All the industry leaders like Wal-Mart, Axa, Citibank, Humana, GE and several others are exploring how Big Data analytics can be used to better understand customer needs, pinpoint risk, improve marketing, enhance the customer experience, combat fraud, and drive profitability. Companies are seeking ways to rebuild their customer relationships in this time of extremely high customer expectations.
They have the challenge of tackling the huge data since 1970s when barcodes were first introduced to scan the products at POS. All sorts of supply chain data came into effect later in 1980-90s while RFID and other sources such as surveillance video cameras started sending humongous data to data centers recently. These have challenged Retailers to capture, store, cleanse & analyze all the data they collect.
Further to flood the data centers are consumer’s interaction with social media & internet which is generating billions of data points that can be measured via clicks, page views, time spent on per page and path traversed from landing to conversion.
Big data analytics is helping retailers to collect and analyze this fine grained shopper visit data and optimize page designs, placements and tailor promotional messages. McKinsey report say that using big data analytics can raise the operating margins by as much as 60%.
Making sense of big data through smarter analytics.
Analytics, today, is the language of boardrooms. CFOs are calling for less data crunching and more analysis. CIOs are looking to glean value from providing the right data to the right people in a secure way. CMOs want to lead the marketing function into where it can collect the right information and make the right recommendations. There is no denying that analytically sophisticated companies outperform their peers. From a basic ‘Descriptive’ maturity phase to an ‘Information Integration’ maturity phase, analytics evolved towards the extraction and consolidation of large volumes of high quality data in data warehouses and organising data in a way that can be analysed efficiently. This enabled the use of data for integrated performance management and resource planning across the enterprise.
Analytics applying statistical and data mining techniques drew deeper insights to predict behavior and events. It answered questions like what could happen, and what if these trends continue? Data can be visualised as a ‘pool’; such a pool is accumulated but stagnant for weeks or months. Enter data as a ‘flowing stream’, characterised by real time information, both internal and external to the enterprise. Such data can provide immediate, current, relevant insights, in addition to insights on past behavior.
