The business world is very familiar with the value of data. But until recently, amassing and storing this information came at great effort and cost. Much of this data had to be actively assembled and contained in-house in CRMs, sales databases, collected through surveys or through external services. Doing it internally required huge manpower investments, and farming it out created billion-dollar practices around finding, gathering and analyzing data for customers. The cost was worth it because the insights revealed were highly valuable.
The emergence of digital communication platforms extends the amount and types of data available. Website traffic, email communication performance, search engine marketing, banner advertising clicks, ad acquisition metrics, and other polls and surveys conducted by organizations give new insight into the activities of current stakeholders. But much of this data is also proprietary and accessible inside the company. If they needed external information or input, companies had to ask it of their target audiences directly or outsource the task. New data gathering techniques and channels, along with expanded data storage and mining capabilities, make parts of the process easier and more effective. But at its core, the process remains very similar to the existing active data collection model.
The emergence of social and digital media is more than just a more efficient way to directly communicate with external stakeholders; these platforms allow companies to access the conversations of their target audiences passively. Companies don't need to ask questions to the outside world or infer possible responses through educated guessing based on past behaviors. All a company needs to do now is listen. The answers to their questions are happening in ongoing conversation threads, reviews, posts, tweets, walls and comments.
Not only is this a great new opportunity for insight, this shift also presents new problems to organizations in both analysis and scale. The sheer volume makes the task of just listening overwhelming. Technologies in data storage and management have continuously improved to be better and cheaper, which has allowed many more organizations to efficiently gather, store and manipulate huge amounts of data. Companies like Amazon have even marketed this massive data storage and management capability (Amazon Web Services or AWS) to the public. Built initially to serve Amazon's internal needs, AWS provides hugely scalable storage and computational services unavailable to smaller companies. Using AWS or a competitor, any size organization can store and process massive amounts of data for relatively low costs.
But technology is only part of the solution. We need to greatly increase our analytical savvy and define real goals, successes and metrics beyond "likes" and "follows." Heavy reliance on the simple (and not very informative) metrics of the early stages of these platforms has understandably led to the current distrust of claims of ROI. Of course, some of this skepticism is deserved because social and digital practitioners in the public relations, policy communications and issues management space have relied too heavily on the novelty of the space and the ignorance of the larger community. But the time for actual quantifiable metrics has arrived, and those that will succeed are the ones that act on this opportunity.
Businesses and organizations are starting to demand this level of precision. Metrics driven communication, advocacy and outreach efforts are now becoming the expectation. The value is clear -- creating a set of tools and metrics that not only clearly tracks "apples" to "apples" over the course of a campaign, but also clearly defines what the "apple" is has shifted the thinking of the communications industry. More granular result tracking is now possible for both performances against KPIs as well as against time targets. The standardization of data measurements also allows us to more closely and accurately track these indicators over the duration of the effort.
Changes in expectations have coincided with improvements in data access, an increase in user participation and a greater level of rigor in social and web analytics. As these capabilities and successes become more widely known, demand for rigorous web data analytics will rise.
This will also drive a growth in data availability from the platforms that house it. This shift has already begun. Application Programming Interfaces (APIs) have expanded to make more data available and increases in user activity (and therefore volume of data) have opened a completely new era in direct-access data acquisition.
There is no reason to believe that this trend is going to slow any time soon. Now that the data genie is out of the bottle, there will not only be increased demand for access and analysis of this information, but also an increased call for making this sort of data content more available from social and digital venues. More data access will increase the demand for measurable results, which in turn will fuel the demand for more data.
More open data APIs and greater transparency will be real differentiators for organizations that possess this kind of information, and internal data mining efforts will be increasingly called upon to produce value.
The "nice-to-have" external data analysis projects of the past and present will be baked into business intelligence efforts of the future.
Data from social networks like Twitter, LinkedIn and Facebook, and search data from Google, Bing, and Yahoo! are not only going to continue to grow in value and importance to organizations, but will also almost certainly be joined by new venues, networks, platforms and channels. Virtual environments, mobile and location-based platforms, niche social media venues, social aggregation, and all the other items currently on a drawing board all represent potential new sources of information and data, intelligence and metrics.
The "Age of Big Data" represents an enormous opportunity for the companies, organizations and communicators who recognize it.