Customer service is an important aspect of any business organization. Businesses keep looking for new ideas to improve their customer service and offer better customer experience, which has led to the advancements in AI based customer service solutions. Although, businesses have been using AI in customer support for a while; the collaboration still holds many facets that are yet to be unfolded in the future.

Following are some of the applications of AI that can be explored for enhancing customer service by businesses:

• Brand Messenger: In recent years, there has been an increased user inclination towards messaging apps. The use of messaging has extended from personal communications to user engagement with brands. This has laid out a path for businesses to incorporate chatbots to interact with new and existing customers. As some major industries (like fashion, tourism, food chains, airline, e-commerce and hotels) have adopted this feature to increase user engagement; it would be exciting to know which industries will follow the suit in future.

• Quick Resolution: Wait time for resolving simple queries is an important determinant in customer satisfaction. Customers seek quick answers to general queries and tend to trust a brand that offers faster answers and streamlined action plans for their queries. Thus, businesses can exploit the capacity of AI to multi-task and handle multiple automated queries. This will help in limiting the response time and generating accurate resolutions.

• Customized User Experience: In addition to making self-service user interfaces more intuitive, AI can help in anticipating customer needs based on previous chat history, contexts and user preferences. AI integrated systems can capture a large amount of data for identifying customer issues, defining customer behavior, determining frequent decisions, prompting with proactive alert messages, suggesting personalized offers and discounts, etc. Such intelligent assistance and pre-emptive recommendations will help companies in offering a quality rich customer service.

• AI Controlled Multiple Support Channels: In addition to providing direct assistance to the customers, AI can be used to control multiple channels of customer support. For example, in case a telecommunications agent is unable to answer a query, AI can determine the issue and direct the customer towards dedicated support channel.

Undoubtedly, these applications support the strengthening of collaboration between AI & Customer Support. However, as the AI systems rely on collecting extensive user data for working efficiently, this gives rise to privacy concerns. The data collecting system can be compromised resulting in a data breach. Thus, business organizations need to pay due attention to data security policies before implementing AI supported customer service systems.

For more information about use of AI in customer service, call Centex Technologies at (972) 375 - 9654.

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A smart application is an software that uses data from user interactions (historical and real-time) for providing actionable insights for better user experience. The insights may be in the form of recommendations, estimates or suggestions to complete a task. A common example of smart applications is retail apps that provide product suggestions to the user based upon previous buying behavior and choices made by the user.

As a business owner, following are the reasons why you should consider to invest in smart applications:

• Operationalize Data: If you are investing your resources for collecting data related to your customers, it will hold no value if this data is not used. Smart applications operationalize the information collected by your data scientists. These applications utilize this information to provide insights to customers and systems for helping them take profitable actions. For example, a smart app utilizes data about the buying history of a user to provide a list of your products that may garner user interest and improve the chances of a sale. This leads to desired outcomes that support your business goals.
• Improve Operational Efficiency: Smart applications are also available for machines, not only human users. Machine-to-machine smart applications can be paired with event-driven architecture for automating the operational processes based on real-time insights. This helps in improving the operational efficiency of an organization.
• New Business Models: Smart applications can be used to analyze data for providing predictive insights for developing more productive business models. The model is based on extensive user data, market research, and thorough analysis. The smart app helps the organization in predicting the results of a business model before investing resources for its implementation.
• Dynamic & Evolving: As new data, insights, and user feedback are collected regularly; the data scientists and developers use this newly acquired information for continuously evolving the smart application. Thus, the smart application is able to provide relevant insights based on real-time data. Also, smart apps have a loosely coupled microservices-based architecture. This makes it easier to implement changes and support continuous evolution.

Undoubtedly, it has become important to adapt smart applications for your business. However, here are some points to be considered before making a decision:

• Do you have robust data practices? Smart applications are data-driven. Thus, an organization requires robust data practices for finding actionable information from a large amount of data.
• Do you imply an agile methodology? For running a smart application, it is important for both data scientists and developers to adopt an agile methodology. Thus, make sure that your data scientists must be able to analyze data and update the algorithms regularly. Also, your developers need to continuously update the smart app.

For more information about next generation smart applications, call Centex Technologies at (972) 375 - 9654.

