Here at InsightaaS, we’ve organized content around the “five Cs” of cloud, collaboration, cognition, communications and clients. In conversation with Information Builders, we explored the “five Vs” of Big Data analytics – volume, variety, velocity, veracity, and value. For those who aren’t schooled in the “Vs,” they refer to:
- Volume: the total amount of data that is available for analysis
- Velocity: the speed with which data is added to the repository that is being analyzed
- Variety: the variation in the types of data (for example, sales data, weather data, social media inputs, video, etc.) that are being analyzed
- Veracity: the integrity of the data; the extent to which analysis based on that data can be trusted to reflect the reality of the situation it represents
- Value: the business value of the analysis of the data
The first three Vs have been part of the Big Data discussion since at least 2001, when they were highlighted in a blog post by Meta Group (now Gartner) analyst Doug Laney. The fourth V, veracity, has gradually come into use as organizations refine their Big Data analytics strategies. The fifth V, value, is not commonly considered to be part of the debate – but it was a central point in our discussion with Information Builders. Brian Joynt, general manager for Information Builders in Canada, sees customers digging beyond questions regarding what to collect and analyze and how to formulate and distribute results; he sees customers looking to understand “how is [Big Data analytics] going to make me money or save me money?” Michael Corcoran, chief marketing officer for Information Builders, expands on this issue: “you can’t just use this technology to service a specialized few people [such as] the data scientists in the back office,” he says, noting that data mining specialists are important, but that analysis in and of itself “is not where you make your money…it’s very difficult to really answer the value/ROI question” when delivery is focused on “just a few pairs of eyes…for us, it’s about big adoption more than Big Data.”
Through the course of our discussion with Information Builders, InsightaaS found six key issues that need to be addressed to ensure that Big Data analytics delivers measurable business benefit. Here (with accompanying explanations from Corcoran and Joynt) are the six key factors:
1. Don’t view Big Data analytics as a generic function – position it as a key element in business-focused applications
In the IT world, there are technologies that take root as stand-alone capabilities (such as CRM), and others (such as video) that end up enabling a swath of other applications, rather than being the primary focus of discrete deployments. For many years, there has been a question as to whether business intelligence (BI) is better suited to the first model or to the second; our observation is that analytics is better positioned as a core component of many different types of systems than as an individual, siloed capability.
This perspective was echoed in our discussion with Information Builders, when we started to dig into the role of Big Data analytics in the cloud. InsightaaS asked Information Builders, “is it the case that BI ends up being an embedded function rather than the focus of the application?” Joynt was immediate and unequivocal in his response: “Absolutely.” He added that cloud adoption is driven primarily by the opportunity to quickly build and deploy customer-facing systems, and that with embedded BI, firms are “able to see the benefits [of new cloud-based systems] much more quickly.” Corcoran added an example of a customer specializing in the production of statements. The customer has moved from paper-based to e-statements, and with the transition, has unlocked opportunities for related data-driven services – “analytics, comparative spend, month over month… where my top five spend is [in the case of] a credit card, or [in the case of a telephony statement] show me my top five phone numbers that I call…” This kind of capability enhances the utility of the statement/service to both business and consumer customers.
2. Get data into the hands of as many users as possible
It has become axiomatic that “data scientist” is a position that is and will increasingly become a position in high demand, as companies look to convert the Big Data that they are collecting into actionable insights. Often, in its early stages, Big Data appears almost like a solution in search of a problem; for example, Gus Hunt, the CIO of the CIA, was quoted (in FCW) as saying, “The value of any piece of information is only known when you can connect it with something else that arrives at a future point in time. Since you can’t connect dots you don’t have…we fundamentally try to collect everything and hang on to it forever.” Big Data scientists are often viewed as the next link in this chain, identifying key connections and surfacing their implications.
3, Use data to support current business objectives – and be prepared to discover additional ways in which Big Data has business impact
There is a pattern common to new technologies: they are often cost-justified on their ability to efficiently address an existing issue, but their value is amplified as adopters discover additional benefits that the new technologies unlock. This is likely to be the case with Big Data, as the availability of previously-unknown patterns and connections creates opportunities for new use cases. Corcoran uses the example of a property management firm that used Information Builders technology to provide insight to the sales force – “inventory of all the customers they covered, when the leases expire, what are the leaseholds, what are the amounts, square footage, properties available.” The cost of the deployment was justified on the reduction in the cost of compiling paper-based reports. Over time, however, the system also became valuable as a recruitment tool, enabling the client to build “a stable of top performers.” Joynt adds that there are other common extensions of Big Data-based sales systems: “for a lot of these firms, the initial implementation driver was retention, just trying to protect their customer base. And what they find, pretty quickly is, while it does serve that need, sales people start using it as a selling tool for new customer acquisition much more quickly than [Big Data adopters] anticipated.”
