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Data intelligence is an important aspect of every organization. It lays the foundation for data analytics and decision making by the company executives. However, data collection and analysis are conducted by computers that store them in languages that are not comprehendible by humans. Thus, in order to facilitate decision making, it is imperative to make data analytics available in natural human languages.

Once the metadata is annotated in human languages, it provides information about events such as when, what, where, how and why they occurred. When the information is available in tangible form, it can be used to gain situational awareness and stimulate thinking by forming patterns or relationships between data. Formatting the data by forming visuals such as tables, charts, and graphs help in understanding the patterns and interdependency of various factors. This understanding defines the course of future actions to achieve desired organizational goals.

In order to understand how to make data analytics human for decision making, let us consider the following aspects:

Type Of Data:

Traditionally, metadata management focused on technical metadata including platform, structure and physical characteristics. However, as the business organizations are now relying extensively on data analytics, equal focus is being laid on collection and correlation of business metadata (business rules, associated applications, and business capabilities) and semantic metadata (business terminology and ontology).

Finding Data Patterns:

A large amount of data is collected on a daily basis. But in order to gain meaningful results, it is required to understand the relationships in the data. An example of data mapping for understanding interrelationships between entities, their properties and relationships is ‘Knowledge Graphs’ pioneered by Google. Although such graphs provide good information, they alone cannot be used for reliable decision making. Thus, more related data has to be collected from parallel platforms and databases to create a ‘Knowledge Platform’.

As the information is classified in classes and concepts across different datasets, it makes it easier to interlink and find related information. Businesses tend to make use of query languages to search for information across the contents of enormous datasets.

Narratives:

After understanding the patterns of data, the next step is to form a data narrative. It includes reasoning and learning in addition to data patterns. To create a narrative, it is important to understand three things:

• Types of questions that may be asked based on data patterns
• Answers to these questions
• Questions that will arise based on previous answers

Data patterns may indicate information such as the effect of a variable on business metrics. But data narrative includes answers to questions such as ‘If metric goes up with time, how will it affect the business?’, ‘Does the metrics accumulate over time or is it point-in-time?’ and ‘What does it mean for our sales?’.

Decision Making:

The data narrative forms the basis of decision making. The decision makers of an organization analyze the narrative, visualize the supporting data, and test the hypothesis to identify gaps. The final decision conveys the required actions for achieving business innovation and goals.

For more information on making data analytics human for decision making, call Centex Technologies at (972) 375 - 9654.

Artificial intelligence has garnered immense applications in various industries including banking, manufacturing, and healthcare. A branch of healthcare that is benefitting from Artificial Intelligence is the ‘Pharmaceutical Industry’. It is met with new challenges in the face of new viruses, mutated antigens, drug-resistant strains, etc. on a daily basis. Additionally, it has become common to see the rendition of once eradicated diseases such as polio. Under these conditions, traditional R&D can be very time consuming and costly.  

Traditional drug discovery methods are objective driven and work well for targets whose structure and interactions in the cell are understood. However, most of the cellular transactions have complex pathways.

In order to overcome these challenges, Artificial Intelligence-powered drug discovery offers an effective alternative. Following are the ways in which AI transforms the drug discovery process:

• AI-powered drug discovery follows a data-driven approach that is based on the vast patient datasets. The data is studied and categorized by complex algorithms into understandable information for facilitating drug discovery at a faster pace.
• It applies machine learning to study new incoming data for recognizing new opportunities and information.
• The algorithms search through vast databases of compound structures to identify a compound that can bind to the antigen protein (even if the structure of the target protein has not been yet identified). This saves a lot of time when compared to manual screening of compounds that can act as drug candidates.

How It Works?

• The first step is to take sample from people with and without a disease. Also, samples are taken from people who are at different stages of disease progression.
• The sample data is then extracted into genomics, proteomics, metabolomics and lipidomics for identifying the target.
• AI and Machine Learning software study the information to identify any differences between the disease and non-disease states, proteins, and other features that may impact the disease state.
• The identified proteins and metabolites are considered to be target candidates by the software.
• The candidates are then queried against databases of patents, publications, chemical libraries, clinical trials, and approved drugs. This facilitates a precision-medicine approach by offering a means to triage the patients in an in-silico manner before entering a clinical trial to determine the effectiveness of a potential drug.

Benefits Of AI-Driven Drug Discovery:

• AI does not rely on predetermined targets which rules out the chances of subjective bias.
• AI amalgamates the latest technology in biology and computing to develop algorithms for drug discovery.
• AI offers high predictive power to define meaningful interactions in drug screening. This reduces the chances of pursuing false potential drugs.
• AI moves drug discovery to a virtual lab where screening results can be obtained at a faster pace and efficiency.