As the IoT network continues to grow, the data has to travel long distances in order to be accessible to every device connected to the IoT network. As the data was formerly stored at a central location, it requires high bandwidth for pushing the data to and fro the nodal devices where it was actually needed. This also resulted in high latency rates. The need to reduce bandwidth requirements and latency rates gave rise to ‘Edge Computing’.

Definition: Edge computing is defined as a part of a distributed computing topology in which the information processing is located close to the edge – where devices or people connected to the network produce and consume the information.

Thus, it is a Microdata center network that processes or stores the vital data locally and pushes all data inward to a central location or cloud storage. Broadly speaking, edge computing is all computing that happens outside the cloud, at the edge of the network, where real-time processing is required. The basic difference between cloud computing and edge computing is that cloud computing feeds on big data while edge computing feeds on real-time data generated by sensors or users.

How Edge Computing Works?

In order to understand how edge computing works, let us consider a corporate scenario. Think about monitoring devices in a manufacturing company. While it is easier for a single device to capture data and send it to cloud storage, the problem arises in the case of multiple monitoring devices as they would produce a large amount of data.

Thus, the edge gateway collects data from the devices and processes it locally to separate the relevant information from junk data. Once the processing is complete, only the relevant information is sent to the cloud storage. Additionally, in case an application needs this information, the edge gateway sends it back in real-time reducing the latency period which would have occurred if the information request was to be processed at cloud location.

Privacy & Security Risks:

As the data is handled by different devices, it gives rise to security and privacy risks.

• Bots: A great degree of edge computing is done via Application Programming Interfaces (APIs). Failing to encrypt the data and authenticate third-party APIs result in a lack of control. This gives rise to a loophole that can be exploited by hackers to steal data or infect the connected devices with malicious code or bots.
• Distributed Denial of Service (DDoS): The hackers may lay silent for an extended period after infecting your system. This gives them time to spread the infection through a larger number of devices while staying unnoticed. Once their code is deep-rooted, they may initiate a DDoS attack which will spread at a greater speed owing to the low latency of edge computing paired with the upcoming 5G network.

It is imperative for organizations to pay attention to data security before implementing an edge computing model in their network. For more information about edge computing and ways to manage privacy issues related to it, call Centex Technologies at (972) 375 - 9654.

Automation helps in enhancing the productivity of a company by streamlining the processes. Technological advancements have helped companies in automating routine tasks for better management of time and resources. Undoubtedly, automation seems like a lucrative option and a large number of companies are grasping the opportunity. However, it has some security risks that should be taken into consideration before incorporating automation into your operations.

• If your business process makes use of IoT enabled devices for data collection and process execution, a through security audit is recommended. It is also important to make sure there are no vulnerabilities in the internet or LAN connection that may install Trojans or malware in the system.
• When automating your company’s payment process, it may be a risk to provide access to multiple people. If multiple employees are authorized to set up and verify payments, the chances of insider threats may increase.
• Relying excessively on automated tools is another security risk for your business. Periodic manual checks to correlate automated process findings should be conducted.
• Like other software, ignoring regular updates of automation software may open a back door for cyber attackers.

Once you understand the risks associated with automation, here are some ways to help you balance automation with security:

• Set Security As A Priority: As the automation market is growing, the service providers have a zeal to launch the latest solutions at a higher pace than their competitors. This may lead to ignorance of security measures during the beta phase of development. Thus, make it a point to ask the automation provider about the built-in security measures. Also, you may consider a trial period to test if the products match your security standard.
• Think Before Scaling Up: Investing in automation for the sake of keeping up with the trend may lead to the obliviousness of security measures. Thus, before you invest in automation, you should consider the need and purpose. Also, pay attention to your system and infrastructure security before scaling up the automation operations.
• Regular Updates: Outdated software has vulnerabilities that can be exploited by hackers and may lead to cybersecurity breaches. So, it is important to be aware of software updates provided by your automation service provider.
• Automate Cybersecurity: Incorporating automation in your cybersecurity strategies can help in improving your security protocol by offering features such as better threat detection, data correlation, etc.
• Training: Before incorporating automation in your operations, train your employees to train them on ways to use the automation tools while considering security protocols.

We, at Centex Technologies, provide state-of-the-art IT security solutions for businesses. For more information on securely incorporating automation tools into your work processes, call us at (972) 375 - 9654.

As quoted by Steve Jobs, ‘Design is not just what it looks like and feels like. Design is how it works.’ UX & UI are an integral part of a website design process that make it more responsive and user-friendly. In order to understand the future of UX & UI, it is first important to understand the terms individually.

