industries. One look at the project portfolio will give you an idea about the range of projects completed by the AI developer. They are already trained and ready for the job. Moreover, their experience in working with different businesses will help in identifying the right tools for your establishment.  Support Services  When you hire offshore AI developers from a company, you get end-to-end services from them. The company will handle everything from planning to maintenance. The AI developer will regularly monitor the systems, iron out the glitches, and make the necessary updates to keep the software ready at all times.  The developer will ensure that there is no downtime (or is at its lowest) so that your business operations will not be affected due to errors in the software or integrations. They will streamline all integrations to create a path for proper data flow within your business.  Remote Collaborations  You don’t need to have an in-house team working in the office when the AI developer can collaborate remotely with your employees. Offshore AI services are all about remote collaborations, irrespective of the differences in time zones. It reduces operational and travel costs. Furthermore, you can hire more than one AI engineer to build/ customize an AI tool for your business. A talented team will be allotted to work on your project once you sign an agreement with the AI service company.  The other advantage of remote teams is that you can hire the best team from anywhere in the world. Geographical restrictions will not limit your search for an AI engineer. There won’t be candidates who can’t join your organization because they live in another country/ region.   Cost-Effective  No matter you are a startup or a more established business, working costs and revenue expenditure are vital factors that affect your finances in the short and long term. In 2018, Gartner estimated that 85% of AI projects are likely to fail by 2022. This is because organizations began adopting AI without proper planning or strategy.  Having unclear objectives, investing too much money in a single AI project, and wanting to build everything

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7 Simple Steps To Develop a BI Strategy

If you are an entrepreneur or someone who runs or administers a business, there are two things that you should keep in mind – systems and processes. One must test different strategies and solutions repeatedly and establish a sweet relationship with cold, hard numbers. This is where numbers tell data-driven stories, and business intelligence strategy comes into the picture.  Business intelligence is the science of using numbers from varied resources to make critical business decisions. With a nicely chartered business intelligence strategy, one gets to sync these systems and processes with multiple goals. On the other hand, business intelligence consulting firms poke in the right direction, pointing to the right tools and training an organization needs to conduct as a part of the strategy.  Additionally, one gets a business intelligence strategy that not only helps the key stakeholders think across the business components but also coordinates plans, objectives, personnel, and more to ensure that implementing the recently developed strategy is a success.    7 Simple Steps To Develop a BI Strategy But how does one develop a BI strategy all in-house or outsourced? Here are seven simple steps to develop a BI strategy: 1. Assemble a Leadership Team and Executive Sponsor To begin with, it is advisory to select individuals who understand the business and can also work in teams to pull levers when needed. This will have a company-wide impact, so choose your leadership and executive sponsor carefully. Eventually, some of the traits that play a major role in defining what sort of leader and team to choose from include: If you are looking at an organizational level of change, these are the characteristics to look out for.  Next, it is time to build out a tech team. This team will work on the technology aspect of the BI team. To simplify it, the executive part of the business intelligence strategy team determines, “How will we infuse BI into our business, and how will BI affect it?”