To take advantage of that potential, business leaders need to know what steps to take in order to make maximum use of their data asset. Business success with big data is not just about choosing the right cloud technologies or hiring smart data scientists – it’s about creating a business-centric approach that connects a company’s data to its business strategy, enables continual improvement, and follows through to impact processes, margins, and customer satisfaction.

In my experience with big data I have developed seven steps that can help any business leader drive more success with their big data initiatives.

1. Create a strategy for your data

Your data needs a strong strategy, one that connects to and underpins your business strategy and also integrates with department-level accountabilities. Develop a plan for where you want to be at each milestone, define your future capabilities clearly, and describe how your data capabilities will be utilized. Then hold your users accountable to where and how they will use the new capabilities, and what business impact they will drive.

For example, if you can use big data to improve in-store sales, you need to not only work with store managers to define capabilities that connect to their strategy, but also ensure the managers are held accountable to using the data capabilities correctly and delivering the intended impact. Doing so connects both your and their goals to the overall business strategy, helps create more usable capabilities, and ensures any needed iterations will be done jointly. A well-conceived data strategy will give you the most bang for the buck on your data investment.

2. Design for agility

Big data systems are just that…big, which means they tend to be inflexible. A great BI system, by definition, will cause a business to change, which in turn will require the BI system to change. Thus, your systems need to adapt quickly to keep pace with your business. Twelve- or 18-month release cycles are appropriate for certain parts of your system, but 3- or 6-month cycles may be appropriate for others. Carefully analyze each component of your big data system, and design for the right amount of agility you need.

You may decide to build higher levels of automation into the layers of your stack that change slowly, while reserving configuration-based approaches for layers of your stack that need to change quickly. In general, the top layers of your stack (e.g., user interfaces and reporting tools) need to be more agile than the bottom layers of your stack (e.g., data collection and storage), but many exceptions to this rule exist. Only careful analysis and understanding of your current and future uses of data will enable you to make the right decisions on agility. Designing for agility will enable your big data investment to keep pace with, and even lead, your business.

3. Understand latencies

Latency is a challenge in traditional BI systems, and big data only amplifies the problem. Big data solutions tend to be architected first as batch systems, with lower latency capabilities being addressed afterwards. Don’t save latency for last – analyze your key use scenarios in terms of latencies, and connect them clearly to business drivers. Focus on delivering the right latency for each need, including the value being driven, and let those needs drive your design.  Certain low latency needs may require bypassing your big data system temporarily, sharing directly between systems in order to deliver specific scenarios.

For example, if your customers tend to interact with system A and system B in parallel or in quick sequence, these two systems may need to share data directly. The data can then be written into the big data system in time to be used by other systems. Delivering data on a real-time or near-real-time basis can be very expensive; thus, it’s better to think in terms of “right-time” data targeted to each need.

Describe latency requirements in detail, and ensure the business justification is sound. Understanding latencies will enable you to deliver data exactly when it’s needed, while keeping costs under control.

4. Invest in data quality and metadata

Data quality in any system is a constant battle, and big data systems are no exception; however, big data systems require much more automation and advance planning.  You should first ensure that data quality is not treated as a project or initiative, but as a foundational layer of your data stack that receives adequate resourcing and management attention.  Second, build in multiple lines of defense – from data mastering (where, for example, you are creating customer accounts) to data collection (where you are recording all of that customer’s interactions with you) to metadata (where you are organizing and dimensionalizing the data to aid in future reporting and analysis).  Third, automate both the processes that identify and elevate data quality issues, and the measurement and reporting of data quality progress.  Empower your data quality team with tools that solve problems at high scale, such as diagnostic and workflow tools.  Efficient data quality practices will enable your big data system to earn its place as a trusted input for key business processes.

5. Get good at prototyping

The data sizes in most big data systems are too large to work with all at once, so it’s typically wiser to build small-scale prototypes to iron out the wrinkles and ensure you are meeting customer requirements. If you are building complex data integrations, online algorithms, or user interfaces, prototyping allows you to learn at a smaller and less costly scale. What’s more, prototypes can be shared early with your user base, which generates valuable feedback as well as excitement.

Prototyping requires somewhat unique skills that you will need to build and refine over time. Prototypers need to be able to move quickly, figure out new designs and technologies, understand user scenarios, actively solicit feedback, and not be afraid to fail. They need to be creative in their approach to solving problems, while still rooted in sound data mechanics. Why is prototyping better than wireframes or feature lists? Since prototypes are “real,” your users will give you better feedback; at the same time you will also understand some of the challenges you will face as you build the full-scale version. Building a strong prototyping capability will help you increase innovation and speed, while reducing the cost of mistakes.

6. Get great at sampling

Sampling will save you a lot of time if you learn how to do it correctly. There are many use cases for which sampling is an effective alternative to using full census (100 percent) data. Certain needs such as creating personalized experiences for each customer, or calculating executive accountability metrics, are not appropriate for sampling. But for many other needs, sampling is a viable option.