For more information on transforming drug discovery through AI, call Centex Technologies at (972) 375 - 9654.

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5G is the fifth-generation mobile network. 5G offers greater download/upload speeds and reduced latency. Some other features of the 5G data network include increased traffic and seamless tech integration. Such features of the 5G data network facilitate it to play a great role in the success of business organizations.

In addition to these benefits, following are the roles that 5G can play in business growth:

• Intelligent IoT: Greater speed offered by 5G is expected to bring an increase in the number of intelligent IoT devices. 5G will provide better data insight and enhance infrastructure diagnose systems to improve the performance of IoT device networks.
• Remote Working: An increasing number of organizations are planning to inculcate remote working culture to improve the productivity of their business. However, slower network speeds and technology restraints have acted as a hurdle in the practical implementation of remote work culture. High data network speed allows seamless AR, VR and connectivity experience for integration of remote workers via quality conference calls, video streaming, etc. to create flexible office spaces.
• Rural Innovation: 5G will enable the government to connect the rural communities for generating opportunities for rural businesses. Alternatively, this will help established businesses in reaching out to a new customer base set in rural areas. 5G will enable marketing professionals in analyzing customer data, demographics, campaign results, and data product performance.
• Network Slicing: Network slicing allows the creation of multiple virtual networks on top of a common shared physical infrastructure. In the case of 5G, multiple virtual networks created from a single physical network can support different Radio Access Networks (RAN). This will allow businesses to set up a network as per their business goals. Businesses can provide an end-to-end virtual system encompassing networking, computing and storage functions.
• Manufacturing: Some common problems being faced by manufacturing companies across the world are timely product delivery, product complexity, skill drain, and process intricacy. 5G will assist manufacturing businesses in automating the processes to achieve process efficiency and cost-effectiveness.
• Retail: 5G offers improved connectivity resulting in a better retail experience. Retailers can make use of AR technology to provide additional information about their products. It will also facilitate services such as retail apps to attract customers and increase in-store as well as online sales.
• Transportation: 5G network will facilitate the usage of self-driving vehicles. Driverless delivery vehicles will enable businesses in ensuring timely and efficient delivery of products.

For more information about 5G internet and its utilities, call Centex Technologies at (972) 375 - 9654.

Bluetooth beacons are hardware devices that use low energy signals to transmit periodic information to electronic mobile devices in close proximity. The beacons transmit their id to the mobile devices, which return this identification number to the cloud server. The server responds by sending the information attached to the identification number. It could be a product detail, a webpage, a phone number, etc.

Business marketing professionals are using Bluetooth beacons in numerous ways to increase sales and revenue:

1. Tracking customer’s in-store movements: Bluetooth beacons can be used to track the movements of a customer in the store and send relevant offers. For example, if the beacon detects that a customer is in shoes section, it makes sense to send her a shoe discount coupon. This motivates the customer to make a purchase.

2. Help Customers In In-Store Navigation: It is common for customers to abandon the store if they can’t find what they are looking for. Bluetooth beacon technology can be used to tackle this problem efficiently. The technology can be used in conjunction with the store app to create an in-store mapping experience. Businesses can develop their apps to assist the customers in creating a shopping list as they enter the store. The app can then be used to show them the location of the selected products. The Bluetooth beacons detect the real-time location of the customers as they move and create a map to show them if they are moving in the right direction or not in relevance to the products.

3. Attract Customers To In-Store Events: Retail businesses commonly organize in-store events to attract customers during holiday seasons. The events may range from free makeup tutorials to gift wrapping presentations. Traditionally, the business marketing professionals used emails or telemarketing to inform their customers about the events. However, customers have a higher probability to walk in the store for attending an event, if they are already in the vicinity. Thus, you can use Bluetooth beacon technology to alert mobile device users in the proximity of your store about the ongoing events. This will help in increasing the footprint traffic, giving you a chance to generate leads.

4. Improve In-Store Conversion Rates: Using Bluetooth beacon technology, you can have an idea of the products that are being purchased by a customer via his online shopping list or in-store location. This information can be used to transmit notifications or reminders to purchase related products. For example, if a customer purchases cereal in a supermarket, a notification such as ‘Do you also need milk?’ can make sense to him. Such targeted messaging helps in increasing in-store conversion rate.

For more information about use of Bluetooth beacons in business marketing, call Centex Technologies at (972) 375 - 9654.

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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.

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.