Big Data The Organizational Challenge | #bigdata #growthhacking #business #organisation #USA #Europe #Asia

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To get in the Big Data game, a company needs three kinds of table stakes. The first is the data itself: large quantities of information in a format allowing for easy access and analysis. Most large companies already have this—in fact, they generally have more than they can use. The second is advanced analytical tools, such as Hadoop and NoSQL. Both proprietary and open-source tools and platforms are widely available these days— all you need are people capable of putting them to work. That brings us to the third, and usually the most challenging, set of table stakes: expertise. Advanced analytics requires staff with state-of-the-art skills in everything from data science to worldwide privacy laws, along with an understanding of the business and the relevant sources of value.

But table stakes alone won’t help you win, because Big Data isn’t just one more technology initiative. In fact, it isn’t a technology initiative at all; it’s a business program that requires technical savvy. So you can’t just add more capacity and expertise, and expect your IT or marketing functions to begin generating data-based insights. Even if they did, the rest of the company would be unlikely to act on those insights. As the analytics leaders have discovered, succeeding with Big Data requires a different approach: You need to embed Big Data deeply into your organization. It’s the only way to ensure that information and insights are shared across business units and functions. This also guarantees the entire company recognizes the synergies and scale benefits that a well-conceived analytics capability can provide. Let’s look at what’s involved.

Ambition Leading companies begin the embedding process by spelling out their ambition. We will embrace Big Data as a new way of doing business. We will incorporate advanced analytics and insights as key elements of all critical decisions. A declaration like this from the senior leadership team is an essential precondition for the kind of behavior change this article will discuss. But the senior team must also answer the question: To what end? How is Big Data going to improve our performance as a business? What will the company focus on? There are four areas where analytics can be relevant: improving existing products and services, improving internal processes, building new product or service offerings, and transforming business models. These objectives often overlap. Progressive’s new “Snapshot” device, which monitors driving behavior, helps the company determine whether a given driver is the right customer for the company. Intuit’s acquisition of Mint.com has helped expand its business beyond purchased software to ad-supported software. Humana, the insurance provider, is using Big Data to transform its business: Using claims data, the company can determine who is likely to end up in a hospital for preventable reasons and then intervene early. Humana and other health insurance carriers are also mining data to help improve patient outcomes and to reward healthy behaviors. Most companies are opportunity-rich when it comes to analytics, and large enterprises can pursue multiple avenues, either simultaneously or sequentially. Still, nearly every company can improve its trajectory by determining priorities and picking the right angle of entry. Horizontal analytics capability With ambition defined, Big Data leaders work on developing a horizontal analytics capability. They learn how to overcome internal resistance, and create both the will and the skill to use data throughout the organization. This is a big job. Organizations don’t change easily and the value of analytics may not be apparent to everyone, so senior leaders may have to make the case for Big Data in one venue after another. They may need to help people change their everyday behaviors and then continue along the new path without backsliding. As with any major initiative, executives and managers have a variety of tools at their disposal. Leading companies typically define clear owners and sponsors for analytics initiatives. They provide incentives for analytics-driven behavior, thereby ensuring that data is incorporated into processes for making key decisions. They create targets for operational or financial improvements. They work hard to trace the causal impact of Big Data on the achievement of these targets. For example, Nordstrom elevated responsibility for analytics to a higher management level in its organization, pushed to make analytical tools and insights more widely available and embedded analytics-driven goals into its most important strategic initiatives. Another global consumer electronics company selected highimpact analytics projects for additional support, creating positive business results stories and additional demand for Big Data solutions. The company added incentives for senior executives to tap Big Data capabilities, and the firm’s leadership has reinforced this approach with a steady drumbeat of references to the importance of analytics in delivering business results. An organizational home The Big Data leaders then create an organizational home for their advanced analytics capability, often a Center of Excellence (CoE) overseen by a chief analytics officer. Creating an organizational home involves several key design decisions. A company has to set its strategy for Big Data deployment. It has to assign collection and ownership of data across business functions, plan how to generate insights, and prioritize opportunities and allocation of data scientists’ time. It must host and maintain the technological infrastructure, set privacy policy and access rights, and determine accountability for compliance with local laws and data security. All of that is a tall order. To get it done, companies typically pursue one of four models: •Business unit led. When business units have distinct data sets and scale isn’t an issue, each business unit can make its own Big Data decisions with limited coordination. AT&T and Zynga are among the companies that rely on this model. •Business unit led with central support. Business units make their own decisions but collaborate on selected initiatives. Google and Progressive are examples of this approach. •Center of Excellence. An independent center oversees the company’s Big Data. Units pursue initiatives under the CoE’s guidance and coordination. Amazon and LinkedIn rely on CoEs. •Fully centralized. The corporate center takes direct responsibility for identifying and prioritizing initiatives. Netflix is an example of a company that pursues this route. Note that in none of these models does IT own Big Data. While IT often plays a critical role in providing and maintaining the infrastructure and tools required to run Big Data analytics, most companies find that it’s a mistake to have IT own or manage the business adoption capability. A company’s choice of model obviously depends on its ambition and its existing operating model. For example, companies with deep analytics capabilities and an emphasis on experimentation and innovation, such as Google and Progressive, can rely on a generally decentralized approach. But many analytics leaders have found that a CoE has the most advantages and the fewest limitations (see Figure 2). A well-functioning CoE enables cross-business-unit access and sharing of data. It takes responsibility for supporting and coordinating every initiative from a business unit, thus providing synergies and scale benefits. On the corporate level, the CoE serves as the go-to organization for analytics strategy and insight support. It sets the road map, and it establishes and maintains privacy policies. A leading European telecommunications company, for example, is in the process of deploying Big Data for a range of purposes, including analyzing customer data to provide better offers and services, and using network traffic data to optimize network management and investments. It will house these capabilities in a variety of settings, but all will be coordinated by a CoE.

Rasalkhaimah, ras, al, khaimah, dubai, university, salford, manchester, @hishamsafadi, hisham, safadi, European, medical, center, business, entrepreneur, startup, economy, money, motivation, education, Leadership,  Transactional,  analysis, emotional, intelligence, organisations,  development,  innovative, technology,  care, health, investor, investment, production, shark, tank, sharktank, USA, UK, London, group, european, canada, india, china, japan, KSA, projectmanagement, datascience, bigdata, IOT, internetofthings, cloud

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