For example, understanding product or feature performance, looking at patterns and trends over time, and filtering for unexpected anomalies can typically be done on sampled data. One approach is to collect 100 percent of the data, but do most of your analysis on samples, and then confirm important conclusions on the full data set. Once you establish process flows to pull sampled data into standard tools such as Excel and/or SQL, you will see analyst productivity increase substantially, which will save you time and money and increase the job satisfaction of your analysts.

To get great at sampling, you need to do three things: first, develop standard sampled data sets that help your analysts address large swaths of business questions, updating them regularly; second, make sure you have at least one highly qualified individual (i.e. a statistician) who can ensure the data is being sampled correctly and results are not misapplied; and third, educate decision makers on the benefits and limitations of sampling so they can get comfortable making decisions with sampled data. The effective use of sampling increases productivity while delivering equivalent business value.

7. Ask for regular feedback

Big data is a learning process, both in terms of managing the data and in driving business value from its contents. Your internal user base is a valuable source of feedback and integral to your learning and development process. Your prototyping program will be a source of feedback, but you should also survey your users and benchmark your progress over time. Areas such as usability, data quality, and data latency are all categories within which users will give you feedback. In addition, you should ask for ad hoc feedback from every level of your stakeholder organizations so they see your commitment to making their business better.

As your data asset’s reputation grows, your stakeholders will give you more and better feedback, which will allow you to develop integrated goals and roadmaps, and drive more business benefit as a result. Regular feedback ensures your big data system is tightly integrated into business decision making, so it can play a lead role in business improvement.

Following the above steps will help you build more effective big data capabilities, saving you time and money, and driving maximum ROI for your business. The big data frontier is here; breaking through it requires an understanding of which steps will help you drive the most impact.

Chad Richeson is the CEO of Society Consulting, a Seattle-based analytics and technology consulting firm that provides business-driven data strategies, solutions, and analytics for its clients. Before joining Society Consulting in 2011, Chad spent 12 years at Microsoft driving analytics and big data solutions for Bing, MSN, Mobile and AdCenter.

Image courtesy of Flickr user Susan NYC.

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Vuzix has announced plans to develop a stylish head-mounted display solution in the form of Smart Glasses, through a licensing partnership with Nokia. The yet-unnamed product would integrate a bright, high-contrast display with a pair of seemingly ordinary-looking sunglasses — sounds like a perfect companion to the ZionEyez in-glasses camera prototype we saw last month. In Vuzix’s words:

This amazing new technology starts with a compact display engine capable of hi contrast and brightness for outdoor use. The output is then relayed into a 1.4 mm thick plastic waveguide lens with input and output hologram structures on the surface which squeezes the light down the waveguide and then two dimensionally expands the image back into the user’s eye, creating an image that is then mixed into the real world.

Naturally, the company envisions its Smart Glasses solution as a web-connected device, letting you watch videos or browse the internet while still being able to see-and-avoid pedestrians as you walk on the sidewalk or obstacles while behind the wheel — try doing that with a Kindle or smartphone (better yet, please don’t). Vuzix expects its Smart Glasses solution to start appearing as early as this summer, but we’ll be getting an early look next week at CES.

Continue reading Vuzix designs Smart Glasses to look like sunshades, tout connected transparent display

Vuzix designs Smart Glasses to look like sunshades, tout connected transparent display originally appeared on Engadget on Sat, 07 Jan 2012 00:01:00 EDT. Please see our terms for use of feeds.

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Nokia’s second generation Extended Depth of Field (EDoF) camera was recently put through the wringer by the folks at All About Symbian, and for a technology that traditionally fails at macro shots, we’re happy to see that the engineers in Espoo have made dramatic advances with this second go-around. Using the same lens and sensor as before, we’re told the improvements come from a refined image processor alone. In a side-by-side comparison, this business was card snapped from 12-inches (30cm) away, first with a C7 and then a 701. While the new setup isn’t perfect, the difference is certainly appreciable. Of course, Nokia has all but ditched EDoF in its high end smartphones — as evidenced by our reviews of the N9 and Lumia 800 — but the technology remains very much alive within its lower-end affairs, and really, who doesn’t like to see improvement? For a complete breakdown of the new technology, be sure to hit up the source link below.

Nokia’s latest EDoF camera demonstrates improvement with macro shots originally appeared on Engadget on Sun, 20 Nov 2011 15:46:00 EDT. Please see our terms for use of feeds.

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In a tech environment where most venture cash seems to be going to consumer-facing startups on the application layer, Qwilt’s plan to introduce new technology into the network might seem peculiar. But the company seeks to solve a serious problem with its technology: How can network operators best deal with the explosion of video data flowing through their networks?

By 2012, video will make up about half of all network traffic, according to Cisco, and will grow to 62 percent by 2015. That’s growing substantially, particularly as more video publishers make their content available over the Internet and as more devices — like connected TVs, Blu-ray players and game consoles, make it easy to watch IP-delivered video in a user’s living room.

All that growth has operators up in arms about the amount of capacity needed to usher that video traffic over the Internet and into their networks. Until now, many have thrown capacity at the problem by upgrading their networks. But that gets expensive, and isn’t a very capital-efficient way of dealing with the problem.

