Analytics

Machine learning has emerged as one of the biggest buzzwords in the recent years. As per International Data Corporation (IDC), spending on AI and ML is expected to grow from $12B in 2017 to $57.6B by 2021. Deloitte anticipates that the number of machine learning pilots and implementations will double in 2018 as compared to 2017.

As a business, how can you benefit to using the Machine Learning wave to your advantage? Here are a few probable applications :

HR, Employee Engagement, Performance Management

Often, HR managers are tasked with a huge load of manual tasks such as monitoring leaves and vacations, mapping resource availability, managing employee productivity etc. Through the power of big data and machine learning, a lot of the exhausting drudgery can be outsourced to machines. As a result, the HR team can free itself to focus on more critical tasks that require creativity and strategy.

Machine learning can also help identify patterns to predict illness among certain employees, thereby promoting better employee health.

Performance Management is an area that prone to much debate and dissatisfaction among employees, because traditional methods of employee performance assessment are often viewed as being vague and unscientific. ML can help analyze even unstructured data such as emails, chat messages etc. to provide much better overall visibility into performance. It can also suggest appropriate trainings or courses to help employees improve on their scores.

Employee engagement can also go up several notches via the clever use of Machine learning. The use of Sentiment analysis by studying the language and phrasing in internal and external communication can provide a great indication of the employee mood.

Reporting & CRM

Despite the perceived negatives about machines taking away human jobs, the fact remains that machines can do exceedingly well at executing mundane, repetitive jobs such as report generation and CRM. Using machines for these tasks is not only effective, it also frees up human time such that they can focus on more high-end tasks that require creativity, strategic thinking.

Analytics allows you to identify the most common challenges and issues and create a set of features that address them. While humans have been doing similar exercises manually for ages now, the efficiency and scale at which the machine does this is what is truly remarkable.

SEO

For any business, ranking high on search engines is a great way to garner new business. Search engine rankings are based on a variety of very complex parameters, not all of which can be influenced.

However, an effective search engine optimization strategy can make a significant difference to company rankings. Even for internal data, an intelligent search engine can help drive better knowledge management.

If you’d like to explore how you could implement some of these into your business, check out Sonata’s analytics offerings to know more: https://www.sonata-software.com/microsites/analytics

Over the next few years, we can fully expect the use of Machine Learning and AI to become mainstream as more organisations discover its many applications. IDC has estimated that by 2019, 40% of digital transformation initiatives will use AI services and by 2021, 75% of enterprise applications will use AI. The possible applications could range from insights to predictions to recommendations on running the business better. As a result, machine learning can help bring about several process improvements too.

Here are some examples:

Superior Customer Service

AI and machine learning can allow organisations to studyhistorical customer service data, current engagement

patterns and use natural language processing to significantly improve the quality of responses to customer queries. Chatbots are increasingly gaining favour, as customers become more comfortable with them. Human intervention is required only to handle complex cases or exceptions, that too only until the time that the bot is able to understand and internalise these situations. This offers an opportunity to provide superior customer service at much lower costs.

In addition, the machine learning algorithm can study transaction patterns and customer behaviour to do a social sentiment analysis. This can help determine which customers are at the highest risk of leaving, thereby helping to map retention strategies. It can also help improve profitability based on buying patterns.

AI insights can also help in predictive maintenance, by anticipating machine failures and putting systems in place to address them. This way, organisations can better plan for service disruptions etc. so that customer inconvenience is minimised.

Automation

Perhaps one of the biggest strengths of machine learning is its ability to automate several labour and time intensive tasks. One great example is in recruitment. For an average job opening, hiring managers need to sift through hundreds of resumes to shortlist a suitable candidate. This is a difficult and lengthy process if done manually. However, with the right algorithms, this process can be highly simplified since the software can evaluate thousands of resumes and shortlist based on the credentials that the company values. Apart from the massive amount of time saved, this also helps human bias that often inadvertently creeps in during the recruitment process.

Finance is another area that offers immense scope for automation using machine learning and AI. Machine learning algorithms can learn from existing processes to recognise patterns and exceptions.

Insight Based Decision making

AI can offer useful and actionable insights to facilitate faster and better decision making. For example, it can help create much more targeted marketing campaigns by tracking data on non- traditional parameters such as the impact of placement of logos in digital advertisements or the number of product mentions. This aids better establish relationships between the action and impact; thereby resulting in more effective campaigns.