.” In contrast, the technical team will decide the specific technical requirements that get business intelligence up and running.  2. Establish KPIs with Key Stakeholders One of the biggest mistakes most companies make at this stage is not having a solid plan. Business intelligence consulting firms suggest you have the data to take your business to the next stage. This solid infrastructure captures all the necessary information at the right time. However, despite the theories, several companies struggle to deliver the expected results.  At this point, the focus of the BI team should be to arm every employee with better information for improved productivity and for stakeholders to solve overarching business problems.  Some of the key business problems that you need to ask about at this stage are: 3. Audit Current Data Infrastructure and Availability Now that you know the key parameters and factors determining your company’s growth, it is essential to have all the key stakeholders audit the current data infrastructure and derive meaningful inferences from the pile of data. At this stage, the discussions primarily revolve around your BI setup’s technical specifications and whether additional data needs to be captured in the process.  Some of the critical questions one needs to ask in the following stage are as follows: 4. Choose The Right Set of Tools Once you have understood the ins and outs of your requirements, it is time to boil it down to real-life implementation. Following are the items to look out for while setting up your data infrastructure or interviewing a business intelligence consulting firm: Data Collection and Management The basic step to begin your data collection practices is to answer the following questions. These are: Storage and Capacity One must decide what kind of setup it needs when dealing with storage. Is it a cloud-based solution or data servers established on-premises? The security of these devices could not be ignored, and hence the technical team should be sound enough to carry out the task effectively.  Data Visualization Tools and Dashboards Delivering insights through data visualization and visual analytics dashboards tools like Tableau or Microsoft Power BI is essential to the success of any BI strategy. However, one must consider the employees’ data literacy while crafting the visualizations. Do they need an extremely interactive tool or something that can be drilled down to explore the nitty-gritty of the problems, or is it a simpler dashboard with graphs? The choice is yours. Data Access and Governance When it comes to data access at various levels in the office, not everyone should know everything about the strategy. This is where data governance comes into the picture. Further, experts suggest measures against data theft (both internal and external); hence, priority should be given to security.  Choosing the Right BI Consultant/Vendor Devising and implementing a BI strategy is a resource-intensive process; hence, outsourcing to a BI consultant or vendor sounds like a sound strategy. These individuals have years of experience with different vertical expertise in business intelligence consulting. Figure out the budget permissions and connect with a consultant or vendor per the requirements.  5. Executing BI Strategies Once you have crafted the strategy, it becomes crucial to point to the right timelines. When will all the components of the strategy be executed? Will the components be executed in one phase or all at once? And so on. This will add clarity to timelines and help you succeed in executing the strategy. Your strategy should delineate timelines and communication methods around the rollout of: And so on.  6. Training When implementing business intelligence problems, most employees (irrespective of level) fail to use such tools. Hence, it becomes critical for the entire company to organize training or boot camps or face a major haul. On the other hand, using BI solutions should not feel like a hassle; hence, with the training and ease, it should come naturally.  7. Launching Your Plan and Measuring Success Now that you have finally made it, all you need to do is now measure the success of the strategy

