Enterprise architecture at Infosys works at the intersection of business and technology to deliver tangible business outcomes and value in a timely manner by leveraging architecture and technology innovatively, extensively, and at optimal costs. Stay up-to-date on the latest trends and discussions in architecture, business capabilities, digital, cloud, application programming interfaces (APIs), innovations, and more.

September 12, 2017

Calling all telcos: Monetize the power of digital using B2B2X model

Author: Vishvajeet Saraf, Principal Technology Architect, Enteprise Architecture

Nowadays, every enterprise is adopting digital initiatives to better serve their customers - and differentiation is the key. Typically, most enterprises collaborate with communication service providers or telecom companies to provide access and mobile solutions to their employees. Now, healthcare, retail, government, insurance, energy, and transport industries are looking for trusted partners who can help them create industry-specific and unique propositions that delight customers through innovative digital services. Think about the emerging use cases of partnering with telcos for smart cities, remote health monitoring, and location-based services. There is significant merit in such a model - partnering with telcos offers significant cost benefits compared to investing from the ground-up to build specific capabilities.


In my opinion, this presents telcos with a very lucrative opportunity to become such trusted partners. They already possess the digital infrastructure needed to provide content, network analytics, access/connectivity, mobility, and IoT services to customers. All they need is the right solution and the right environment. 

telecom digital incubator.jpg

Digital incubators - Test before you invest

All innovation begins with testing. Only by failing early and failing fast can new ideas improve and grow to become value-generating services. So, firstly, enterprises need an ecosystem where they can test their proposition before they invest - and this is precisely what telcos can offer. Telcos can provide enterprises across industries with an ecosystem that prototypes, builds and rolls out propositions for their customers - a 'digital incubator', one might say. Such digital incubators are a win-win opportunity: On the one hand, enterprises can test ideas and roll-out successful propositions. On the other hand, telcos gain a new revenue stream that accelerates the B2B2C model.

Here is how telcos can monetize this opportunity for maximum returns:

  • Position themselves as digital service providers (DSPs) of traditional telco services for telephones,     broadband, fixed data, etc., while partnering with content providers to provide content and media services along with access to NFV/SDN, IoT ecosystem, data/network probes/analytics to enterprise customers
  • Create a digital platform that delivers these capabilities as lightweight digital services or APIs for enterprise customers 
  • Become a digital incubator by creating an ecosystem based on open API methodology that allows enterprises to carry out trials, test and build unique propositions for their customers

digital incubator ecosystem.png

How to build an incubator ecosystem?

Below are the key elements of an open API-based digital incubator ecosystem that is built around a digital platform.

incubator ecosystem components.png

I want to highlight that, in such an environment, it is critical to make APIs publicly available. Let me explain why: Publicly-available APIs provide developers and enterprises with programmatic access to the digital capabilities of the chosen telco. In this way, enterprises can develop custom offerings through apps and other channels instead of relying on their telco partner to develop industry-specific offerings. In a manner of speaking, it is about creating a 'You do, I support' model, instead of an 'I do everything' model

Here are the key components for building an incubator ecosystem:

  • Open API specs portal: Through this portal, enterprises and developers can access publicly available API details such as API specifications, sample payloads, error codes, access details, and limitations (if any) 
  • Developer portal and accelerators: This portal not only allows developers to register their apps, but also educates them. Through this channel, telcos can receive feedback and suggestions from enterprises and developers about APIs exposed by them. Several API gateway products provide this feature, thereby helping DSPs  build developer portals
  • Test environments: These allow enterprises to trial and prototype services with the exposed APIs 
  • Digital studios: This is important and requires some investment by enterprises. Digital studios act as demo centers where enterprises can demonstrate their propositions to their customers (B2B2X). Telcos can also provide  value-added services such as rapid prototyping tools as well as designers to help enterprises roll out demos
  • Revenue models, API monetization and partner on-boarding: Needless to say, an open API ecosystem will trigger new revenue models. So, it is important to plan for these new revenue models and create partner/enterprise on-boarding processes that cater to different scenarios and industry verticals. Many API gateway products provide key capabilities for API monetization

Infosys has done it already!