 What Is UI?

UI or user interface is the point of contact between human and computer. It includes anything that a user interacts with in order to use an application or website like screens, touchscreens, keyboards, sounds, lights, etc.

What Is UX?

As defined by ISO 9241-210, UX or user experience is "a person's perceptions and responses that result from the use or anticipated use of a product, system or service".  It includes all the users' emotions, beliefs, preferences, perceptions, psychological responses, behaviors and accomplishments that occur before, during and after use. 

Talking in terms of business websites, UI is more focused on how a product’s surface looks and works; whereas UX is focused on a user’s journey of using your product.

Here are some future trends that you should look forward to:

Future UI Trends For 2020

• Custom-Made Graphics: Your business website should help people relate to your brand. So, making use of photos or imagery with people who are relevant to your business is a good idea. Instead of using stock photos, the future will see creation of custom-made graphics for business websites and other digital content.
• Motion Graphics & Motion Effects: With the rise of 5G, it will become easier to load complex animations. So, this will pave way for websites with a creative UI having animated texts, fonts, graphics, etc. instead of static images.
• Responsive Colors: This trend has been around for a while and is expected to be adopted by more brands. Instead of sticking to a single symbolic color, the brands tend to include an array of responsive colors to the website design. These may include a set of pre-defined colors or a dynamic color system that adapts according to the color of environment for a more symmetrical view.

Future UX Trends For 2020

• Designing For Speed: As data transfer speed and latency rate are improving significantly, UX is directly related to loading speed of your website. For 2020, the focus will be on optimizing your website for speed. Also, some good UX practices such as loaders (button that shows your webpage is loading) will become irrelevant. The only use of loaders will be in call-to-actions such as order confirmation, transferring funds, etc.
• AR & VR: AR & VR are becoming an integral part of UX design. Common examples of including AR & VR in design are apps like Snapchat, Sephora, etc. The trend is being extensively adapted to offer user convenience.
• Device Synchronization: Users tend to use a website across multiple devices like desktop, laptop, smartphone, etc. With increasing use of mobile devices, device synchronization is an important future trend for UX design.

For more information on UI & UX, contact Centex Technologies at (972) 375 - 9654.

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Small-medium size businesses (SMBs) pose as an easy target to the cyber criminals. The reason behind an increased number of crimes against SMBs is that majority of cyber-attacks have an underlying motive of stealing personal data for identity theft and credit card fraud. Since SMB networks tend to be less secure, it becomes easier for the hackers to launch a breach successfully.

As there is an alarming increase in breach incidents, it has become important for SMB owners to pay more attention to cybersecurity. Some cybersecurity practices that SMBs should adopt are:

Document Your Cybersecurity Policies: It is important to document the cybersecurity policies, installed updates, analysis reports, etc. SMBs can make use of online planning guides to initiate the documentation process. Also, many portals offer online training, tips and checklists related to prevailing cybersecurity trends. This is an important step for SMBs to keep a track of their cybersecurity protocols.

Educate Your Employees: As the cyber-attacks are becoming more complex, the cybersecurity policies are also evolving. In addition to regularly updating the protocols, SMBs should define internet use guidelines and establish consequences of cybersecurity violations. The employees that have access to the network should be thoroughly educated about these updates and guidelines. They should be properly trained on security policies and ways to detect malware or infection.

Firewall: Make sure that your employees should use a firewall when accessing business network in office or at home. Firewalls act as fist line of defense against cyber-attacks targeted to access sensitive data. For an additional line of defense, SMBs should consider installing internal firewalls in addition to external firewall.

Mobile Device Security: As the BYOD culture is gaining popularity, most employees prefer using their own mobile devices to access business network and sensitive data. Since employees tend to download numerous applications or software on their mobile devices, they pose as a threat by accidentally downloading malware. A hacker can compromise the mobile device and gain access to the sensitive business data. Thus, educate your employees on the requirement to encrypt their data, install trusted security apps and password protect their devices.

Password Policies: Teach your employees to use strong passwords. You can ensure this by setting well-defined password policies for network access. Also, it is advisable for SMBs to use multi-factor authentication for granting network access to the employees and consumers. SMB owners can also lay out the policy that requires employees to change their passwords after a few months.

