The Sentient Enterprise. Ratzesberger Oliver

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СКАЧАТЬ the curve in making data and analytic muscle more accessible to the diverse workforce that needs these resources to innovate.

      On an organizational level, failing to leverage data for innovation and decision support can put your whole business on a downward trajectory. Success today requires navigating a constantly expanding data universe, and companies that don’t fully embrace the data available to them are operating on borrowed time.

      We’ll explore in this book how the five-stage Sentient Enterprise capability maturity model can put data in the hands of more business users, part of a broader revolution into how companies listen to data, conduct analysis, and make autonomous decisions at massive scale in real time. In the process, we’ll visit with top analytics professionals at some of today’s largest and most successful organizations – Verizon, Dell, Cisco, General Motors (GM), Wells Fargo, and Siemens, just to name a few – to see how this revolution has, in many ways, already begun.

      “We remind ourselves every day – it’s even in our Company Credo – that being big is not the same as being the best,” said Grace Hwang, Executive Director for Business Intelligence and Advanced Analytics at Verizon Wireless, one of the top executives who gave extensive interviews for the writing of this book. “Our job is to leverage economy of scale – but at the same time to be nimble and proactive.”

      In the pages to come, we’ve packed lots of real-world perspective from Verizon and other influential companies that have agreed to share their stories – their headaches and challenges, their insights and solutions – as they innovate their way to success. Throughout this book, in fact, we prioritize on-the-ground relevance and accessibility for a wide range of readers.

      We’ve designed this book to be accessible and succinct for the lay business audience, with plenty of bread crumbs for more technophile information. While we are indeed talking about capabilities made possible by servers, nodes, data warehouses, and the skein of other infrastructure and software resources that go into any large analytics infrastructure, we do so from a perspective that’s not too wonky or overly technical.

      COLLABORATION WITHOUT CHAOS

      Especially when working with many experts and massive infrastructure that might scale all the way to the global production level, it’s easy for collaboration to veer into chaos if you don’t have the proper platforms and hassle-free governance to help people stay in their lanes. But it’s important for people to still collaborate effectively with those in other parts of the business, so silos don’t develop as barriers to agility.

      We’ll see in the chapters to come how that one word —agility– is key to getting the enterprise to the sentient point where it can analyze data and make autonomous decisions at massive scale in real time. Agile systems and processes enable this by loosening IT roadblocks, democratizing data access, breaking down silos, and avoiding costly inefficiencies like data duplication, error, and just plain chaos.

      Merriam-Webster’s Collegiate Dictionary defines agile as “marked by ready ability to move with quick easy grace” or “having a quick resourceful and adaptable character.” In the corporate world, business agility is usually defined as a company’s ability to rapidly respond and adjust to change or adapt to meet customer demands. For our purposes, however, let’s entertain a more targeted definition:

      Agility is the ability to decompose or break big problems and systems into smaller ones, so they’re easier to solve and collaborate around.

      In our effort to build this new agile environment for analytics, we looked across many industries for other examples of agility. This cross-industry perspective can solve problems in one sector by looking to other kinds of business settings for challenges met and lessons learned. The context may be different, but the insights and solutions can be strikingly similar.

      For instance, we can learn much about an agile decomposition approach to tomorrow’s data architectures by examining the Open Systems Interconnection (OSI) model that the telecommunications industry deployed as far back as the 1970s. OSI was developed to segment complicated infrastructure (wiring, relay circuits, software, etc.) into manageable chunks for better collaboration among various specialists.

      By designing modular but interoperable parts of the system known as abstraction layers, OSI ensured that the work of software programmers, for instance, didn’t conflict with what engineers and line workers might be doing in the field – or vice versa. We like the OSI example because, even though it was developed four decades ago, the technique of segmenting big systems into overlapping but distinct and manageable elements is a powerful ingredient for agility – one that we continue to see in some cutting-edge settings today.

      Check out a technology called Docker (https://www.docker.com/) to see what we mean. Docker lets you break down the app-building process into a series of manageable steps. Through a simple Docker Engine and cloud-based Docker Hub, the company lets you assemble apps from modular components in a way that can reduce delays and friction between development, quality assurance (QA), and production environments. By breaking things down into smaller components, Docker aims to make the app-building process more manageable and reliable.

      Another example is the entire “microservices” approach to building software architectures. Unlike traditional service-oriented architectures (SOAs) that integrate various business applications together, microservices architectures involve complex applications built from small, independent processes. These processes communicate with each other freely using application programming interfaces (APIs) that are language agnostic.

      With microservices, you’re still building powerful architectures; but it happens more efficiently, with modular elements broken down to focus on discrete small tasks. As a result, microservices architectures can be tremendously agile. They facilitate continuous-delivery software development and let you easily update or improve services organized around distinct capabilities such as user interfacing, logistics, billing, and other tasks.

      AN EVOLUTIONARY JOURNEY (THAT’S ALREADY BEGUN!)

      These examples show how we’re on a journey away from monolithic and nonagile IT applications. But a caveat along this journey – one we’ll emphasize often in the course of this book – is that you must fold in the right kind of governance, so your newly agile systems don’t create more problems than they’re solving. We’ll talk through the Wild West pitfalls of data anarchy and error that arise when we try to loosen old systems and rules without putting some kind of (seamless and hassle-free) governance in place to support our new and agile methodologies.

      We’ll also see how most of the steps a company takes on the journey to sentience follow this definition of agility as decomposing problems into manageable components. The word is even embedded in the first of the Sentient Enterprise’s five stages – the Agile Data Platform – proof of how front and center agility needs to be for anyone looking to survive and compete in today’s data-driven marketplace.

      Fortunately, we’re not at square one in fulfilling the mandate for more agility and sentience around data in the enterprise; far from it! During his time at eBay, and now at Teradata, the practitioner on your coauthor team (Oliver) has worked to create collaborative and agile platforms for analytics. In the same spirit as OSI’s abstraction layers, analytic platforms help data scientists and other users convene and extract insights around data safely and profitably.

      The Sentient Enterprise now elevates this platform approach for collaboration to an entirely new and coordinated level at scale. Among other things, you’ll learn about the Layered Data Architecture, which we’ll discuss more fully in Chapter 2. In a nutshell, it’s like an OSI-style nerve center for your data architecture – a tiered system for concurrent and customized access by many users of different СКАЧАТЬ