How Marico gains agility in decision making process?
Marico's move to Azure SQL Data Warehouse enables complete control of data management.BY Shipra Srivastava | Oct 05, 2017 | comments ( 0 ) |
Operating in the wellness and beauty space, Marico Ltd. ranks amongst India's leading consumer products companies. It is currently present in 25 countries across Africa and Asia and has multiple brands in the categories of skin care, hair care, health foods, edible oils, fabric care and male grooming. Marico clocked a turnover of Rs 61 billion in FY 2015-16.
In the FMCG space, the volume of data being generated fluctuates greatly during certain periods. This puts unforeseen burden on the existing IT infrastructure in managing the data flux. “The last five days of a month and the first five days of the next month are extremely important for business, as during these 10 days critical decisions are to be made. This window of planning, therefore, needs high business visibility,” said Girish Rao, Head - IT and Business Analytics at Marico.
However, the window for extraction, transformation and loading of data of the magnitude of 3-4 GB per day was only 3-4 hours. Moreover, in those 10 days, the data volume spiked to 8-9 GB even as the window remained the same.The first and foremost challenge that we wanted to overcome was to gain control over the churn and speed of data,” said Rao. “Secondly, the variability in data volume made us refresh our hardware again and again, which was cumbersome as well as expensive. Thirdly, our existing database was going out of support, and as we were already on SQL, it was a logical progression to move to the cloud,” he added.
With the decision to move to the cloud finalized, Rao went ahead with Microsoft Azure SQL Data Warehouse – the fully-managed, highly-elastic, petabyte-scale, warehouse-as-a-service solution. The big advantage of aligning with Microsoft was its offer of an end-to-end data solution (database, data warehouse, big data, IoT, visualization, advanced analytics) that unlocks insights from any data.
The starting point for the project was consolidation of the data and reworking of the individual data structure orientation from serial computing to parallel computing.“Microsoft constantly helped us in our preparedness to move to the cloud solution. The project was initiated in December-January, 2017 and completed in March, 2017,” Rao revealed.
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