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7 Innovative Applications of Computer Vision in Agriculture

The agricultural sector is among the critical and crucial industries around the globe, especially in India. For 58% of the Indians, agriculture is the primary source of income. The gross value added (GVA) of agriculture, forestry, and fisheries was estimated at Rs. 19.48 lakh crores ($276.37 billion) in FY20. According to the first flash estimates of FY22 national income, the percentage of agriculture and related sectors’ GVA  (at current prices) is 18.8% of the total GVA. Consumer spending in India is set to grow again in 2021, rising by up to 6.6% post-pandemic. Agriculture and related activities recorded a growth rate of 3.6% at constant prices in FY21.   India’s food industry is facing tremendous growth and increasing its contribution to the global food trade every year due to its immense value creation potential, especially in the food processing industry. The Indian food and grocery market is the sixth-largest in the world, with retail accounting for 70% of sales. India’s food processing industry accounts for 32% of the country’s total food market. Total exports of agricultural and related products in FY21 were US$41.25 billion.   As digital technologies revolutionize all industries, agriculture is no exception. Like any other sector, the agricultural sector faces various challenges including climate change, labor shortages, and the disruptions caused by the pandemic. Digital technologies such as computer vision can help the agricultural sector meet these challenges and achieve efficiency, resilience, and sustainability. Computer Vision Application – What Is It and Why Does It Hold Importance? Computer vision can be called an extension of AI which enables machines with the efficiency and capability of organized thinking and interpreting the data as human counterparts. Computer vision technology requires analysis of a plethora of visuals from footage to images and it has the ability to identify, track, precisely predict and assess specific objects within the stored visuals.  Smart farming is about harnessing the power of technologies like artificial intelligence, computer vision, and many more to automate and improve the quantity and quality of crops. Even with fluctuations in commodity prices, a ~5% improvement in crop yields can ensure farming remains profitable.  From using computer vision technology for crop and soil monitoring to disease detection and predictive analytics, agribusiness is entering a whole new phase of evolution thanks to AI.   It’s not just future potential, but growing interest and investment in the agricultural sector:  Challenges That Can Be Solved By Computer Vision in Agriculture Agricultural management is a complex subject due to its vast breadth and the sheer complexity that is bound with it. Manual inspections are therefore very time-consuming and expensive.  Plants, that are grown in greenhouses often use humidity, temperature, and light sensors to ensure optimal growing conditions for plants. However, many sites will continue to rely on manual logging and monitoring of environmental conditions, allowing for a quick response to changes or technical issues, such as the failure of a heating unit is prevented.   In agriculture, climatic factors such as precipitation, temperature, and humidity play an important role in the life cycle of agriculture. Increasing deforestation and pollution are causing climate change and making it harder for farmers to make decisions about soil preparation, seed planting and harvesting. Each culture requires specific nutrition in the soil. The soil needs 3 main nutrients: nitrogen (N), phosphorus (P), and potassium (K). Nutrient deficiencies can lead to poor crop quality.   If not properly controlled, it can lead to an increase in production costs and also absorb nutrients from the soil, which can lead to nutrient deficiency in the soil. The Main Problems to Concern About  1. Irrigation In order to successfully produce crops, suitable irrigation functionality is required. The ML algorithm can improve irrigation leading to the following :   Here are some irrigation systems  in the  machine learning realm:  2. Predictive Analytics Making the right choice or decision is a key factor in farming. The correct decision leads to better income outcomes. Predictive analytics is a great machine learning tool that plays an important role in making the right decisions.  3. Soil Defect Diagnosis  Farming is all about calculated risks, but what if risks can be calculated and cured in advance? The anomaly analysis can help you identify the strengths and weaknesses of the soil, which generates more revenue and saves a lot of time.  4. Right Seed at the Right Area  Agriculture faces a serious problem today. There is a huge scope for improvement as present conditions require specific solutions. Simply put, harvests are not properly monitored. Classification analysis is the key to efficient damage control and higher ROI than before.  5. Predicting Production Based on Weather Conditions The climate has now predominantly become a data issue. Previously, inaccurate weather forecasts have yielded a devastating loss of many crops, along with wastage of invested time, effort, and money. The good news is, that technology has significantly improved and one of the key solutions for this can be Regression analysis which can help with precise and accurate forecasting.  6. Weed Detection In developing countries, around 40-50% of the total crops are lost because of post-harvest issues, crop diseases, and pests. Even in developed countries such as the US, around 20-30% of crop yield is lost to similar culprits.  However, you can use image analysis to detect the present object in the field/farm. By classifying the objects, one can easily spot weeds on the farm, which can be dealt with, ensuring better crop growth.  7. Recommender System We know that acquiring a new customer is harder and more expensive than retaining a  paying customer. Analysis of the recommender system will assist you in identifying the customers who’re most likely to buy your product/service and the probability of your existing customers. In addition, this leads to customer loyalty and broadens the overall range of innovations in service. 8. Water Treatment The correct minerals are the most important requirement for plant growth. Spotting anomalies through unattended analysis helps you choose the required volume of minerals, leading to faster-growing plants and helping you produce

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7 Benefits of Artificial Intelligence in Advertising