AI can also help ensure smoother supply chains by using contextual data on suppliers to predict any disruptions, thereby enabling better planning.It can also help detect any anomalies in patterns, both based on internal and external data, thereby helping in early fraud detection.

Click here to read more about how your business can improve processes and benefit from AI and machine learning or write in to us @ info@sonata-software.com

Each and every Retail business is at multiple levels of maturity with regard to leveraging their data assets for customer acquisition, customer experience and operational efficiency. However, a future-proof retail analytics ecosystem is needed to address the following challenges (boldfaced) with the adjoining solutions:

Absence of a data foundation for analytics – Delivering actionable insights across the enterprise is the key goal, but the ability to integrate packaged and homegrown data sources for a single view of it, needs to be honed. This can be done with having a resolute data foundation for retail analytics replete with good data governance, data cleansing processes and ultimately, optimal data quality in place.

Intangible business impact – Ongoing result-oriented data initiatives need to be dealt with continuously at a revenue oroperational metrics level, by laying emphasis on a sincere approach to reduce time-to-value complexities – the resultant effect being an upsurge in the ability to boost business and create a tangible impact, with the help of robust data analysis methods.

Dearth of re-usable assets – Successful AI/ML implementations in Retail mandates a plethora of re-usable assets that can prove to be powerful inside-out, with adequate availability. However, the life-breath of aggregating these re-usable assets is to ensure that even with the best of what is available, a sense of totality in incorporating the implementations can have a total reach as well.

Cognizance of Sub-vertical specifics – Data Analytics across grocery/fashion/apparel/cpg, etc. have intrinsic variations which need to be factored in for best outcomes when implementing Retail analytics. These(outcomes) help in monitoring these sub-verticals which herald a dynamic environment that are specific to them.For eg., heightened predictability of consumer behavior, that is a direct outcome of Retail analytics, helps reduce inventory and opportunity costs for grocery retail. Similarly for fashion retail, predictive analytics is used to analyze Sales against demand and supply.

In lieu of the above, it necessitates that retail organizations looking to maximize their RoI from analytics investments, need to :

• Focus on a collaborative and consultative approach between their business, IT and partner ecosystem Ensure data initiatives are phased and aligned to existing organizational and data maturity
• Implement a continuous delivery model with intermittent, measurable outcomes rather than a big-bang approach
• Leverage re-usable assets in terms of pre-built tested algorithms, data models and plug-n-play vertical specific tools for rapid iterations and validations

Data and analytics is inarguably the single most impacting factor driving differentiation and growth for Retail organizations. It’s no more a luxury but a necessity to have a strategic and business driven outlook in the analytics journey with short and long-term business objectives. At the same time, deriving RoI intermittently is key to justify investments and build on top of a sustainable data foundation.

Business outcomes for Data & Analytics investments are correlated to:

• Time to value – Are you seeing impact soon enough or is it still a vision without tangible outcomes?
• Addressing business nuances – Are you analyzing factors with specifics of your business/as-is maturity and not just what is prevalent in the industry as a whole?
• Commitment to the cause – Is the organization committed to the data analytics journey in terms of investments, skills and timeframe to move up the analytics value chain?
• Partnering for the cause – Analytics is not the focus of a Retail Company, but your business is. It’s important to identify the right partner to provide the requisite thought leadership and walk with you on the journey. Do you have the right partner?​

We at Sonata Software are helping retail enterprises globally with retail analytics plug-n-play tools (Retina) , re-usable assets for AI/ML driven business use cases and helping build a future proof, sustainable foundation to their data-driven digital transformation journey.

Click here for you to learn more.

Couple of years before when I spoke about Robotic Process Automation (RPA) with one of my prospects, they asked me ‘where the robots are, how they work, if they are battery operated, how they are recharged and lastly how long they can work on a single charge?’ I think we have come a long way past that discussion. Today, prospective customers are well conversed with the RPA concept and many of them have started taking advantage of using them to automate their business processes. Now the question is how to go about and become successful in this journey. I would like to highlight the overall process, challenges in each step and best practices in this blog. I believe that each organization has a commitment to digital transformation and many of them have initiated the digital journey. Digital transformation means different things for different customers, but everyone will agree with me that an essential part of the digital journey is Business Process Automation (BPA). Let’s understand what BPA means to any organization.

