Announcing GeneraX – Affine’s Generative AI Product Suite

Affine has a rich legacy of developing AI-powered solutions. Right from its inception, there has been a strong emphasis on not just developing superior quality solutions but enhancing our learning curves and innovation opportunities. This approach helped us open up new avenues to solve business problems effectively. Thus, it has been the single most important differentiator, allowing us to build production-grade AI solutions for several global businesses.

Our accolades from global AI hackathons across multiple industries are a testament to the depth of knowledge we have in AI while signifying our advanced practices. It should be noted that in hackathons like Datacentric AI, Hackerearth, and Kaggle hackathons, we were the only AI company that made a spot in the top percentile among dedicated academic researchers in the field.

In the World of NLP:

Affine’s mastery in leveraging Transformer technology is displayed well in our NLP solutions. We were able to combine our Deep Learning expertise with open-source technologies like BERT, RoBERTa, etc., to deliver ground-breaking solutions that helped organizations reduce a significant amount of manual effort and deliver more accurate results. Some of the most recent solutions we developed were – Document summarizer, Context-based enhanced search, and Contextual AI Chatbot. You can contact us to know how these solutions can help your business.

In the World of Vision:

Specialization in Stable Diffusion matured during the development of our Satellite Image Segmentation product – Telescope. We used Stable Diffusion to create synthetic data that could be used to train the Image Segmentation Model. Telescope was thus developed with the intent to save millions of dollars and months of effort that would go into land surveys in multiple industries. We also created a mechanism using GAN models to create new gaming characters.

The Upcoming Generative AI Product Suite – GeneraX

The last few months have witnessed the widespread adoption of Generative AI, such as Open AI’s GPT in text generation, Dall-E 2 for image generation, and Google’s Bard chatbot. Despite some limitations, these AI implementations are revolutionary and provide excellent results. However, they are not completely business ready. A significant effort is required to ensure that these implementations give professional-grade, meaningful, and usable outcomes to businesses.

The grueling hours of learning the in-depth working of different AI technologies have always been guided by our intent to build the best real-world solution that could be used and benefit businesses. Affine’s knowledge of how things work under the hood is coming together with GPT 3 and Dall-E 2 to create enterprise-level SaaS products. The GPT and Dall-E APIs have helped us speed up development, give wider scope and convert the boutique solutions we pride ourselves on into plug-and-play products.

We’re kicking off our Generative AI product suite – GeneraX – with CreAItive!


“Are you a marketer frustrated with the prolonged ideation of designing creatives? And you spend hundred-thousands of dollars to create marketing-ready creatives and get only a handful of variations. It’s time to get over this creative generation cycle. Introducing Affine’s Image Segmentation and Stable Diffusion powered CreAItive. It’s a one-stop-shop for design ideation, experimentation, and creation of 100+ market-ready images on the go at a fraction of time and cost.”

Are you ready to scale up your business with the power of AI? Watch out – this space for demo links and to gain access to the early adopter benefits on GeneraX!

For a product demo, contact us today!

How will Artificial Intelligence Transform the Business Landscape in 2023?

Over the last two years, businesses of all sizes across the world have embraced AI in various forms and seen a tangible outcome. As a result, Artificial Intelligence is expected to make significant advancements considering the massive investment and continuous innovation that has occurred in the last couple of years, with the potential to significantly improve our lives and the way organizations work in the digital transformation landscape.

AI has already revolutionized many industries, from healthcare to finance, and its applications are only going to grow. Accelerated AI automation has seen the most advancement in the recent past, especially in Generative design AI or AI-augmented design and Machine Learning code generation. We can expect AI-driven automation to power businesses to make better decisions, reduce costs, and increase efficiency.

AI-powered robots and autonomous cars are providing us with a new level of convenience. AI technology drastically improving healthcare delivery and becoming more integrated into our lives has grown into an essential part of our day-to-day life. The next phase of AI is going from narrow-scope to wide-scope ensembles. This will also be the time when AI governance and security will be developed, scrutinized, and standards will be set. We are heading into an era where AI engines driving decisions in silos for different business functions will be ensembled and synchronized for maximum efficiency and profitability at an enterprise-level.

Generative AI will gain prominence!

Generative AI has become the buzzword of recent months, with applications like ChatGPT taking the world by storm (it crossed 5 million users within five days). In the context of such AI models—where computers generate text rather than simply copying it from other sources and rearranging it to form new sentences—ChatGPT illustrates how generative technology will grow more ubiquitous as time goes on. With the advent of generative AI technology, it’s possible to create not just text but also images, videos, music and even entire websites. The usefulness of this technology lies in its ability to automate content generation, provide personalized content and generate a high volume of quality material. In 2023, we can expect generative AI apps to accomplish even more.

With new technologies, we often face challenges even greater than those faced by previous generations. We expect that scalability, privacy and security issues will arise as well—and, of course, copyrights. For AI to become the next creator, it will have to take on some of those roles itself—and that means addressing ethical concerns around how machine learning models are trained. Industrial enterprises must set up frameworks that enable the democratization of information. The scope of Generative AI is large enough to warrant monitoring these challenges closely.

Advances in AI will lead to a rise in AI governance

As enterprises adopt more AI technology, this will result in better data governance practices – mainly driven by increased awareness among the public and regulating authorities. The burgeoning application of Artificial Intelligence has outpaced attempts to create a framework for regulating it. As a result of increasing public concern about the impact of artificial intelligence on society, we can expect to see more countries implement regulations such as the EU Artificial Intelligence Act, data and policies (GDPR) in order to protect citizens.

As enterprises ramp up their use of AI, they will need to assess the potential risks involved and incorporate ethical standards in their strategies. Ethical use and governance of AI models/ tools will be critical for all enterprises deploying them.

AI can help businesses detect and mitigate cybersecurity risks

AI will be instrumental in helping organizations implement proactive cybersecurity measures. By anticipating and preventing existing and emerging threats, AI will create a shield against any potential dangers.

As the number of cyber-attacks has increased each passing year, so too has their complexity. Responding quickly to these concerns in real-time is critical and the need of the hour. But how can you use all that data effectively? Machine learning models can learn from vast amounts of information quickly and respond to changing patterns; Artificial Intelligence will help increase efficiency through automation as well as allow experts better allocate resources toward more pressing problems.

The current decade will unfold full-fledged AI ecosystems

According to Gartner’s hype cycle on emerging technologies, cloud sustainability and cloud data ecosystems will reach the “Plateau of Productivity in the next five years while different Accelerated AI Automation (like Casual AI, Foundation models, Generative design AI, ML code generation, etc. will reach “Slope of Enlightenment” and start moving into “Plateau of Productivity”.

This means that in the next five years, we will see organizations will consciously start replacing the stand-alone AI engines making localized decisions with a wholesome digital ecosystem. The ecosystem is housed in the cloud, operated by Automated AI systems and interacting with business stakeholders and users via immersive technologies and blockchain-based transactions.

A retail customer would no longer be limited by traffic congestion, parking availability or distance to the store to try out merchandise in the virtual reality store. A surgeon’s exceptional skills could be deployed miles away in an area of need without having to wait for the duration of a flight, saving countless lives. Construction and infrastructure development can be tested to ensure stability with great accuracy and high speed with agile adjustments.

This sounds a little sci-fi, but the technology for the future is ready now. It just needs to be brought together.

