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August 30, 2017

Simple definitions to get smarter: From 'big data' to 'AI'

Author: Ramkumar Dargha, AVP and Senior Principal Technology Architect, Enterprise Architecture

Today, business parlance is peppered with words such as big data, data analytics, data science, machine learning, artificial intelligence, and automation. We have all heard these terms being used; some even interchangeably. For those of you wondering what these terms actually mean, fear not. In this blog, I will attempt to demystify these new trends, highlight their significance and, most importantly, explain how they play a vital role when it comes to automation and artificial intelligence. I want to point out that none of these explanations come from industry-standard definitions or existing literature. They are drawn from my experience and shared with you in the hope of making these complex terms more comprehensible.

Business jargon 101

Let me begin with an illustration. The following diagram depicts how each trend works individually and within an ecosystem. While this may seem confusing now, I recommend you refer to it once each term is better understood.

View image

Data science - Firstly, the word 'data' refers to all types (like unstructured data) and all sources of data (like traditional data warehouses). So, data science is a field that encompasses the entire journey or lifecycle of data. It includes steps such as ingesting data, processing data, applying algorithms, generating insights, and visualizing actionable insights

Big data - This refers to data characterized by the four Vs, namely, high volume, high speed (velocity), high diversity (variety), and high veracity (abnormality or ambiguity). On second thought, we may even say five Vs, since big data adds significant value to enterprise operations! Much like data science, big data also represents the entire data lifecycle, which may cause some confusion - but let us bear with this. This brings me to the next important term.

Machine learning - Machine learning is the ability of machines to learn on their own through data - just as humans do through their environment. In machine learning, machines understand and learn from data, apply the learning and, based on the results, revise previous learning from new data. All this is done iteratively. Here, learning refers to the process by which machines convert the data to insights and apply those insights to take action. As you may have observed, data is key, particularly big data. However, ML can also use traditional data for algorithms like classification, linear regression, clustering, etc.

Data analytics - But, how do machines learn from data?  This is where data analytics comes in. Data analytics uses machine learning algorithms like those mentioned above to uncover patterns hidden in input data. These patterns are applied to new (but similar) datasets to create inferences based on past data. These inferences then become insights for future business actions. To know more about how to get data analytics right, check out my blog on "Data Analytics: Doing it Right!".

AI - In 5 steps

In my opinion, artificial intelligence (AI) has five main steps, which are described below: 

  1. Curate/acquire knowledge using approaches such as natural language processing (NLP), optical character recognition (OCR), etc
  2. Generate business rules using knowledge gained through the knowledge curation process or from insights/intelligence acquired through various machine learning techniques (as mentioned above) 
  3.  Leverage an automation engine that stores the collected knowledge and insights as code
  4. Take business actions either automatically through the automation engine or manually where human intervention is required
  5.  Use the feedback loop for continuous improvement by learning new patterns and un-learning old ones (when needed) in an iterative manner, just as humans learn, unlearn and re-learn on a continuous basis     
O   One clarification to be made here is: Some literature considers the generation of insights through machine learning and data analytics (Step 2) as part of knowledge curation (Step 1). I have intentionally separated these two here. According to me, knowledge curation is about acquiring knowledge from an information source such as literature and existing documents through NLP, search, OCR, etc. Alternatively, gaining insights through machine learning is done by applying ML algorithms on existing machine data. In my opinion, these two are distinct processes of acquiring knowledge. There are also traditional sources of knowledge such as human research, discovery, etc., that can be used to create business rules. This is represented as 'other knowledge source' in diagram and it does not necessarily come under the scope of AI.

I hope this piece has helped you better understand these complex concepts. Any thoughts or suggestions on how to improve these definitions? Please feel free to leave your comments and suggestions below.



Infosys EA Blogging Series

Our Enterprise Architecture blog series covers all aspects of business, information and technical architecture in order to demonstrate how we work with all teams across Infosys to provide innovative and coherent technology strategy and Chief Architect expertise to our clients worldwide. For more information on our Enterprise Architecture services, please find us here 

August 29, 2017

Blockchain - New kid on the block

By Ramanath Shanbhag, Senior Principal Technology Architect

In some of the conversations I've had with our clients, I get asked about how to identify the applicability of blockchain to their organization. There are many articles on technical aspects of blockchain as well as their implications in various industries. There is a very good PoV on Infosys digital site on few applications of blockchain in some industries. 

Without going into the history of block chain, which started with crypto-currencies called bit-coins, blockchain at a basic level is a distributed ledger system. What you record within the ledger is a matter of your business. You could choose to record the value of money itself, in which case it becomes a crypto-currency, or you could record payments trail, in which case it becomes a payments system. One could choose to record proof of evolution, as in case of agriculture demonstrating the origins of a particular produce, in which case it becomes a certificate of assurance. Or one could choose to record a contract, so that the terms and conditions are tracked, audited and enforced. Whatever you log within the ledger, it will guarantee immutability and audit trail.

The key characteristic of blockchain is its ability to establish trust worthiness by demonstrating audit trail. So what are the key characteristics of a block chain process and how do we find processes in which blockchain can be implemented. I would define a worthy blockchain process as having few distinct characteristics which can be uncovered by asking the below questions.