Business Process Automation:

• Improves the operational efficiency and productivity
• Saves overall cost and reduces the cost of operation
• Provides greater transparency and consistency
• Streamlines the overall process
• Minimizes manual work and Increases the accuracy
• Provides opportunity for the existing workforce to move from routine/mundane work to customer facing and decision-oriented activities

One may question the need for BPA when we have traditionally used a tool such as Business Process Management (BPM) for the same purpose. There is nothing wrong with BPM; howeverthe scope is much larger, expensive and it takes a considerably longer time to implement. RPA can complement BPM in the journey of BPA. I believe that RPA is a means to BPM and fits well into the BPM culture of an organization. The actual benefits for RPA depends on the process and industry where it is being implemented. However, there are common benefits across all industries and processes.

Speed, Efficiency and Productivity – RPA is changing the way the business and business models are working today. It can speed up existing processes, and integrate with multiple technology systems in a non-invasive way, thereby increasing productivity.

Cost Effectiveness – Digital Robots operate 24/7 without any vacation except during the system maintenance or outage. Robots require monitoring as opposed to supervision. It requires minimal maintenance.

Better Accuracy & Quality – RPA automates business processes which havea probability of human error. Robots can work tirelessly and reduce re-work. Robots provide the highest level of consistency across each process all the time. This improves better output quality.

Enhanced Customer Experience – Bots can complete a large amount of routine/mundane work in a relatively short timeframe. It also frees up human resources to facilitate better customer interaction. Faster processing coupled with accuracy and increased customer interaction enhances customer satisfaction significantly.

Flexibility and Scalability – Recruiting, training, motivating, managing and retaininga human resource is a continuous and costly affair. Robots can easily be scaled up or down for a given process without any major hassles.

Versatility – RPA has the capability to work across industries to perform different tasks for a variety of processes.

Non-Invasive – RPA robots work likehuman beings and carry out same activities on same applications. RPA does not require any changes to the existing application and integrates seamlesslywith systems without any changes to the same.

Better Return on ROI – It is not easy to calculate the ROI for RPA,as it brings variousintangible benefits along with ROI benefits on the project cost & license cost vs. labor replacement. However, the aboveROI benefits would be significant for any RPA program. The maximum ROI comes when a CoE for RPA is established.

Constraints of Implementing RPA. Though RPA brings lot of benefits to process automation, there are few constraints which organizations need to take in its stride in pursuit of digital transformation:

• Initial Cost – The license cost of the RPA toolcoupled with engineering expense spent on RPA, means a few organizations are resistant to the changes consistent with embarking on the journey of RPA.
• Change Management – Adapting the new technology will result in changes in existing processes or Standard Operating Procedures (SOP).Enterprises need to work with the technical teams to ensure that the impact is smoothly handled.Our advice for any RPA implementation is to take it slow and have both existing and RPA processes side by side until confidence is gained before moving to the next stage.
• Workforce Displacement – RPA will result in the workforce being displaced from their current repetitive work to other activities.This requires to be carefully managed in terms of cross-skilling, motivation and educating the workforce on benefits of movement, before embarking on the RPA journey.

Identification of a Use Case

The success of a RPA program relies on the identification of the right use case. Based on our experience, we can provide a fewguidelines to choose the right one and we would advisecompanies to start the journey with a simple use case and subsequently move to more complex use cases.

• Repetitive and Manual – A process that is repetitive in nature across applications (done manually) would be an ideal candidate for the RPA use case.
• Volume – It is important to identify the number of times the same process is recurs per unit of time, which would identify the current amount of human effort being spent on accomplishing that task (use cases). This would be a key factor in selecting the use case.
• Rule-based – For a use case or process, there would be business rules which need to be followed.If these rules are documented, then automating this business process using RPA would be the ideal candidate to start with.
• Structured Document –It is wise to start with a structured document vs. un-structured text for RPA use case. RPA uses NLP and OCR techniquesusing whichunstructured can also be processed, however our advice would be to start with structured and then move to unstructured.
• Error-prone – Ideally a use casewhere there is precedence of manual errors, can be the right choice for RPA robots. Manual mistakes can cause difficulties in regularity compliance besides customer dissatisfaction. The accuracy and unbiasedness would be an advantage for RPA
• Mature and Stable System – A process(use case)or application which undergoes frequent changes would not be an ideal candidatefor RPA. A stable system would beideal for RPA implementation.
• Revenue – Identifying a use case or process whose ROI or revenue can be easily computed, would be an ideal candidate for RPA to create a quick impact.
• Irregular Demand – Any process (use cases) that requires ramp up and down of human individuals to support it, would be a good candidate for RPA, as RPA bots can be scaled up or down based on the demand cycle.Also, aspects of induction, training, attrition would not be there like it is, in human workforce.
• New System Roadmap – Any process or system that is likely to change in near future may not be the right candidate for RPA.
• Data Privacy – The management team has toensure that all necessary approval/permission are obtained for theRPA bot, as it would behave like a human in terms of accessing data and writing data back from multiple systems for processing.

