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Volume, Velocity, Variety and Veracity of your data, the 4V challenge, has become untamable. Wait, yet another big data blog? No, not really. In this blog, I would like to propose a cognitive app approach that can transform your big-data problems into big opportunities at a fraction of the cost.
Everyone is talking about big data problems but not many are helping us in understanding big data opportunities. Let's define a big data opportunity in the context of customers because growing customer base, customer satisfaction and customer loyalty is everyone’s business:
- you have a large, diverse and growing customer base
- your customers are more mobile and social than ever before
- you have engaged with your customers where ever they are: web, mobile, social, local
- you believe that "more data beats better algorithms" and that big data is all data
- you wish to collect all data - call center records, web logs, social media, customer transactions and more so that
- you can understand your customers better and how they speak of and rank you in their social networks
- you can group (segment) your customers to understand their likes and dislikes
- you can offer (recommend) them the right products at the right time and at the right price
- you can preempt customer backlash and prevent them for leaving (churn) to competitors and taking their social network with them (negative network effects)
- all this effort will allow you to forecast sales accurately, run targeted marketing campaigns and cut cost to improve revenues and profitability
- you wish to do all of this without hiring an army of data analysts, consultants and data scientists
- and without buying half-dozen or more tools, getting access to several public / social data sets and integrating it all in your architecture
- and above all, you wish to do it fast and drive changes in real time
- And most importantly, you wish to rinse and repeat this approach for the foreseeable future
The advances in technologies like in-memory databases and graph structures as well as democratization of data science concepts can help in addressing the challenges listed above in a meaningful and cost-effective way. Intelligent big data apps are the need of the hour. These apps need to be designed and built from scratch keeping the challenges and technologies such as cognitive computing[1] in mind. These apps will leave the technology paradigms of 1990s like "data needs to be gathered and modeled (caged) before an app is built" in the dumpster and will achieve the flexibility required from all modern apps to adapt as the underlying data structures and data sources change. These apps can be deployed right off the shelf with minimum customization and consulting because the app logic will not be anchored to the underlying data-schema and will evolve with changing data and behavior.
The enterprise customers will soon be asking for a suite of such cognitive big data apps for all domain functions so that they can put the big data opportunities to work to run their businesses better than their competitors. Without dynamic cognitive approach in apps, addressing the 4V challenge will be a nightmare and big data will fail to deliver its promise.
Stay tuned for future blogs on this topic including discussions on a pioneering technology approach.
Stay tuned for future blogs on this topic including discussions on a pioneering technology approach.
[1] Cognitive computing is the ability to analyze oceans of data in context with related information and expertise. Cognitive systems learn from how they’re used and adjust their rules and results dynamically. Google search engine and knowledge graph technology is predicated upon this approach.
This blog has benefited from the infinite wisdom and hard work of my former colleagues Ryan Leask and Harish Butani and that of my current colleagues Sethu M., Jens Doerpmund and Vijay Vijayasankar.
Image courtesy of MemeGenerator
Image courtesy of MemeGenerator
