By Shammy Narayanan
Imagine a familiar scenario wherein you had booked tickets to take your spouse for the night show of the latest movie, and when you are about to log off, a meeting request devilishly pops up from your super boss. Your well-laid plans are thwarted, and you also have to face the music at home (most of the time, you will end up with additional gifts to placate …don’t get me going on this!). Now reimagining the same scenario, the moment you accept the late invite, assume your data management system is smart enough to understand the conflict; it acknowledges the movie plan has to be cancelled. It initiates/prescribes corrective action such as accessing your Ticket booking app and rebooking the tickets for a day/time convenient for your family (by syncing their calendars), triggering an automated message to your spouse explaining the unavoidable delay and passing the glad news on rescheduled tickets (also ordering set of fresh flowers from your nearest boutique). Life will be far better in the reimagined scenario, with less pain and an actionable plan; that’s Data Fabric for you.
Data Fabric is an architecture that accesses disparate data sources (in this case, your calendar, booking app, messaging system, etc.), understands the actors/nodes (you and your spouse), relationship between the actors (in this example, it’s family) and models the system to minimize the impact from any unplanned/unexpected event. Let’s apply this concept to a business scenario, visualize the effect of what this architecture can save on the factory floor when it understands the delay from a supplier on the critical parts, besides alerting management on the possible and potential impact it can prescribe the best course of alternatives with the revised supply schedule. That’s True Analytics in action. From detecting Fraudulent claims, Identifying Money laundering trails, and Connected cars (Jaguar Land Rover), DataFabric is becoming the single word response to the enigmatic queries of the future.
DataFabric is not an entirely new concept, but it integrates several building blocks which were part of the Data ecosystem. MetaData catalogs, Knowledge Graphs (Netflix, FB, and Google had been extensively embracing this technique), Data Governance, and Data Integration pipelines have been in existence for a while. Similar to Lego Block, Data Fabric brings all these elements together under a common architectural umbrella. Once the foundation is established, DataFabric attempts to understand the semantics of real-time data and propagates the impact to the rest of the system to minimize the loss. In essence, now our data architecture is not a mere thermometer indicating the temperature but a thermostat that adjusts to the varying weather. While this concept is intriguing, how far are our organizations prepared to embrace this change?
Let’s start at the very base; many of the companies that I know of are still undecided on what data to collect; a few of them attempts to collect everything and soon hit the wall in not knowing how to translate them into actionable analytics, the remaining few are going in vicious circles, waiting for a magic formula that will tell them on what to collect and what not to. In practice, there is no silver bullet. For example, a hospital chain in New Jersey collated Outpatient waiting duration and turned it to boost customer satisfaction scores (that story for a different blog). John Deere, a tractor manufacturer, started collecting data on soil quality (temperature, moisture, etc.) and created a separate revenue stream by selling it to its customer base. I can keep enumerating such different use cases (Rolls Royce, Dickey’s Barbecue, etc.) wherein data not only transformed the business but created a new revenue stream for their corporation. If this hemming and hawing are just with the fundamental decision on data collection, items such as Multi-cloud and Knowledge graphs appear to be far-fetched. So if you are still stuck at the starting point, know that you are getting behind the curve with each passing day. Data space amply rewards risk-takers; you need to take the plunge and keep investing in Proof Of Concepts(POC) every quarter; if we wait for all the waves to settle, the org will remain a helpless and hopeless position with the exodus never reaching the Promised land.
In conclusion, DataFabric is just the first ray of light before sunrise. The best is yet to come; while DataFabric can precisely prescribe the corrective course of action based on real-time data, imagine the possibilities when it’s fused with a Predictive modelling system or by integrating with a Natural Language Processing (NLP) system performing behavioural analysis to evolve following best possible action. DataFabric is unleashing an endless streak of wild and incredible Opportunities, and the journey has just begun.
Shammy is a 8x Cloud certified, Distinguished IT Specialist/Architect for Cloud, Data Transformation, and Automation, with 21+ years of experience in providing technical leadership for complex business problems in the IT services industry. Shammy is a well-seasoned, business-driven, solution-oriented, Cloud, Automation Techno-Business Leader and Executive IT Architect who has led many large client engagements and provide solutions on various technology platforms, such as Azure, AWS, Google Cloud Platform with leg