In a nutshell

We are moving towards the integrated AI ecosystem panning across all facets of our daily lives and every business at an unstoppable pace. The way businesses interact and transact with consumers is going to be revolutionized with this ecosystem. Entire new business models are being created around this technological evolution impacting organizations of all industries and sizes.

While enterprises and consumers are getting more and more familiar with different AI interactions, architects and engineers will get onto the trend where they will be “parenting” AI engines on what to do, how to do it, how well it learns and how well it functions.

Can AI ease the messy chaos of Revenge Travel? 

Recently Heathrow Airport saw incidents of mass flight cancellations, delays, and baggage issues thanks to the resurrection of the zeal for traveling amongst people, owing to the bottleneck caused by global travel restrictions. Such is the effect of the revenge travel phenomenon.  

Tired of being locked down for over a year due to the pandemic, people started storming to nearby holiday destinations to break free from the humdrum activities and routine life.  

The travel industry was subject to unavoidable impact due to the Covid shutdown. According to Statista, the worldwide travel and tourism GDP saw a 50% freefall from 10% to 5% in 2020.  

With any unnatural imbalance, an adverse effect is imminent, and in this case, a new trend emerged – Revenge Travel.  

New work trends have paved the way for Revenge Travel

The exhaustion of staying inside their homes for a continued period led to this reactive global phenomenon. Once the cases started to decline and countries across the globe began easing travel restrictions, the vacation-starved populace rearing to make up for lost time and confinement started the trend of revenge traveling. 

While traveling was always an option for people, the revenge travel phenomenon saw its inception as animosity towards not having a choice of leaving their homes.  

As with contemporary trends, revenge travel saw an immense foothold, and people started booking airline tickets like there was no tomorrow. Staycation and workcation trends have emerged amongst organizations across the world, opening possibilities to travel more than usual. People even preferred domestic traveling, and domestic flight bookings beat international flight bookings in July 2021.

So, what exactly is the solution? Like other industries, can technology play an aiding role in easing these issues? Can it help accelerate the performance of the travel industry?

Travel and Tourism –can AI be beneficial? 

Messy travel experiences are an issue for customers, while businesses cannot afford to lose face. Everyone has been the recipient of a messy travel experience at least once in their lifetime. Being allocated a different room and tickets booked for the wrong date or time is something everyone has faced. The classic story of a travel agent messing up one of the most important adventures of people’s life is not something new.  

But travel aggregators have changed the landscape for travel and tourism businesses. AI has made the life of travelers a lot easier by being able to book without visiting travel agents.  

For businesses, AI offers to increase profitability in many ways. Pioneers in AI and data analytics have designed and developed solutions specific to the Travel & Tourism industry, benefiting both businesses and customers. Let us explore some AI-based Travel & Tourism solutions that can drive growth for the industry.  

Managing heavy demands & cancellations 

One of the major effects of the rise in revenge travel is the volatile demand. Flights, hotels, and tourist destinations were overwhelmed at once and the unpredictable nature of this demand brought instability and took the travel and tourism industry by surprise. 

The availability of big data is such a valuable potential to tackle this challenge for many of the players in the industry. Leveraging data to forecast demand based on several factors like customer behavior, price trends, and upcoming events can be the game-changer and help ease the unforeseen demand and excessive cancellation situation that plagues the industry.  

Demand & Cancellation Prediction & Management is an analytical OTA solution from Affine that does this along with predicting inclement weather and the resulting flight delays. By doing this, the solution also helps OTAs equip themselves to handle and assist customers, resolve queries, and manage rebooking in case of cancellations. 

This data powered analytical solution helps OTAs predict demand, reduce cancellations and manage refunds, while improving cash-flow for the business. Effectively managing cancellations and refunds also result in a smooth customer experience and increased brand loyalty. 

Automated query handling – the need of the hour for both OTAs and customers 

With the revenge travel chaos and ever rising flight and hotel bookings, customers have many qualms and queries. The sheer volume of queries paired with the skyrocketing number of customers makes this a herculean challenge for OTA players. 

While agents are necessary to solve certain queries and issues, manual efforts simply can’t hold up to this excessive number of requests and a sea of travelers. 

OTAs need to automate the initial levels of travel queries for a smoother process. Furthermore, chatbots are far superior to manual labor in terms of time management and efficiency in handling the sheer volume of customers.  

Affine’s Contextual AI – Chatbot & analyticsis an AI-based chatbot that handles major customer queries and manages them. Live agents are necessary to solve certain issues but this chatbot only transfers the customer to the live agent when it is absolutely necessary, thus easing the load on agents while efficiently handing most mundane queries thanks to its intelligent capabilities. 

For OTAs, this solution helps improve operational costs and reduce customer service costs by having fewer agents as the chatbot handlesthe majority of the traffic. It also helps understand customer interactions helping improve customer experience and overall customer satisfaction. 

These are just examples of a few solutions, and there are tailor-made solutions to improve almost every aspect of the travel & tourism industry like  

  • Conversion rate 
  • Acquisition cost 
  • Ad impressions and many more. 

 As people are getting more dependent on technology day by day, providing a smooth customer journey is essential in the long run for players in the travel industry. Leveraging the abundance of data and the excellence of AI and ML technology provides an airtight business practice headed towards sustainability & success. 


The post-pandemic era has brought some drastic changes to the lifestyle of people all over the world. The innate yearning for traveling has burst and traveling has become the de-stressing factor for the majority. Hybrid working models for offices and work from anywhere trends have opened the possibilities to travel with just a laptop and an internet connection. 

Revenge travel may be a one-time phenomenon, but it has awakened the deep desire to travel within the populace across the world.  

Revenge travel is just a setting stone for what is in store for the travel and tourism industry. The travel and tourism industry needs solutions that will help them operate efficiently and rake in higher margins. Booking agents are history and travel aggregators are competing across the industry, but AI-specific travel solutions will help travel and tourism businesses equip themselves with the future-ready foolproof tools required to sustain.  

What does Affine bring to the table?   

Affine is a pioneer and a veteran in the data analytics industry and has worked with space-defining Logos like Expedia, HCOM and Vrbo to name a few. From travel & tourism to game analytics, & media and entertainment, Affine has been instrumental in the success stories of many Fortune 500 global organizations; and is an expert in personalization science with its prowess in AI & ML.   

Learn more about how Affine can revamp your Travel and Tourism business!  

What are legacy systems? How Can Modern Data Platforms Bring Revolutionary Change?

Affine’s Analytics Engineering Practices is kicking off a new series on “All that you need to know about Modern Data Platform.” Read the second part of the series here. You can also read part one of the series here.

What are Legacy Systems? Does your business have one?

 Legacy systems comprise ETL systems, data warehouses, and other traditional software/hardware data architectures. Organizations retain legacy systems if it is expensive to transition to modern data platforms because of data migration or if the legacy system is critical to the business.

Why organizations need to adopt Modern Data Platform?

Investments in modern data platforms can transform business practices in the long term. The following are four fundamental reasons; why an organization should adopt powerful modern data platforms.

Modern Data Platforms – 4 Potential Reasons Why They Are Revolutionary?

1. Enhancing data discovery efforts

A robust modern data platform can synthesize different data types. It can parse through structured or unstructured data in the cloud or organize it according to user requirements.

