The Transformative Power of Computer Vision Across Industries

Computer technology has always been a rapidly evolving field which outdates previous innovations in a short timespan. The advancements in computer technology range from wireless charging to the ever-increasing memory storage. Many digital trends have come and gone since the advent of smartphone technology. Nevertheless, artificial intelligence is an endowment of computer technology that has set a benchmark in the field of technological innovations.

Over recent years, AI technology has advanced further with new techniques like image processing, OCR, pattern recognition, and handwriting recognition. The computer vision techniques have become more accurate at recognizing patterns from images, videos, and real-life experiences as compared to human cognitive systems. The goal of computer vision algorithms is to extract and analyze the content from digital media files. Read further to explore how computer vision can transform various industries the way they serve humans.

Computer Vision Applications in Different Industries
Healthcare: Computer vision has the potential to complement routine diagnostics that require time and assistance from human physicians. For instance, Gauss Surgical is producing a real-time blood monitor that eliminates the problem of inaccurate blood loss measurement during injuries and surgeries. The monitor comes with an intuitive app that uses an algorithm to analyze pictures of surgical sponges. It then predicts the amount of blood loss during surgical treatment.

Automotive: Recent data from the World Health Organization (WHO) shows that more than one million people die every year in car accidents. These accidents mainly happen due to human errors or lack of attention while driving. Computer vision can make a real difference in the automotive industry by adding important safety features to the vehicles. If a car could detect danger, it would stop before an accident happens, saving countless lives. Several self-driving systems use computer vision to detect traffic signals and drive smoothly without causing accidents or damage to the property. Other applications include parking safety which aims to reduce car accidents that happen during reversing or parking in tight spaces. For example, reverse cameras equipped with parking sensors can detect obstacles and trigger warning signals when a car approaches an object. Similarly, front cameras can ensure that the integrated computer assesses the distance from a vehicle in front. Keeping vehicles at a safe distance reduces the risk of accidentally bumping into other vehicles.

Agriculture: Cattle rearers can use facial recognition techniques to identify different types of animals in a herd. They can use advanced herd monitoring systems that require less human intervention. Produce growers for some of the leading grocers around the world, including Walmart, are using computer vision. It enables them to detect pests in their crops or different types of plant diseases. Further, they can learn more about their plants in general and grow high-quality crops for better returns on sales.

A company called SlantRange uses drones with computer vision cameras to scan the crops and identify potential threats. The drone hovers at an altitude of about 400 feet with a 4.8 cm/pixel resolution camera. Once it is airborne, the camera takes pictures of the crops to detect the possible hazardous conditions. It includes instances such as infestation and lack of water nutrition. Moreover, it accurately predicts crop yields and determines the right time to harvest. Hence, it allows farmers to take the necessary actions to save their crops beforehand.

Retail Stores: Last year, Amazon launched a retail store that enables shoppers to bypass a queue and pay for their merchandise right away. The underlying technology behind the store is called Just Walk Out. Shoppers need to activate the iOS or Android mobile phone app before entering the store’s gates. Computer vision facial recognition cameras are used to let employees know when something is taken from the shelves. If an item is returned to the shelf, the system can also remove that item from the customers’ shopping cart. The network of cameras enables the app to track people in the store at all times. It ensures that the billing is done for the right items and to the right shopper as they walk out. The app generates an online receipt and charges the cost of products to their Amazon account.

Banking: Banks and financial institutes can use image recognition software to prevent fraud instances by authenticating documents via machine learning. Know Your Customer (KYC) is an essential process for authenticating computer vision applications. The technology enables banks to shift their focus from duplicate databases and use biometrics for identification of their prospective clients. Computer vision technology streamlines the KYC process by enabling prospective customers to open accounts via smartphones. The European bank BBVA has attracted numerous customers, enabling them to open accounts by taking a selfie or via video calls. The solution enables banks to increase customer convenience and move further in the customer-centricity journey.

