Business Intelligence Platform RESTful Web Service

Albert Alan

Restful API

RESTful Web Services are REST architecture based web services. Representational State Transfer (REST) is a style of software architecture for distributed systems such as the World Wide Web. In this architectural style, data and functionality is considered resources and are accessed using Uniform Resource Identifiers (URIs), typically links on the Web.

RESTful Web Service

REST has some advantages over SOAP (Simple Objects Access Protocol) but is similar in technology since it is also a function call via HTTP protocol. REST is easier to call from various platforms, transfers pure human-readable data in JSON or XML and is faster and saves resources.

In the basic idea of REST, an object is accessed via REST, not its methods. The state of the object can be changed by the REST access. The change is caused by the passed parameters. A frequent application is the connection of the SAP PI via the REST interface.

When to use Rest Services

  • You want to access BI platform repository objects or perform basic scheduling.
  • You want to use a programming language that is not supported by another BI platform SDK.
  • You want to extract all the query details and number of records per query for all the reports like Webi and Crystal, etc.
  • You want to extract folder path of all reports at once.

Process Flow

RESTful Web Service

RESTful Web Service Requests

To make a RESTful web service request, you need the following:

  • URL – The URL that hosts the RESTful web service.
  • Method – The type of HTTP method to use for sending the request, for example GET, PUT, POST, or DELETE.
  • Request header – The attributes that describe the request.
  • Request body – Additional information that is used to process the request.

Common RWS Error Messages

RESTful Web Service

Restful Web Service URIs Summary List

URLResponseComments
  /v1Service document that contains a link to the /infostore API.This is the root level of an infostore resource
  /v1/infostoreFeed contains all the objects in BOE system/v1/infostore
  /v1/infostore/ <object_id>Entry corresponding to the info object with SI_ID=./v1/infostore/99
      /v1/logon/longReturns the long form for logon, which contains the user and password authentication template.Used to logon to the BI system based on the authentication method.
  /v1/users/ <user_id>  XML feed of user details in BOE systemYou can Modify user using PUT method and DELETE user using DELETE method.
    /v1/usergroups/ <usergroup_id>    XML feed of user group details in BOE systemSupport GET and PUT and DELETE method. You can Modify user group using PUT method and DELETE user group using DELETE method.
  v1/folders/ <folder_id>XML feed displays the details of the folder, can be used to modify the details of the folder, and delete the folder.You modify the folder using PUT method and DELETE the folder using DELETE method
  /v1/publicationsXML feed of all publications created in BOE systemThis API supports GET method only.

Extended Workflow

 The workflow is as follows:

  • To Pass the Base URL

GET http:///localhost:6405/biprws/v1/users

  • To Pass the Headers

  • To Get the xml/json response

Automation of Rest Call

The Business Intelligence platform RESTful Web Service  (BI-REST-SDK) allows you to programmatically access the BI platform functionalities such as administration, security configuration and modification of the repository. In addition, to the Business Intelligence platform RESTful web service SDK, you can also use the SAP Crystal Reports RESTful Web Services  (CR REST SDK) and SAP Web Intelligence RESTful Web Services (WEBI REST SDK).

Implementation

An application has been designed and implemented using Java to automate the extraction of SQL query for all the webi reports from the server at once.

Tools used:

  • Postman (Third party application)
  • Eclipse IDE

The structure of the application is as below:

The application file comprises of the required java jar files, java class files, java properties files and logs. Java class files (SqlExtract) are the source code and will be compiled and executed using command prompt as:

Step 1

  • Javac -cp “.;java-json.jar;json-simple-1.1.jar;log4j-1.2.17.jar” SqlExtract.java

 The above command compiles the java code.

Step 2

  • Java -cp “.;java-json.jar;json-simple-1.1.jar;log4j-1.2.17.jar” SqlExtract.java

 The above command runs the compiled java file.

The java properties file (log4j) is used to set the configurations for the java code to run. Also, the path for the log file can be set in the properties file.

RESTful Web Service

The logs (SqlExtractLogger) consist of the required output file with all the extracted query for the webi reports along with the data source name, type and the row count for each query in the respective folder in the path set by the user in properties file.

RESTful Web Service

The application is standalone and can run in any windows platform or server which has java JRE (version greater than 1.6 – preferred) installed in it.

Note: All the above steps required to execute the application are consolidated in the (steps) file.

Conclusion

SAP BO provides Restful web service to traverse through its repository, to fetch structural info and to modify the metadata structure based on the user requirements. When integrated with programming languages like python, java, etc., extends the scope to a greater extent, allowing the user to automate the workflows and to solve the backtracking problems.

Handling Restful web service needs expertise in server administration and programming as changes made to the metadata are irreversible.

References

About the Author –

Alan is a SAP Business Intelligence consultant with a critical thinking and an analytical mind. He believes in ‘The more extensive a man’s knowledge of what has been done, the greater will be his power of knowing what to do’.

Artificial Intelligence in Healthcare

Dr. Ramjan Shaik

Scientific progress is about many small advancements and occasional big leaps. Medicine is no exception. In a time of rapid healthcare transformation, health organizations must quickly adapt to evolving technologies, regulations, and consumer demands. Since the inception of electronic health record (EHR) systems, volumes of patient data have been collected, creating an atmosphere suitable for translating data into actionable intelligence. The growing field of artificial intelligence (AI) has created new technology that can handle large data sets, solving complex problems that previously required human intelligence. AI integrates these data sources to develop new insights on individual health and public health.

Highly valuable information can sometimes get lost amongst trillions of data points, costing the industry around $100 billion a year. Providers must ensure that patient privacy is protected, and consider ways to find a balance between costs and potential benefits. The continued emphasis on cost, quality, and care outcomes will perpetuate the advancement of AI technology to realize additional adoption and value across healthcare. Although most organizations utilize structured data for analysis, valuable patient information is often “trapped” in an unstructured format. This type of data includes physician and patient notes, e-mails, and audio voice dictations. Unstructured data is frequently richer and more multifaceted. It may be more difficult to navigate, but unstructured data can lead to a plethora of new insights. Using AI to convert unstructured data to structured data enables healthcare providers to leverage automation and technology to enhance processes, reduce the staff required to monitor patients while filling gaps in healthcare labor shortages, lower operational costs, improve patient care, and monitor the AI system for challenges.

