“The secret of success is to be ready when your opportunity comes”, said a former British Prime Minister in the 19th Century. How apt it is even in today’s’ context, as the opportunity wave today is AI!. Irrespective of which space the enterprise functions (internet/non-internet) , AI is imperative.Some evangelists have drawn comparison – AI is to technology revolution what electricity was to the industrial revolution. So get ready to ride the wave, as it has the power to transform any industry.
So How to Become AI Ready ?
As a start, lets focus on the two building blocks of AI – Data Science and Data Engineering. Let see what differentiates the two; Data Science is primarily an offshoot of applied statistics whilst Data engineering grew out of systems engineering. Hence, Data Engineering focuses on Data Acquisition, Wrangling, ETL etc whereas Data Science is a science of deriving insights (both historical and predictive) from the available data.
We have observed, that businesses often struggle and lose their way coordinating between these two pillars. The key to success largely depends on how well these two pillars have been harmonized.
- Absence of Unified Storage : Data Science requires input data from various sources ( both in-house and external ) , since this data is not available in a single place , data engineers have to rely on complex ETL pipelines to create a unified view .
- Varied People Skill Sets : One is Engineering ,while the other is Science Driven.
- Ease vs Optimization : Data Scientists like to have their data represented in a particular consumable format, whereas data engineers look for optimizing storage and performance .
- Data Freshness : Since most of the data engineering results come in time based batches, often data scientists struggle to update their models for incoming / new data sets
To ensure that relationship between Data Engineering and Data Science is coupled, enterprises need to proactively review their (legacy) architecture. Illustrated below is a new architecture (lets call it Inverse Lambda) where all the required data is centrally collected and stored – resulting in a centralized schema (which is being updated on a real-time basis) leading to creation of single source of truth ( as in diagram below).
The above adoption would enable de-coupling of the aforesaid disciplines to create ideal harmony and thus result in faster intelligent analytics. Our nextgen IZAC Data Integration Platform is built on this premise and will empower enterprises to become AI ready in a shorter time frame. This of course, would help the organisation to maintain the competitive edge with its peers and also experience better customer satisfaction.
Stay tuned….in the next series of this blog we will share and discuss the building blocks of IZAC in detail. For a fastStart discussion, please write to us at: firstname.lastname@example.org