The Invisible Bullet-Holes Pierced By Big Data Analytics Analysis
The Invisible Bullet-holes Pierced by Big Data Analytics in Financial Services and Their Fixes
Even though data started becoming available in the late 19th century for a few now-developed countries, its analysis remained a question mark. Processing the 1880 Census results in the US took 7 whopping years! In the vintage times, every company, universally took all key decisions intuitively. Until recently, financial institutions too that wanted to examine large pools of data were first required to devote significant time and resources into organizing structured data which may be scattered across several departments and data warehouses. Fortunately, technological advances are now expediting the organization of structured data and allowing large …show more content…
Organizations are substantially migrating from being product-centric to customer-focused. In the financial services industry where a firm’s competitors could eat it up before even chopping it down into pieces, literally saying, identifying valuable customers, retaining them by offering them attractive services that they find appealing and finally managing these customer relationships, are indispensable measures to achieving competitive advantage. Successful implementation of these will directly contribute to the bottom line of the financial institutions. Risk management, on the flip side, may not explicitly add to an institution’s bottom line but application of analytics can be instrumental in tracing rogue trading or other non-compliant …show more content…
To offset this, these firms are under constant pressure to develop new products while still pitching existing ones to new audiences. This requires continuous innovation that has to be sourced from the ability to accurately and critically analyze the full spectrum of data. At the root of the challenge, lay data silos preventing firms from gaining insights on existing & potential customers, products, and sales & distribution channels. Effective big data analytics overcome the handicap in handling enormous volumes of data but are accompanied by the challenge to analyzing and acting on the data in real time using business intelligence and predictive
Even though data started becoming available in the late 19th century for a few now-developed countries, its analysis remained a question mark. Processing the 1880 Census results in the US took 7 whopping years! In the vintage times, every company, universally took all key decisions intuitively. Until recently, financial institutions too that wanted to examine large pools of data were first required to devote significant time and resources into organizing structured data which may be scattered across several departments and data warehouses. Fortunately, technological advances are now expediting the organization of structured data and allowing large …show more content…
Organizations are substantially migrating from being product-centric to customer-focused. In the financial services industry where a firm’s competitors could eat it up before even chopping it down into pieces, literally saying, identifying valuable customers, retaining them by offering them attractive services that they find appealing and finally managing these customer relationships, are indispensable measures to achieving competitive advantage. Successful implementation of these will directly contribute to the bottom line of the financial institutions. Risk management, on the flip side, may not explicitly add to an institution’s bottom line but application of analytics can be instrumental in tracing rogue trading or other non-compliant …show more content…
To offset this, these firms are under constant pressure to develop new products while still pitching existing ones to new audiences. This requires continuous innovation that has to be sourced from the ability to accurately and critically analyze the full spectrum of data. At the root of the challenge, lay data silos preventing firms from gaining insights on existing & potential customers, products, and sales & distribution channels. Effective big data analytics overcome the handicap in handling enormous volumes of data but are accompanied by the challenge to analyzing and acting on the data in real time using business intelligence and predictive