AI is playing a significant role in medical imaging and clinical practice. Providers and healthcare organizations have recognized the importance of AI and are tapping into intelligence tools. Growth in the AI health market is expected to reach $6.6 billion by 2021 and to exceed $10 billion by 2024.  AI offers the industry incredible potential to learn from past encounters and make better decisions in the future. Algorithms could standardize tests, prescriptions, and even procedures across the healthcare system, being kept up-to-date with the latest guidelines in the same way a phone’s operating system updates itself from time to time.

There are three main areas where AI efforts are being invested in the healthcare sector.

  • Engagement – This involves improvising on how patients interact with healthcare providers and systems.
  • Digitization – AI and other digital tools are expected to make operations more seamless and cost-effective.
  • Diagnostics – By using products and services that use AI algorithms diagnosis and patient care can be improved.

AI will be most beneficial in three other areas namely physician’s clinical judgment and diagnosis, AI-assisted robotic surgery, and virtual nursing assistants.

Following are some of the scenarios where AI makes a significant impact in healthcare:

  • AI can be utilized to provide personalized and interactive healthcare, including anytime face-to-face appointments with doctors. AI-powered chatbots can be powered with technology to review the patient symptoms and recommend whether a virtual consultation or a face-to-face visit with a healthcare professional is necessary.
  • AI can enhance the efficiency of hospitals and clinics in managing patient data, clinical history, and payment information by using predictive analytics. Hospitals are using AI to gather information on trillions of administrative and health record data points to streamline the patient experience. This collaboration of AI and data helps hospitals/clinics to personalize healthcare plans on an individual basis.
  • A taskforce augmented with artificial intelligence can quickly prioritize hospital activity for the benefit of all patients. Such projects can improve hospital admission and discharge procedures, bringing about enhanced patient experience.
  • Companies can use algorithms to scrutinize huge clinical and molecular data to personalize healthcare treatments by developing AI tools that collect and analyze data from genetic sequencing to image recognition empowering physicians in improved patient care. AI-powered image analysis helps in connecting data points that support cancer discovery and treatment.
  • Big data and artificial intelligence can be used in combination to predict clinical, financial, and operational risks by taking data from all the existing sources. AI analyzes data throughout a healthcare system to mine, automate, and predict processes. It can be used to predict ICU transfers, improve clinical workflows, and even pinpoint a patient’s risk of hospital-acquired infections. Using artificial intelligence to mine health data, hospitals can predict and detect sepsis, which ultimately reduces death rates.
  • AI helps healthcare professionals harness their data to optimize hospital efficiency, better engage with patients, and improve treatment. AI can notify doctors when a patient’s health deteriorates and can even help in the diagnosis of ailments by combing its massive dataset for comparable symptoms. By collecting symptoms of a patient and inputting them into the AI platform, doctors can diagnose quickly and more effectively.   
  • Robot-assisted surgeries ranging from minimally-invasive procedures to open-heart surgeries enables doctors to perform procedures with precision, flexibility, and control that goes beyond human capabilities, leading to fewer surgery-related complications, less pain, and a quicker recovery time. Robots can be developed to improve endoscopies by employing the latest AI techniques which helps doctors get a clearer view of a patient’s illness from both a physical and data perspective.

Having understood the advancements of AI in various facets of healthcare, it is to be realized that AI is not yet ready to fully interpret a patient’s nuanced response to a question, nor is it ready to replace examining patients – but it is efficient in making differential diagnoses from clinical results. It is to be understood very clearly that the role of AI in healthcare is to supplement and enhance human judgment, not to replace physicians and staff.

We at GAVS Technologies are fully equipped with cutting edge AI technology, skills, facilities, and manpower to make a difference in healthcare.

Following are the ongoing and in-pipeline projects that we are working on in healthcare:

ONGOING PROJECT:

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PROJECTS IN PIPELINE:

AIOps Artificial Intelligence for IT Operations
AIOps Digital Transformation Solutions
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Following are the projects that are being planned:

  • Controlling Alcohol Abuse
  • Management of Opioid Addiction
  • Pharmacy Support – drug monitoring and interactions
  • Reducing medication errors in hospitals
  • Patient Risk Scorecard
  • Patient Wellness – Chronic Disease management and monitoring

In conclusion, it is evident that the Advent of AI in the healthcare domain has shown a tremendous impact on patient treatment and care. For more information on how our AI-led solutions and services can help your healthcare enterprise, please reach out to us here.

About the Author –

Dr. Ramjan is a Data Analyst at GAVS. He has a Doctorate degree in the field of Pharmacy. He is passionate about drawing insights out of raw data and considers himself to be a ‘Data Person’.

He loves what he does and tries to make the most of his work. He is always learning something new from programming, data analytics, data visualization to ML, AI, and more.

Center of Excellence – Network

The Network CoE was established to focus on Network solution design, Network design, Advanced Network troubleshooting, Network consulting, Network automation, and competency development in Next Generation Network technologies. It is also involved in conducting Network and Network security assessments in the customer’s IT infrastructure environments focused on optimization and transformation.

Network and Network Security Certification drive

As part of Network CoE, we focus on upgrading the skill sets of L1, L2, L3 Network engineers so that their competency levels are high. This is achieved by Network certification drives organized by Network COE. There are many certification drives focusing on Routing, Switching, Network security, Data Center Technologies, and Network automation driven by Network CoE like CCNA, CCNP, PCNSE, CCNA Data Center and Cisco Certified DevNet Associate. There is an active participation in these certification drives, and many GAVS engineers got themselves certified.

Standard Best Practices and Standard Operating Procedures

In Network CoE, the focus is on industry best practices. Standard Operating Practices are created for various technologies within Networking and Network security and used for Network operations.  We have Standard Operating Practices for Monitoring, NOC, switching, routing, WIFI, load balancers and Network security.

Next generation Network Transformation

The Network and Network Security Industry is undergoing key changes in terms of next generation technologies,Next Generation Firewall, Software defined Networks, WIFI 6 standard. There is an added impetus to Network automation, Intent based Networking. We enable Network transformation by enabling these technologies in customer environments.

Network Automation

We are focusing on Network automation of Standard Operating practices pertaining to Network and Network Security technologies. Instead of usual script-based automation, we focus on automation through Network Programmability via standard API interfaces. This gives much finer control and increased functionality in automation.