Post identification of the usecase, the next challenge is to convince the management and impacted parties before starting on the RPA journey.

Management Buy-in – The management needs to understand the following besides theexpected ROI from the implementation of RPA:

• How will the work process change?
• What would be the new skill requirement for the organization?
• What would be the impact on the existing workforce?
• What would be the customer impact?
• What is the time frame required for the implementation?

Once a process is identified for RPA, the team should prepare the document to outline each of the above. Now, the cost benefit analysis and ROI is to be calculated before making the presentation to the management for acceptance.

Impacted Team – It is essential to have an honest, open and candid discussion with the team about how RPA will affect them and what changes it will bring to the organization and processes. It would be helpful for smooth implementation.

• The supervisor or the team in charge needs to be taken to confidence about the benefits of RPA, and how the existing team can be better utilized needs to be explained, so that he/she can convince other team members.
• A plan for training of the new team members (selected) is to be prepared.
• An up-skill plan needs to be prepared for the redundant team members and their next assignment to be finalized well in advance.
• HR team needs to be used to take care of emotional and motivational aspects related to the current employees, so as to not be detrimental for a successful RPA implementation.

The next step is to choose the right Partner and Tool Selection

The selection of the RPA tool and a reliable partner are two most important actions in the intelligent automation journey of any organization. Today, we have several leading product vendors who claim to have the best features in the field of RPA. However,many business houses still struggle to choose the right product for their needs.Most of the leading RPA product vendors claim to fit all sizes. I therefore think, that it is a difficult task to choose the right product and the partner. Based on our years of experience working with multiple RPA tools, starting from selection to successful deployment, we believe that a set of criteria may be useful for an organization to finalize the best tool for RPA initiatives.

Architecture – There are three different types of robots that exist today. The assisted, unassisted and Hybrid. These digital robots are deployed both at front-end and back-end automation. The design and structure of the tool have implications for how and where it can be used. The adaptability and efficiency of the product’s architectural and functional capability to meet your requirements would be key in the selection process of the RPA tool. The other consideration would be an enterprise vs. non-enterprise level tool.

Ease of Use - Ease of use can play a big role in selecting the right tool. It should be flexible to accommodate basic process automation with built-in features. Better usability will lead to quicker growth, greater ease of deployment and higher levels of adoption. Lastly, the benefitis proportionality with the ease of use. The tool should be easily configurable and manageable by business users. It should have a quick learning curve and ensures a great level of reusability features that determine the ease and speed of the implementation.

Scalability – Organizations are operating with on-premises, Cloud and hybrid models. It would be wise to choose an RPA partner who can provide a solution both, for on-premises and Cloud. In selecting a tool, both, infrastructure and operational scalability are important. The tool can be adjusted on-demand and be allowed to execute multiple workflows. Organization can start with a small pilot and scale in a cost- effective way.

Reliability – This is an important criterion to select the right tool. The automation framework based in the tool that is built, should be robust and very reliable. The tool should be able to handle different types of use cases and high volume of data without degrading the performance or service level.

Flexibility –Identify the customizations required for the organization after going through the demonstration of tool features. The cost and elapsed time of these customizations need to be discussed with the product vendor during the product evaluation stage. The organization needs to make sure the RPA product works across multiple operating systems.

Security –Robotic Process Automation is going to process sensitive data of an organization, and therefore the right kind of security control should be an essential part of the selection criteria.

Integration -In most of the cases, RPA tools need to interact with multiple systems in an organization. The ease of integration with other systems would be another selection criterion for the right RPA tool for an organization.

Technology Spectrum –RPA tools come with OCR and some level of cognitive capabilities. One should look at the tools’ ability to take advantages of futuristic technologies like AI/ML and Chatbots etc. This may not be the current requirement for an organization, but it would be worth to have a look into the technology landscape from the point of view of investment and new technologies.