The technology also enables organizations to analyze customer behavior in real-time through emotion recognition by analyzing micro-expressions, pupil dilation, and eye movements. Banks can capitalize on the information by offering personalized products and convenient accounts-related processes. Similarly, computer vision can track the intent of individuals near cash points and detect threats in real-time. As such, it will provide a safe environment for their customers.

Read More: Zooming into the world of Computer Vision applications

Conclusion
In its current state, computer vision is an important part of the digital factory. It has the potential to transform every field that one can imagine. Businesses can explore new opportunities using AI-based solutions that have computer vision integrations. AI solutions with computer vision can identify faces and analyze customers’ sentiments to offer them relevant suggestions and services.

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Power BI vs Tableau

In your search for a Business Intelligence (BI) or data visualization tool, you have probably come across the two front-runners in the category: Power BI and Tableau. They are very similar products, and you have to look quite closely to figure out which product might work the best for you. I work for Encore Business Solutions; a systems partner that specializes in both Power BI and Tableau. We’ve seen more than a few scenarios in which Tableau was being used when the company really should have gone with Power BI, and vice-versa. That was part of the inspiration for this side-by-side comparison.

Unfortunately, the internet is full of auto-generated and biased pages regarding which product trumps the other. The truth is, the best product depends more on you, your organization, your budget, and your intended use case than the tools themselves. It is easy to nit-pick at features like the coding language that supports advanced analysis, or the type of maps supported — but these have a minimal impact for most businesses. I’m going to do my best to stay away from these types of comparisons.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

In writing this comparison, I did a lot of research. The result was more than just this article: I also created a tool that can generate a recommendation for you based on your response to a short questionnaire. It will generate a score for both Power BI and Tableau, plus provide a few other things to think about.

Tableau Software
Founded in 2003, Tableau has been the gold-standard in data visualization for a long time. They went public in 2013, and they still probably have the edge on functionality over Power BI, thanks to their 10-year head start. There are a few factors that will heavily tip the scales in favour of Tableau, which I’ll cover in the next few paragraphs.

Tableau: Key Strengths
Let’s make one thing clear from the start: if you want the cream of the crop, all other factors aside, Tableau is the choice for you. Their organization has been dedicated to data visualization for over a decade and the results show in several areas: particularly product usability, Tableau’s community, product support, and flexible deployment options. The range of visualizations, user interface layout, visualization sharing, and intuitive data exploration capabilities also have an edge on Power BI. Tableau offers much more flexibility when it comes to designing your dashboards. From my own experience, Tableau’s functionality from an end-user perspective is much farther ahead of Power BI than the Gartner Magic Quadrant (below) would have you believe.

Tableau built their product on the philosophy of “seeing and exploring” data. This means that Tableau is engineered to create interactive visuals. Tableau’s product capabilities have been implemented in such a way that the user should be able to ask a question of their data, and receive an answer almost immediately by manipulating the tools available to them. I have heard of cases in which Tableau actually declined to pursue the business of a customer in the scenario that the customer didn’t have the right vision for how their software would be used. If you just want something to generate reports, Tableau is overkill.

Tableau is also much more flexible in its deployment than Power BI. You can install the Tableau server in any Window box without installing the SQL server. Power BI is less flexible which I will discuss in Power BI Weaknesses.

Tableau can be purchased on a subscription license and then installed either in the cloud or an on-premise server.

Finally, Tableau is all-in on data visualization, and they have their fingers firmly on the pulse of the data visualization community’s most pressing desires. You can expect significant future improvements in terms of performance when loading large datasets, new visualization options, and added ETL functions.

Tableau Weaknesses
Unfortunately, Tableau comes at a cost. When it comes to the investment required to purchase and implement Tableau – 9 times out of 10 it will be more expensive than Power BI, by a fair margin. Often, Tableau projects are accompanied by data-warehouse-building endeavours, which compound the amount of money it takes to get going. The results from building a data warehouse and then hooking up Tableau are phenomenal, but you’ll need an implementation budget of at the very least $50k – plus the incremental cost of Tableau licenses. Learn more from Power bi online course

Of course, a data warehouse is not a requirement. Tableau connects to more systems out-of-the-box than Power BI. However, Tableau users report connecting to fewer data sources than most other competing tools. Overall, considering the investment required to implement a data warehouse is a worthy indicator of the commitment required to get the most out of Tableau.