Legacy systems are less effective in handling advanced data discovery and processing. They store data in isolated silos that neither the system nor a user can reconcile. This flaw makes legacy systems less efficient since the user will have to manually manage or organize the data with other tools and then process it. Only after they complete these steps can they obtain insights from the data.

On the other hand, a modern data platform will jump right to the last step to generate insights for business.

2. Promoting Data Democratization throughout the organization

The idea behind data democratization is to enable the business user a smoother way to access staged data so businesses can leverage data to transform the workflow by unleashing the value of information locked up in the data store. A modern data platform facilitates effective data democratization while making accessibility easier to empower users to obtain the relevant data points and insights independently and quickly.

A transparent process and platform to access data enable domain experts such as data scientists to skip logistical hoops and effortlessly home in on the data points they need. Thus, it might not be the case for legacy systems, which often have redundant interim steps, such as report request processes.

3. Prioritizing data safety and privacy

Modern data platforms are equipped with multiple layers of security to prevent data breaches. Most organizations follow the regulations such as CCPA, HIPAA, FCRA, FERPA, GLBA, ECPA, COPPA, and VPPA. These data protection laws provide governing frameworks for data usage, storage, and deletion, which are easier to process in modern data platforms to ensure data security and privacy.

Most businesses that use legacy systems find it difficult to implement the regulations required to meet security and privacy standards.

4. Ensuring self-service of data

A streamlined modern data platform smoothly enables self-service of data to your internal customers. It is well equipped to identify various data points and efficiently cater to internal customers’ requirements. Thus, a modern data platform reduces the complexity and allows users to access the data swiftly when they need it most. Legacy systems lack self-service, and all data requirements must be routed through IT and data teams.

How to overcome legacy systems confrontations, and where should you invest?

Legacy systems are in rapid decline. Organizations are choosing to store a significant chunk of their data on modern data platforms to optimize their data processes and decision-making. Modern data architectures need to keep up with the rapidly growing data-driven needs of businesses. As a result, modern data platforms have emerged as the most efficient solutions and promise to take the business world by storm.

However, before making a significant financial investment in the space of Modern Data Platforms, organizations must find a suitable, competent technology partner to partner with them on this journey. An expert like Affine’ s Analytics Engineering practices can make this transition seamless and effective. Are you ready to begin your journey to true data centricity? We are here to help. Schedule a call today!

This blog is the second episode that signifies “All You Need to Know About Modern Data Platforms.” In the next episode, we’ll compare traditional vs. cloud hosted data platforms to determine which would be better for your business.

AI to fuel the Film industry’s future

The worldwide revenue for theatres fell from an all-time high of $41.7 billion in 2019 to a jaw-dropping $11.9 billion in 2020. The film industry took a deadly hit from the pandemic, and the following lockdown brought the industry to its knees and raised questions about its future.

Source: Statista

Ever since the onslaught of OTT platforms, the media and entertainment industry has shaken up, and a new form of revolution has set the foundation. The film industry is one such domain that has been the recipient of the adverse effects of this revolutionary transformation in the past decade.

While the big screen and an unparalleled cinematic viewing experience are still unchallenged to an extent, access to home entertainment and content on demand is a dent to the box office.

The Pandemic Saga

One of the biggest jolts for the film industry to date has been the pandemic, which brought things to a screeching halt and left the industry high and dry. Movie theatres had to shut down due to lockdown measures, and people confined to their homes took an interest in gaming and streaming shows on their couches as alternatives.

The result? Box office revenues plummeted to an all-time low!

The challenge lies in the future

The 2020 numbers look dreary, but as lifestyles return to normalcy again post-pandemic, the film industry still has a challenging task. Consumer behavior has changed. The average content consumer has seen value from OTT platforms that provide quality content on tap, and film as a product has deteriorated in value. Video on Demand offers immense value, and this is a critical film industry challenge that needs addressing.

If the five-year forecast from 2020 to 2025 is anything to go by, it is not going to be a smooth journey for the film industry. The OTT platforms have wreaked havoc with value entertainment at their tap and dethroned the film industry, aided by the unforeseen pandemic.

Source: Statista

But the charm of watching a movie on the big screen is unparalleled. The industry needs to revamp its practices in the process of film production. While a passion for the craft fuels the art of filmmaking, the technical and strategic processes stand to immensely benefit from AI practices explicitly designed for the film industry.  

Production and promotion- areas that need efficiency the most

A film’s success or failure has always been a gamble, but the production effort and cost are constant across most film titles. Solutions implemented right from the pre-production phase can result in substantial, measurable impacts.

Many studios spend an insane amount of funds on marketing and promoting their movies. With the current advertising landscape seeing a transformation, thanks to the latest content consumption habits, promotional budgets need to be scrutinized irrespective of the production scale.

Source: Statista

Save for the slump brought by the pandemic, the promotional budget for movies has seen an upward surge in the previous years and is back on track for 2021, which means higher spending and a bigger overall budget. While this amplifies the reach of the film across the globe, there are two main challenges here:

  1. Many small and medium-sized studios cannot splurge on sky-high budgets to promote their movies.
  2. Even big production houses sometimes go overboard with the promotions, and the movies earn less than expected.

Efficient promotions are the only way to go forward irrespective of the might of the production houses.

Commercial Forecasting System

Hollywood is no stranger to big-budget titles bombing at the box office while total underdogs clinch big victories. Sometimes there have been instances of a movie bombing locally but performing exceptionally well at international box offices like China.

This AI (Artificial Intelligence) based project management system from Affine helps production companies execute smart, efficient insight-filled decisions across the film’s production processes.

With this AI solution, production companies can predict the performance of their movies on local and international markets and across various demographics and populace at respective production stages of the film.

Production industries can stand to gain benefits as mentioned below by leveraging the Commercial Forecasting System:

  • Ascertain key foresight into film performances well in advance
  • Make necessary changes in the preliminary stages of production
  • Project realistic output numbers
  • Carry out efficient and data-driven marketing/promotional activities in tune with the film’s predicted performance across demographics and media types

Script Analysis

Time and time again, it has been proven that a good script is a foundation for a successful movie. With the diversity of content today, it is challenging to design a script that will assure superior performance at the box office.Script Analysis is an AI and ML (Machine Learning) solution that learns from the plethora of data fed into it and analyzes the storyline to determine its success in respective release regions, even at a pre-production phase. Historic film data helps the solution analyze similar script performances and predict the outcome with near-perfect accuracy over the micro level of demographics and age groups.

With the Script Analysis solution, production companies can leverage the benefits mentioned below:

  • Predict the near-accurate outcome of a script if it’s shot into a movie
  • Ascertain valuable insights that help make data-driven business decisions well before the production stage
  • Green-light scripts that are assured of performing well while making necessary changes to scripts that are not as optimal for business

Talent and Casting Analytics

Many great movies have had surprise castings that worked for them and changed fortunes for both – the filmmakers and the talent. But there have been cases of miscasts that have ruined good movies as well. Leaving casting to gut feeling is not feasible anymore and must be treated like any other business process.

Many production businesses have already adopted AI-based casting methods to choose the right talent optimally. Affine’s Talent and Casting Analytics leverages data to generate insights on the impact of key talent on a movie’s box office performance.