Network Assessments and Recommendations

We undertake Network Assessments which focuses on Networking and Network security infrastructure including devices and monitoring tools. We focus on various device types like routers, switches, firewall, WIFI controllers, WIFI access points, load balancers, Layer-3 switches, collaboration devices, SD-WAN devices, MPLS devices, VPN devices, IPS devices, etc. We also focus on Network monitoring tools.  We have a GAVS tool called GAVS topology mapper which can be used to discover network topology and its serves as one of the inputs during Network assessment. We apply standard best practices and come out with findings and recommendations. The recommendations will be directed towards Network optimization and/or Network transformation.

Solutions for Pain Points

We identify customer paint points in Networking and Network security areas and address it with comprehensive solutions. A case in point is where we designed a disaster recovery solution for an enterprise network, where the main site and DR site had different subnet schemes and for the Disaster recovery solution to work the VMs in main site and DR site need to have the same IP address.

Network Maturity Model

In GAVS, we have a Network Maturity Model. We have various levels with the Model. We use the Network Maturity Model to rate Network and Network Security setup.

Network Maturity Levels
ScoreLevel
5Optimised
4Managed
3Defined
2Repeatable
1Ad hoc
Network Design

We undertake Network design of Green Field projects (New Network) or Network re-design of Brownfield projects (Existing Network).  A case in point is where we re-designed an existing data center for better resiliency.

Data Center Design

We have designed Data Centers with N+1 Redundancy based on Cisco Nexus 9K and ACI as part of Data Center move and consolidation.  We used spine and leaf architecture for high availability. We have migrated Catalyst 6000 based data center to a Data Center with Nexus 9K.

Advanced Network and Network Security Services

We undertake several Advanced Network and Network security services. We have done large scale Cisco Identity Service Engine (ISE) Hardening and upgrade. We also migrated to DMVPN for several hundreds of sites.

Advanced Network and Network SecurityTroubleshooting

There are situations when a problem will involve two or more towers, e.g., Networking, server applications etc., we get involved and crack these kinds of problems.

For example, a problem which involved DHCP Network service running in a server. The DHCP network service became slow. We systematically analysed and found out that the actual problem is the server slowness and not the DHCP Network service. In another situation, we found out that DMZ firewall was having 90% CPU utilization which led to connection drops of Applications and we fixed it by upgrading the firewall devices.

Conclusion

We continue to partner with GAVS Customer success managers to provide unique experience to customers in the Networking area.

If you have any questions about the CoE, you may reach out to them at COE_NETWORK@gavstech.com

CoE Team Members

  • Ambika Tripathi
  • Andrew Ellis
  • AvineshYokanathan
  • Deepak Narayanaswamy
  • Durai Murugan Prakash
  • Faheem koyatty
  • Ganesh Kumar J
  • Gayathri R
  • Ibrahim Silver Nooruddin
  • JettiTarakesh
  • Justin Robinson
  • Krishnakumar R
  • Nabiulla A
  • Nandhini Prabhu
  • Navaneetha Krishnan
  • Palanisamy Sakthivel
  • Prasad R
  • Rajeshkanna S
  • Ravichandran V
  • Shafi H
  • Shamini P
  • Shanmukha Ganesh
  • Sridhar
  • Srijith
  • Suresh Chander
  • Venkata Manikrishna Soma
  • Vishal Manuhar

Center of Excellence – .Net

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“Maximizing the quality, efficiency, and reusability by providing innovative technical solutions, creating intellectual capital, inculcating best practices and processes to instill greater trust and provide incremental value to the Stakeholders.”

With the above mission,we have embarked on our journey to establish and strengthen the .NET Center of excellence (CoE).

“The only way to do great work is to love what you do.” – Steve Jobs

Expertise in this CoE is drawn from top talent across all customer engagements within GAVS. Team engagement is maintained at a very high level with various connects such as regular technology sessions, advanced trainings for CoE members from MS, support and guidance for becoming a MS MVP. Members also socialize new trending articles, tools, whitepapers and blogs within the CoE team and MS Teams channels setup for collaboration. All communications from MS Premier Communications sent to Gold Partners is also shared within the group. The high-level roadmap as planned for this group is laid out below.

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The .NET CoEfocused on assistingourcustomers in every stage of theengagement right from on-boarding, planning, execution, technical implementation and finally all the way to launching and growing. Our prescriptive approach is to leverage industry-proven best practices, solutions, reusable components and include robust resources, training, and making a vibrant partner community.

With the above as the primary goal in mind the CoE group is currently engaged inor planning the following initiatives.

Technology Maturity Assessment

One of the main objectivesof this group is to provide constant feedback to all .NET stack project for improvement and improvisation. The goal for this initiative is to build the technology maturity index for all projects for the below parameters.

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Using those approaches within a short span of time we were able to make a significant impact for some of our engagements.

Client – Online Chain Store: Identified cheaper cloud hosting option for application UI.

Benefits: Huge cost and time savings.

Client – Health care sector: Provided alternate solution for DB migrations from DEV to various environments.

Benefits: Huge cost savings due to licensing annually.

Competency Building

“Anyone who stops learning is old, whether at twenty or eighty.” – Henry Ford

Continuous learning and upskilling are the new norms in today’s fast changing technology landscape. This initiative is focused on providing learning and upskilling support to all technology teams in GAVS. Identifying code mentors, supporting team members to become full stack developers are some of the activities planned under this initiative.  Working along with the Learning & Development team,the .NET CoE isformulating different training tracks to upskill the team members and provide support for external assessments and MS certifications.

Solution Accelerators

“Good, better, best. Never let it rest. ‘Till your good is better and your better is best.” – St. Jerome

The primary determinants of CoE effectiveness are involvement in solutions and accelerators and in maintaining standard practices of the relevant technologies across customer engagements across the organization.

As part of this initiative we are focusing on building project templates, DevOps pipelines and automated testing templates for different technology stacks for both Serverless and Server Hosted scenarios. We also are planning similar activities for the Desktop/Mobile Stack with the Multi-Platform App UI (MAUI) framework which is planned to be released for Preview in Q4 2020.

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Additionally, we are also adoptingless-code, no-code development platforms for accelerated development cycles for specific use-cases.

As we progress on our journey to strengthen the .NET CoE, we want to act as acatalyst in rapid and early adoption of new technology solutions and work as trusted partners with all our customer and stakeholders.

If you have any questions about the CoE, you may reach out to them at COE_DOTNET@gavstech.com

CoE Team Members

  • Bismillakhan Mohammed
  • Gokul Bose
  • Kirubakaran Girijanandan
  • Neeraj Kumar
  • Prasad D
  • Ramakrishnan S
  • SaphalMalol
  • Saravanan Swaminathan
  • SenthilkumarKamayaswami
  • Sethuraman Varadhan
  • Srinivasan Radhakrishnan
  • Thaufeeq Ahmed
  • Thomas T
  • Vijay Mahalingam

Center of Excellence – Java

The Java CoE was established to partner with our customers and aid them in realizing business benefits through effective adoption of cutting-edge technologies; thus, enabling customer success.

