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Top 6 Benefits of Point of Sales (POS)



The Point of Sale (POS) is becoming the latest trend in the digital business world. Point of Sale platform is where the most regular manual activities are automated in order to simplify and fasten up the work progress. It is always a great advantage to be aware of the benefits of new things/trends before starting to use them.

So, here I’m listing the top 6 benefits of using POS solutions, and let’s dive deep into them:

1] Easy Stock Management

The advanced POS solutions support you in maintaining the stock by providing real-time stock data reducing your time spent in stock management. So that you can use the time in some other tasks that require more of your attention.

2] Managing Employees

The POS system has a clock feature using which the employees can check-in and check-out just in the pos software by verifying the employee identity and also helps to keep an eye on the employees’ performance.

3] Detailed Reports

This system provides us with all data of our business performance helping us to keep track of our business at our fingertips and also the real-time data from the central database make well-planned business decisions.

4] Ease Payment Modes

One of the major advantages of using the POS application is that it can accept payments from any part of the world and be done using any payment modes, such as credit/debit cards, NFC, mobile wallets, etc… Thus allowing the customers to pay from their favorite payment modes attracts more customers increasing the business revenue.

5] Optimizing Check Out

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The billing process is made much easier as the detailed list of products picked by the customer is sent to the billing department/system within the blink of an eye time of customer finishes their purchase. Thus optimizing the entire check out process.

6] Cut Off Works

As we all know the computers and mobile devices are very less likely to make mistakes when compared to humans. The usage of point of sale systems helps in cutting off the possible manual errors from the complete business process.

Point of Sale System for a New Business:

In this digital era, we can see many new businesses coming up and existing businesses transforming digitally. As a part of this digital revolution, many businesses started to implement POS systems in their firms. Along with the growing digital businesses, the companies providing custom POS systems have also increased. So now if we wish to implement a POS system in our business we don’t need to be technology expertise we can just get in touch with these custom POS system providers and get a customized POS software/application suitable for our business needs.

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Obstacles and Opportunities of Democratizing AI for Organizations



Obstacles and Opportunities of Democratizing AI for Organizations

Enterprise deployment of artificial intelligence (AI) is positioned for tremendous growth.

Artificial intelligence is set to change the business world by improving predictive analytics, sales forecasting, customer needs, process automation and security systems.


Source: Everest Group

IBM’s Global AI Adoption Index revealed that a third of those surveyed will be investing in AI skills and solutions over the next 12 months. 

More expansive use of AI democratizes AI, providing access to insights to more people – technologists and non-technologists alike. The latter group might include people in leadership, sales, finance, human resources and operations. This is where AI will shine, empowering business teams to make AI-driven decisions.

Imagine: business teams do not have to know how to code or be schooled in the intricacies of AI’s backend. Instead, they will use AI like you and I use a mobile phone for efficiency (if we’re running late, we merely send a text notifying the other person), access information faster (if we’re in the grocery store and need a recipe, we look it up), make better decisions (GPS gives us the fastest route).

Just as mobile technology works without us understanding complex circuitry, algorithms or software, the democratization of AI across enterprises will be integrated in much the same way.

So, what will hold AI back and how will AI help enterprise companies gain traction?

3 Obstacles and Opportunities Organizations Face by Implementing Artificial Intelligence


Artificial intelligence deployment approach | Source: IBM

Obstacle #1: Data in disarray. Data that does not provide a complete picture and single version of truth because of data silos and various data formats within an organization.

Opportunity: Employing a data fabric. Using a data fabric to help organizations use data more effectively and get the right data to users regardless of where it is stored. One significant advantage of a data fabric is that data governance rules may be automatically set for compliance.

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Having one information structure to garner insights and analytics from, integrating security to protect sensitive data and establishing a framework for implementing trustworthy AI positions AI as part of the business strategy, not solely an IT strategy so that AI directly impacts business operations.

It all comes back to connecting data with business drivers and a data fabric helps accomplish this. It is what I call “point-to-point” thinking – knowing the business imperatives, business drivers, the different levels of raw data, who is consuming the data, who will have access to the data, and why the data is important in decision-making and then, the big payoff with AI, how it will elevate experiences: customer experience, workforce experience, supply chain experience, strategic partner experience, community experience. In “point-to-point” thinking we don’t hoard data, but share it – securely.

Obstacle #2: Varied skill levels. A lack of AI technical skills across the enterprise and a reliable, open platform to bring AI to more people.