Production companies can indeed gain advantages from the Talent and Casting Analytics solution in the following ways:

  • Provides casting suggestions based on historical roles and in the actor’s portfolio
  • Use the cast as a variable to determine the film’s performance at the box office
  • Rank and simulate talent options based on their economic impact across the film industry like media type, genre, and key territories

AI-powered box office predictor system

The sheer number of filmmakers has grown over the years, and many are challenging each other at the box office, which may be a treat for the viewers, but as a business, production houses can end up with losses.

At the end of the day, the commercial success of a film is just as crucial, if not more than its critical acclaim. If all the above solutions are the factors of the success equation of a movie, then an AI-powered box office predictor system is the main act.

With this solution, production houses, independent filmmakers, and distributors can predict the movie’s box office performance up to 6 months in advance. The plethora of business opportunities this solution provides is immensely insightful and can help film businesses make valuable decisions.

With the Affine’s solution, you can leverage the following:

  • Predict film revenue at the box office well in advance with the highest accuracy rate
  • Decision makers take steps for ROI (Return on Investment) improvement
  • Forecast the promotional/marketing effort required per box office performance across regions, genres, and many other factors

The film industry will sustain AI behind the scenes

Films are not going anywhere, irrespective of the competitors. But the post-pandemic era comes with many changes due to multiple factors, ranging from content consumption behavior to global inflation.

People worldwide are in a price-sensitive phase, which brings the need for film production companies to improvise the game-plan. With the Film industry-specific AI practices, they stand to benefit from box office success and an efficient production, casting, and marketing process, contributing to the overall ROI.

What does Affine bring to the table?

Affine is a pioneer and a veteran in the data analytics industry and has worked with giants like Warner Bros Theatricals, Zee 5, Disney Studios, Sony, Epic, and many other marquee organizations. From game analytics to media and entertainment, Affine has been instrumental in the success stories of many Fortune 500 global organizations; and is an expert in personalization science with its prowess in AI & ML.

Learn more about how Affine can revamp your film production business!

What Is Modern Data Platform? Why Do Enterprises Need It?

Affine’ s Analytics Engineering Practices is kicking off a new series on “All You Need to Know About Modern Data Platforms.” Read the first episode of the series here.

What is a Modern Data Platform?

Legacy systems are outdated. Your business needs a modern data platform!

But first, what is a modern data platform? It is a future-proof modern data architecture focused on delivering high-quality business analytics for your ever-growing business undergoing transformation. It combines modern data warehousing, AI and ML, and real-time data ingesting and processing. Modern data platforms for enterprises are agile with workloads and yield rapid value from your data.

In short, modern data platforms deliver what organizations need to become data-driven and deliver value to their ever-changing customers. They help organizations become modern and future-proof. Modern data platforms process massive volumes of data and make data-led insights available at the click – of a button. They have a scalable storage system that ingests unprecedented volumes of data and meets the demands of the business in delivering customer insights and making efficient decisions. Statistics-driven analysis, efficient data processing, reliable prediction, and low-latency information delivery are other benchmarks for an efficient modern data platform.

Why do enterprises need modern data platforms?

Your customer is changing. They demand – always-on, always-connected service and settle for nothing less than speed, agility, and reliability. They want hyper-personalized experiences without compromising on their current SLAs.

Your business is changing as a result of the data-driven environment transforming businesses across industries. Organizations must embrace an insight-driven approach to meet the changing customer demands. It would be best to have a solution that gives you easy access to insights, especially one that provides swift ROI from technological and marketing interventions by breaking down data silos in your business.

Modern Data Platform accelerates the journey to data-centricity with the following features:

How to build your own or buy a Modern Data Platform?

It depends on your requirement and budget.

Needless to say, the cost of building an in-house modern data platform could be an expensive affair. In order to accelerate the data-centricity journey, businesses rely heavily on purchased platforms. For example, modern data platforms of the big three cloud providers equip their customers with a wide range of data analytics tools. It empowers them with the capability to analyze vast volumes of customer, business, and transactional data quickly, securely, and at a low cost. Companies must soon assess their data analytics capabilities and chart a course for transformation to a data-driven enterprise. Given the rapidly changing nature of technology and the marketplace, becoming more responsive to customers and market opportunities and greater agility is crucial.

Do you have a strategy for the Modern Data Platform?

Adopting a modern data analytics platform for enterprises will change everything—how your organization makes everyday business decisions.

Businesses need a cultural change to make the most of the modern data platform opportunity. They must reform and reinvent strategies and redesign operating models. Organizational structures need to be amended, roles need to be redefined, and resources need to be upskilled. Realigning existing data models for a better result and strategizing performance tracking to understand the core capabilities are also critical. Most importantly, it would be necessary to upskill your technical and business teams to make the most of the modern data platform and leverage it in everyday decision-making. 

Investing in a modern data platform for business is no mean task. You need a technology partner who understands the space holistically and can deliver quick time to value. A partner like Affine’s Analytics Engineering Practices. Are you ready to begin your journey to true data centricity? We are here to help. Schedule a call today!

This is the first in a series of blogs that depicts “All That You Need to Know About Modern Data Platform”. The next blog will outline Modern Data Platforms vs. Legacy Systems.

The Future of Pay-TV in the age of streaming

Technology is transformative in its revolution across our industries. In the early 2000s, top executives of Netflix met the top brass of Blockbuster, which was then a $6 billion behemoth in the entertainment industry. Netflix had a meager 300 subscribers and a loss running close to 8 figures. They were looking to be acquired by Blockbuster for a measly $50 million. Blockbuster execs felt the asking price was too high and rejected the deal.

The rest is history. Netflix emerged as the glorious gladiator in the streaming domain, leading the pack, innovating content consumption, and revolutionizing the home entertainment business like no other. According to Statista, as of 2022, Netflix was leading the OTT race with a whopping paying global subscriber count of over a 221million!

Netflix Subscribers over the years

Source: Statista

As with any industrial revolution, something had to feel the adverse effect of this global expansion, and in this case – cable TV.

How cable TV fell from grace

The cable TV industry was undisputed in the early to mid-2010s, with more than 105 million U.S. TV households as subscribers. They had set up shop and dominated the majority of homes as a reckonable home entertainment force.

Reaching a peak in 2012-13, cable networks saw an astronomical amount of ad commitments, and cable TV became a staple in every household. There was an exceptional revenue flow to the parent companies of network providers then, but the cable network is currently a shadow of its former self.

1.8 million Subscribers canceled their Pay-TV subscriptions in 2020. Such was the impact of streaming services and the revolution brought upon them. Simply put, streaming is the decisive force that has powered cord-cutting.

Streaming services provide users with absolute control over viewing options and content diversity, something Pay-TV has always lacked. Pay-TV also had the added deadweight of multiple immersion-breaking ads that drastically impacted viewer engagement even with good program content.

According to Statista, Pay-TV subscribers had fallen to 83 million users as of 2020; and a declining trend is forecasted for the future. But it isn’t a home run for the streaming services either; they are just arriving at the tip of the iceberg as new challenges emerge.

Two’s a company; three’s a crowd

Netflix has revolutionized the home entertainment industry. It has been the leader in streaming services despite the competition; and has instrumentally driven a behavioral change in people’s content consumption habits.

The pandemic was an unpredictable event that turned profitable for streaming services as people started banking on OTT platforms for content consumption, thanks to the worldwide lockdown. The additional factors of access to high-speed internet and a device-agnostic approach to content consumption only added to the boost for the accelerated growth.