Power BI
Power BI is Microsoft’s data visualization option. It was debuted in 2013, and has since quickly gained ground on Tableau. When you look at Gartner’s most recent BI Magic Quadrant, you’ll notice that Microsoft is basically equal to Tableau in terms of functionality, but strongly outpaces Tableau when it comes to “completeness of vision”. Indeed, the biggest advantage of Power BI is that it is embedded within the greater Microsoft stack, which contributes to Microsoft’s strong position in the Quadrant.

Power BI: Key Strengths
Though Tableau is still regarded by many in the industry as the gold standard, Power BI is nothing to scoff at. Power BI is basically comparable to all of Tableau’s bells and whistles; unless you care deeply about the manifestation and execution of small features, you’re likely to find that Power BI is fully adequate for your BI needs.

As I mentioned, one of the biggest selling points of Power BI is that it is deeply entrenched in the Microsoft stack – and quickly becoming more integrated. It’s included in Office 365, and Microsoft really encourages the use of Power BI for visualizing data from their other cloud services. Power BI is also very capable of connecting to your external sources.

Because Power BI was originally a mostly Excel-driven product; and because the first to adopt Microsoft products are often more technical users, My personal experience is that Power BI is especially suitable for creating and displaying basic dashboards and reports. My own executive team really likes being able to access KPIs from the Office portal, without having to put much time into the report’s creation, sharing, and interactivity.

Power BI’s biggest strength; however, is its rock-bottom cost and fantastic value. For a product that is totally comparable to the category leader, it’s free (included in Office 365) for basic use and $10/user/month for a “Pro” license. This increases adoption of the product as individuals can use Power BI risk-free. For companies that don’t have the budget for a large Business Intelligence project (including a data warehouse, dedicated analysts, and several months of implementation time), Power BI is extremely attractive. Companies that are preparing to “invest” in BI are more likely to add Tableau to their list of strongly considered options.

Power BI is available on a SaaS model and on-premise; on-premise is only supported by Power BI Premium licensing.

Microsoft is also investing heavily in Power BI, and they’re closing the small gaps in their functionality extremely fast. All of those little issues some users have with Power BI are going to disappear sooner rather than later.

Power BI Weaknesses
As I’ve mentioned, Tableau still has the slight edge on Power BI when it comes to the minutiae of product functionality; mostly due to their 10-year head start. But perhaps Power BI’s greatest weakness is its lack of deployment flexibility. For Power BI on-premise you need to install the Power BI Report Server as well as the SQL Server.

I also mentioned that Tableau works well for users with large amounts of data and for users that want on-premise systems. You should be aware that there are some new features being added to Power BI via Power BI Premium that help catch Microsoft up to Tableau in the areas of large datasets and on-premise capabilities – but Power BI Premium adds significant cost, and these features are relatively new. Tableau still reigns in these areas.

To get more knowledge of Power BI and its usage in the practical way one can opt for Power bi online training Hyderabad from various platforms. Getting this knowledge from industry experts like IT Guru may help to visualize the future graphically. It will enhance skills and pave the way for a great future.

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Power BI In Brief – 2020

Every month, we bring you news, tips, and expert opinions on Power BI? Do you want to tap into the power of Power BI? Ask the Power BI experts at ArcherPoint.