Objectives

  • Be the go-to team for anything related to Java across the organization and customer engagements.
  • Build competency by conducting training and mentoring sessions, publishing blogs, whitepapers and participating in Hackathons.
  • Support presales team in creating proposals by providing industry best solutions using the latest technologies, standards & principles.
  • Contribute a certain percent of revenue growth along with the CSMs.
  • Create reusable artifacts, frameworks, solutions and best practices which can be used across organization to improve delivery quality.

Focus Areas

  1. Design Thinking: Setting up a strong foundation of “Design Thinking and Engineering Mindset” is paramount for any business. We aim to do so in the following way:
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2. Solution and Technology: Through our practice, we aim to equip GAVS with solution-oriented technology leaders who can lead us ahead through disruptive times

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3. Customer success

  • Identify opportunities in accounts based on the collaboration with CSMs, understand customer needs, get details about the engagement, understand the focus areas and challenges.
  • Understand the immediate need of the project, provide solution to address the need.
  • Java council to help developers arrive at solutions.
  • Understand architecture in detail and provide recommendation / create awareness to use new technologies
  • Enforce a comprehensive review process to enable quality delivery.

Accomplishments

  • Formed the CoE team
  • Identified the focus Areas
  • Identified leads for every stream
  • Socialized the CoEwithin GAVS
  • Delivered effective solutions across projects to improve delivery quality
  • Conducted trainings on standards and design-oriented coding practices across GAVS
  • Publishedblogs to bring in design-oriented development practices
  • Identified the areas for creating re-usable artefacts (Libraries / Frameworks)
  • Brainstormed and finalized the design for creating Frameworks (For the identified areas)
  • Streamlined the DevOps process which can be applied in any engagement
  • Built reusable libraries, components and frameworks which can be used across GAVS
  • Automated the Code Review process
  • Organized and conducted hackathons and tech meetups
  • Discovered potential technical problems/challenges across teams and offered effective solutions, thereby enabling customer success
  • Supported the presales team in creating customized solutions for prospects

Upcoming Activities

  • Establishing tech governance and align managers / tech leads to the process
  • Setting up security standards and principles across domain
  • Buildingmore reusable libraries, components and frameworks which can be used across GAVS
  • Adopting Design Patterns / Anti-patterns
  • Enforcing a strong review process to bring in quality delivery
  • Enabling discussions with the customers
  • Setting up a customer advisory team

Contribution to Organizational Growth

As we continue our journey, we aim to support the revenue growth of our organization. Customer Success being a key goal of GAVS, we will continue to enable it by improving the quality of service delivery and building a solid foundation across all technology and process streams. We also want to contribute to the organization by developing a core competency around a strategic capability and reduce knowledge management risks.

If you have any questions about the CoE, you may reach out to them at COE_JAVA@gavstech.com

CoE Team Members

  • Lakshminarasimhan J
  • Muraleedharan Vijayakumar
  • Bipin V
  • Meenakshi Sundaram
  • Mahesh Rajakumar M
  • Ranjith Joseph Selvaraj
  • Jagathesewaren K
  • Sivakumar Krishnasamy
  • Vijay Anand Shanmughadass
  • Sathya Selvam
  • Arun Kumar Ananthanarayanan
  • John Kalvin Jesudhason

RASA – an Open Source Chatbot Solution

Maruvada Deepti

Ever wondered if the agent you are chatting with online is a human or a robot? The answer would be the latter for an increasing number of industries. Conversational agents or chatbots are being employed by organizations as their first-line of support to reduce their response times.

The first generation of bots were not too smart, they could understand only a limited set of queries based on keywords. However, commoditization of NLP and machine learning by Wit.ai, API.ai, Luis.ai, Amazon Alexa, IBM Watson, and others, has resulted in intelligent bots.

What are the different chatbot platforms?

There are many platforms out there which are easy to use, like DialogFlow, Bot Framework, IBM Watson etc. But most of them are closed systems, not open source. These cannot be hosted on our servers and are mostly on-premise. These are mostly generalized and not very specific for a reason.

DialogFlow vs.  RASA

DialogFlow

  • Formerly known as API.ai before being acquired by Google.
  • It is a mostly complete tool for the creation of a chatbot. Mostly complete here means that it does almost everything you need for most chatbots.
  • Specifically, it can handle classification of intents and entities. It uses what it known as context to handle dialogue. It allows web hooks for fulfillment.
  • One thing it does not have, that is often desirable for chatbots, is some form of end-user management.
  • It has a robust API, which allows us to define entities/intents/etc. either via the API or with their web based interface.
  • Data is hosted in the cloud and any interaction with API.ai require cloud related communications.
  • It cannot be operated on premise.

Rasa NLU + Core

  • To compete with the best Frameworks like Google DialogFlow and Microsoft Luis, RASA came up with two built features NLU and CORE.
  • RASA NLU handles the intent and entity. Whereas, the RASA CORE takes care of the dialogue flow and guesses the “probable” next state of the conversation.
  • Unlike DialogFlow, RASA does not provide a complete user interface, the users are free to customize and develop Python scripts on top of it.
  • In contrast to DialogFlow, RASA does not provide hosting facilities. The user can host in their own sever, which also gives the user the ownership of the data.

What makes RASA different?

Rasa is an open source machine learning tool for developers and product teams to expand the abilities of bots beyond answering simple questions. It also gives control to the NLU, through which we can customize accordingly to a specific use case.

Rasa takes inspiration from different sources for building a conversational AI. It uses machine learning libraries and deep learning frameworks like TensorFlow, Keras.

Also, Rasa Stack is a platform that has seen some fast growth within 2 years.

RASA terminologies

  • Intent: Consider it as the intention or purpose of the user input. If a user says, “Which day is today?”, the intent would be finding the day of the week.
  • Entity: It is useful information from the user input that can be extracted like place or time. From the previous example, by intent, we understand the aim is to find the day of the week, but of which date? If we extract “Today” as an entity, we can perform the action on today.
  • Actions: As the name suggests, it’s an operation which can be performed by the bot. It could be replying something (Text, Image, Video, Suggestion, etc.) in return, querying a database or any other possibility by code.
  • Stories: These are sample interactions between the user and bot, defined in terms of intents captured and actions performed. So, the developer can mention what to do if you get a user input of some intent with/without some entities. Like saying if user intent is to find the day of the week and entity is today, find the day of the week of today and reply.