Governance and Visibility –An organization’s RPA strategy should include the Governance, Operating Model, Organization Structure and Change Management process. Management and governance of various bots should be visible to all intended members through dashboards. A mechanism must be in place in case of malfunction.

Vendor Support and Documentation –The product maturity andPartner’s ability to support each customer’s needs, would be one of the primary factors while selecting a Partner to employ the RPA tool. RPA is a journey and the regular interaction with the Partner is important. A matured support organization along with theavailability of right documentation both online and offline, will help faster deployment and reduce maintenance time to a greater extent.

‘Path to Success with RPA’ -Once an organization chooses the right RPA partner and the tool, the next step is to execute the first use case successfully. The organization along with partner should create a work plan for the execution and should be ready with the infrastructure and right resources ahead of it. This process will showcase the strength of RPA, as well as its pitfalls. This step will establish the framework and the operational models for a smooth transition into the long-term RPA strategy.

The initiative to implement RPA is more business-driven than IT. RPA will take over certain manual processes which were repetitive, manually intensive, voluminous, time consuming, and error prone. Each step of the RPA implementation process should be clearly communicated to each affected employee for smooth transition and reduce the margin of errors. RPA is a very powerful transformational tool which can improve efficiency, productivity, and make the organization more competitive in the marketplace. It has a potential to improve end-customer satisfaction significantly.

QL Server 2008 and Windows Server 2008 are quickly approaching their end of support (EOS) dates. Without Microsoft fixing bugs and running security updates, hackers will vie to exploit security gaps in the server and your company’s sensitive data will be at risk.

To avoid compliance issues, security breaches and other losses that come with unsupported servers, it’s important your company upgrades now. As you likely know by now, cloud-based servers are becoming the new standard at companies around the world. By migrating to the cloud, you’re able to give your business a competitive edge. In this article, you’ll get what you need to know about each step of the process so you take action with confidence.

Step 1: Create a Plan

If you’re running SQL Server 2008 or Windows Server 2008, it’s time for a new plan. Your options are:

1. Upgrade your on-premise servers to a newer version

2. Keep the servers you have and pay for extended support as a temporary solution

3. Migrate your SQL Server to a cloud-based server like Microsoft Azure

The first two options won’t set your business up for long-term success, as trends suggest that migrating to a cloud-based workload is inevitable for businesses in the years ahead. Plus, extended support for 2008/2008 R2 servers is a costly option. Meanwhile, Microsoft promises to provide free Extended Security Updates until 2022 when you migrate your SQL 2008/2008R2 Server database to Azure.

Step 2: Public, Private or Hybrid?

In order to move to the cloud, you first have to choose whether you want to be on a public, private or hybrid cloud. Public clouds are entirely hosted and managed by a provider in their data centers. Microsoft Azure, Amazon Web Services (AWS) and Google Cloud are some examples of leading providers.

With a private cloud, you’re responsible for your own hardware, storage and devices required for hosting the cloud. You can create a private cloud for your company using a platform such as VMware, HPE, IBM or OpenStack.

Alternatively, you can choose to operate on multiple clouds, including both private and public. According to a survey from RightScale, more and more companies are opting for a multi-cloud strategy, and as much as 85 percent of companies in 2017 utilized more than one cloud. The main motive behind a hybrid solution is that it gives you the best of both worlds--you can use a private cloud environment for your IT workloads and have strong security and guaranteed resource availability. At the same time, using a public cloud to accommodate occasional or seasonal traffic spikes, can help you stay within budget and scale more quickly.

Step 3: Choose Your Cloud Provider

Microsoft Azure isn’t the only cloud provider, but it is leading the way to the cloud for businesses around the world. If you’re currently running SQL Server 2008 or Windows Server 2008 or a later platform from Microsoft, Microsoft Azure can secure you the safest and most seamless migration to the cloud. Its cloud service is reliable, compliant with GDPR and several other privacy laws, and provides multi-level data protection to ensure endpoint security. Whatever provider you choose, you want to look into their security certifications, reliability and performance, as well as the level of support they offer.

Step 4: Get Professional Help with Migration

Be careful not to underestimate the complexity of data migration and the critical planning process it involves. Getting on the cloud is a big step that will pay off in the foreseeable future and far beyond. Lack of attention to detail can cause migrations to fail, causing a huge setback. When you work with specialists to build your migration, you entrust the process with experts able to customize and optimize your cloud infrastructure in a way that achieves your business’s unique goals.