The Infosys Enterprise Architecture team has helped several telcos build digital platforms for successful digital transformation. We help telcos develop the building blocks for digital incubators by leveraging our proprietary accelerators and frameworks such as:

  •  Infosys Digital Enterprise Architecture (I-DEA) for defining the ecosystem architecture
  • Infosys Cornerstone Platform to accelerate building microservices as well as digital platform delivery 
  • Experienced Infosys enterprise architects and reusable artifacts to strategize and build an open API ecosystem for large telcos and government organizations
  • Infosys Digital Studios, a unique differentiator, to help telcos showcase initial demos to their enterprise customers

Inspiration behind writing this blog is Binooj Purayath.  


Models and partner on-boarding templates by TMForum



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 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 16, 2017

Architecture for digital world of ecosystems, platforms and the mesh

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


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.



  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    

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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 


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 

July 31, 2017

5G to unleash new wave of disruption - are you ready?

Author: Shreshta Shyamsundar, Principal Technology Architect, Enterprise Architecture 

The 5th generation of wireless systems - abbreviated as 5G - is almost here! While it is expected to be commercially available in 2019, testing is scheduled to begin in the winter of 2018 in Seoul, South Korea. Many companies are already taking steps to prepare for its rollout. In the last quarter, Qualcomm announced that a commercially viable 5G modem will be available. Meanwhile, Qualcomm is supporting OEMs in building next-gen cellular devices and aiding operators with early 5G trials and deployments.

Originally, 5G will be designed for multimode mobile broadband that works through 4G LTE and 5G devices. But, it will also be extensible as a fixed wireless broadband device. With 5G, customers will experience and consume higher value content at much greater speeds. By enabling interworking and cohesive connectivity, 5G promises to enhance the quality of the broadband experience.

As a step above 4G, 5G will radically increase the speed of data transfers across the network - going beyond merely sending texts, making calls and browsing the web. As a digital user, 5G will enable you to instantly and easily download and upload Ultra HD and 3D videos. Sustaining the hyper-connectedness brought on by the Internet-of-Things (IoT), 5G will help seamlessly connect and support thousands of connected devices across personal and work environments.

Getting the right hardware

However, 5G will come with its own challenges. While we can look forward to greater data rates, more devices and lower latency, rolling out 5G means re-thinking the entire network stack, particularly radio access networks and network hierarchies. Our current network communication hardware cannot support the 1ms latency that 5G commands. Providers will have to consider software-defined networking (SDN) to manage traffic and network function virtualization (NFV) to virtualize network traffic.


These thoughts have been well-articulated in an NGMN white paper. According to the authors, 5G will demand extreme innovation in 6 key areas, namely 1) user experience, 2) system performance, 3) devices, 4) enhanced services, 5) business models, and 6) network deployment and operations. The paper talks about building "architecture that leverages the structural separation of hardware and software, as well as the programmability offered by SDN/NFV. As such, 5G will be a native SDN/NFV architecture covering aspects ranging from devices, (mobile/fixed) infrastructure, network functions, value-enabling capabilities and all the management functions to orchestrate the 5G system. [2]"

The authors add, "On the radio access side, it will be essential to provide enhanced antenna technologies for massive MIMO at frequencies below 6GHz and to develop new antenna designs within practical form factors for large number of antenna elements at higher frequencies[2]."

Energy savings from 5G


5G demands lesser energy to power devices, thereby supporting (theoretically, at least) a greater density of endpoints. In fact, Telstra and Ericsson are collaborating to create "the first national IoT-enabled mobile network[3]". With this technology, the average upload speed will rise to 200-400 KBPS. We can also expect to see greater number of cost-effective and energy-efficient sensor devices. Here, the use-cases are far-reaching. According to the news report, "a sensor network deployed at Pooley Wines in regional Tasmania [can] collect data like soil moisture and temperature, rainfall, solar radiation and wind speed.[3]"

Opportunity for enterprise architects


5G is already in trial phase and pre-user production testing is expected to commence within 18 months. This means there isn't any time to waste: Telcos must focus on building a roadmap for 5G enterprise architecture, particularly for time-bound technology refreshes. They need to prepare for increased data volumes, faster time-to-market and a 5x reduction in latency from 5ms to 1 ms.


In fact, many network operators across continental US, Europe and Asia are already assessing the impact of 5G on existing systems, applications, processes, and operating models. Insights from these assessments can give enterprise architects a head-start on designing offerings that implement 5G in organizations. This will equip them with a competitive edge when it comes to presenting a business case, performing gap analysis and recommending strategic initiatives.