RASA Stack

Rasa has two major components:

  • RASA NLU: a library for natural language understanding that provides the function of intent classification and entity extraction. This helps the chatbot to understand what the user is saying. Refer to the below diagram of how NLU processes user input.
RASA Chatbot

  • RASA CORE: it uses machine learning techniques to generalize the dialogue flow of the system. It also predicts next best action based on the input from NLU, the conversation history, and the training data.

RASA architecture

This diagram shows the basic steps of how an assistant built with Rasa responds to a message:

RASA Chatbot

The steps are as follows:

  • The message is received and passed to an Interpreter, which converts it into a dictionary including the original text, the intent, and any entities that were found. This part is handled by NLU.
  • The Tracker is the object which keeps track of conversation state. It receives the info that a new message has come in.
  • The policy receives the current state of the tracker.
  • The policy chooses which action to take next.
  • The chosen action is logged by the tracker.
  • A response is sent to the user.

Areas of application

RASA is all one-stop solution in various industries like:

  • Customer Service: broadly used for technical support, accounts and billings, conversational search, travel concierge.
  • Financial Service: used in many banks for account management, bills, financial advices and fraud protection.
  • Healthcare: mainly used for fitness and wellbeing, health insurances and others

What’s next?

As any machine learning developer will tell you, improving an AI assistant is an ongoing task, but the RASA team has set their sights on one big roadmap item: updating to use the Response Selector NLU component, introduced with Rasa 1.3. “The response selector is a completely different model that uses the actual text of an incoming user message to directly predict a response for it.”

References:

https://rasa.com/product/features/

https://rasa.com/docs/rasa/user-guide/rasa-tutorial/

About the Author –

Deepti is an ML Engineer at Location Zero in GAVS. She is a voracious reader and has a keen interest in learning newer technologies. In her leisure time, she likes to sing and draw illustrations.
She believes that nothing influences her more than a shared experience.

Design Thinking 101

Vasudevan Gopalan

Is the end-user at the center of everything you do? Do you consider human emotions while conceptualizing a product or a solution? Well, let us open the doors of Design Thinking

What is Design Thinking?

  • Design thinking is both an ideology and a process, concerned with solving in a highly user-centric way.
  • With its human-centric approach, design thinking develops effective solutions based on people’s needs.
  • It has evolved from a range of fields – including architecture, engineering, business – and is also based on processes used by designers.
  • Design thinking is a holistic product design approach where every product touch point is an opportunity to delight and benefit our users.

Human Centred Design

With ‘thinking as a user’ as the methodology and ‘user satisfaction’ as the goal, design thinking practice supports innovation and successful product development in organizations. Ideally, this approach results in translating all the requirements into product features.

Part of the broader human centred design approach, design thinking is more than cross-functional; it is an interdisciplinary and empathetic understanding of our user’s needs. Design thinking sits right up there with Agile software development, business process management, and customer relationship management.

5 Stages of Design Thinking

Office 365 Migration
  • Empathize: This stage involves gathering insights about users and trying to understand their needs, desires, and objectives.
  • Define: This phase is all about identifying the challenge. What difficulties do users face? What are the biggest challenges? What do users really need?
  • Ideate: This step, as you may have already guessed, is dedicated to thinking about the way you can solve the problems you have identified with the help of your product. The product team, designers, and software engineers brainstorm and generate multiple ideas.
  • Prototype: The fourth stage brings you to turn your ideas into reality. By creating prototypes, you test your ideas’ fitness.
  • Test: You present the prototype to customers and find out if it solves their problem and provides users with what they need. Note that this is not the end of the journey; you need to get feedback from the users, adjust the product’s functionality, and test it again. This is a continuous process similar to the build-measure-learn approach in the lean start-up methodology.
Design Thinking

Benefits of Design Thinking in Software Development

1. Feasibility check: Design thinking enables software development companies to test the feasibility of the future product and its functionality at the initial stage. It enables them to keep end-user needs in mind, clearly specify all requirements and translate all this into product features.

2. No alarms and no surprises: Once you’ve tested your MVP and gathered feedback from users, the team can confidently proceed to the product development. You can be quite sure that there will be little to no difference between the approved concept and final version.

3. Clarity and transparency: Design thinking approach allow product designers/developers to broaden their vision, understand and empathise with the end-users’ problems and have a detailed blueprint of the solution they should eventually deliver.

4. Continuous improvement: The product can be (and sometimes should be) modified after its release when user feedback is at hand. It becomes clear which features work and which can be done away with. The product can undergo some series enhancements on the basis of feedback. This leaves place for continuous improvement and software development process becomes flexible and smooth.

Real-world Success Stories

1. PepsiCo

During Indra Nooyi’s term as PepsiCo’s CEO, the company’s sales grew 80%. It is believed that design thinking was at the core of her successful run. In her efforts to relook at the company’s innovation process and design experience, she asked her direct reportees to fill an album full of photos of what they considered represents good design. Uninspired by the result, she probed further to realize that it was imperative to hire a designer.

“It’s much more than packaging… We had to rethink the entire experience, from conception to what’s on the self to the post product experience.”, she told the Harvard Business Review.

While other companies were adding new flavours or buttons to their fountain machines, PepsiCo developed a touch screen fountain machine, a whole new interaction between humans and machines.

“Now, our teams are pushing design through the entire system, from product creation, to packaging and labelling, to how a product looks on the shelf, to how consumers interact with it,” she said.

2. Airbnb

Back in 2009, Airbnb’s revenue was limping. They realized that poor quality images of rental listings may have something to do with it. They flew some of their employees to a city and got them to take high quality photos and upload it on their website. This resulted in a 100% increase in their revenue.

Instead of focusing on scalability, the team turned inward and asked, ‘what does the customer need?’ This experiment taught them a few big lessons, empathy being just as important as code was one of them.

3. Mint.com

Mint.com is a web-based personal financial management website. Part of their success is attributed to the human-centric design of the website which tracks and visualizes how a person is spending their money. Bank accounts, investments, and credit cards can easily be synchronized on Mint, which then categorizes the expenses to help the user visualize their spending. They built a product that illustrates a core principle of design thinking: truly understanding the position and mindset of the user. They had 1.5 million customers within 2 years.