Reasons why people still have a Cable TV subscription

Source: Statista

An ace card Pay-TV usually had was sports content. Rights to sports content fetched a fortune for these organizations, and the pandemic brought these events to a screeching halt, accelerating cord-cutting. Even though the pace has picked post-pandemic, sports is one of the biggest reasons Cable-TV subscriptions make sense for many people.

Netflix’s initial USP was its unique strategy for acquiring publishing rights for shows and movies. By paying more upfront, it was able to build a content-rich streaming library.

Players like Disney and HBO built their streaming platforms and have gotten back many of their titles from Netflix, landing a devastating blow. However, Netflix had foresight about this and had started producing original content with shows like BoJack Horseman, House of Cards, and Stranger Things doing wonders for the company.

But that wasn’t enough anymore.

On top of that, the players in the streaming market are leveling up their game, producing quality content to woo viewers. HBO Max and Apple TV have performed excellently, thanks to this strategy.

Currently, there is a considerable challenge for subscription services. Viewers are now at a crossroads as multiple streaming services own the rights to a plethora of content, and respective subscriptions are necessary to access the content.

Source: Bloomberg

The market is overcrowded, and everyone wants a piece of the streaming pie. Netflix has lost over $50 billion of its market cap, and its panicked investors resorted to selling their shares. On top of it, Netflix laid off many employees recently.

The combined impact of all these factors is so severe that Netflix plans to introduce a ‘basic’ account option with ads to attract more viewers and handle price-sensitive geographies. Amazon prime has already rolled out this model, and the other players may have to succumb to this trend not too far away in the future.

What’s the future for Pay-TV and streaming services?

The problem with Pay-TV isn’t just streaming services; it’s the business model. Not having the freedom of choice in terms of content and watching through many ads despite paying a monthly subscription fee were the factors for the downfall of Pay-TV. But all hope is not lost for the cable businesses.

The biggest weapon they always had was sports content. Other than that, players like Paramount, Comcast, and Viacom are already in the business of streaming, so it’s not exactly a battle to the throne. The truth is that viewer preferences and behavior have changed drastically over the years. Pay-TV needs to stand out and offer an incentive to attract users.

Pay-TV businesses can take a page out of the streaming model playbook and implement it for a taste of success. Players like Amazon are already running ads on streaming services, and Netflix plans to do it eventually with its basic account. So, running ads isn’t the issue here; the irrelevancy and bombardment of ads is, however, a critical issue. The solution – relevant, crisp ads placed in appropriate slots and personalized to suit the viewer.

Pay-TV businesses also need to stop bundling and let viewers build a package. In short, personalization is the key for both ads and content. Nobody wants to pay for a pre-built bundle of channels just to watch a couple of relevant shows, movies, and content while unnecessarily paying for content they don’t consume.

AI & ML are the elixirs for a sustainable future for Pay-TV

One of the biggest reasons for the success of streaming services was the drastic evolution of the internet and its infrastructure. Data thus became a vital bloodline for every business, and the personalization game was accessible to the streaming services thanks to their online business model.

Pay-TV always had to rely on viewership ratings and TRP from rating agencies, and there was always a time and logistical delay in obtaining data. Now that all the players have a hand in the streaming game, accessing data is not a pipe dream anymore, but wisely leveraging it is not a simple task either. Insights are only valuable when actionable output is obtainable from them.

Ads relevant to particular users, strategically placed within the content, can be a game-changer for businesses. Data analytics and intelligent algorithms are essential in performing real-time monitoring and decision-making..

The plethora of data from user profiles and viewership data powered by the right AI solution can turn fortunes around for Pay-TV to bounce back into the home entertainment business and sustain itself in the long run.

Pay-TV businesses are now assisting the streaming services by bundling several streaming options with the cable package and integrating the viewership experience across cable and streaming TV set-up boxes. With this, there isn’t a head-on clash but a prospect to co-exist and sustain for both.

For broadcast services, a marriage between Pay-TV and streaming services opens the door for valuable data that can be leveraged to offer exciting packages customized to user preferences while overcoming the cord-cutting challenge.

Can AI solutions benefit the home entertainment domain?

Pay-TV players not only need to have a quality content library but also the tools to deliver it to viewers. Relevancy is imperative, and content recommendation is the ace that wins the game. A solution called the Live TV Recommendation System provides recommendations for new programs using a content-based algorithm that finds similarities, overcoming the cold start problem. It also leverages watching habits to provide timeslot-appropriate TV recommendations to viewers.

Ad consumption has become a tricky affair. Gone are the days of long ads between programs and high-demand segments. Viewers don’t put up with ads as much as they used to. Optimal Ad Space Recommendation is a solution that considers viewer demography, preferences, and other crucial factors to analyze viewer behavior. This helps choose the best ad to display amongst your ad inventory for maximum ad engagement.

Many such solutions tailored for Pay-TV, Broadcast, and OTT platforms can accelerate their performances while efficiently achieving organizational goals. Affine has an arsenal of such solutions across multiple verticals and a track record of working with major clients across the industry.

What does Affine bring to the table?

Affine is a pioneer and a veteran in the data analytics industry and has worked with giants like Warner Bros Theatricals, Zee 5, Disney Studios, Sony, Epic, and many other marquee organizations. From game analytics to media and entertainment, Affine has been instrumental in the success stories of many Fortune 500 global organizations, and has mastered personalization science with its prowess in AI & ML.

Learn more about how Affine can revamp your media-entertainment business!


Statista, DetroitNews, Bloomberg, Forbes, Srtreamingmedia, CNBC, Broadbandtvnews

How to Ace Cross-Cloud Migration?

Enterprises moving to the cloud from on-prem is an enormous effort that can span years together. And, once the migration to the cloud is complete, an enterprise might decide to move entirely to a new cloud or parts of the current applications to a new cloud platform for cost, performance, or other reasons. This is a more tedious effort than on-prem to cloud migration. In this blog, we will discuss the cross-cloud migration checklist, workloads optimization, and key considerations that help businesses move from one cloud vendor to another successfully.

Business Purpose:

We need to clearly understand the pros and cons of the source cloud platform, which we expect the team to have as it is their incumbent environment. We must deeply analyze the target cloud platform as it should solve all the cons and have all the pros mentioned above, like performance, low cost, and powerful app features.

Recognize and include stakeholders:

Connect with core team members throughout your organization, including IT and business partners. Early commitment and backing will result in a smoother, quicker cloud migration process.

