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“Big Data: Leveraging Competitive Intelligence In Retail" focused on the next wave – enabling real time decisions and real-time responses through big data. Here are the Lounge47 key takeaways: 1. Large enterprises have this far used big data to focus on process improvement and variety of data (Process improvement 47%, Variety of data 26%, Volume of data 16%, Cost Saving & Efficiency 8%, Velocity of Data 3%) 2. Big data is not a new problem; at any point of time, our ability to produce data has always been greater than the sophistication of the tools available to process and make it usable 3. Companies like Uber and Amazon, with products like “Surge Pricing” or “Dynamic Pricing” are ushering in the paradigm of “fast data” to make instant decisions and gain a competitive advantage 4. “Fast Data” unlike “historical data, is live, interactive, automatically generated, and often self-correcting” – the volume and nature will be further accelerated through the Internet of Things (IoT) 5. In the retail vertical – data enablers that push micro decisions in real time and serve to answer – what inventory to hold? or what products to promote? - pose a powerful value proposition 6. A plethora of data products, web-based, Apps, API’s, reports could be built to help enterprises take decisions E.g. a “Color” report that tells a fashion retailer that their inventory should carry more items in blue 7. Data products could serve - ecommerce companies, sellers, brands – each stakeholder, with very specific requirements and specific problems to solve E.g. brands value reports on product discounts offered to flag policy violation 8. Solving the big data challenge would involve the following generic steps – data extraction and aggregation, cleaning, normalizing, standardizing, sorting, storing. Analytics. Visual data presentation, via dashboard interfaces, reports etc. 9. Big data sounds like a simple problem to solve however the challenges are many a) Data acquisition: crawling public websites could be limited if volume and speed of query impact service to users, thus slowing the data collection b) Data cleaning & standardization: raw data could be messy or have gaps c) Storage and retrieval d) Data Accuracy: Careful management of massive machination with minimal human audits to keep the margin of error suppressed 10. Some Big data products: Price comparison by the hour and across competition, color report on product inventory, Market & Business intelligence products, discount tracking of basket of products 11) Finding a “give-back” to encourage E-Commerce companies to part with private data would allow big data companies to build an ecosystem that is mutually beneficial to all stakeholders.
While big data is an often used buzz word, and challenges like “new technology deployment” and the “collection, analysis and measurement of data” are being solved, the full power of this paradigm will be realized when organi