Design thinking is a human-centred approach to innovation that draws from the designer’s toolkit to integrate the needs of people, the possibilities of technology, and the requirements for business success.

References

https://www.researchgate.net/publication/226141981_Design_Thinking_A_Fruitful_Concept_for_IT_Development

https://blog.brainstation.io/how-5-ceos-used-design-thinking-to-transform-their-companies/

About the Author –

Vasu heads Engineering function for A&P. He is a Digital Transformation leader with ~20 years of IT industry experience spanning across Product Engineering, Portfolio Delivery, Large Program Management etc. Vasu has designed and delivered Open Systems, Core Banking, Web / Mobile Applications etc.
Outside of his professional role, Vasu enjoys playing badminton and focusses on fitness routines.

JAVA – Cache Management

Sivaprakash Krishnan

This article explores the offering of the various Java caching technologies that can play critical roles in improving application performance.

What is Cache Management?

A cache is a hot or a temporary memory buffer which stores most frequently used data like the live transactions, logical datasets, etc. This intensely improves the performance of an application, as read/write happens in the memory buffer thus reducing retrieval time and load on the primary source. Implementing and maintaining a cache in any Java enterprise application is important.

  • The client-side cache is used to temporarily store the static data transmitted over the network from the server to avoid unnecessarily calling to the server.
  • The server-side cache could be a query cache, CDN cache or a proxy cache where the data is stored in the respective servers instead of temporarily storing it on the browser.

Adoption of the right caching technique and tools allows the programmer to focus on the implementation of business logic; leaving the backend complexities like cache expiration, mutual exclusion, spooling, cache consistency to the frameworks and tools.

Caching should be designed specifically for the environment considering a single/multiple JVM and clusters. Given below multiple scenarios where caching can be used to improve performance.

1. In-process Cache – The In-process/local cache is the simplest cache, where the cache-store is effectively an object which is accessed inside the application process. It is much faster than any other cache accessed over a network and is strictly available only to the process that hosted it.

Data Center Consolidation Initiative Services

  • If the application is deployed only in one node, then in-process caching is the right candidate to store frequently accessed data with fast data access.
  • If the in-process cache is to be deployed in multiple instances of the application, then keeping data in-sync across all instances could be a challenge and cause data inconsistency.
  • An in-process cache can bring down the performance of any application where the server memory is limited and shared. In such cases, a garbage collector will be invoked often to clean up objects that may lead to performance overhead.

In-Memory Distributed Cache

Distributed caches can be built externally to an application that supports read/write to/from data repositories, keeps frequently accessed data in RAM, and avoid continuous fetching data from the data source. Such caches can be deployed on a cluster of multiple nodes, forming a single logical view.

  • In-memory distributed cache is suitable for applications running on multiple clusters where performance is key. Data inconsistency and shared memory aren’t matters of concern, as a distributed cache is deployed in the cluster as a single logical state.
  • As inter-process is required to access caches over a network, latency, failure, and object serialization are some overheads that could degrade performance.

2. In-memory database

In-memory database (IMDB) stores data in the main memory instead of a disk to produce quicker response times. The query is executed directly on the dataset stored in memory, thereby avoiding frequent read/writes to disk which provides better throughput and faster response times. It provides a configurable data persistence mechanism to avoid data loss.

Redis is an open-source in-memory data structure store used as a database, cache, and message broker. It offers data replication, different levels of persistence, HA, automatic partitioning that improves read/write.

Replacing the RDBMS with an in-memory database will improve the performance of an application without changing the application layer.

3. In-Memory Data Grid

An in-memory data grid (IMDG) is a data structure that resides entirely in RAM and is distributed among multiple servers.

Key features

  • Parallel computation of the data in memory
  • Search, aggregation, and sorting of the data in memory
  • Transactions management in memory
  • Event-handling

Cache Use Cases

There are use cases where a specific caching should be adapted to improve the performance of the application.

1. Application Cache

Application cache caches web content that can be accessed offline. Application owners/developers have the flexibility to configure what to cache and make it available for offline users. It has the following advantages:

  • Offline browsing
  • Quicker retrieval of data
  • Reduced load on servers

2. Level 1 (L1) Cache

This is the default transactional cache per session. It can be managed by any Java persistence framework (JPA) or object-relational mapping (ORM) tool.

The L1 cache stores entities that fall under a specific session and are cleared once a session is closed. If there are multiple transactions inside one session, all entities will be stored from all these transactions.

3. Level 2 (L2) Cache

The L2 cache can be configured to provide custom caches that can hold onto the data for all entities to be cached. It’s configured at the session factory-level and exists as long as the session factory is available.

  • Sessions in an application.
  • Applications on the same servers with the same database.
  • Application clusters running on multiple nodes but pointing to the same database.

4. Proxy / Load balancer cache

Enabling this reduces the load on application servers. When similar content is queried/requested frequently, proxy takes care of serving the content from the cache rather than routing the request back to application servers.

When a dataset is requested for the first time, proxy saves the response from the application server to a disk cache and uses them to respond to subsequent client requests without having to route the request back to the application server. Apache, NGINX, and F5 support proxy cache.

Desktop-as-a-Service (DaaS) Solution

5. Hybrid Cache

A hybrid cache is a combination of JPA/ORM frameworks and open source services. It is used in applications where response time is a key factor.

Caching Design Considerations

  • Data loading/updating
  • Performance/memory size
  • Eviction policy
  • Concurrency
  • Cache statistics.

1. Data Loading/Updating

Data loading into a cache is an important design decision to maintain consistency across all cached content. The following approaches can be considered to load data:

  • Using default function/configuration provided by JPA and ORM frameworks to load/update data.
  • Implementing key-value maps using open-source cache APIs.
  • Programmatically loading entities through automatic or explicit insertion.
  • External application through synchronous or asynchronous communication.

2. Performance/Memory Size

Resource configuration is an important factor in achieving the performance SLA. Available memory and CPU architecture play a vital role in application performance. Available memory has a direct impact on garbage collection performance. More GC cycles can bring down the performance.

3. Eviction Policy

An eviction policy enables a cache to ensure that the size of the cache doesn’t exceed the maximum limit. The eviction algorithm decides what elements can be removed from the cache depending on the configured eviction policy thereby creating space for the new datasets.

