![\"sumo\"](\"https:\/\/www.retailprophet.com\/wp-content\/uploads\/2013\/10\/sumo1.jpg\")
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By Doug Stephens<\/p>\n
It\u2019s hard (read impossible) to go a day without hearing that big data is poised to revolutionize business.\u00a0 The speed, volume and variety of data now available at our fingertips, holds incredible promise for companies in all categories. For the average marketer, big data must seem like a giant catapult being rolled into a battle that\u2019s currently being waged with sticks and stones. \u00a0\u00a0It is indeed a new war machine and by most accounts, including my own, it will be a formidable force. Of that there seems little question.<\/p>\n
Within this new data landscape, companies are scrambling to comprehend the technical aspects of big data\u00a0 – what it is, how it works, what it costs and the true capabilities it might bring.\u00a0 These are early days on the big data hype curve, and it\u2019s particularly challenging to separate fact from fantasy in terms of what the average company can actually expect to do with big data once they harness it.\u00a0 And the truth is that grasping this new and awe-inspiring technology is not without some challenges.<\/p>\n
But as is often the case, understanding the technology is only a small portion of the full test that companies embarking on big data will face.\u00a0 Far less obvious but infinitely more critical are the human<\/em> issues prompted by the use of big data analytics. These, to my mind, are the true challenges that companies should be addressing early, honestly and sensitively.<\/p>\n I see these challenges affecting three key constituents:<\/p>\n <\/p>\n Many of today\u2019s corporate leaders were developed to believe that \u201cstrong leadership\u201d means gathering all relevant facts and data, performing thorough and astute analysis and then making the most sage and correct decision possible. Measurement of the decision may come weeks or months later.\u00a0 Then and only then might there be any course correction.\u00a0 Decision makers have been expected to be firm and unwavering, because in today\u2019s corporate world, equivocation and plasticity of thinking have often been seen as signs of weakness.<\/p>\n Big data leadership however, requires a completely different decision-making style.\u00a0 It involves making nimble and quick decisions based on limited information and best guesses, and then implementing through a series of tests, course corrections and pivots based on an immediate, robust and constant flow of data. It\u2019s like navigating a ship using a GPS unit versus an antique sextant and the stars. In essence, the big data leader won\u2019t set out to be \u201cright first time\u201d but to be \u201cless wrong over time.\u201d<\/p>\n The best retail leaders therefore, will no longer be those that aspire to be the most intuitively correct but instead those that are the most innately curious, and capable of asking the best questions of their data.\u00a0 In other words, instead of having the right the answers, it will be the leaders smart enough to develop the right queries that will drive results.<\/p>\n This requires a fundamental re-learning of what it means to be a leader and my guess is that many organizations (and their leaders) will fail to make the transition.<\/p>\n <\/p>\n Cameras can record employee interactions with customers. \u00a0RFID tags can track worker’s bodies through the store and measure their attentiveness to consumers and pace of work.\u00a0 Social filters can sift through millions of conversations to find mentions of staff names or their store locations for commendation or condemnation. So, companies venturing into the use of big data will have to ask themselves what sort of relationship they want to have with their employees.<\/p>\n Will big data be used as a device purely for routing out the weak or as a valuable means of developing the strong? Does the collection of data work to help the employee excel or does it work for the company only? And are employees entitled to their own performance (big) data or is it merely stored and deployed at them?<\/p>\n This new and microscopically invasive tool can be either remarkably positive or devastatingly destructive depending on how it\u2019s positioned, shared and executed with the workforce.<\/p>\n <\/p>\n Take it from someone who has scoured the earth for good retail big data use cases, there aren’t many. \u00a0In fact, the best big data case studies tend to be found in other areas, such as health, government, medicine and education. Why? Because in these situations, consumers have little choice but to share accurate and complete data. \u00a0\u00a0They have no such obligation in most retail situations. Retail customers lie, share loyalty cards and coupons, move and yes, even die, all without informing retailers. Consequently, at any given time, much of the data in a retailer\u2019s system is garbage\u2026big garbage.<\/p>\n The only way for retailers to gather good data is to compel customers to willingly and even happily share it with them.\u00a0 This requires two things; implicit trust on the part of the consumer and a one-to-one sharing of value between the two.\u00a0 If data is, as some call it, a new currency, what does the customer get when they spend their data currency with you<\/em>?\u00a0 More advertising or a tangibly better shopping experience?\u00a0 More junk mail or more relevant and personalized communications? It\u2019s a critical philosophical question that your company has to align on.\u00a0 Trust will represent as great a competitive advantage as a fabulous product or standout service \u2013 perhaps even greater. So that privacy policy that currently sits as an obscurely written, 5 page, legal footnote on most websites?\u00a0 Well it may be time to put it in plain English and move it up to the header.<\/p>\n <\/p>\n Three to five years from now, I suspect we\u2019ll take big data for granted. \u00a0It will be By Doug Stephens It\u2019s hard (read impossible) to go a day without hearing that big data is poised to revolutionize business.\u00a0 The speed, volume and variety [\u2026]<\/span><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[32,1],"tags":[241,243,46,24,118],"class_list":["post-3187","post","type-post","status-publish","format-standard","hentry","category-technology","category-uncategorized","tag-big-data","tag-hadoop","tag-marketing","tag-retail","tag-retail-prophet"],"yoast_head":"\nLeaders<\/b><\/h2>\n
Employees<\/b><\/h2>\n
Customers<\/b><\/h2>\n
Table stakes<\/b><\/h2>\n
![\"Get-My-Book.001\"](\"https:\/\/www.retailprophet.com\/wp-content\/uploads\/2013\/04\/Get-My-Book.001.jpg\")
table stakes to play in chain retail, and most companies will have honed a relatively acute understanding of big data’s technical traits.\u00a0 They\u2019ll get that piece pretty quickly.\u00a0 But it\u2019s the companies that recognized early on that big data is not a technical<\/i> challenge but rather a human<\/i> challenge that will be more likely to master it\u2019s true potential. \u00a0Get the human piece wrong, however, and all the distributed computing, in-memory processing and Hadoop databases in the world won’t help you.<\/p>\n","protected":false},"excerpt":{"rendered":"