Data Backup: Invest in off-shore backup plans to ensure data retrieval in case of any disaster or data loss. Make it a point to back up the data at regular intervals. If possible, consider using automatic data backup settings.

 For more information about cybersecurity practices for SMBs, call Centex Technologies at (972) 375 - 9654.

Companies accumulate large amount of data every year. The data may include important information like trade secrets, customer information, client database, product/service information, marketing strategies, etc. It is important for the companies to keep this data secured to prevent financial, trade and reputation loss. However, an increasing rate of data breach incidents indicate that most companies fail to secure their data.

Here are some common mistakes that the enterprises make leading to loss of data:

• Lack of Security Testing: New security features are launched at regular intervals. While it is recommended that businesses should update their security features with newer versions; the switch should be made after proper testing. The companies make the mistake of skipping the beta phase of testing (a testing phase where vulnerabilities of a new security feature are detected and rectified by the technical team of organization). Implementing any new security feature without thorough testing puts the business data at the risk because hackers get the chance to exploit the vulnerabilities and launch a data breach.

• Forgetting To Map Data: Data movement is an essential component for managing the operations of any business. As the use of online resources is increasing, data movement forms the basis of marketing/ sales strategies, collaborative meeting of on-shore & off-shore employees, process handling between different teams, etc. As the data is regularly moving, it becomes important to keep a track of it. Mapping data is the process of marking the origin, journey and destination of data flow. It also involves keeping a track of every person who interacts with the data, and the changes made to it. This helps the data monitoring team to detect data handling patterns and recognize unexpected interactions at an early stage. However, companies usually commit the mistake of neglecting this important process.

• Relying Solely On Anti-Virus: Although it is important to install anti-virus software into the computer systems of the organization to detect the malware; it should not be treated as the backbone of the cybersecurity strategies of the organization. Businesses make the mistake of relying solely on anti-virus software instead of installing other security measures that can detect and flag potentially malicious incoming data before it enters the network.

• Using Outdated Versions Of Security Networks: When considering security networks, companies have to pay attention to three aspects namely security software, security hardware and internal network of company’s systems. Companies often update one or two of these aspects which leaves them at the risk of improper integration of security networks. The outdated versions lead to vulnerabilities in the system which can be exploited by hackers.

It is advisable for the businesses to focus on proper cybersecurity strategies to prevent data breach instances.

For more information about ways to secure data, call Centex Technologies at (972) 375 - 9654.

Artificial Intelligence (AI), Machine Learning and Deep Learning are commonly used interchangeably. However, in technological context; Machine Learning and Deep Learning are subsets of AI. In order to understand the difference between these terms, it is important to know the actual meaning of individual term.

Artificial Intelligence (AI): Artificial Intelligence is a term that defines the simulation of human intelligence processes by computer systems. The processes include learning, reasoning and self-correction. AI is broadly classified as weak (narrow) AI and strong AI. Weak AI systems are designed to do a particular task. The most common example of weak AI is the virtual personal assistants. On the contrary, strong AI systems are equipped with generalized human cognitive abilities. These systems are able to find a solution to any problem independent of human intervention.

Machine Learning (ML): ML is an application or subset of Artificial Intelligence. Under this application of AI, a machine is programmed to access and manipulate data. The machine can analyze the data to identify patterns and learn from these patterns. This allows the machine or computer system to modify decisions as per any change in data without explicit programming. Machine Learning is driven by algorithms and stat models. The common usage of Machine Learning can be found in apps such as email filtering, optimization, internet fraud detection, etc. Machine Learning methods are widely grouped as supervised and unsupervised ML.

• Unsupervised ML: These methods group interpret data based only on input data. Clustering methods are an example of unsupervised ML.
• Supervised ML: Supervised ML methods use both input and output data to develop a predictive model. Classification and Regression methods are listed as supervised ML.

Deep Learning (DL): It is a broader subset of AI. Deep Learning involves collection of large unstructured data and combing through it to generate classified structured information. The basic difference between Machine Learning and Deep Learning is that ML is task oriented learning, whereas DL is more general. It is used to derive meaning or identify patterns in unstructured data. This, in turn, helps in spotting large scale trends or irregularities. Some common applications of Deep Learning include self-driving cars, fraud news detection, natural language processing, visual recognition, etc.

For more information about Artificial Intelligence, Machine Learning and Deep Learning, call Centex Technologies at (972) 375-9654.