The Last Mile of Analytics
The opening line of Charles Dickens’ “The Tale of Two Cities” said: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were all going direct to Heaven, we were all going direct the other way – in short, the period was so far like the present period, that some of its noisiest authorities insisted on its being received, for good or for evil, in the superlative degree of comparison only.” Does this reflect what we see around us today in the Big Data/Analytics/Data Science cloud? It is the Best of Times: The Rising Buzz… If you type Big Data in Google Trends, the steep upward slope of the graph reflects the buzz we are seeing around us. Replace it with any of the other buzzing buzzwords, the graph would be similar. …Leads to the Inevitable Search… If you type in “Analytics” in Google Search, you get 61,00,00,000 results. This is as of this minute. As I speak, more results will get thrown, from new companies to new case studies to new buzz makers to new technologies. Everyone seems to be having an opinion about it. …For Impact Search through back covers of Economist or `through the recently reported stories in The Forbes or HBR. Case studies of success stories through Analytics and features of cool companies solving the impossible through data are as hard to miss as the stories on #GreeceCrisis. So is the new Gold Rush for Data? It is the worst of Times: With all the buzz around us, what many people are not mentioning, is the confusion, the frustration and at times, the sheer failure from this Big Data Deluge The Ignition Key.. Industry analysts such as Gartner, Forrester and others report that Analytics is at the top of Technology spending for CXOs across industries. But, if you see the Top 10 Big Data Challenges, the one thing that stares glaringly at us, is the perennial chicken and egg story. When is the right time to start and how do companies start unlocking the magic key? …Lost in the Gear of Choice The sheer number of companies claiming to provide Big Data and Analytics solutions, the list of people claiming to be Chief Data Scientists, the plethora of open jobs in the Analytics/Data Science Disciplines, the display of new trainings that aim to arm people with the right tools: they all add up to one thing. A problem of choice for Decision Makers. It’s like needing a simple cup of coffee to start your morning and the man at the counter reciting 15 flavors of exotic coffee brews instead. … and the Break of Indecision A recent article in HBR claimed that just 11% of Customer facing Marketing decisions are actually taken with the help of data. If a similar survey is done across functions, how many Strategic Business Decisions will we see being really enabled with Data? Is Analytics being restricted to solving structured and repeatable business problems while the real, strategic and unstructured problems are still tantalizingly out of reach of the Data Gods? Where does that lead us? If you cut through the buzz and see the real picture, what stares at us more clearly is the confusion that echoes through this mayhem of buzz. How many Big Data and Analytics projects are really successful? Are people speaking about what’s failing and why? How many companies have really seen sustained impact from Analytics and taken strategic decisions through data? How many companies have been able to really embed Analytics in their DNA? Succeeding in Analytics requires a lot more than just Data. It requires one to touch the core of the company’s Culture. And we believe, unless that happens, we will never see the Last Mile of Analytics. Let’s showcase one simple framework for reaching the Last Mile of Analytics How do we read this framework? Succeeding in Analytics and getting to the Last Mile of embedding Analytics in decision-making is not just about dealing with data. The journey from Data to Decisions requires one to look at how Analytics is operationalized in the business. And the missing piece that actually can drive or enable Analytics is not data but Culture. Can culture be enabled by Analytics? To make Analytics actually a part of the Company culture, it’s not enough to have a set of people providing Analytics solutions. Analytics needs to be embedded in technology accelerators that can directly enable decisions at the point of action. This makes Analytics accountable for real business decisions rather than just providing more data. Making Analytics work across the business requires Collaboration and the right choice of Engagement Model which would vary based on the maturity of the organization and its decision-making, not its data needs. The business models could range from Products, Services, and Managed Solutions to Analytic Marketplaces. Unless the right business model is chosen, Analytics will remain a discrete project. The Last Mile: We are far from closing the book on Analytics. Most people are still looking for the book, some have started reading the initial few chapters. A few people have figured out the narrative arc, the characters and the story. It’s time then to turn the page to the chapter that’s still being written, The Last Mile. We would love to hear your stories! Read the BRIDGEi2i Thought Paper “Last Mile of Analytics“ This blog is written by Debleena Roy, Director – Strategic Marketing at BRIDGEi2i
The opening line of Charles Dickens’ “The Tale of Two Cities” said: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it […]
The opening line of Charles Dickens’ “The Tale of Two Cities” said: “It was the best of times, it was the worst of times, it was the age of wisdom, it was
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["big data", "data science", "Go", "AI", "Aim", "ViT", "analytics", "Rust", "GAN", "R"]
["AI", "data science", "analytics", "Aim", "R", "Go", "Rust", "big data", "GAN", "ViT"]
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