Translation Results Requested Through Conyac Made Public

1. Resist a “collect data now, figure out later” approach

To ensure future analytics capabilities, companies must invest now in a platform that enables fast, efficient onboarding of new datasets. They should consider how their business will operate in the future with regard to data ingestion and federation and how the transition from legacy systems to end-to-end automated data and analytics should take place.

Central to this is the ability to invest in a new platform that scales purposefully, carefully, and transparently, as opposed to collecting data without a clear objective or investing effort to interpret the data being collected.
2. Re-architect legacy data applications — no matter how painful it seems

Many companies are overly reliant on outdated legacy systems with high maintenance overheads where the cost to upgrade or make strategic change is de-prioritized. That’s true even of major tech companies — for instance, although Samsung’s SmartHub TV software runs on the cloud, all its financial transactions are still handled on-premises due to the cost of moving them.
The net result is that in many organizations, data is siloed across many divisions. Some data — such as social media stats — are even stored outside the company, creating another layer of complexity. To innovate in big data, companies must revamp legacy data applications with a focus on greater operational transparency across a variety of departments.
3. Take a modular and multi-granular approach to data management

The further you can shape raw and insightful data into modular, well organized entities at varying levels of granularity, the more likely you will be to able to efficiently leverage business insights as well as remain nimble to react to the ever changing big data landscape — this is how you can defuse the big data time bomb.

(Thanks to Poornima Apte and Edward Newell of Hippo Reads for their research help on this story.)

Cameron Sim is CEO of Crewspark.