Opportunity: Creating a bridge to AI for people within the enterprise. Palantir for IBM Cloud Pak for Data is one of the great innovations of our time because it doesn’t require coding skills. People in non-technical roles can go from raw data to data insights quickly using application templates (think of all the designs being produced with minimal design experience because of apps like Adobe Photoshop and Canva). This is truly the path to democratizing AI.

People can now use AI to make better decisions in real time and improve business outcomes. These teams include sales and marketing, manufacturing operations, campaign managers, branch managers, franchise operators, human resources, among others.

An example: a customer walks into their regional bank. The banking professional greets the customer, invites them to sit down and pulls up their profile. They see, not only account information, but a 360-degree view of the person sitting across from them. Through a data fabric, non-tabular visualizations gathered from previously siloed data originating from different systems provides an AI-infused perspective.

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This might include two algorithmically recommended customer offers inspired by marketing analyst data and intelligent customer segmentation and campaign propensity scoring powered by Watson models.

Going further, feedback from the customer can then be entered and that data influences future offers because it goes right back into IBM’s data and AI platform. IBM Cloud Pak for Data, which helps to simplify data management and protect sensitive data by establishing a framework for implementing trustworthy AI.

Obstacle #3: Solving for the wrong “x.” In hundreds of conversations I’ve had with enterprise leaders over the years about AI, one common failure I see not identifying the right problem or identifying use cases that will yield high return from AI.

Opportunity: Clearly articulating the problem to be solved. With AI, we are talking about a machine making reasonable conclusions based on data. Better defining the problem is akin to asking better questions.

Imagine the difference if you were in a store and asked someone if they sold products. The question is too vague to expect a meaningful answer. Ask where the tomatoes are and you get a clear answer. Both are valid questions, but one is more focused. That’s how defining the problem should be (this is not just for AI purposes; I devote a lot of space in my book, Ascend Your Startup, to defining the customer problem because I believe building the wrong solutions plagues many companies)

Key Takeaways

How Artificial Intelligence Is Revolutionizing the Advertising Industry

In an interview with famed Mount Everest climber George Mallory, a reporter asked him why he wanted to climb the formidable mountain. His answer: “Because it’s there.” AI is very much the same thing. It has obstacles, yet it has the allure of opportunity and of making measurable progress.

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Here are the big three takeaways for enterprise companies:

•  Use a data fabric. Information is powerful – and it exists! Don’t let siloed data and inconsistent data formats hold people back from making better decisions.

•  Give people what they need to succeed in their jobs. Tools such as low code/no code enable business users to rapidly leverage data and apply AI in their decision making. 

•  Go back to square one and define the problem. Solving for “x” without fully understanding “x” wastes precious time, causes unnecessary frustration and marginalizes the experience for everyone involved.

The Rise of AI


Source: Statista

In a Forbes article on the topic of AI, author Manas Agrawal writes, “With rapid learning and adoption, AI is no longer a crystal ball technology but something that humans now interact with in nearly every sphere of life.”

In a very short time, we won’t be talking about AI adoption as people see it as part of doing business and part of making life more efficient. AI then will shift to being part of an enterprise’s business strategy, delivering value for non-technical people working in many different areas like customer experience, brand differentiation, HR, research and development, management and sales.

This is what the democratization of AI looks like at the crossroad of technology and humanity to improve outcomes for people leading successful enterprise businesses.

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5 Proven Tips to Implement Machine Learning the Right Way



5 Proven Tips to Implement Machine Learning the Right Way

Businesses that realize the value of data and contemplate machine learning implementation are usually faced with similar challenges, that can be overcome by following a few simple guidelines.

Business leaders are increasingly becoming aware of data science and machine learning’s role in supporting and enhancing business growth. In an attempt to leverage the technology, businesses either jump into implementation without planning or are stuck in the planning phase for too long, both leading to suboptimal outcomes. As business and technology leaders involved in machine learning implementation, you should keep the following tips in mind to ensure that you are doing it the right way:


1. Use the Right Data

Like any other analytical or logical application, the principle of Garbage in, Garbage Out holds true for machine learning. When it comes to machine learning implementation, although the algorithm is considered to be an important factor for driving success, the data that is fed to it is equally important, if not more. The quality and relevance of data used in machine learning helps extract highly valuable insights and sets the machine learning initiative in the right direction. Choosing the right variables to track and process through the algorithm requires asking the right questions and verifying the data quality.

