1. Data scientists will be in short supply, while data warehouse and BI folks will try to migrate over. Yet lack of experienced big data architects will represent the real hold-up for now. As Tom Groenfeldt of Forbes says, "it’s really a matter of degrees when it comes to labeling someone a big data scientist. It’s also clear that practitioners of precursor fields of big data will be lining up to get involved, yet often lack the new thinking required to master the field. But the biggest shortage in my opinion will be in the enterprise-scale strategists capable of crafting and realizing a big data vision, one step-at-a-time."
This observation is backed up by others:
There will be a shortage of talent necessary for organizations to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions.-www.mckinsey.com2. Analytics vendors will start down the big data path.
Many won’t get far, but a few will make the transition. When venerable old-guard analytics companies like SAS start releasing big data reports, you know it’s the buzz word du jour. Yet this is inevitable with any important new technology trend. What’s more significant is that very few vendors will have a comprehensive blueprint or framework for big data, most will be providing point solutions, and that’s fine. In this early wave, big data suites as such really don’t exist and it’s up to companies to curate a set of capabilities.
3. Everyone will label everything big data in 2012. This will make it hard to see what makes the approach or technology stand apart and provide a unique solution. The issue of signal-to-noise with big data marketing and hype will threaten to obscure its real meaning for some, yet others will take what big data represents — a set of innovative new approaches to solving new and long-standing business problems in a much more agile, integral, and high impact manner — as a call to significant action. The Register recently published an effective cross-check of what big data really means to those on the ground. Regardless of the term itself, from their surveys it’s clear there’s a broad perception that big data will let organizations tackle problems that were previously ‘too hard or too expensive’.4. Everyone will look for simple analysis tools for big data, but few will find them. Companies looking for instant nirvana with one-click setup and zero-configuration of big data solutions will increasingly have their needs met, but slowly at first. The problem with effective big data is that it’s not just about predetermined buckets or templates for business intelligence; it’s about meaningful analysis and processing of information in a way that’s highly relevant to the business.
5. Progress will be made. Codifying the domain of a business in order to ‘teach’ an organization’s big data platforms will turn out to be one of the outstanding challenges, but some initial solutions will emerge. Most of the better big data stories, such as the health care company that’s collecting ubiquitous fertility data, instead of just from those having fertility problems (and skewing it for everyone), are specific to a domain or industry. In other words, they’re custom-built and designed. Big data is often more about the democratization of data as Bradford Cross once put it, to be liberated for use inside and across the business, instead of limited to data scientists in white lab coats, tackling a small number of well-defined problems. To do this, we need better ways to adapt big data appliances to the details of our business. Important initial headway is being made here (example: Appistry‘s industry-specific big data solutions) and I think we’ll see much more of it this year, especially in social business fields such social marketing, Social CRM, social product development, and crisis management as well as specific domains such as life sciences, defense, and especially financial services.
6. Consumerization of big data will be one of the primary vectors into the organization for tactical needs, making ‘shadow’ big data a nascent but important new trend. I’ve made the argument that Google search is a great example of a simple big data appliance anyone can use. It analyzes the contents of most of the world’s Web sites in near real-time, allows all of it to be quickly searched using a simple interface, provides recommendations when it thinks you’re asking the wrong question, and so on. It’s in use at virtually every company in the world. It will be followed up by numerous SaaS big data services over the next few years that will bring consumer-like simplicity and power to the field. They will be so easy to start using that many workers will prefer them to any home grown solution. While this won’t always be the case (partially because the internal data is typically quite difficult to load into external services by the average worker), companies will see plenty of unsanctioned big data solutions. Not that I thinkthis is a real problem.
7. The more bureaucratic the company, the more it will struggle to embody its strategy and policy in an operational big data life cycle, despite this being the best way to obtain value. It’s hard for rigid processes and hierarchies to change, and companies either poor at using technology to solve problems or those that aren’t very agile will have more of an uphill challenge to activating on big data. I don’t expect big data to appear in these organizations in 2012, but early adopters will appear in technology, finance, healthcare, insurance, government, media, and retail businesses if they have either stiff competition or are already rapidly growing (and hence already changing) more than other businesses. Correspondingly, big data vendors that supply these industries will experience the most lift.
8. Rich data, such as audio, images, and video, will remain opaque to most organizations this year, despite advances in machine analysis of both and their growing prevalence.I’m basing this on looking at most big data offerings today, which don’t emphasize these types of data very much if at al, despite the explosion of images, audio, and high-definition video in recent years. For now, the lion’s share of big data will focus on textual processing.9. Social media and big data will have significant lift this year, though semantic processing will remain in its early stages. I recently identified nine top uses cases for social business intelligence, for which big data will be a leading solution. Social media, because it requires linguistic and natural language processing, is an ideal candidate for big data analytics yet it will be in areas requiring sophisticated link analysis like automated reputation/ influencer tracking and segmentation that will be of primary interest this year. Perhaps more importantly, big data will “complete” social business as a capability that allows the company to listen and intelligently analyze their constituents contextually and in scale (see figure above.)
10. Companies will do surprisingly well integrating early big data capabilities. This will be especially signifigant in their social business efforts. That the big software vendors are moving into the big data fray (such as with Oracle’s new big data appliance) speaks volumes. And offerings are focusing on the ease-of-use factor, both in installation and maintenance as well as operation, clearly understanding the immense competition and pressure they’ll be experiencing from online providers. I’ll be publishing a breakdown of the consumer big data app soon, but I’m virtually certain it will show a list of well-known and up-and-coming household names that you’ll be seeing in the enterprise this year as companies throw big data solutions at their issues to see what sticks.
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