In the past, advertisements were created with traditional methods such as art and copywriting. However, with the prevalence of advertising on social networking sites, it is now crucial for companies to get their message across in a way that is concise and compelling. To gain a competitive edge through distinguishing ads, they need to understand how their customers are engaged with the current ads. Brands need to be smart to not only reach their audiences but also capture their attention. Yet, the growing competition can be challenging for businesses trying to build client relationships and more importantly, loyalty. So, how are brands coping with this increasingly demanding scenario? In the current digital wave, brands are improving advertisement targeting by using machine learning techniques. It has led to a new wave of advertisements that are more realistic and engaging than ever before. According to the Times of India, “AI is predicted to take up to 80 percent of global advertising spend in the world.” Additionally, using artificial intelligence enables targeted marketing campaigns by automatically recognizing consumer preferences and behaviors. By understanding customer preferences and behavior, advertisers can create more personalized ads that are more likely to be effective.  What Is Artificial Intelligence (AI) and How Does AI Work in Advertising? Artificial intelligence (AI) is a branch of computer science that leads to the designing of computer programs that can simulate human intelligence. With programs that can reason, learn, and act autonomously, they can make decisions, solve problems, and understand complex concepts like humans. Furthermore, AI is gaining popularity due to its application in different industries such as HR, education, and retail to name a few, that are helping humans resolve complex issues and develop credible solutions.  With artificial intelligence (AI) evolving every day, the advertising industry is also growing. With advances in AI, companies can target ads more accurately and create more engaging content. It has led to a rise in new marketing techniques such as chatbots and natural language processing. The use of artificial intelligence for advertising also allows companies to measure the effectiveness of their advertising campaigns in real time. AI can help create targeted ads, personalize customer experiences, and optimize campaigns. But how does AI work in advertising? Benefits of Using AI in Advertising Artificial intelligence is still a relatively new technology, but its potential applications are endless. Furthermore, how ai is used in advertising is proving to be particularly useful for the advertising industry. Some benefits of using AI in advertising include: 1. Target Your Ads More Effectively Through Data Advertisers are always looking for new and effective ways to target their ads. Recently, there has been a lot of talk about using big data to help improve ad targeting. Big data is a term used to describe a large amount of data that is now available due to the growth of digital technologies. This data can be used to predict consumer behavior and preferences for creating targeted ads. According to Forbes, “The most recent research indicates that a combination of AI and big data can automate nearly 80% of all physical work, 70% of data processing work, and 64% of data collection tasks”. 2. Helps Optimize Your Campaigns for Better Results Artificial intelligence is being used increasingly in digital marketing, as it can help optimize campaigns for better results. AI can identify what is working and what isn’t in a campaign, and make changes accordingly. Optimized campaigns designed as per the audience’s needs help improve customer engagement and retention. It also provides companies with increased ROI. 3. Helps Save Money and Time With Increased Sales AI can help you save time and money because it can swiftly categorize the huge amounts of data that are available in this day and age. It also spots trends and patterns which are useful in making better advertising decisions. Companies like Amazon are using AI for driving pricing strategies that help to reduce prices for more sales. They have also used AI to understand and leverage pricing strategies, like when the demand for a product increase, they increase the product price too. Thus, helping companies augment more sales and revenue.  4. Reduce the Risk of Losses in Ads By using AI to help you with the marketing process, you can reduce the amount of money that you would otherwise lose if there were an error in your campaign. One of the benefits of using AI for ad campaigns is that it can help identify which ads are most likely to result in a sale. This allows businesses to focus their efforts on the ads that are most likely to be successful, reducing wasted money and time on ineffective campaigns. Another benefit of AI is that it can help identify potential threats early on in the campaign lifecycle. By identifying threats early, businesses can take steps to mitigate the risks. 5. Improving Customer Experience With AI, you can have better customer communication and a more personalized style of service. It can help companies learn about their customers, understand their needs and desires, and then predict their future behavior. It allows companies to provide customers with personalized and the best possible service and products. For example when Nike leveraged AI to provide its customers with the capability to design their sneakers in-store. It helped them to collect more data and design future products accordingly. Such AI-backed advertising techniques are immensely crucial for customers who are more likely to purchase when they feel that their preference is being well taken care of.  6. Reach More Customers With Your Ads In the age of social media, finding new and innovative ways to reach your target audience is more critical than ever. AI can also help you target your ads more effectively by learning what works and what does not. Like Coca-Cola, the largest beverage company analyzed 120,000 pieces of content on social media to understand the demographics and customer behaviors. They also analyzed customers who actively talked about their product and brand. Thus,

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How Can Businesses Adopt AI Technology to Achieve Their Goals