2. Experiment with Algorithms

Successful machine learning implementation requires a combination of quality data and a robust algorithm. It may not be easy to get everything right with your algorithms in the first attempt, requiring you to make improvements based on trial and error. Investigating your algorithms to pick out behaviors that are desirable, as well as, the ones that are not, will enable you to modify algorithm parameters to achieve the ideal results. You should also realize that algorithms that track highly complex data, such as that associated with humans, need constant re-evaluation and re-programming to ensure sustained effectiveness.

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3. Collaborate with Research and Academic Institutions

One way to make up for lack of expertise in machine learning implementation is partnering with academic institutions that research and teach machine learning and analytics. This will enable organizations to make use of subsidized expertise while making progress using machine learning. An example of such collaboration between business and academia is the Prototype optimization model made by Wayne State University that saved $12 million for Ford Motor Company on the first use. As a long-term initiative, businesses should invest in data science labs, to promote such fruitful collaborations with academic institutions.

4. Train Employees in Machine Learning Implementation

Another way to compensate for inadequacy in know-how is to ‘upskill’ existing employees through training to make them proficient in data science applications. Employees for an upskill should be selected based on aptitude for work in question. The most suitable candidates to receive such a training are the ones with a high aptitude for math and statistics, and the ability to translate data into useful insights. Organising training seminars and workshops are among the most common ways to train employees in a chosen skill.

5. Hire Third-Party Expertise

As there is high demand for data science experts, businesses can consult third-party specialists to help them initiate and integrate machine learning into the business. Experienced third-party professionals can guide your business through planning and execution of pilot projects, and educate organizational personnel on data science and machine learning.

Keeping these tips in mind will enable you, as a CIO or technology leader, to keep your machine learning implementation on the right track. To make the most of any technology, the best way is to gain enough practical knowledge about it. In addition to the tips mentioned above, you should also acquaint yourself with some machine learning best practices.

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Best Practices in Machine Learning



Best Practices in Machine Learning

Machine learning (ML) has given rise to several practical applications that fulfill real business interests such as saving time and money.

It has the potential to dramatically impact the future of your organization. Through applications such as virtual assistant solutions, machine learning automates tasks that would otherwise need to be performed by a live agent. Machine learning has made dramatic improvements in the past few years, but we are still very far from reaching human performance levels. Many a times, a machine needs the assistance of a human to complete its task. This is why, it is necessary for organizations to learn best practices in machine learning.

For the correct implementation of a machine learning algorithm, organizations are required to study machine learning use cases and execute best practices. Some such best practices include:



When your organization has too much data, there is a temptation to take some files and drop rest of them. Dropping data while training your machine learning algorithms can cause several issues. Importance weighting means that if you decide that you are going to sample example X with a 30% probability, then give it a weight of 10/3. Thus, by importance weighting, all of the calibration properties are discussed and addressed.

Reuse Code

You must reuse code between your training pipeline and your serving pipeline whenever it is possible. Batch processing methods are different than online processing methods. In online processing, you have to handle each request as it arrives, whereas, in batch processing you have to combine tasks.

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At serving time, you are doing online processing, while training is a batch processing task. For using the code, you can create an object that is particular to your system. You should be able to store the result of any queries or joins in a very human readable way.

Then, once you have gathered all the information, during serving or training, you should be able to run a common method for bridging between the human readable object that is specific to your system and whatever format the machine learning system expects.

Avoid Unaligned Objectives

While measuring the performance of your machine learning system, your team will start to look at issues that are outside the scope of the objectives of your current system. If your product goals are not covered by the existing algorithmic objective, then you must either change your objective or your product goals. For instance, you may optimize clicks or downloads, but make launch decisions based in part on human raters.

Keep Ensembles Simple 

Unified models are those models that take in raw features and directly rank content. These models are the easiest models to debug and understand. However, an ensemble of models works better. To keep things simple, each model must either be an ensemble, only taking the input of other models, or it can be a base model taking many features, but not both

If your organization is having models on top of other models that are trained separately, then combining such models can result in bad behavior. You must use a simple model for ensemble that takes only the output of your “base” models as inputs. You can enforce properties on these ensemble models. For example, an increase in the score produced by a base model should not decrease the score of the ensemble.

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Thus, implementing these best practices can ensure successful implementation of machine learning algorithms.

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