  1. Does the process involve ecosystem players, especially outside of your organization's control?

  2. Are there existing processes which are costly in terms of operations, because of issues of trust between ecosystem players?

  3. Are there processes / businesses which are un-explored / not possible today because of issues of trust and provenance in the ecosystem?

The fundamental problem that blockchain solves is the problem of trust between multiple parties in an ecosystem by providing immutability to distributed transactions. Let's take the example of shipping industry. A simple process of shipping goods from one location to another involves multiple parties including the sender, receiver, shipper, freight forwarders, ocean carriers, port & customs, bank. A lot of effort, time and costs goes managing and co-ordinating documents across these different parties, given the nature of the entire process. There is obviously a lot of cost savings which can occur if we implement blockchain to resolve some of the issues of trust. But it's not just the system that needs to be built. It's the entire co-ordination within the ecosystem which needs to be built, so as to ensure that the cost savings and time and transparency benefits are shared. That is a business process re-engineering exercise and involves business and management effort, not just technology.

Let's take another example of agriculture industry. Within agriculture, a lot of focus is on sustainable agriculture practices. Companies have committed to adhere to GAP and sustainable farming and procurement policies. Consumers are becoming more health conscious and may be willing to pay for products adhering to sustainable agriculture practices. The ecosystem has disparate players from farmers to commodity processors to FMCG manufacturing companies / retail outlets. It is difficult to provide proof of the origin of the produce and adherence to GAP at a batch level. Will the ability to provide proof of provenance, allow companies to create products / brands with higher value for the players? Will customers be willing to pay more for such brands? If technology is able to provide this proof of provenance, will companies be able to create new differentiated products? These are questions that need to be validated with market research and refined using design thinking and business ideation. And if the answer to the above questions is that more value can be created by increasing trust and transparency in the ecosystem, then block chain is the right building block for the process.

Blockchain can be an enabler, but by itself, it may not be sufficient to create value within the ecosystem. Supported by other technologies such as IoT and mobile, it can create new ecosystems. Take for example energy industry where microgrids can be created so that supply of energy which is typically centralized, can be de-centralized. By leveraging blockchain, IoT and mobile applications, a new ecosystem driven by local communities can be created to supplement an existing ecosystem of energy distribution, as has been demonstrated by the Brooklyn microgrid.

Block chain, is the new kid on the block. But it can be useful only if we are able to discover the right use cases for the tool, and engage with the right ecosystem of partners to extract value from the value chain or to create a new value chain. At Infosys, we can help you find the right processes for your organization and help implement the same. For more details on Infosys block chain offerings refer to https://www.infosys.com/blockchain/#offerings.

The question remains as to which organization will take the lead in creating this eco-system and what additional benefits can be accrued from building this ecosystem. Well, the answer depends on who is the dominant player in the ecosystem who wants to take the lead. Or is it about who wants to become the dominant player in the ecosystem by taking the lead. Do share with us your views about the potential of blockchain for your organization.


August 16, 2017

Architecture for digital world of ecosystems, platforms and the mesh

Author: Bora Uran, DPS, Senior Principal Consultant, Enterprise Architecture

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I have noticed new buzzwords mushrooming in business literature thanks to the surge of digital transformation programs - these include 'digital ecosystems', 'digital platforms', 'platform ecosystems', and 'platform economy' to name a few. Most of these terms refer to ways through which we can increase value by forging relationships between organizations, systems and connected things.


"The Mesh" is another one that has been added recently.  In the "Top 10 Strategic Technology Trends for 2017" research note, Gartner mentions "An intelligent digital mesh is emerging to support the future of digital business and its underlying technology platforms and IT practices. The mesh focuses on people and the Internet of Things (IoT) endpoints, as well as the information and services that these endpoints access".1


Depending on the business or technology context, each of these three words may have a different subjective meaning. However, one cannot argue that they all represent a common theme. Together, they symbolize a future of seamless connectivity where digital and physical worlds merge and traditional boundaries between organizations, systems and technologies are broken.


New architecture for new enterprise ecosystems


I am excited at the prospect of such a future and I am certain that successful digital transformation programs will help us get there. But first, enterprises need architecture that:


  •   Blends different technologies with varying levels of maturity
  •   Integrates diverse range of applications, services and devices
  •  Supports multiple client channels with optimized user experiences
  •  Enables continuous and agile delivery of new features
  •  Increases performance and scalability for high-volume and real-time interactions

 n  In order to satisfy these requirements, enterprise application architecture is evolving from monolithic to modular and flexible structures.There are new deployment models emerging with higher degrees of agility and scalability. From what I have observed, the two major shifts are happening in the areas of:


  •  Service-oriented architecture (SOA) - SOA is adopting new architecture styles, patterns and protocols such  as  web oriented architecture, microservices architecture and REST
  •  Application development - This is transforming to provide: 

o    Modern software application architecture and frameworks such as back-end and front-end Java or Java Script frameworks

o     Lightweight infrastructure with cloud-based and containerized workloads

o     Automation and agility with DevOps and agile development practices 


In the "Top 10 Strategic Technology Trends for 2017: Mesh App and Service Architecture" report, Gartner mentions the following: "Applications need a different architectural approach to support digital business ecosystems. That approach is mesh app and service architecture (MASA)". 2 Here, some of the key architecture styles are cloud-based, serverless, service-oriented, API-led, and event-driven. Implementing such architecture can be challenging. So, enterprises must focus on increasing the maturity of services-based computing and exploring new technologies such as containerization of workloads and serverless architecture. Most importantly, they should build new skills in areas of modern software frameworks, hybrid platforms and continuous delivery and integration.  


