"Content Smart Management" to "Smart Content Management" for Media and Information Services

Content is King in Media, and the question continues to come up "How to make Content smarter and monetize?"

Content is core to Media & Entertainment and that's what it monetizes, hence this question has been coming up again and again. We, at TCS Media & Entertainment Practice, have spent last 2 decades, maturing our content capabilities and our M&E offerings. We have productized Smart Content Management Backbone (SCMB) as our Patented IP 15 years back, and happy to see it is still as relevant and get mentions in analyst’s report. [ Everest Product Engineering Services Report -?https://www.tcs.com/who-we-are/newsroom/press-release/tcs-positioned-leader-software-product-engineering-services-everest-group]. As I look back, at what the team has done, I wanted to pen down the memory lane to today's evolution of Smart[er] Content Management.

1. The Era of Content Agility [ 2005 - 2014]: Automation and Smarter Workflows

At the core, our concept of Smart Content started with how to make content agile (and not stored in dark stores) - how to make content flexible/granular/ tagged/linked and enriching it, thereby making it repurpose-able and easily monetize-able. Earliest version of our Smart Content Platform (SCMB), started with a digital publishing platform for media publishers with capabilities to automate the media workflow from content ingestion to distribution. With majority of content in unstructured format and contextual knowledge with SMEs, the challenge was how to make enrichment process less manual and store it as granular pieces preserving the relationship. The platform found use cases with M&E customers on Flexible Content Management, Digital Asset Management, Editorial Workflows, Learning Management Solutions, Publishing Media Authoring system with ability to quickly repurpose content and federated Content Discovery with recommendations. Our Platform approach with service-oriented architecture with NoSQL/Document db, taxonomy model, Word like editorial interface enabled us to quickly take in whole or parts of platform to build solutions for different segments of media customers.

2. Enter the Age of Smart Answers [ 2015 - 2020 ]: Focus on Monetization with Context, Content, Connectivity

Smart Content by then had metamorphosed into Question Answering Machine. Ask a question in natural language and the platform has to provide the relevant answer. We all became Googlefied, and discussions centered on how content can be monetized easily and how to engage the audience/customers. Some media and information services customers started branding themselves as industry centric intelligent answer companies. Explosion of mobile devices and connectivity provided the opportunity to reimagine workflows and provide avenues of content monetization that earlier was not available. This is also the digital economy where all forms of unstructured content, especially videos started to grow exponentially. Cloud provided scale and quick innovation capabilities to experiment with new forms of content monetization quickly. Platforms started to use AI models, and mine content using NLP for cognitive enrichment. Content was stored in graphDB and GraphQL to quickly provide answers and insights. Not just search, but training models with large curated data-sets to bring context and relevancy in answers.

TCS SCMB morphed with rejuvenated technology - now on cloud and exploiting the capabilities of machine enrichments like entity extractions, computer vision and ontology driven discovery. Personalization and recommendation algorithms were married with customer journeys to allow for contextual relevance. Now in addition earlier use cases, SCMB found popularity in extreme workflow automation consolidating media silos and content monetization with newer distribution models. Prebuilt workflows with templates-models were added for financial information extraction (for financial information services), legal editorial cognitive workflow (human-machine collaboration), Summarization of articles/news, Sentiment/emotion analysis, Video and Image auto enrichments. We started to work on NLG (Natural Language Generation) to be able to copywrite content for marketing/websites ( or at least provide a good draft ). Our product team structure now had lot more Cloud techies, ML engineers and data scientists, and engineers relied on newer AI services available in public cloud, semantic contextual search, fault tolerance and performance.

3. Age of Man-Machine Co-pilot [ 2021 -?]: Reimagine our world with Co-pilot Content Creation and Creativity??

Pandemic firstly, moved us almost 100% home and forced us to reimagine our life and business - fully digital, video conferences all the time, OTT consumption instead of theatres and work-life meaningfully rebalanced (including hours!). A completely digital universe of Metaverse is unfolding and World of Generative AI is now available to think about content very differently. General purpose AI models are now capable of multi-dimensional analysis which provides us with ability to do man-machine collaboration in a completely transformed way. There is lot more data and content generated in every interaction and hence data management at scale has become more important.

ChatGPT has generated so much interest lately, now every business is exploring newer use cases of Generative AI in M&E. We are also working with our customers to advise and explore cases they should invest. As we started evolving our SCMB to new generation, we are advancing on the AI models we had build and taking advantage of GPT-* LLMs from Microsoft, Google Bard/VertexAI and AWS services. We also find that our customers are building their version of GPT based on OpenAI, so we are creating maps to execute right models. Interesting new use cases are coming up in Synthetic Data Creation, Advertising workflows, Content for marketing,?thumbnail clips, content production, highlights automation in sports clips and so forth. News workflow will have to facilitate the new of of man-machine orchestration and we will likely see a new generation of co-pilots (not workflows).

As we build our SCMB - Smart Content Platform, we had to choose tools and technologies that will evolve with time. I am happy to see that the architecture and most of the technologies we chose for SCMB have lasted to this far. While cloud movement and leveraging AI services turned out to be right track, MongoDB was another good choice. From the days of using MongoDB as Document Db to leveraging them for content analytics/data platform and powerful digital solution enabler. As we go into the Generative AI era, I am expecting cloud providers will rapidly roll out Gen AI services, and MongoDb newer services like Vector Search/Stream Processing to reimagine content process innovatively with SCMB. While AI bias, ethics are big themes which needs to be resolved, I am excited about the new beginning of the world of Smart(er) Content Management.?

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