There are various popular eviction algorithms used in cache solution:

  • Least Recently Used (LRU)
  • Least Frequently Used (LFU)
  • First In, First Out (FIFO)

4. Concurrency

Concurrency is a common issue in enterprise applications. It creates conflict and leaves the system in an inconsistent state. It can occur when multiple clients try to update the same data object at the same time during cache refresh. A common solution is to use a lock, but this may affect performance. Hence, optimization techniques should be considered.

5. Cache Statistics

Cache statistics are used to identify the health of cache and provide insights about its behavior and performance. Following attributes can be used:

  • Hit Count: Indicates the number of times the cache lookup has returned a cached value.
  • Miss Count: Indicates number of times cache lookup has returned a null or newly loaded or uncached value
  • Load success count: Indicates the number of times the cache lookup has successfully loaded a new value.
  • Total load time: Indicates time spent (nanoseconds) in loading new values.
  • Load exception count: Number of exceptions thrown while loading an entry
  • Eviction count: Number of entries evicted from the cache

Various Caching Solutions

There are various Java caching solutions available — the right choice depends on the use case.

Software Test Automation Platform

At GAVS, we focus on building a strong foundation of coding practices. We encourage and implement the “Design First, Code Later” principle and “Design Oriented Coding Practices” to bring in design thinking and engineering mindset to build stronger solutions.

We have been training and mentoring our talent on cutting-edge JAVA technologies, building reusable frameworks, templates, and solutions on the major areas like Security, DevOps, Migration, Performance, etc. Our objective is to “Partner with customers to realize business benefits through effective adoption of cutting-edge JAVA technologies thereby enabling customer success”.

About the Author –

Sivaprakash is a solutions architect with strong solutions and design skills. He is a seasoned expert in JAVA, Big Data, DevOps, Cloud, Containers, and Micro Services. He has successfully designed and implemented a stable monitoring platform for ZIF. He has also designed and driven Cloud assessment/migration, enterprise BRMS, and IoT-based solutions for many of our customers. At present, his focus is on building ‘ZIF Business’ a new-generation AIOps platform aligned to business outcomes.

Autonomous Things

Machine learning service provider

Bindu Vijayan

“Autonomous things (AuT), or the Internet of autonomous things (IoAT), is an emerging term for the technological developments that are expected to bring computers into the physical environment as autonomous entities without human direction, freely moving and interacting with humans and other objects…”

To put it simply, Autonomous Things use AI and work unsupervised to complete specific tasks without humans. Devices are enhanced with AI, sensors and analytical capabilities to be able to make informed and appropriate decisions.  They (these devices) work collaboratively between humans and the environment and provide superior performance.  Today AuT work across several environments with various levels of intelligence and capabilities. Some popular examples of these devices are drones, vehicles, smart home devices among others. The components of Autonomous things – software and AI hardware are getting increasingly efficient. With improved technologies (and significantly reducing sensor costs), the variety of tasks and processes that can be automated are increasing, with the advantage of bringing in more data and feedback that can efficiently improve and enhance the benefits of autonomous things.

The technology is used in a wide variety of scenarios – as data collectors from a variety of terrains and environments, as delivery systems (by Amazon, pizza deliveries, etc.), medical supplies to remote areas, etc. Robotics used in the supply chain has proven it reduces/elevates the danger out of the hitherto human tasks in warehouses.  And they probably have the most economic potential currently, followed by autonomous vehicles.  Drones are used to collect data across a wide variety of functions –  for surveillance, security, stock management, weather forecasting, obtaining air data, oceanic data, agricultural planning, etc.

Some fascinating use cases:

Healthcare

Drones are proving to be more and more effective in several ways – they are currently used extensively for surveillance of disaster sites that have biological hazards.  There is no better relevance than the current times when they can actually be used in epidemiology to track disease spread,  and of course for further research and studies.  Drones are facilitating on-demand healthcare by providing medicines to terrains that are difficult to access.  Swoop Aero is one such company that provides medicines via drones.  Drones have brought healthcare into the most remote areas with diagnosis and treatment made available. Remote areas of Africa have their regular medical supplies,  vaccine supplies, lab samples collected, emergency medical equipment made available through Drones. They are also used in telementoring, for perioperative evaluation and so on.  Drones have been very efficient in accessing areas and providing necessary support where ground transport is not reliable or safe or impossible.  Today, most governments have Drones on their national agenda under various sectors. The Delft University of Technology is developing an ambulance drone technology that can be used at disaster sites to increase rescue rates..

Retail

In a world where we have virtual assistants do grocery shopping, replenish stocks, and cooking machines making food, when there is a need to go out shopping, shoppers want to have an easy, fast and frictionless process.  Today, customers do not want to wait in queues and go through conventional checkouts, and Retailers know that they might be losing customers due to their checkout process.  And autonomous shops like Amazon Go are giving that experience to customers where they can purchase without the inconvenience of checkout lines.

Providers of checkout-free shopping technology like ‘Grabango’, use sensor vision and ML to actually hold a virtual shopping basket for every person in the store.  The technology is reputed to process a multitude simultaneous checkout transactions. “Grabango’s system uses high-quality sensor hardware and high-precision computer algorithms to acquire the location of every item in the store. This results in a real-time planogram covering the entire retail environment.” They say it results in increased sales and loyalty, streamlined operations and inventory management and out of stock alerts.

Construction

Companies like Chicago based, Komatsu American Corp., have autonomous haulage stems that have optimized safety in the mining industry like never before. They “help you continue to meet your bottom line while achieving zero-harm” while their focus has been on developing autonomous mining solutions, they have been doing it for more than three decades now! Their FrontRunner AHS has moved more than two billion tons of surface material so far in driverless operations.  Catepillar would be deploying their fleet of autonomous trucks and blast drills for the iron mine in Western Australia – Rio Tinto Koodaideri.  The industry is thriving with autonomous and semi autonomous equipment, and it is evident that it has brought improvements to productivity, and increased profitability. At the Australian mine “autonomous vehicles operated on average 700 hours longer and with 15 per cent lower unit costs”… Similarly, there are other companies like Intsite, a heavy machinery company; their autonomous crane ÁutoSite 100’ does autonomous operation of heavy machinery.