Evaluate the services of source platform:

  • Perform end-to-end analysis of all the services in source platforms, applications, data, etc.
    • Thorough analysis of the data sources, data flows, data models, and ETL processes
    • Detailed info on databases, tables, partitions/clustering/indexing, data dictionaries
  • Have a close working session with the incumbent developers and IT in the team 
  • Decide on KPIs to measure and report on (Duration, Delay, Disruption, Costs per service/bandwidth)
  • Categorize source data into Hot, Warm, and Cold

Analogous target platform Tools/Services

  • Identify the suitable services/service models (SaaS, PaaS, IaaS) in the target platform which can replace services in the source platform and perform as expected according to the business use cases
  • Implement a proof of concept (POC) to validate the approach for each service

Cost Estimates:

  • Analyze the egress and ingress charges from source to target environment
  • Identifying the overall cost of the source architecture at the service level from the billing dashboards that the source cloud vendor provides
  • Analyze the cost of the services to be billed on the target platform, and it should be comparatively the same or less than the source platform cost

Optimize Workloads for better Performance

  • Remodeling the Data Schema: Rearchitect the Data Schema to better fit with the new services in the target platform, based on its features and processes.
  • Removal of Duplicate Data: Delete the redundant/duplicate data after detailed cross-checking with the business while migration can improve performance.
  • Conversion of SQL: Convert the complex SQLs to simple ones for ease of maintenance and performance. 
  • Revalidation: Re-validate all optimization techniques to ensure better performance as this migration effort is an opportunity to reduce technical debts and address performance. 
  • Rearchitecting the Application: Re-architecting the applications to be functional in the target cloud platform could result in less resource utilization and better performance.
  • Lift and Shift
    • As various cloud vendors follow different approaches to data storing and data accessibility/retrieval, we need to deep dive into their techniques and rearrange our data accordingly
    • 90% of the cross-cloud migration projects will not adhere to Lift and Shift and might need an entire re-architecture
  • Huge data Migrations
    • We need to watch out for the data pulling from source cloud vendors as they charge egress costs and bandwidth costs. Try to compress the data and make it as small as possible, then plan out the migration
  • Long Dependency on an old Cloud provider
    • If we move to a new cloud vendor, try to cut off using services from the old cloud vendors. if not, we will end up paying for both the cloud providers
  • Serverless option for Performance-based processes
    • For services that depend more on performance and readiness, plan wisely to choose between the dedicated pool and serverless options. You might see a 2x to 2.5x performance difference for massive data sets (Terabytes)
    • Observe the performance differences between the two different cloud providers. It may be extremely diverse, and since migration costs are associated, this will become a bottleneck if not planned for in advance
  • Data Cleansing
    • Clean the data as much as possible before getting into the migration process. It will help shrink the record count/size of the data, which is directly proportional to lower migration costs.
  • Pilot run 
    • Before migrating complete data, performing a proof of concept (POC) with a sample of data is essential. This approach will give you a clarity of thought about the process execution and the plan
  • Parallel loads
    • During the migration planning phase, make a checklist of items that need to run in batches and in real-time; prepare the data accordingly. If it is not well planned, the entire system performance might have to be addressed during the later phases, which might include a re-architecture of specific non-performant items
  • Automated testing
    • The absence of automated source-target validation would result in a larger chunk of time taken only for validations or would result in cutting corners for this activity. Loaded data needs to be validated and reconciled with the source system in an automated manner. 
  • Capacity planning
    • Data migration could be a resource-intensive operation and may require capacity planning. It’s always wise to plan the size of CPU, memory, storage, hardware, tools and have them readily available before the beginning of migration so that the migration effort can be successful.
  • Pipeline overload
    • Too many ETL activities result in blockages in debugging and dependent/ waiting in deadlock for previous actions. Try to have pipelines in smaller and granular pipes as much as possible. It will be easy to maintain, debug, upgrade, and replace

Summing Up!

Any cross-cloud migration should be considered an extensive effort that might start as an easy lift and shift and potentially change into an extensive migration and rearchitect effort. There is expertise required in both target and source cloud platforms to ensure no loss of functionality, eliminate tech debts, and to improve performance. This is something that Affine can help you with as we have extensive experience in migrating between all three big-cloud providers. Re-define your business goals by leveraging our Cloud Practices. Schedule a call today and talk to our cloud experts.

Price Elasticity: How vulnerable is your product in the market?

Product Pricing

What elements do we consider when selecting a product’s price? Is it entirely dependent on customer demand? Firms & marketers are constantly striving to unfold the relationship between sales demand and price fluctuation to find a pricing point that is best for their products. Let us look at price elasticity to reveal the facts behind this relationship.

What is Price Elasticity?

Price elasticity of demand (PED) is an economic measure representing the responsiveness, or elasticity, of the quantity demanded to the change in the price of a product or service. To simplify, it is the ratio of percentage change in quantity demanded of a product in response to the percent change in its price.

Most markets are sensitive to the price of a product or service, the cheaper the product higher the demand, and vice versa. While this need not be true for all products and services, price elasticity as a quantifiable phenomenon shows precisely how sensitive customer demand can be for a product price. For starters, let us look at questions professionals in marketing try to answer when determining the elasticity of their products:

  • How much more can you sell by lowing the product price?
  • How will the rise in the price of one product affect sales of the other products?
  • How will a decrease in the market price of a product affect the volume of production & market supply?

What Is Price Elasticity of Demand & Supply?

Let’s look at Coca-Cola to try and understand these concepts. Assuming that a bottle of Coca-Cola regularly costs $1, if the price surges to $2, it will likely result in a dip in demand as most people would consider it expensive. On the other hand, if you drop its price to 10¢, you will notice a significant rise in its demand.

Price elasticity springs from the fundamental economic law of supply and demand:

  • Cheaper the product, the higher the demand
  • And the more expensive a product becomes, the lower the demand

How likely is Sales Demand to Change When Price Changes?

Price elasticities are usually negative; when our price decreases, our sales demand increases. It makes sense, doesn’t it? However, positive price elasticities are found in rare cases where products do not conform to the law of demand. 

Yes, these case scenarios happen when we have Veblen goods. Veblen goods are typically high-quality, exclusive, and a status symbol. Example – Louis Vuitton

What will the Price vs Consumer Demand Curve Look Like?

The chart above shows that a 33% increase in price point decreases consumer demand by a million, whereas demand doubles if we drop it by 50%.

Mathematical formula to calculate the price elasticity –

Price elasticity (E) is the percentage change of an economic outcome (which is generally the number of units sold) in response to a 1% change in its price:


% Change in Quantity Demanded = (New Quantity – Old Quantity)/Average Quantity,

% Change in Price (P) = (New Price – Old Price)/Average Price

For example, let’s say that a clothing company raised the price of a coat from $100 to $120, and the 20% increase in price caused a 10 % decrease in the quantity sold from 1,000 coats to 900 coats. Formulating these numbers gives you a price elasticity of demand which is 0.5, as mentioned below:

-.10 / -20 = -0.5 or 0.5

Types of Price Elasticity

  1. Perfectly elastic: Minimal change in price results in a substantial change in the quantity demanded
  2. Relatively elastic: Minor changes in price cause a tremendous change in quantity demanded (E > 1)
  3. Unit elastic: Any change in price is matched by an equal change in quantity (E =1)
  4. Relatively inelastic: Substantial change in price causes minor changes in demand (E < 1). Gasoline is a good example. As an essential commodity, demand stays relatively the same even with an increase in its price.
  5. Perfectly inelastic: Quantity demanded does not change even with a price change. There is no elasticity of demand or supply for these products. Perfect inelasticity happens with products or services where the consumers do not have any other substitute goods. For example, food, medication, etc.

Factors of Price Elasticity:

  1. Purchase probability: The likelihood of a customer purchasing a product from a particular category. For instance, in the case of beer, where there may be various brands with differing prices from the same product category, purchase probability determines how likely a customer will buy one brand instead of the other. Assuming we can compute the aggregate price of a category and according to the rule of demand, the higher the price, the lesser the demand, and the likelihood of purchasing beer decreases with the aggregate rise in its price. Calculating the price elasticity reveals this change in demand.
  2. Brand choice probability: Brand choice probability defines the customer’s choice. If you work for Oreo, you are more likely to be concerned with Oreo’s brand compared to the overall biscuit sales. That is why marketers focus on persuading clients to select their brand over their competitors. When the cost of a product from a brand increase, the chance of purchasing that brand decreases.
  3. Purchase quantity: Purchase quantity represents a customer’s expected purchase.

