Speaker Interview- Indranil-Bandyopadhyay

Q-What are the vital elements that make your data more accessible to provide deeper data insights?

Democratizing data and deriving meaningful insights from it should be part of any organization’s strategy. There are a few steps to this dance.
One: provisioning of data from various sources
Two: ensuring that the data is stored in a meaningful way – centralized or decentralized DataMarts.
Three: the consumption piece – a standardized role-based process to consume data for analytics purposes. This can be achieved by using tools like Power BI that sits on the data repositories and makes it easier for the business users in deriving insights. Once the data is understood it can be extended to predictive and prescriptive analytics.

Q-What measures would you take to break down operational and data silos across risk, finance, regulatory, customer support, and more?

The people in the respective silos need to be compensated on the bases of effective organization wide data consumption. In the absence of that there will be always silo. From a data strategy point of view, one needs to create separate DataMarts for the operational silos with the ability to fetch fit for purpose data across the DataMarts/Data reservoir.

Q-Can you highlight 5 innovation strategies to apply advanced analytics for integrated insights and visibility?

I suggest the following considerations to enable a business to democratize advanced analytics:

• Data Quality management Strategy -- To define and monitor KPIs, data profiling rules, metrics and monitoring tools
• Metadata Strategy -- To implement metadata types, data lineages, business data and dictionary
• Data Architecture Strategy -- To define and manage standards for data modelling, data warehousing and ETL tools
• Technology architecture strategy -- To define and manage system infrastructure and processes, commodity hardware, and Data platforms
• Data Tool strategy – To use tools that can put guard rails and democratise data science by prescribing algorithms and hand hold the analytics journey

Q-What are your thoughts on a cloud-based data architecture that makes your data more accessible? audience?

I am supportive of the cloud-based data architecture because of the ease of storage, data accessibility and usage of standard APIs for advanced analytics. What worries me though is the cost of accessing data from the cloud. While it is relatively cheap to store data in the cloud, accessing the same (depending on size) can get very expensive.

Q-What changes need to be made in embracing a data culture and adopting strategies for data collection and access to clean data?

I will suggest the following:

• Improving and leveraging data be an element of an organisation’s strategy
• Decision making on available data points – opinion without data points is mostly invaluable
• My experience says that the data consumption strategy lags the data provisioning strategy. So, a strong data consumption strategy execution helps detect the gaps in data collection and quality of data.

Q-What initiatives and measures should the government and regulators be taking on developing reliable data on a national level?

First is the political will to develop a reliable data framework
Second is keeping national interest in mind, the assistance from corporates to create this framework
Third is the reduction of internet data cost Last but not the least putting capable people to lead the data transformation journey.

Q-How can an organization standardize and verify actionable data that is critical to implement effective strategies?

Strategically, effective data hosting and consumption needs to be top of mind for any organisation. Tactically, one can only get to actionable data by going through the discovery stage of what the available data is telling. This is followed by insights and trend analysis. Once all of this is done hopefully actionable data insights will be obtained. If not, go back to the first step. Unfortunately, there is no easy way in getting to actionable data.