Data lakes are increasingly becoming a popular approach to getting started with big data. Simply put, a data lake is a central location where all applications that generate or consume data go to get raw data in its native form. This enables faster application development, both transactional and analytical, as the application developer has a standard location and interface to write data that the application will generate and a standard location and interface to read data that it needs for the application.
However, left unchecked, data lakes can quickly become toxic, becoming a cost to maintain whereas the value delivered from them shrinks or simply does not materialize. Here are some common mistakes that can make your data lake toxic.
Your big data strategy ends at the data lake.
A common mistake is to choose a data lake as the implementation of the big data strategy. This…
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In the early 1960s, chaos theory pioneer Edward Lorenz famously asked, “Does the flap of a butterfly’s wings in Brazil set off a tornado in Texas?” Lorenz theorized that small initial differences in an atmospheric system could result in large and unexpected future impacts.
Similar “butterfly effects” can surface in the increasingly interconnected and complex universe of enterprise partnerships and supply-chain and cross-product relationships. It’s a world where new or evolving products, services, partnerships, and changes in demand can have unexpected and surprising effects on users and other products, services, traffic, and transactions in a company’s ecosystem.
Monitoring these complex relationships and the potentially important changes that can reverberate through an enterprise’s network calls for an interconnected system of virtual “sensors,” which can be configured and tuned to help make sense of these unexpected changes. As enterprises increasingly interface with customers, partners, and employees via apps…
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“That’s great, it starts with an earthquake, birds and snakes, an aeroplane, and Lenny Bruce is not afraid.” –REM, “It’s the End of the World as We Know It (and I Feel Fine)”
REM’s famous “It’s the End of the World…”song rode high on the college radio circuit back in the late 1980s. It was a catchy tune, but it also stands out because of its rapid-fire, stream-of-consciousness lyrics and — at least in my mind — it symbolizes a key aspect of the future of data analytics.
The stream-of-consciousness narrative is a tool used by writers to depict their characters’ thought processes. It also represents a change in approach that traditional analytics product builders have to embrace and understand in order to boost the agility and efficiency of the data analysis process.
Traditional analytics products were designed for data scientists and business intelligence specialists; these…
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A large number of enterprise report a high level of inertia around getting started with Big Data. Either they are not sure about the problems that they need to solve using Big Data or they get distracted by the question of which Big Data technology to invest in and less on the business value they should be focusing on. This is often due to a lack of understanding of what business problems need to be solved and can be solved through data analysis. This causes enterprises to focus their valuable initial time and resources on evaluating new Big Data technologies without a concrete plan to deliver customer or business value through such investments. For enterprises that might find themselves in this trap, here are some trends and ideas to keep in mind.
Commoditization and maturation of Big Data technologies
Big Data technologies are going to get…
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Product success can be measured in numerous ways, including the rate of user signups, the number of popular features, the frequency of use and the duration of sessions. But the one metric that’s hardest to measure but most significant is delight.
In short, delight produces long-lasting loyalty and passion in users. It persuades and convinces them to not only continue using a product but also encourage everyone around to do so, too.
Delightful products stand out from the competition. Often, such products have little to no advertising because it’s not needed. These products are characterized by the ease of discovery, learning, use and reuse. Delightful products are talked about, tweeted about, shared and possess extensive word-of-mouth.
Members of a development team should understand what delight looks like. They need to postulate, hypothesize and understand what it would mean. They should determine how to detect the difference between a delighted user and an indifferent…
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Users love products and services that make them feel smarter. The more efficiently they can spend their valuable attention, time and money, the smarter they feel. The smarter that users feel when interacting with your product, the more they love it. We call this the smart-user theorem.
Strong examples of the smart-user theorem in action abound. Facebook and Instagram save users time by enabling them to connect and share with friends and family quickly and efficiently. Similarly, apps have become popular and ubiquitous, partly because of their availability to fulfill virtually any need or task.
The simplicity of the interface and the entire value chain on the iPad, the ease of planning a trip on Expedia via a mobile device or using Dropbox to store files — these are more examples that offer powerful guiding principles for enterprises as they engage customers with their products and…
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