Analytical Model Implementation

We understand the business context of price elasticity and how important it is for every business to maintain a healthy financial sheet. The following operational question is how to solve / implement it. We have used Linear regression, either the Ordinary Least Square (OLS) or Recursive Least Squares (RLS) method, to predict quantity from the price change over time for any specific product.

Linear Regression Equation:


β is the beta coefficient (slope). Generally, beta is negative with respect to quantity sold, except for few exceptions where it can be positive.

Multivariate Linear Regression:

If additional supporting factors are directly linked with sales, we may utilize them in multivariate linear regression alongside the price variable. 

Price Elasticity Score:

To get the elasticity value of a product, we need to use equation #1 from above:

As price & unit have a linear relationship, beta (dy/dx) will remain constant. Hence, we can use the average value of price & units to get the elasticity score.

Business Application:

Once ready with the price elasticity model, the next step is to apply the business learning to reveal a product’s sensitivity in the market

The above chart shows that the Samsung-65 Class LED TV with a negative price elasticity of -17.68 will have 170.6% more demand with a 10% drop in its price or lose 170.6% of its sales demand with a 10% rise in price.

Whereas the Sony XBR-X850E-Series 75-Class TV with a positive price elasticity of 7.19 will notice a 71.2% drop in its sales demand with a 10% price reduction and a 71.2% boost in sales demand with a 10% increase in its price.

What Next?

New problems arise the moment you solve a business problem. For instance, what is next? Or is there a better approach? What can we do additionally to improve the implemented solution? The case is the same for price elasticity models. We see that changing the price of a product affects its sales. But what happens with a change in the price of a competitor’s products? The phenomenon that causes a shift in demand for one product from a change in the price of competing products is known as Cross-Price Elasticity. But more on that in the next blog!


What is AIOps? & Why do Businesses Fail to Take Full Advantage of AIOps Capabilities?

Building responsible technology has become a top priority for enterprises across industries as understanding and scrutiny of the challenges connected with AI have grown. Responsible AI evolves from a “best practice” to the “high-level principles” and guidelines required to drive system-level change, building trust around the business world. Regulators are taking notice, also advocating for regulatory frameworks surrounding AI that include enhanced data protection, governance, and reporting standards.

Can AIOps Help Your Business?

The current business environment across industries is changing rapidly, causing software and systems to grow more complicated. Cross-functional teams; are expected to generate solutions more quickly and efficiently in these circumstances. Site Reliability Engineering (SRE) and Network Operation Center (NOC) teams in the DevOps setup; are continuously flooded with a mountain of data. At the same time, these teams are increasingly under pressure to handle many dashboards, frequent alerts, and signals from multiple tools, making it harder to identify the root cause of problems/instances. AIOps as a solution; assists these teams in finding solutions to the problems more quickly and effectively, allowing them to focus more on creative work and the software development process.

Businesses are intrigued to start investing/buying an AIOps solution in order to transform their business. But factually, many business leaders and technologists fail to understand that success with AIOps lies within the right preparation and not just limited to the technology adoption.

Resilient. Agile. Ease. – “Shift Left” makes it hassle-free for SRE and NOC teams in DevOps setup

Enhancing the application development lifecycle is a potential use case for AIOps. Applications are constantly changing as a result of digital transformation, with new iterations frequently surfacing each day. A domain-agnostic solution can be “Shift Left” in the lifecycle to provide observability and control to Site Reliability Engineering (SRE) and Network Operations Center (NOC) teams in DevOps set up that were previously available to ITOM and ITSM teams.

Why are businesses failing to take full advantage of AIOps capabilities?

Before discussing the bottlenecks of businesses implementing AIOps, let’s understand AIOps in a nutshell…! 

What is AIOps?

– According to Gartner, “AIOps combines Big Data and Machine Learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination.”

It defines artificial intelligence in IT operations and workflow. It refers to the strategic application of AI, Machine Learning (ML), and Machine Reasoning (MR) technologies across IT operations to simplify, streamline, and optimize the use of IT resources.

In a nutshell – AIOps is designed to address the growing challenges of complex IT infrastructures and help organizations make the transition to a better, more efficient future. AIOps can be used to improve IT productivity and reduce costs, automate repetitive tasks, create a more predictive environment by collecting data from multiple sources and analyzing it using ML/MR technologies, reduce time-to-market for new innovations, etc.

Supporting this fact are the other key findings that say –

  • 53% of AI adopters mentioned “lack of transparency” as one of their significant bottlenecks
  • 54% of respondents expressed concerns about making bad decisions based on AI recommendations
  • 55% of respondents worried about the liability for decisions and actions taken by AI systems

And the reason they fail to implement or take full advantage of AIOps!

Challenges: Top 5 Reasons why AIOps implantation Could Fail 

  1. Incompatibility with existing tools: Is it feasible for your software systems to communicate data in an efficient way? In order to provide valuable insights, an AIOps-based analytics platform will need data from other software systems. The failure of your AIOps transformation could be due to interoperability with existing software platforms. 

    If current software systems prevent you from collaborating with other products or systems, it’s time to think about an IT transformation. Let’s assume you are using a digital service desk AI software to generate the tickets, and your current legacy tools fail to process the request. It will land you in trouble. Thus, make sure that the tickets generated by the services desk are forwarded to your legacy tools for analysis. If your existing devices are compatible with an AIOps based analytics platform, it will automatically process the events/instances from the service desk and generate actionable insights.
  2. Lack of awareness to identify problem areas: You aren’t undergoing AIOps transformation simply to implement cutting-edge technology. The primary objective of employing AI in operations management services is to boost the productivity of your IT operations. Apart from keeping up with the current AI trends, it would help to concentrate on the areas where an AIOps transformation is required. 

    Some of your IT operations may be efficient enough that you don’t need the help of an AIOps-based analytics platform. Adopting AIOps can be costly; thus, it’s best to identify the major bottlenecks that reduce the ROI (Return on Investment). Even the best AIOps tools and products have specific use cases and cannot assist you with a problem right away.
  3. Outdated strategies to train data: In order to achieve scalable results, you need to feed the AIOps-based analytics platform training data. Data serves as fuel for AI/ML algorithms, allowing them to learn about IT processes. Organizations fail to provide training data to AIOps-based analytics platforms, resulting in AIOps transformation failure. 