Sonata Software helps enterprises migrate to the cloud with their unique “Platformation” approach focused on connectivity, efficiency and scalability. It enables companies with on- premise servers to migrate to the cloud while staying in complete alignment with Microsoft offerings.

As your business transitions to the cloud, Sonata Software delivers expertise from the beginning to the end, starting with the decision-making process. They can help you find a provider that fits your enterprise best by analyzing your existing applications and infrastructure and determining which platform would give you the best ROI.

Sonata will help monitor your onsite and offsite structure, disseminate built-in migration reports and test your workloads before, during and after migration to make sure the entire project is completed without a glitch.

Getting Started with a Successful Migration

As EOS for 2008 servers approaches, migrating to a cloud platform will prevent the risk of your workload and sensitive data being hacked. Partnering with Sonata Software optimizes your process and improves your end result with better efficiency and innovation. Take the burden off your IT team and concentrate on familiarizing the rest of the company with the new cloud infrastructure. Meanwhile, let industry experts help you through the tough decisions and infrastructure management.

With SQL Server end of support nearing, your IT department needs a gameplan. Not only does your company face huge security risks running unsupported software, but also you miss out on the functionality of a better server. Why not use EOS as an opportunity to upgrade to a server that boosts your bottom line and saves you overhead costs in the long- run? There are several options out there for handling EOS, but doing nothing shouldn’t be one of them. Here are the risks your company faces if you aren’t prepared:

1. Compliance Gaps

By storing your customers’ data on unsupported platforms, you’ll quickly end up with compliance gaps. In today’s business world, GDPR, European privacy law and other privacy laws are too important to ignore. Failure to comply can result in large fines, breaches in consumer trust, a bruised reputation and even shutdowns or worse.

2. Security Attacks

As soon as EOS arrives for SQL 2008 and Windows Server 2008, Microsoft will stop running security updates and patching the software. This gives hackers a window of opportunity they’re sure to jump on. Consequently, your company will be exposed to a serious risk of cyber attack if you don’t take action.

According to research, When a cyber attack strikes, 1 in 5 enterprises end up losing customers, and 1 in 3 lose revenue. Not to mention, there’s a huge toll taken on productivity, employee morale and brand trust.

3. High Costs

Without upgrading, you’re bound to run into high costs to maintain the equipment and fix bugs, which is pointless because you’ll have to upgrade eventually. There is an option to extend your support temporarily so you can keep the servers you have. However, this requires a large fee, whereas migrating to Microsoft Azure gives you free Extended Security Updates until 2022.

4. Losing Out on Profit

Besides having to spend more in the long-term by not upgrading, you also miss out on opportunities that reduce overhead spending and increase your bottom line. Cloud computing gives your business the chance to scale more quickly and with less overhead because you can always increase your computing power with the click of a button. With a “utility” pay structure, cloud computing requires you only pay for the resources you use.

5. Becoming Obsolete

According to analysts, the migration to cloud computing is inevitable for all businesses in the foreseeable future, as technology is rapidly evolving the modern work landscape. As more and more enterprises migrate to the cloud, you need it to stay competitive. Business is becoming more data-driven, and cloud computing offers strategic value for businesses focusing on data growth, insights and analytics. It gives you the flexibility to scale up or down with the demands of business, the productivity to keep your employees working efficiently, and it cuts in-house overhead costs.

Don’t Become Obsolete

Don’t let your data infrastructure go unprotected by the time EOS comes around. Take action sooner rather than later so your business can start reaping the benefits of cloud computing. While it may seem tempting to simply extend your support for your 2008 software, taking this step will only hold your business back in the long run. Migrate to Microsoft Azure so you can get top-of-the-line security on the cloud with ample support and reliability.

To know more about how Sonata can help you manage the SQL 2008 EOS better, please reach out to sukrupa.g@sonata-software.com.

To know more about our Data & Analytics offerings, please click here.

With popularity of mobile payment systems, QR codes can be found almost anytime, anywhere in daily life. From luxury shopping centres to street vendors, consumers can make payments easily by scanning the QR code with their smart phones. The awkwardness of forgetting your wallets at home no longer exists. If you have a mobile payment set up on your phone, you can virtually always go cashless in daily life.

