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Legacy systems are outdated.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.
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!
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.
Assume you’re in the real estate business or own agricultural land and want to discover every detail about a bit of land/location sitting a thousand miles away. Doesn’t that sound a little too intricate? Well, Telescope helps you to do it in a snap!
What is Telescope?
It’s a next-generation AI satellite image segmentation solution capable of resolving complex business and significant operational requirements. Telescope uses a machine learning framework to classify information in a digital image (i.e., buildings, roads, grasslands). It then generates output segmentation data, which can be utilized for diverse business purposes such as pattern identification and object tracking.
Telescope emphasizes the concept of leveraging AI and has established a software package that utilizes cloud services to allow you to extract valuable data pertaining to your business. This platform lets users perform real-time image analysis on high-resolution satellite images and view adjacent locations with the accurate coverage percentage of greenery, land, buildings, and water bodies.
Telescope uses a cutting-edge combination of Computer Vision and GIS technologies that allows to automatically retrieve high-resolution satellite images of sites with up to 100 square km of magnification
It enables single or multiple pairs of lat-long parameters or location names in various formats
Uses a Deep Learning Feature Pyramid Network (FPN) model with a point rend module for precisely predicting the label maps
It will allow you to assess variations in water bodies or land shapes like dams, rivers, deserts, and mountains
Telescope provides a broad range of real-world applications such as monitoring deforestation, urbanization, traffic recognition of natural resources, urban planning, etc. This novel technology can also be used in critical missions for the uniformed forces and in monitoring catastrophic catastrophes like volcanic eruptions, wildfires, and floods, among other things. With an intuitive interface and a substantial reduction in manual effort, it automates land surveys and minimizes the requirement for data collection associated with mapping and construction operations.
In other words, this solution enables faster and more accurate data processing, reducing time and cost, and is very handy for professionals. Furthermore, the solution’s atomized approach yields quicker and more accurate outcomes, with much less reliance on third-party data sources.
What is More Exciting About Telescope?
Its segmentation technique automates the process of extracting structures like land, lake, etc., from satellite images without the requirement of any advanced skills in Computer Vision or geospatial data
It has been built using advanced image processing algorithms, offering the most accurate results and seamless integration with real-time API services
The solution can be readily incorporated into current corporate GIS systems or used as a standalone solution to handle geographic data
Exports multiple high-resolution images leveraging Google Maps as a default source for satellite images
Highly interactive dashboard for reporting and visualization
Highly accurate percentage-wise representation of the areas covered in various categories
Save your planning time up to 70% and costs up to 30%
A Bigger Picture Beyond the Flaws
Whether it’s a mission for any uniformed forces, monitoring catastrophic events, feasibility analysis for mobile tower setup, or planning a smart city, Telescope is a one-in-all solution for your business need. Request a demo to learn how to leverage this solution for your business need. Be ahead of the technology crux!
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