Transportation

Most of us think Tesla when we think autonomous vehicles.  Elon Musk’s dream of providing autonomous ride-sharing has Tesla working on getting out one million robotaxis on the road this year. We will have to wait and see how that is going to pan out. Though autonomous vehicles are the most popular, I suppose it might take a little more time before it finds answers to the regulatory challenges, definitely not an easy task.  It gets quite overwhelming when we think of what we are expecting from autonomous vehicles – it assumes correct performance no matter the uncertainties on the roads and the environment, as well as the ability to face any sort of system failures on its own, and AI is a very critical technology when we are talking real-time decision making. Those sort of scenarios call for a strong computing platform in order to do the analysis at the edge for faster decision making.  The new V2X, which is the 5G vehicle-to-everything is expected to make autonomous vehicles mainstream because the vital information would get transmitted as structured data to the vehicle. V2X is expected to have vehicles interfacing with anything, be it pedestrians, roadside infrastructure, cyclists, etc.

Today, technology is also looking at ‘vehicle platooning’ – “Platoons decrease the distances between cars or trucks using electronic, and possibly mechanical, coupling. This capability would allow many cars or trucks to accelerate or brake simultaneously. This system also allows for a closer headway between vehicles by eliminating reacting distance needed for human reaction.” It has a group of self driving vehicles moving at high speed but safely, as the trucks are in constant communication with each other and use this intelligence to make informed decisions like braking, speeds, etc.  And autonomous trucks and cars can automatically join these platoons or leave, this has the advantages of reduced congestion, fewer traffic collisions, better fuel economy, and shorter commutes during peak hours. 

Conclusion

Studies show that Autonomous things are fast moving to ‘swarm’ or a bunch of intelligent devices, where multiple devices will function together collaboratively, as against the previously isolated intelligent components/ things. They are going to be intelligently networked among themselves and with the environment, and the wider that becomes within every industry, they are going to show phenomenal capabilities. But let’s not forget there is a whole other side to AI, given how unpredictable things are in life, AI would sooner or later have to respond to things that it never saw in training… we still are the smarter ones…

References:

https://en.wikipedia.org/wiki/Autonomous_things

https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/

https://worldline.com/en/home/blog/2020/march/from-automatic-to-autonomous-payments-can-things-pay.html

https://en.wikipedia.org/wiki/Self-driving_car

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6174005/

https://www.komatsuamerica.com/

https://en.wikipedia.org/wiki/Platoon_(automobile)https://grabango.com/

Hyperautomation

Machine learning service provider

Bindu Vijayan

According to Gartner, “Hyper-automation refers to an approach in which organizations rapidly identify and automate as many business processes as possible. It involves the use of a combination of technology tools, including but not limited to machine learning, packaged software and automation tools to deliver work”.  Hyper-automation is to be among the year’s top 10 technologies, according to them.

It is expected that by 2024, organizations will be able to lower their operational costs by 30% by combining hyper-automation technologies with redesigned operational processes. According to Coherent Market Insights, “Hyper Automation Market will Surpass US$ 23.7 Billion by the end of 2027.  The global hyper automation market was valued at US$ 4.2 Billion in 2017 and is expected to exhibit a CAGR of 18.9% over the forecast period (2019-2027).”

How it works

To put it simply, hyper-automation uses AI to dramatically enhance automation technologies to augment human capabilities. Given the spectrum of tools it uses like Robotic Process Automation (RPA), Machine Learning (ML), and Artificial Intelligence (AI), all functioning in sync to automate complex business processes, even those that once called for inputs from SMEs,  implies this is a powerful tool for organisations in their digital transformation journey.

Hyperautomation allows for robotic intelligence into the traditional automation process, and enhances the completion of processes to make it more efficient, faster and errorless.  Combining AI tools with RPA, the technology can automate almost any repetitive task; it automates the automation by identifying business processes and creates bots to automate them. It calls for different technologies to be leveraged, and that means the businesses investing in it should have the right tools, and the tools should be interoperable. The main feature of hyperautomation is, it merges several forms of automation and works seamlessly together, and so a hyperautomation strategy can consist of RPA, AI, Advanced Analytics, Intelligent Business Management and so on. With RPA, bots are programmed to get into software, manipulate data and respond to prompts. RPA can be as complex as handling multiple systems through several transactions, or as simple as copying information from applications. Combine that with the concept of Process Automation or Business Process Automation which enables the management of processes across systems, it can help streamline processes to increase business performance.    The tool or the platform should be easy to use and importantly scalable; investing in a platform that can integrate with the existing systems is crucial. The selection of the right tools is what  Gartner calls “architecting for hyperautomation.”

Impact of hyperautomation

Hyperautomation has a huge potential for impacting the speed of digital transformation for businesses, given that it automates complex work which is usually dependent on inputs from humans. With the work moved to intelligent digital workers (RPA with AI) that can perform repetitive tasks endlessly, human performance is augmented. These digital workers can then become real game-changers with their efficiency and capability to connect to multiple business applications, discover processes, work with voluminous data, and analyse in order to arrive at decisions for further / new automation.

The impact of being able to leverage previously inaccessible data and processes and automating them often results in the creation of a digital twin of the organization (DTO); virtual models of every physical asset and process in an organization.  Sensors and other devices monitor digital twins to gather vital information on their condition, and insights are gathered regarding their health and performance. As with data, the more data there is, the systems get smarter with it, and are able to provide sharp insights that can thwart problems, help businesses make informed decisions on new services/products, and in general make informed assessments. Having a DTO throws light on the hitherto unknown interactions between functions and processes, and how they can drive value and business opportunities.  That’s powerful – you get to see the business outcome it brings in as it happens or the negative effect it causes, that sort of intelligence within the organization is a powerful tool to make very informed decisions.

Hyperautomation is the future, an unavoidable market state

hyperautomation is an unavoidable market state in which organizations must rapidly identify and automate all possible business processes.” – Gartner

It is interesting to note that some companies are coming up with no-code automation. Creating tools that can be easily used even by those who cannot read or write code can be a major advantage – It can, for e.g., if employees are able to automate the multiple processes that they are responsible for, hyperautomation can help get more done at a much faster pace, sparing time for them to get involved in planning and strategy.  This brings more flexibility and agility within teams, as automation can be managed by the teams for the processes that they are involved in.

Conclusion

With hyperautomation, it would be easy for companies to actually see the ROI they are realizing from the amount of processes that have been automated, with clear visibility on the time and money saved. Hyperautomation enables seamless communication between different data systems, to provide organizations flexibility and digital agility. Businesses enjoy the advantages of increased productivity, quality output, greater compliance, better insights, advanced analytics, and of course automated processes. It allows machines to have real insights on business processes and understand them to make significant improvements.

“Organizations need the ability to reconfigure operations and supporting processes in response to evolving needs and competitive threats in the market. A hyperautomated future state can only be achieved through hyper agile working practices and tools.”  – Gartner

References: