Management information systems
Take a look at this, I think it will help you with chapters 1 and 2.
Doing Business The Digital Way: How Capital One Fundamentally Disrupted the Financial Services Industry
To stay competitive, companies must stop experimenting with digital and commit to transforming themselves into full digital businesses. Here are seven traits that successful digital enterprises share.
The age of experimentation with digital is over. In an often bleak landscape of slow economic recovery, digital continues to show healthy growth. E-commerce is growing at double-digit rates in the United States and most European countries, and it is booming across Asia. To take advantage of this momentum, companies need to move beyond experiments with digital and transform themselves into digital businesses. Yet many companies are stumbling as they try to turn their digital agendas into new business and operating models. The reason, we believe, is that digital transformation is uniquely challenging, touching every function and business unit while also demanding the rapid development of new skills and investments that are very different from business as usual. To succeed, management teams need to move beyond vague statements of intent and focus on “hard wiring” digital into their organization’s structures, processes, systems, and incentives.
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There is no blueprint for success, but there are plenty of examples that offer insights into the approaches and actions of a successful digital transformation. By studying dozens of these successes—looking beyond the usual suspects—we discovered that effective digital enterprises share these seven traits.
1. Be unreasonably aspirational
Leadership teams must be prepared to think quite differently about how a digital business operates. Digital leaders set aspirations that, on the surface, seem unreasonable. Being “unreasonable” is a way to jar an organization into seeing digital as a business that creates value, not as a channel that drives activities. Some companies frame their targets by measures such as growth or market share through digital channels. Others set targets for cost reduction based on the cost structures of new digital competitors. Either way, if your targets aren’t making the majority of your company feel nervous, you probably aren’t aiming high enough.
When Angela Ahrendts became CEO of Burberry, in 2006, she took over a stalling business whose brand had become tarnished. But she saw what no one else could: that a high-end fashion retailer could remake itself as a digital brand. Taking personal control of the digital agenda, she oversaw a series of groundbreaking initiatives, including a website (ArtoftheTrench.com) that featured customers as models, a more robust e-commerce catalog that matched the company’s in-store inventory, and the digitization of retail stores through features such as radio-frequency identification tags. During Ahrendts’s tenure, revenues tripled. (Apple hired Ahrendts last October to head its retail business.)
Netflix was another brand with an unreasonably aspirational vision. It had built a successful online DVD rental business, but leadership saw that the future of the industry would be in video streaming, not physical media. The management team saw how quickly broadband technology was evolving and made a strategic bet that placed it at the forefront of a surge in real-time entertainment. As the video-streaming market took off, Netflix quickly captured nearly a third of downstream video traffic. By the end of 2013, Netflix had more than 40 million streaming subscribers.
2. Acquire capabilities
The skills required for digital transformation probably can’t be groomed entirely from within. Leadership teams must be realistic about the collective ability of their existing workforce. Leading companies frequently look to other industries to attract digital talent, because they understand that emphasizing skills over experience when hiring new talent is vital to success, at least in the early stages of transformation. The best people in digital product management or user-experience design may not work in your industry. Hire them anyway.
Tesco, the UK grocery retailer, made three significant digital acquisitions over a two-year span: blinkbox, a video-streaming service; We7, a digital music store; and Mobcast, an e-book platform. The acquisitions enabled Tesco to quickly build up the skills it needed to move into digital media. In the United States, Verizon followed a similar path with strategic acquisitions that immediately bolstered its expertise in telematics (Hughes Telematics in 2012) and cloud services (CloudSwitch in 2011), two markets that are growing at a rapid pace.
This “acqui-hire” approach is not the only option. But we have observed that significant lateral hiring is required in the early stages of a transformation to create a pool of talent deep enough to execute against an ambitious digital agenda and plant the seeds for a new culture.
3. ‘Ring fence’ and cultivate talent
A bank or retailer that acquires a five-person mobile-development firm and places it in the middle of its existing web operations is more likely to lose the team than to assimilate it. Digital talent must be nurtured differently, with its own working patterns, sandbox, and tools. After a few false starts, Wal-Mart Stores learned that “ring fencing” its digital talent was the only way to ensure rapid improvements. Four years ago, the retail giant’s online business was lagging. It was late to the e-commerce market as executives protected their booming physical-retail business. When it did step into the digital space, talent was disbursed throughout the business. Its $5 billion in online sales in 2011 paled next to Amazon’s $48 billion.
In 2011, however, Wal-Mart established @WalmartLabs, an “idea incubator,” as part of its growing e-commerce division in Silicon Valley—far removed from the company’s Bentonville, Arkansas, headquarters. The group’s innovations, including a unified company-wide e-commerce platform, helped Wal-Mart increase online revenues by 30 percent in 2013, outpacing Amazon’s rate of growth.
Wal-Mart took ring fencing to the extreme, turning its e-commerce business into a separate vertical with its own profit and loss. This approach won’t work for every incumbent, and even when it does, it is not necessarily a long-term solution. Thus Telefónica this year recombined with the core business Telefónica Digital, a separate business unit created in 2011 to nurture and strengthen its portfolio of digital initiatives. To deliver in an omnichannel world, where customers expect seamless integration of digital and analog channels, seamless internal integration should be the end goal.
4. Challenge everything
The leaders of incumbent companies must aggressively challenge the status quo rather than accepting historical norms. Look at how everything is done, including the products and services you offer and the market segments you address, and ask “Why?” Assume there is an unknown start-up asking the exact same question as it plots to disrupt your business. It is no coincidence that many textbook cases of companies redefining themselves come from Silicon Valley, the epicenter of digital disruption. Think of Apple’s transformation from struggling computer maker into (among other things) the world’s largest music retailer, or eBay’s transition from online bazaar to global e-commerce platform.
Digital leaders examine all aspects of their business—both customer-facing and back-office systems and processes, up and down the supply chain—for digitally driven innovation. In 2007, car-rental company Hertz started to deploy self-service kiosks similar to those used by airlines for flight check-in. In 2011, it leapfrogged airlines by moving to dual-screen kiosks—one screen to select rental options via touch screen, a second screen at eye level to communicate with a customer agent using real-time video.
We see digital leaders thinking expansively about partnerships to deliver new value-added experiences and services. This can mean alliances that span industry sectors, such as the Energy@home partnership among Electrolux, Enel, Indesit, and Telecom Italia to create a communications platform for smart devices and domestic appliances.
5. Be quick and data driven
Rapid decision making is critical in a dynamic digital environment. Twelve-month product-release cycles are a relic. Organizations need to move to a cycle of continuous delivery and improvement, adopting methods such as agile development and “live beta,” supported by big data analytics, to increase the pace of innovation. Continuous improvement requires continuous experimentation, along with a process for quickly responding to bits of information.
Integrating data sources into a single system that is accessible to everyone in the organization will improve the “clock speed” for innovation. P&G, for example, created a single analytics portal, called the Decision Cockpit, which provides up-to-date sales data across brands, products, and regions to more than 50,000 employees globally. The portal, which emphasizes projections over historical data, lets teams quickly identify issues, such as declining market share, and take steps to address the problems.
U.S. Xpress, a US transportation company, collects data in real time from tens of thousands of sources, including in-vehicle sensors and geospatial systems. Using Apache Hadoop, an open-source tool set for data analysis, and real-time business-intelligence tools, U.S. Xpress has been able to extract game-changing insights about its fleet operations. For example, looking at the fuel consumption of idling vehicles led to changes that saved the company more than $20 million in fuel consumption in the first year alone.
6. Follow the money
Many organizations focus their digital investments on customer-facing solutions. But they can extract just as much value, if not more, from investing in back-office functions that drive operational efficiencies. A digital transformation is more than just finding new revenue streams; it’s also about creating value by reducing the costs of doing business.
Investments in digital should not be spread haphazardly across the organization under the halo of experimentation. A variety of frequent testing is critical, but teams must quickly zero in on the digital investments that create the most value—and then double down.
Often, great value is found in optimizing back-office functions. At Starbucks, one of the leaders in customer-experience innovation, just 35 of 100 active IT projects in 2013 were focused on customer- or partner-facing initiatives. One-third of these projects were devoted to improving efficiency and productivity away from the retail stores, and one-third focused on improving resilience and security. In manufacturing, P&G collaborated with the Los Alamos National Laboratory to create statistical methods to streamline processes and increase uptime at its factories, saving more than $1 billion a year.
7. Be obsessed with the customer
Rising customer expectations continue to push businesses to improve the customer experience across all channels. Excellence in one channel is no longer sufficient; customers expect the same frictionless experience in a retail store as they do when shopping online, and vice versa. Moreover, they are less accepting of bad experiences; one survey found that 89 percent of consumers began doing business with a competitor following a poor customer experience. On the flip side, 86 percent said they were willing to pay more for a better customer experience. 1
A healthy obsession with improving the customer experience is the foundation of any digital transformation. No enterprise is perfect, but leadership teams should aspire to fix every error or bad experience. Processes that enable companies to capture and learn from every customer interaction—positive or negative—help them to regularly test assumptions about how customers are using digital and constantly fine-tune the experience.
This mind-set is what enables companies to go beyond what’s normal and into the extraordinary. If online retailer Zappos is out of stock on a product, it will help you find the item from a competitor. Little wonder that 75 percent of its orders come from repeat customers.
Leaders of successful digital businesses know that it’s not enough to develop just one or two of these traits. The real innovators will learn to excel at all seven of them. Doing so requires a radically different mind-set and operating approach.