    Even matured businesses fail to provide AI/ML algorithms with sufficient training data to improve their performance. Your AIOps-based analytics platform will not deliver relevant insights if your training data is cluttered and contains many outliers. The organization data is constantly fragmented across many software platforms and is unstructured. AIOps-based analytics tools cannot perform to their full potential without a comprehensive view of the organization’s data.
  4. Lack of performance metrics understanding: How would you know if something is wrong with your AIOps transformation? One option is to wait and see how the attempted AIOps transformation impacts your ROI. Another method for determining the value of AIOps adoption is to use performance metrics. If you discover inefficient AI DevOps platform management services in a timely manner, you may be able to do transition to an alternative transformation plan. The following are some of the KPIs that can help in measuring the impact of AIOps transformation:
    • MTTD (Mean Time to Detect): It refers to the amount of time spent investigating an IT incident. The MTTD should decrease if AI is used for application monitoring.
    • MTTR (Mean Time to Detect): It signifies the amount of time it takes to resolve an IT issue. The use of AI in operations management services should always result in a considerable reduction in MTTR.
    • Service availability: AIOps systems will constantly improve the availability and reliability of your services. If the availability of your services isn’t improving, it’s time to modify your AIOps strategy.
  5. Reluctant or inability to adapt to the changing IT culture: You need to understand that AIOps adoption is not just limited to technological changes; it will dramatically change your IT culture. For instance, it would be difficult for your cross-functional teams to trust the decisions suggested by AI data analytics monitoring tools. So, understanding its capabilities and how it works is crucial for the teams involved in the process. You need to conduct the workshops and training sessions to educate them in a timely manner. In this regard, reskilling and upskilling the respective teams could give you faster results denoting cultural shift the way teams think and work for an organizational goal. On the other hand, you can leverage open-source AI/ML tools that can be tailored to match your current IT culture.

How to overcome challenges in AIOps implementation?

  • Observe: Get started with real-time big data processing by identifying and collecting incidents/logs/alerts/request raised/event history from all underlying systems (application, network, infrastructure, etc.). 
  • Think: Machine learning/deep learning techniques can be used to enable AI-based insights and recommendations. Start with AI-based insights, such as noise reduction through event de-duplication and grouping and real-time anomaly detection. Event correlation for causal analysis, automated RCA, application failure prediction, and change impact analysis are some of the more advanced use cases you can consider in the process.
  • React: RPA, ITPA, scripts, and orchestrators can be used to initiate auto-heal/self-remediation operations. You need to have an experienced team or train them to manage the workflows using these methodologies.
  • Learn: Allow AI-based learning to learn from previous incidents from both successful/failed attempts made in the process and use it to predict future outcomes to plan your next attempt. This approach is paramount to understanding your challenges in every possible way and learning how to overcome them strategically.

AIOps Market Report & Recommendations 

One of the critical factors driving market growth of AIOps is the growing demand for automation across industry verticals, such as banking, financial services, BFSI, healthcare, automation, IT, and logistics.

Businesses use AIOps to prevent and control security breaches by monitoring the activities and transactions of employees, customers, and external agencies. In accordance with this, the Covid-19 influence has resulted in widespread enterprise adoption of the work-from-home (WFH) trend, which is considerably contributing to market growth. When working remotely, AIOps is applied to improve information security and many other operational workflows effectively.

Various technological breakthroughs in cloud-computing solutions are also contributing to the AIOps growth. Businesses across industries are using cloud-based solutions for major business applications, performance, network, and security management that are gaining popularity at pace. AIOps also contributes to improving the overall efficiency of the infrastructure for service delivery with less manual efforts. In other words, considering the relentless improvements in the IT infrastructure, specifically in boosting the economies with extensive research and R&D initiatives, are expected to propel the market growth.

“There is no future of IT operations that does not include AIOps. This is due to the rapid growth in data volumes and pace of change (exemplified by rate of application delivery and event-driven business models) that cannot wait on humans to derive insights.”

~ Gartner

AIOps Market Size, Share and Trends – Industry Forecast 2022 to 2027

“The global AIOps market is expected to exhibit a CAGR of 21.2% during 2022-2027”

The past and current aftermath COVID-19 outbreak has given the AIOps market a significant boost. As AI-based IT operating solutions become more common, the industry has accelerated significantly, allowing businesses to reduce workflow and resource effort; allowing to focus more on innovation. During the pandemic, AIOps provided various benefits to businesses, including automating monotonous processes, delivering meaningful reporting, and improving risk management. As a result of these efforts, the industry’s post-COVID-19 repercussions have increased drastically.

 AIOps Market Size, Share and Trends

What are the AIOps trends in the U.S, Singapore & Germany? Where its market stands in these regions? 

Domain-agnostic tools is the next big thing in the U.S region 

In the United States, the domain-agnostic segment is expected to produce roughly USD 500 million in revenue by 2023. These solutions primarily rely on monitoring tools to collect data and respond to changing customer prerequisites. AIOps platforms are being used by businesses across the U.S to compete with and replace several traditional monitoring tools in practice. IaaS and observability monitoring is being done comprehensively within AIOps platforms, specifically if the company’s whole IT infrastructure is in the cloud.

AIOps trends and Market size in the U.S

AI-based solutions to assist DevOps capabilities is gaining popularity in Singapore

The SME segment in Singapore is expected to grow at a 30% CAGR through 2027. It has been observed that SMEs are increasingly turning to AI-based IT operations tools to improve service quality and respond to changing customer needs in a more flexible manner. In contrast to this observation, below graph illustrates the enterprises vs. SMEs AIOps scenario in 2020 and its growth prediction in 2027. 

Enterprises vs. SMEs – AIOps trends and Market size in Singapore

Real-time analytics and Infrastructure management are the driving force in Germany 

In 2020, the real-time analytics segment in Germany amassed 35% market share followed by other segments such as application performance management, network and security management, and infrastructure management. Since then, AI is increasingly being used by businesses around the country to make insightful decisions rather than depending on human interventions, and to empower IT teams to take immediate action. The below graph denotes, by the year 2027 real-time analytics and infrastructure management sharing the equal market share.

AIOps trends, applications and market size in the U.S

BFSI sector is expected to boost the AIOps market growth

The rapid growth and requirement of AIOps applications in the BFSI sector is one of the primary factors for the global AIOps market’s rise. As part of banking operations, employees, clients, and external agencies engage in a variety of regular and irregular activities and transactions. These operations must be closely monitored due to their complexity and confrontations. Considering these developments, AIOps is expected to boost market growth in 2022 and ahead while offering wide verity of applications to monitor real-time data and automated issue resolution.

Top 4 considerations for I&O leaders for successful AIOps implementation 

I&O leaders must help their organizations become more agile and responsive to the needs of business units. The right operations services, such as monitoring and metrics, can help you continuously improve your systems’ performance and responsiveness. It should start with focusing on the development of a “change engine” within the organization, using automation to reduce manual tasks, errors and inconsistencies and leveraging technology to meet compliance requirements. Thus, these I&O leaders should focus on:

  • Invest and use an incremental strategy to replace rule-based event analytics and expand into domain-centric workflows like application and network diagnostics, putting practical outcomes ahead of ambitious goals
  • Allow the use case to define whether AIOps should be domain-centric or domain-agnostic. To start with use specialized use case, leverage domain-centric AIOps features built into a monitoring tool, and deploy a domain-agnostic stand-alone solution with a roadmap encompassing multiple use cases
  • You need to select an AIOps platform that offers bidirectional interaction with ITSM tools to enable task automation, knowledge management, and change analysis. Ensure you’re not choosing the tools that are limited to basic search-and-display functionality
  • Enable continuous insights across ITOM by following the methodology of observe, think, react and learn which is discussed in the earlier section of this article

Summing Up!

AIOps will result in a sweeping change in how AI-driven IT operations are managed. Even though the primary objective for AIOps adoption is to address operational issues arising from a highly complicated IT ecosystem and keep up with a fast-paced business environment, the ability of humans to understand and trust its insights, decisions, recommendations, and predictions will be extremely crucial.

Copyright © 2024 Affine All Rights Reserved

Manas Agrawal

CEO & Co-Founder

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