| | APRIL 202219call BI and Data Science the 'Yin' and 'Yang' of data driven value creation for the business as they are complementary to each other and each has a place that will solve different business problems. To put it in simple terms, my view is that Business Intelligence (BI) includes operational reporting & dashboards, descriptive analytics, predictive analytics, prescriptive and disruptive analytics. All these approaches provide some kind of intelligence to the business through data analysis and insights by answering different types of business questions (e.g.known knowns, known unknowns, unknown unknowns) and expectations. This is achieved with the support of various technologies, tools, methodologies and techniques e.g., Visualization tools, R, Hadoop, SQL, Python, Tensorflow, Machine Learning, Deep Learning, intelligent automation, AI solution, and infrastructure to handle different types of data assets of varying volume, velocity and veracity. One size approach to create a data storage and processing environment to analyze data will not work, as it is a costly exercise and it is important that the data driven value creation strategy and approach should be based on the maturity and capability of the organization, as it is should be viewed as 'holistic data driven cultural transformation journey' by the organization. This means that data assets are the 'crown jewel' of the organization as its strategic and competitive assets and therefore, the journey should be an enterprise effort and not dictated by specific functions like technology and data analytics only.Fundamental Challenges that Organizations Need to Overcome to Create Value Out of DataIrrespective of whether an organization uses BI or Data Science or AI (what each means, varies between organizations) to drive data driven digital strategies or intelligent automation or customer experience to drive value creation using data, the challenges that an organization needs to overcome are common. The crucial point is that any organization that invests into these areas without having a solid data foundation and management strategy and practice, will struggle. Any processes or solutions that are built on poor data infrastructure and data processes will be slow, untrustworthy, poor quality, resource intensive and expensive.Whether an organization's larger goal is to achieve digital transformation, 'compete on analytics', or be-come 'AI-first', embracing and successfully manag-ing data in all its forms is an essential prerequisite. Critical obstacles with regard to managing data still must be overcome before companies begin to see meaningful benefits from their big data, analytics, data science and AI investments. Bringing BI, Data Science & AI Together on a Common 'Data Playground'How you create a common data layer/infrastructure/environment that would help derive business value out of data through various initiatives such as BI, Data Science and AI is critical. It is critical for all these initiatives to support each other and work together in a common and consistent well defined environment. Following are the some of the key initiatives that organizations should consider as part of their holistic data strategy to create the common 'Data playground'.· Data Democratization: Ensuring that the organization's data assets flow seamlessly and interoperate across its business processes & technology systems and reaches the hands of the all types of users with minimum fuss with minimum help. This enables business users to seamlessly access data that they can use for value creation. This helps create a data-driven culture throughout the entire organization. Without data democratization, consumers of data waste time searching for data, accessing the data, and waiting for approval. However, it is important to ensure that potential risks such as data ethics, data privacy, and misuse of data & compliance requirements are managed. Data democratization is not a trivial problem, but is fundamental to driving a data driven organization culture. A democratized data environment creates an opportunity for Business intelligence, Data science and AI to work hand-in-hand to derive data driven value to the business in a collaborative, efficient, effective, consistent and reliable manner by seamlessly working with the same data.· Semantic Data Layer: It is common that data assets of all types in organizations are distributed and silo in nature due to various reasons ­ legacy systems, legacy processes, legacy culture, various servers, public cloud services, and data centres. This has resulted in a massive gap between data sources and business users. Progressive companies are using semantic data layer to help bridge this gap. Semantic Data Layer maps/integrates complex enterprise data Ram Kumar,Chief Data & Analytics Officer
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