It’s important to note that I didn’t title this post “Implementing a Data Governance Architecture”. Data governance is not a technology space, tool – or architecture. As our data governance framework illustrates, tools and architecture represents but one of many facets needed to support an enterprise data governance competency. But once you’ve defined your vision and business case with a clear approach for managing the people, process and policy facets, technology can play a significant role in determining the ultimate success or failure of your data governance efforts. Complex and poorly integrated current state architectures present a significant obstacle to applying common standards for the delivery of trusted and secure data across the enterprise. Data architects play a pivotal role in enabling data governance by designing and evangelizing the data management reference architecture to support data quality and privacy requirements. In addition, these architects must recommend enabling technologies to support data governance and stewardship workflows that aid the core processes of discovery, definition, application and measurement and monitoring (Stay tuned – I’ll be sharing a lot more about these core data governance processes in a future post discussing the “Defined Processes” facet of our framework). Whatever you do, don’t fall into the all-too-common IT trap of selecting the tools before the goals, strategy and processes of data governance are in place. If you skip these steps and just try to build it, they (‘the business’) most assuredly will NOT come.
Architectural Components To Consider For Holistic Data Governance
As the graphic below represents, enterprise and data architects must consider the full lifecycle of critical enterprise data. This includes:
· Traditional upstream on-premises transactional/operational applications, systems and processes that create, update, import, or purchase data.
· Traditional downstream on-premises analytical applications, systems and processes that consolidate, reconcile, deliver and consume data.
· Exploding growth of off-premises sources and targets of data including Cloud-based applications and platforms, Social data, Mobile devices, third party data feeds, sensor data, and Hadoop analytic environments.
· Supporting data management infrastructure that enables and ensures compliance with your organization’s unique requirements for delivery of the “Right data at the right time with the right latency of the right quality and security in the right context”. Right?
· Assessment and delivery of the Shared Capabilities that must be made available across your enterprise data architecture – and not confined within specific applications or tools. A common approach includes investments in single vendor data management platform technologies that deliver many of these capabilities through pre-packaged data services. But many also design best of breed architectures leveraging existing software and infrastructure investments and deliver many of these shared capabilities through service-oriented architectures or similar data services approaches to ensure standardization, reuse and policy compliance across their data ecosystems.
Many of our clients also ask what specific enabling software capabilities they should consider to help get their data governance efforts off the ground. Some early investments to consider include:
· Data profiling. Data profiling software helps business analysts and data stewards answer the questions “what does our data look like today”, “how does data in one system relate to data in another system” and “what rules and policies should we consider defining to improve”.
· Data discovery. While data profiling allows in-depth analysis of specified data sets, data discovery allows you to identify where any data anomaly or business scenario occurs across all data sources. Many use data discovery for the purpose of figuring out “what data do I have that is relevant to this analysis or business decision?” Example: “I’m building a 360 degree customer view. What data do I have that is relevant to the kind of customer view that I want to create?” As another example, your data privacy organization may require the ability to identify where Personally Identifiable Information is used, and how that relates to specific business rules/processes where obfuscation or data masking needs to be used.
· Business glossary. A business glossary allows your business and IT stewards to capture and share the full business context around your critical data. In addition to the expected definitions of core data entities and attributes, context can also include rules, policies, reference data, free form annotation, links, and data owners, to name a few. Many organizations simply manage their shared definitions in Word documents or spreadsheets, which typically focuses on the terms and definitions but misses the broader context. A strong packaged business glossary enables collaboration across the business and IT roles that create, approve, and consume these definitions – minimizing the risk of redundancy, definition stagnation and versioning conflicts.
· Metadata management/data lineage. The ability to reconcile and provide transparency and visibility to the supporting metadata of your most critical data is a foundational element of your data management reference architecture. Data lineage visualization and auditing capabilities also allow data architects and stewards to effectively assess impact analysis of potential changes to data definitions, rules or schemas – as well as root cause analysis capabilities when responding to a data quality or security failure. IT staff ranging from data modelers, enterprise architects, business systems analysts, developers and DBAs often manage the technical metadata, while business analysts and business stewards are often responsible for the business-oriented metadata. Ideally your business glossary should be well integrated with your metadata solution.
In addition to the above, architects should also consider where and how they want to manage their data modeling, process modeling, data quality, data privacy, master data, data monitoring and auditing, workflow management, and collaboration capabilities. In addition, they must determine how data stewards will be notified and how these stewards should mitigate exceptions to any established data quality or privacy rules.
What I’ve shared here is fairly generic, meant to be used only as a guide for enterprise architects and data governance program drivers. Every organization will need to assess its own unique current state architecture and technology capabilities, and design its optimized future state data management reference architecture based on its data governance vision and business case. While the business must own the definitions of trusted, secure data – and be held accountable for the business impacts of that data – designing an effective supporting architecture with recommendations for the most appropriate enabling technologies to support all the roles within a data governance organization is a job for IT.