4. Extend data reach into the hands of the customers
In our analysis of the changes that cloud is likely to bring to corporate IT, InsightaaS is on record as saying that the traditional “3 C” approach to technology, in which systems are used to count/categorize, communicate, and control processes, will be supplemented by the “3 Es” of engage, entertain and envelop – new modes of deployment that move IT from a discrete corporate function to a core component of customer-facing business processes. Increasingly, new technologies will used to extend market reach, and will demonstrate their value in terms of increased revenue and customer retention.
For Information Builders, the movement towards customer-facing analytics has been, well, customer driven. It started, Joynt said, when two of the initial customers of the web version of the company’s BI system asked “I could put this outside, on the internet as well, right, because its web technology?” One of these was the Spanish Railway System, which wanted to deploy an online scheduling system that would be accessed by millions of people. The trend has continued from there; Corcoran reports that Information Builders has just started working with Yellow Pages on a system that will enable small businesses to do “predictive what-ifs – what if I increase my ad space? What if I increase my spend in this area? What kind of expected outcome can I get?” – and will then track actual results against the chosen approach.
5. Understand that data is a potential source of customer engagement and revenue
One of the seminal business texts on cloud, Consumption Economics, holds that an essential key to unlocking the value of cloud is understanding that much of this value is contained in the data that cloud-based systems create. InsightaaS has already documented examples where the information generated from cloud-based systems can increase the strategic importance of a supplier’s service, increase the stickiness and/or value of the service, and even expand a firm’s total addressable market.
While the value of insights gleaned from cloud-based systems will likely become evident in many industries, it is clear already in certain sectors, notably transaction processing businesses that are collection points for large volumes of data. Corcoran notes that firms such as credit card processors “have figured out that they can put up hosted applications to provide [analytics] back to the merchants, especially those who don’t have their own investments in IP, and therefore, don’t have point of sale analytics.” He adds that while this presents clear opportunities for service provision to smaller firms, “there’s also interest from third parties…[for example], sometimes the government is interested in trends, and those credit card processors have collected great data, from multiple retailers, from multiple consumers…[we] constantly see that there are new uses for data. I think it creates a lot of excitement, a lot of opportunity” for many different types of firms – and of course, for suppliers like Information Builders that enable cloud-based Big Data analytics.
6. With M2M and social media, Big Data (and its application) is likely to get much, much bigger
There are many examples of how machine-generated data can create volumes of information that are nearly unimaginable today. For example, it is estimated (by the GSMA) that there are 30 million connected vehicles on the road today, a figure that will grow to 600 million in 2025. The U.S. Department of Transportation currently assumes that each of these vehicles will communicate 150 mb worth of data each month. We might expect that this figure will grow over time – but even if it does not, 600 million vehicles will add more than one zettabyte (one billion terabytes) in wireless information to our current sources. If we consider that vehicles are just one of literally thousands of potential M2M data sources – and that the zettabyte figure is most likely low for 2025 data transmission from this one source – we start to get a sense as to how M2M data will radically reshape the body of information that is available for analysis.
Information Builders uses other examples to illustrate how M2M and social data may impact the creation and consumption of information. Corcoran looks at a scenario from healthcare, where “you’re in pre-op, and they’re taking your vitals every half-hour, There are a lot of researchers who want to know, ‘what about all that other data in between those two half-hour reads? We’d like to see what happened when you were given an adrenaline shot or when you give this medication. We’d like to see the fluctuations in heart rates and things…’” With respect to Big Data from social media, Corcoran sees the value being unlocked when insight from social trends is connected “back to the enterprise,” so that a manager responsible for a marketing campaign can align multiple relevant feedback points: “I can see what people are saying about us. I can see the impact on revenue. I can take and tie it to what our own employees are saying about this campaign because I have that [range of input information] reachable, at different points of integration.”
Concluding thoughts: These are still early days in the Big Data journey; InsightaaS’s “SoLoMoN” timeframe calls for it to become a core component of cloud systems in 2016. However, we are certainly close enough to that point to begin to understand how Big Data will be deployed, and the various ways that it will contribute value to lead adopters. The six issues identified here – positioning Big Data analytics as a key element in business-focused applications, getting data into the hands of as many users as possible, looking at Big Data both as a means of addressing current needs and a means of providing future value, extending data into the hands of customers, appreciating the customer benefits of Big Data access, and planning for large-scale increases in available data caused by M2M and social networks – provide signposts that early and potential adopters of Big Data analytics can use to understand and capture potential sources of value. By understanding how to align the “5 Vs” against these objectives, IT and business managers can formulate Big Data strategies that will evolve with new opportunities arising inside and outside their organizations.