The Infosys SMART EA offering [4] helps operators find numerous ways to implement 5G technology for greater value. However, they need to start thinking about the potential IT roadmap now to reap benefits and gain a substantial market advantage.


[1] - Ofcom 2017 report - 15 June 2017 - http://telecoms.com/479494/ofcom-publishes-beginners-guide-to-5g/

[2] - Next generation mobile networks - 27 Feb 2017 - https://www.ngmn.org/5g-white-paper/5g-white-paper.html

[3] - Telstra gets a Jump in 5G Race - 27 Feb 2017 - http://www.theaustralian.com.au/business/technology/telstra-gets-jump-in-5g-race/news-story/c5cfe570b75a8adb6f198602af01ae21

[4] - Infosys Enterprise Architecture practice - 15 June 2017 https://www.infosys.com/enterprise-architecture/Pages/offerings.aspx#Strategize


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 

July 19, 2017

Data Analytics: Doing it right!

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

Companies across industries are realizing that there is enormous data within and outside their organization that can be leveraged for high performance and thus, turning data into a strategic asset.  Data analytics is the cornerstone to make this happen. For, Data is of no use unless we convert that data into insights. Such insights are of no use unless those insights help us to solve a business need or a business problem. Simply put, Data analytics help translate raw data into insights. Data analytics make data a strategic asset. So getting data analytics right is key to successfully leveraging data as a strategic asset. So, how to get data analytics right?

 Here are the salient points, I believe, one has to follow to get this most important thing right. The data analytics!

 Always link data to a business decision and the business decision, in turn, to a business KPI. That means, data has to result into an insight. That insight has to result into a business decision and that business decision should positively impact one or more business KPIs. Analyze every step in the business process map and evaluate how data can make that step or a process more simplified, or more efficient, or more intelligent or even made redundant. Think end-to-end business process. The business process can be an operational process, IT process, a marketing process or a sales process. Every step in the business process may have a potential to leverage data.

2.       Do not underestimate Data preprocessing and Exploratory Data Analysis (called EDA in analytics terminology. It is a step we do before applying the algorithms to data. EDA helps us to understand the data better in terms of its distribution, any missing data, any inherent relationships or correlations. This understanding helps us to clean the data so that the data is in right shape to be used as input to subsequent algorithms).   In fact, this is the most important step in the data analytics process. This step can take nearly 70% of the time spent. Wrong data leads to wrong analytics, which leads to wrong prediction and wrong business decisions and degraded KPIs - Garbage in Garbage out! Models and algorithms (however great or sophisticated they are) will not come to your rescue if we give inappropriate data to them. Give them right data in right form and apply the models/algorithms for the right business situation. They become precious. Not standalone on their own.

3.       Data analytics is always a probability game. Do not expect or wait for perfection in data analytics. What does this mean? If we make our decisions based on the insights from data analytics and by following the right process, it just means that we are more probable to succeed. It increases our probability to succeed. Sometimes multi-fold. But it does not mean that insights will exactly predict the future. Data analytics is not meant for that. It makes us more probable to succeed! That's a great value-add actually!

4.       Measurement and Evaluation of models. There are multiple techniques available to evaluate effectiveness of a model. For example, model accuracy, sensitivity, false positive rate, lift charts, gain chart - for classification models. MSE, SSE, R-square - for estimation/regression models. Use them to check accuracy and usefulness of models before employing them in production.

5.       Ensemble. In simple words, Ensemble means leveraging multiple models that are similar, to improve prediction. For example - CART, C5.0, logistic regression, Neural Networks for the same classification problem. The principle of collective intelligence applies. For a classification problem, create a group (of ensemble) of models as mentioned above. Check the classification output results from each of the models. Apply an averaging mechanisms like majority voting or propensity averaging. In almost all cases, ensembles increase the prediction capability of models.

6.       If a model is too good to be true, it actually is. For example, if you happen to come across a classification model which claims 90% accuracy in predicting, it is too good to be true. Either the model has memorized the training data set (model over-fitting) or there could be other issues in training data (like auto-correlation etc.). Such model works well for already known cases, but might lack generality.  Such models are of limited use in predicting results for new data points (which is the real purpose of data analytics). Prefer a classification model which is 75% accurate (rather than 90 % accurate) but which has better generality (ability to predict from unseen or new data points better). Consider this for example. 100 data records out of which only 5 cases of fraud transactions. The first classification model can predict all data non-fraud and still have an accuracy of 95%. But the second model will predict 25 cases as frauds out of 100 records including those 5 fraud cases. The accuracy of the second model is only 80% . But the second model is more useful than the first one!