Power BI Desktop – Feature List
More exciting updates for August—as always:

• Reporting - Perspectives support for Personalize visuals; rectangular lasso-select for data points; additional dynamic formatting support to more visuals
• Analytics - Direct Query support for Q&A
• Visualizations - Linear Gauge by xViz; advanced Pie & Donut by xViz; ratings visual by TME AG; toggle switch by TME AG; fdrill down Pie PRO by MAQ Software; ADWISE RoadMap; updates to ArcGIS Maps; extending Admin capabilities for AppSource visuals
• Template Apps - Agile CRM analytics for Dynamics 365
• **Data Preparation ** - Text/CSV By Example
• Data connectivity - Cherwell connector; Automation Anywhere connector; Acterys connector

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Power BI Developer Update
And the updates continue—this time, for developers:

• Updates in embedded analytics
• Automation & life-cycle management
• New API for updating paginated reports data sources
• Get dataset/s APIs return new additional properties
• Embed capabilities
• Persistent filters support for embedding in the organization
• Phased embedding
• Control focus behavior for create/clone visual
• Additional Javascript API enhancements
• Selected learning resources

Multiple Data Lakes Support For Power BI Dataflows
And if that’s not enough, Microsoft also announced improvements and enhancements to Azure Data Lake Storage Gen2 support inside Dataflows in Power BI. Improvements and enhancements include: Support for workspace admins to bring their own ADLS Gen2 accounts; improvements to the Dataflows connector; take-ownership support for dataflows using ADLS Gen2; minor improvements to detaching from ADLS Gen2. Changes will start rolling out during the week of August 10. Read more on multiple data lakes support in Power BI dataflows.

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Comparing Power BI with other tools

the Business Intelligence (BI) world has been moving towards self-service BI. As expected, several vendors created tools empowering regular users to gain insights from their data. Among the many, there is Power BI. Nowadays, users want to understand the differences between Product X and Power BI.

One of the most common questions in conferences and user group sessions is likely, “can you provide a comparison between this product and Power BI?”.

The answer is almost always, “No, I cannot compare them, because they are too different”. First, one needs to understand the deep difference between Power BI and most other reporting tools on the market. Only later does a comparison make any sense. As a matter of fact, I think Power BI can be compared to only a few products on the market today. I would like to add my point of view to the discussion.

To get in-Depth knowledge on Power BI you can enroll for a live demo on Power BI online training

Indeed, Power BI is a tremendously powerful data modeling tool that happens to come with a pretty face; most other products are beautifully crafted reporting tools with a pretty face. The only thing they have in common is the pretty face. If you stop at what they have in common, you are only comparing a small fraction of the whole product, and that would be unfair.

To go further, a deeper understanding of basic BI concepts is needed.

Beware: this article is biased. I love Power BI and I make my living out of it. Nevertheless, I am a BI professional; I started working with Business Intelligence many years ago and I have gathered experience that I can share. I will try to be as fair as I can in this post, as my goal is not to provide a comparison with any tool. The goal of this post is to help you understand what you really need to evaluate when making (or reading) any comparison between different BI products.

At the top level, any Business Intelligence solution is composed of three layers:

Raw data: these are the data sources that one wants to analyze. Raw data comes as is.
Semantic model: this is where data is re-arranged to optimize it for analysis. Here you also define the calculations required by the reports.
Reports: these are the nice dashboards you can build with the tool.

Power BI manages all three layers: you start from raw data, you can build a semantic layer, and finally you prepare reports. Most other reporting tools are focused on the last layer and are limited in the previous two. In other words, they are missing the capability to build a real semantic layer. It is important to clarify what a semantic layer is, to understand what you would miss by choosing a different product.

In the old ages of BI, there was a clear separation between users and developers. A BI developer would build a project to help users extract insights from their data, and build reports. Users did not need to understand tables, relationships, or calculations. The developer oversaw shaping the tables, providing predefined calculations and giving sensible names to entities. Leveraging the semantic model, users did not have to know DAX, MDX or SQL.

A semantic model lets users interact with business entities like customers, sales, and products. Users would place those entities in reports made with Excel or with other reporting tools. Regular users were happy with just Excel and a Pivot Table. More advanced users wanted more powerful tools, and this led to the creation of several reporting tools with their ad-hoc programming language to create more advanced formulas. Regardless, the important thing is that no matter how powerful those tools are, they were still reporting tools based on the existence of a previously crafted semantic model. No semantic model, no reporting.