Artificial intelligence is a dynamic force that keeps the industry moving forward to conquer more technologies. From manufacturing to hospitality to retail and aerospace, AI is being adopted by several organizations across all industries. However, businesses are still in varying stages of adopting AI in their enterprises. While the top companies have added AI technology as an integral part of their systems, SMEs still use AI to develop pilot projects for certain departments like sales, marketing, etc. Let’s learn about the adoption of AI in certain industries and how they leverage it to achieve their goals. Why Artificial Intelligence Is Important The social applications of AI, such as home assistants, smartphone applications, etc., are different from enterprise-level applications. The latter is on a much complex and wider scale, encompassing various systems and processes in the business.  Artificial Intelligence Types  Unlike, in theory, AI is not a one-size-fits-all solution. It is vital to understand what the enterprise needs before investing in AI solutions. While pre-designed software is useful, it doesn’t deliver results like customized AI systems. But to understand this difference, we need to know more about artificial intelligence, the types, and what AI aims to achieve as an end goal.  AI is classified as follows- Narrow AI It is used for goal-oriented purposes such as facial recognition, speech recognition, etc., where machine learning algorithms complete the task based on a set of narrow constraints. General/ Strong AI It mimics human intelligence and behavior so that it can solve any problem and not be limited to specified tasks. We still haven’t successfully created AI machines with cognitive abilities.  Artificial Superintelligence This is currently in the theoretical stages. ASI machines are supposed to not just mimic humans but become one of them by being self-aware. Artificial Intelligence vs. Machine Learning In simple terms, AI is an umbrella concept where machine learning and deep learning, understand data and make decisions based on it. ML algorithms are used to quickly process vast amounts of data and deliver valuable insights.  What is the End Goal of AI?  AI sounds fascinating, and we can see that large-scale enterprises and global organizations are already getting results from AI. But questions are still aplenty for new businesses that want to hire AI services to revamp their systems.  The ultimate aim of using AI is to get measurable results to prove that the enterprise is better than its competitors. AI works the best with a centralized system where data is collected from several sources and is stored in a data warehouse or a data lake for further cleaning and processing.  Artificial intelligence drives ROI for businesses and delivers long-term results when the technology used aligns with the systems in the enterprise. AI is not limited to scaling but also to measure the results and get the most out of data.  Why Should Companies Adopt AI? What makes AI so important that companies adopt and integrate it as a part of their business processes? How does AI help organizations overcome challenges and become industry leaders?  The following are some uses of artificial intelligence that empower businesses to perform better in the competitive market and grab the opportunities that come their way.  1. Artificial intelligence enables better customer service and support.  AI is used to create and deploy chatbots, provide smart suggestions in emails, develop human-like interactive voice response systems, automate ticket generation and routing complaints, and make it easy for customer service agents to solve an issue in less time. The live chat option on many websites has customized chatbots to respond to users’ queries. 2. AI helps with target marketing and generating more leads. Artificial intelligence is used to create content for promotional emails, develop ads for each customer segment, provide product recommendations based on customers’ purchase and search history, speech recognition, etc. Amazon’s website is the best example of target marketing using AI. 3. Use AI in the HR department to hire eligible candidates and create customized training programs for employees. HireVue is a well-known AI-powered platform that helps more than 700 leading brands to hire some of the best talents in the market. Vodafone, Unilever, and Oracle are some companies that use HireVue for recruiting purposes. Similarly, artificial intelligence helps create different training modules based on employees’ skills. 4. AI streamlines the supply chain and shortens the delivery cycle.  Amazon has the best warehouse management system powered by AI. Artificial intelligence helps in managing inventory, increasing safety for warehouse employees, planning the travel route for deliveries, finding alternate delivery methods, and so on.  5. AI makes the business more secure by enhancing cybersecurity. Many of the latest antivirus software and malware are developed using artificial intelligence. The software is trained to identify phishing attacks and malicious links before employees fall prey to cyberattacks. The FaceID access feature of iPhones is a small yet everyday example of how AI is used for cybersecurity. 6. AI technology is used to detect fraudulent transactions and minimize losses.  AI processes vast amounts of data in a few milliseconds and identifies patterns in transactions. Many banking and financial institutions have started to use AI and ML-based fraud detection software to identify unscrupulous customers and cybercriminals with stolen identities. Teradata provides AI-powered fraud detection software to banks. 7. Data is put to good use through AI applications.  Data collected from social media and other sources is processed in real-time to extract meaningful and actionable insights. AI tools are used for predictive analytics and data visualization. Tableau is an example of AI-based data visualization software.  8. AI makes the workplace safer for employees, especially in industries like oils and mining. Artificial intelligence is used to monitor the work environment using drones, surveillance cameras, etc., and determine that the workers are not at risk. AI is used with IoT (Internet of Things) to collect data and process it to identify any signs of accidents or risk. AI helps the management create a safer working environment in factories.  9. AI optimizes the use of resources and helps the enterprise and employees

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