But things are changing as we speak. QR codes are just a step in the evolution of mobile payment technology and they may soon be a thing of the past. In fact, soon people may be able to forget about QR codes and pay with virtually nothing but... themselves. This new payment method we are talking about is facial recognition, which has been implemented by many tech giants in various industries. The facial recognition market is expected to grow to $7.7 billion in 2022 from $4 billion in 2017. That’s because facial recognition has all kinds of commercial applications. It can be used for everything from surveillance to marketing.

How does facial recognition work?

You might be good at recognizing faces. You probably find it a cinch to identify the face of a family member, friend, or acquaintance. You’re familiar with their facial features — their eyes, nose, mouth — and how they come together.

That’s how a facial recognition system works, but on a grand, algorithmic scale. Where you see a face, recognition technology sees data. That data can be stored and accessed. Using a series of algorithms, facial recognition technology works by scanning your face using a digital camera, analysing it based on a variety of physical traits. Using this analysis, the system can create a faceprint - a unique code of individual face, which is stored and accessed through an identity database.

Facial recognition software often includes a “liveness test”, where it requires the users to shake their head slightly when analysing the face. This prevents users from simply holding a photo to foul the camera.

So how is this technology applied to real life?

Unlock Phones: A variety of phones including the latest iPhone are now using face recognition to unlock phones. This technology is a powerful way to protect personal data and ensure that, if a phone is stolen, sensitive data remains inaccessible by the perpetrator.

Help The Blind: Listerine has developed a ground-breaking facial recognition app that helps the blind using face recognition. The app recognizes when people are smiling and alerts the blind person with a vibration. This can help them better understand social situations.

Make Air Travel More Convenient: Airlines have already started using face recognition to help people check bags, check into flights and board planes faster. It seems like we are quickly moving toward a future in which air travel is not only safer than ever before, but also more convenient than any period in history.

Banks: Facial Recognition has also been adopted by several banks with thousands of ATMs placed across the nations. With these ATMs, customers can withdraw cash by scanning their face, without the need for a bank card or even a mobile phone. Thanks to this new technology, the money withdrawal process becomes much faster and more convenient.

Hotels: Marriott International has launched facial recognition check-in kiosks, placed at two locations in China, will be able to scan and identify the guests’ faces, then match it with their reservations in the system and check them in. Thanks to the new system, the check-in times can be reduced from three minutes to one.

Restaurants: Customers can now order their meal by smiling in front of the self-serving screens. Then, by adding a phone number and scanning their faces the customer’s identity will be verified before proceeding with the payment.

Supermarkets: Super market giants are also leveraging facial recognition in its chain of unmanned stores. As the name suggests, there is no staff in the store, but only shelves of products with digital price tags providing updated price discounts and offers in real time. Customers can buy everything from the stores by picking up products and walking through the "smart payment lanes" to check out. When entering the store, the smart cameras will bind your face to your payment information, so that you will be able to pay automatically by simply walking out of the store.

Pros and Cons:

Pros:

Complete Automation: Facial Identification technology can be used for automating the complete identification process. Just pair it up with cameras and you don’t need to hire guards for identifying people.

Higher Accuracy Levels: The precision level of identification systems has further been secured with facial recognition software. Infrared cameras teamed up with 3D facial identification technology allow users to track every corner of their facility and monitor who comes in and goes out.

Fraud-Proof: Almost every identification system comes with its own flaws, however, it is not possible for anyone to get in as someone else or get out of door as someone else. It keeps employees from gaining access to confidential information and prevents frauds of any kind. It is easy to log in through passwords, but biometric systems cannot be falsified.

Cons:

Quality: Image quality is of supreme importance in terms of facial recognition. For better quality image you will need an advanced software. When a facial software captures a picture, it compares the face with stored photo and if the picture fails to match it raises an alarm. Thus, quality of image would deeply influence complete identification process. If there is any flaw in this respect, it would give faulted results.

Storage and Processing: Storage is the basic requirement in digital environment. Everyone who owns a facial recognition system would need to store lots of data for future prospect. You will need extra space for storing hundreds of low-quality visuals and if the images are high quality then you will need extra space.

Physiological Changes: There are several physiological changes that might not be registered by facial identification software. Change in hair colour, length, weight change or any other major change in appearance can easily throw system off.

Conclusion: Facial recognition systems for verification and identity is just the tip of the iceberg. There is no reason to doubt in further evolution of this technology that would further open new doors for growth and development. As this technology would keep on growing, it will pave way to numerous pros, cons as well as moral questions.