Infosys is a key partner with leaders in enterprise architecture and innovation. We help clients realize value from their transformation journey by offering services such as insights-driven enterprise transformation, digitization, ecosystem integration and management, and hybrid cloud enterprise transformation3. Our value proposition stems from our expertise in areas of microservices architecture, API-led connectivity, DevOps and Agile, open source, and cloud adoption.


References

 

  1. Top 10 Strategic Technology Trends for 2017 by David W. Cearley et al, Published 14 October 2016 ID: G00317560
  2. Top 10 Strategic Technology Trends for 2017: Mesh App and Service Architecture by David W. CearleyGartner Published: 21 March 2017 ID: G00319580
  3. Infosys Smart EA offering   
  4. Service Technology Architecture Guidance by Steven Schilders, Infosys    




Infosys EA Blogging Series

Our Enterprise Architecture blog series covers all aspects of business, information and technical architecture in order to demonstrate how we work with all teams across Infosys to provide innovative and coherent technology strategy and Chief Architect expertise to our clients worldwide. For more information on our Enterprise Architecture services, please find us here    

August 9, 2017

DevOps as key catalyst in digital transformation

Author: Sridhar Murthy J, Principal Technology Architect, Enterprise Architecture 

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Today's digital marketplace is governed by fierce competition - and falling behind is a risk no business can afford. The key to succeeding as a digital business is to embrace emerging technologies and innovation. With digital transformation programs sweeping across industries, enterprises are finding new ways to deliver top-quality products and services to end-users, manage vendors and optimize internal processes.  

 

When it comes to digital transformation, IT has a significant role to play as a strategic driver of digitization, agility and innovation. The DevOps approach is already revolutionizing software delivery for many companies. However, enabling DevOps is challenging, and requires commitment and careful planning. Often, enterprises choose to adopt DevOps because they truly want to differentiate their market offerings and increase speed-to-market. However, without a clear goal, the drivers for DevOps remain elusive and, in such cases, enterprise IT is unable to support true transformation.

 

My suggestion is this: If you want to gain a competitive edge through digital transformation, you must first re-evaluate how you develop software and how you harness the talent of development and IT teams.


Transforming the IT landscape

Meeting ever-changing customer needs means having the ability to build innovative, quality and scalable products. However, businesses cannot innovate in isolation. This 'collaboration for innovation' is exactly what DevOps offers.


The DevOps methodology combines agile with constant feedback to continuously roll out new changes. The outcomes of each rollout are meticulously assessed and fed back to enterprises by customers and end-users using a technique known as the 'canary release'. DevOps also helps enterprises leverage micro-services architecture to achieve modular services with business capabilities. In doing so, DevOps ensures constant innovation and refined digital transformation.   

 

So how do you, as an enterprise, adopt DevOps to revolutionize IT?


Roadmap for digital transformation

In my opinion, there is a mutually dependent relationship between cloud and DevOps. They share common patterns such as infra-as-code, auto-scaling, proactive monitoring, infra-resiliency, and immutable environments. Even as cloud maximizes the value of DevOps, the benefits of cloud can't be fully realized without DevOps-driven automation.

 

Step 1: Redefine your enterprise vision to align with the short-term and long-term goals of digital transformation.

Step 2: Create a roadmap to transition from the current methodology to DevOps. Here, it is better to start with specific projects and then scale up progressively.

Step 3: Define the tools required to automate workflows and underlying activities to achieve agility, higher quality and shift-left testing.

Step 4: Carefully select the technology you need that will maximize automation and collaboration

Step 5: Automate everything you can in workflows and implement a pattern where 'everything is code'


Securing the last mile

Finally, let's not forget security. DevOps promotes secure development lifecycles where security is in-built rather than tested separately. It requires security checks to be established across the software development lifecycle with a seamless feedback loop into development.


I think the best feature about DevOps is how it enables 'operation by design'. This means that DevOps builds resilience and testability while providing proactive monitoring and self-healing capabilities. Further, it ensures that all environments are stable. Development, quality assurance, user acceptance testing, and production remain consistent, giving you top-quality software.


With all its unique capabilities, I believe that DevOps is one of the key catalysts for digital transformation. Are there others? Let me know what you think.




Infosys EA Blogging Series

Our Enterprise Architecture blog series covers all aspects of business, information and technical architecture in order to demonstrate how we work with all teams across Infosys to provide innovative and coherent technology strategy and Chief Architect expertise to our clients worldwide. For more information on our Enterprise Architecture services, please find us here 

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