7.       Do not believe in models which do not make logical intuitive sense. Note that data analytics will give us mathematical proof and a way to represent and codify the inherent relationships, classifications, predictions etc. But even if you come across a model with high accuracy to predict (for the test data set) but the relationship uncovered by the model does not make logical intuitive sense, then it makes sense to discard that model.

8.       Data analytics is mainstream. Nearly 80% of data analytics problems can be solved by following straightforward algorithms like classification, regression, clustering and collaborative filtering (recommendation systems). There are multiple tools, frameworks, technologies available today to solve such problems. The algorithm knowledge is no more a barrier. The nuances and complexities of the algorithms are pretty much part of libraries available in standard languages like Scala, R, Python etc. What we need to know is how to apply the algorithms/models to new data and new scenarios that we come across. Do not get overwhelmed by assuming that data analytics (and thus AI, ML, data science etc.) is only for a selected few who have PHDs. It is ready being adopted mainstream.  However, move onto the next point. (BTW, how is data analytics related to AI, ML, and Data Science etc.? That will be the topic of my next blog)

9.       Self-Service BI. It is useful. However, do not get carried away by this. Sometimes it makes sense to let business users do the BI themselves through self-service BI, for instance - for slicing and dicing or what-if analysis. But predictive analytics is not just traditional BI. Predictive analytics is a discipline in itself.  It needs people who are knowledgeable and experienced in data analytics. Remember, data analytics is easy to get it all wrong. Provide ability to do self-service BI where appropriate. But do not forget that there is lot more value to be gained beyond self-service BI.

10.   Time is of essence. Once you find a model which has good accuracy and makes logical intuitive sense, proceed quickly and put that into production. As I mentioned earlier, no model is perfect. But as long as every new model or a next version of an existing model is better than the status quo, proceed. But iterate to improve as you go along. This brings us to the last point.

11.    Continuous improvement. Every model has a shelf life. Fraudsters are as intelligent, if not more, than data analysts'/data scientists. A fraud prediction model which is good today will not be good tomorrow. The inherent relationships will change. Always keep measuring the accuracy of models in production on an on-going basis. Once we find that the current model is no longer serving the purpose, it is time to go back to the drawing board and re-work the model or create a new one.

 I have identified some of the most important points. There could be more. Let me know what you think.

March 7, 2017

Is your Enterprise Architecture SMART?

Author: Mohammed Rafee Tarafdar, VP - Unit Technology Officer

Enterprise architects are operating in a very dynamic environment and the enterprise context in which they operate is evolving significantly. This evolution is  across multiple dimensions - stakeholders, operating model, engineering, and technology. A few key observations:

Continue reading "Is your Enterprise Architecture SMART?" »

January 3, 2017

Horizontal communication integrations trends and impact on IT architecture

 Authors: Marie Michelle Strah- Senior Principal-Enterprise ApplicationsVishwanath N Taware- Senior Principal-Enterprise Applications

Over the last 18 to 24 months, we have seen a lot of mergers and acquisitions (M&As) in the US telecommunications industry. These M&A activities can be categorized as horizontal (network expansions or strategic service consolidations) and vertical (broadcasting or content players with cable or telecom service providers).

Continue reading "Horizontal communication integrations trends and impact on IT architecture" »

Telecom vertical acquisition and approach for enterprise architecture

Authors: Marie Michelle Strah- Senior Principal-Enterprise ApplicationsVishwanath N Taware- Senior Principal-Enterprise Applications

Portfolio diversification, creation of new revenue streams and business model transformation are driving vertical acquisitions.

Continue reading "Telecom vertical acquisition and approach for enterprise architecture" »

January 2, 2017

Is your IT team ready for M&A, Demerger Business Events?

Author: Abhay Narayan Joshi, Senior Principal Technology Architect

In my observation, since last ~8+ years, the business environment changes such as M&A (Mergers and Acquisitions), Demerger and Partial sell-offs scenarios are happening at relatively higher frequency than ever before. It is happening in every industry verticals including, Banking, Financial Services, Insurance, Healthcare, Manufacturing, Retail, Telecom, Media, Energy & Oil etc.

Continue reading "Is your IT team ready for M&A, Demerger Business Events?" »

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