Qwilt’s solution relies on transparent caching technology designed to ensure that popular videos from services like Netflix, Hulu and YouTube can be stored at the edge of the network. That means video files are served up closer to the end user, which results in better video quality, at the same time that it offloads traffic that otherwise would travel over aggregation and core network infrastructure. By doing so, Qwilt can reduce network overhead by 60 to 80 percent, CEO Alon Moar told me in an interview.

That message resonates with some ISPs who are happy to reduce the amount of traffic that flows through their pipes. Moar told me Qwilt is in conversations with about 50 ISPs and trialing the technology with five ISPs. The caching product, which is primarily software-based and can run on any commercial off-the-shelf hardware, will be sold to ISPs for placement in their edge networks. With trials underway, Qwilt expects general availability of the product in the first half of 2012.

Qwilt has about 30 employees, with an engineering team in Israel and sales offices in London and Silicon Valley. On Qwilt’s board are chairman Yuval Shahar, former CEO of P-Cube (which was acquired by Cisco in 2004); Accel Partner Richard Wong, who invested in companies like Facebook, Riverbed and Infinera; former Accel partner Peter Wagner; Crecent Point partner Ohad Finkelstein; serial entrepreneur Giora Yaron and former RealNetworks CEO Rob Glaser.

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The computer tablet is actually quite often known as the pc tablet. Given that the isn’t a Computer, plus some tablets are expected to be Linux based, the computer tablet is a correct name. While it’s bigger than the cell phone, it really is smaller compared to a notebook.

This mobile computer system is normally a flat display which has a digital keyboard set that may appear on the actual screen. You may use your hands and fingers or simply a pen type product for you to type around the pc tablet. Nearly everyone is touch empowered letting you make use of your hands to be able to “switch” web pages, enlarge, shrink and navigate around your monitor.

Around 2000, Microsoft utilised the phrase “Microsoft tablet Pc”, in those days however the tablet pc was generally some sort of note taking gadget constructed for business people. Plagued with functionality difficulties as well as far too heavy for longer use, this failed to turn into a item customers wished for. After that Apple was ready to take the particular pc tablet to the industry. Within the year of 2010, the iPad ended up being rapidly ordered by Apple and gadget addicts. About it, your touch user interface was in fact changed plus making it a fundamental part of the new pc tablet engineering. Right now in time, you will get a selection of free tablet pc with phone contracts.

The touchscreen provides for modifications that a computer with key-board fails to. Those who employ a stylus pen often understand writing. The particular touchscreen technology groundwork helps make the tablet pcs more easy to use as they are usually incredibly perceptive. Along with a stylus pen or maybe a hand, electronic digital artwork and drawing are a lot easier as compared to any personal computer mouse that provides excitement for painters of all types.

Typically the pc tablet personal computer is higher priced at the moment as compared with a few laptops, but that cost is likely to tumble. The void of a key pad indicates entering inputs are sluggish, having said that you’ll find computer keyboard attachments being put together. Since you’re constantly coming in contact with the actual display with your hand or stylus there is a risk to damage. In addition given that it frequently carried as well as handled far more, they could suffer problems with blows and improper use. Though with all this information demand for tablet pcs is expected to continue to rise till a different pc presents itself.

Led by scientists Stefan Luther and Flavio Fenton, the team claims that by using a series of five pulses of less potent shocks (instead of a single concentrated charge), docs can see an 84 percent reduction in damaging power. This new technology — coined low-energy antifibrillation pacing (LEAP) — can also be used in implanted defibrillators, not just the well-known flappy paddles. Due to the relatively low emissions, both the patient and such implants have extended lives. And that, friends, is good for us all — given the impending Robot Apocalypse, we’ll be needing those extra years just to hold down the fort.

Scientists find less damaging defibrillation method, heart tissue relieved originally appeared on Engadget on Sat, 16 Jul 2011 01:52:00 EDT. Please see our terms for use of feeds.

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Continue reading Sony MiniDisc Walkman to stop spinning in September (video)

Sony MiniDisc Walkman to stop spinning in September (video) originally appeared on Engadget on Fri, 08 Jul 2011 22:25:00 EDT. Please see our terms for use of feeds.

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But those results are bogus, Hitz said. As one example, he said that when he goes to approve his assistant’s time card, he ends up in a payroll system in the cloud. “But people will say, ‘Payroll is not a mission critical application.’ To which I say, try not paying your employees and see how long your company lasts,” Hitz said.

But it’s not just mission critical applications like payroll that are powered by the cloud; In the case of NetApp, Hitz said policy management applications, the consumer-facing website and even the company’s blog are all cloud-based. Hitz said it reminded him of the days when he would visit customers and they would say they didn’t have a PC strategy. And yet, all of their employees would have computers in front of them.

That is, just because a company hasn’t built a strategy around usage of a new technology doesn’t mean it’s not already using it. For organizations, however, it’s important that they embrace and understand those technologies. “You have to get your arms around it,” Hitz said.

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