Picture this: a BI tool lets a developer build a semantic model. A reporting tool lets a user build a report on top of an existing semantic model. You need both to create a BI solution.Learn more from Power BI online course

Unfortunately, building a BI project takes time. Users were hungry for reports. This led to the start of the Self-Service BI era. Self-service BI is the idea of users building reports themselves, to reduce development time and to build a democratic knowledge about data. Sounds cool and terrifying at the same time.

Anyway, this is where we are today. Obviously, driven by the market several vendors started to build self-service BI tools. A few new products appeared on the market. Rather, existing tools evolved into new ones, targeting self-service BI. Keep in mind: any self-service BI tool requires the functionalities to build both the semantic model and the report in the same tool. Thus, depending on where you start, you have two options to have an existing product evolve into a self-service product:

If you already have a semantic model tool, you need to add reporting capabilities. You need to make it easier to use, because the target is no longer a BI professional but a regular user instead.
If you already have a reporting tool, you need to add the capability to build a semantic model because your users need to massage the data and build calculations on top of the resulting model.

In both cases, in the end you obtain a tool that mixes the capabilities to create a semantic model and to build reports. After this first step, you can add tons of different features like sharing with other users, building wizards to automatically connect to other services, improving the formula language and so on. But the core is always the same: a semantic model and a reporting tool, bound together in a nice package.

Even though we consider Power BI to be a new product, it is actually the evolution of Power Pivot and Analysis Services Tabular (semantic model), Power Query (querying tool), and Power View (the first version of the reporting tool released with Excel and SharePoint). Other vendors took similar steps, with different starting points. It is fair to say that several vendors started from a reporting tool, adding the semantic model to it.

Now, if you need to compare two BI tools, you need to compare at least these two features: the semantic model and the tools to build a report.

Say you want to compare Product X with Power BI; you show me how easy it is to build a gorgeous report on top of an SQL view, much easier and much more powerful than Power BI. Cool, but you are only comparing a fraction of both products. Reporting-wise, sure, Product X is better than Power BI. But there are other considerations: can you load multiple tables in Product X? Can you build relationships between them? Can you use a programming language to author complex calculations that involve scanning different tables? All these operations belong to the semantic model. A fair comparison needs to apply to all the features.

This is what Power BI offers you:

Power Query – a data transformation tool which is easy to use and yet incredibly powerful. It can load virtually anything and join data from different sources.
A modeling environment where you can build different kinds of relationships between tables and build powerful models. It does not hurt that it runs on top of one of the fastest databases I have ever seen.
DAX – a programming language which is not easy, but lets you author nearly any query and calculation. Yes, on this I am biased for sure!
Power BI – a reporting engine which is very good in building dashboards and reports. It can also be extended with custom visuals and third-party products.

Then, there is web-based reporting and sharing, a mobile experience, the ability to load from nearly any data source in the cloud or on premises and many other useful features. Yet, the core is composed of the four features above. If you want to compare apples to apples, you need to compare at least these four parts. Be mindful: you need all of them. A tool that requires you to build a single table because it does not let you relate two tables is nothing but a nice reporting tool. Comparing it to Power BI does not make much sense to me.

Moreover, it does not come by chance that to learn Power BI, one needs to learn new programming languages. Each feature has its own language, and this is just the right thing to have.

Finally, reporting. Reporting is only the last part, even though it is the most visible one. You might find other products are better than Power BI when it comes to reporting. This is fine, if you are aware that you are only comparing a fraction of Power BI with the whole of Product X.

I love Power BI, and I would really love to see a fair comparison between Power BI and any other product. We could learn a lot from the topic. But for it to be fair, it cannot just be based on how easy it is to build a pie chart (just kidding! You are not using a pie chart, are you?). One needs to evaluate everything both products have to offer.

To get in-depth knowledge of this technology and to develop skills to make a great career in this regard one can opt for Power BI online training Hyderabad.

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