Data ScienceTechnology & IT

What Does Big Data Mean in 2015?

The Accenture Technology Vision Report of 2014 predicted Big to be the Next Big Thing and sure enough their predictions were spot-on. With yet another concurrent year of staggering growth in Big Data, it’s safe to say, the myth that Big Data is just a Big Hype can be put to rest.

In this year’s BARC study, findings show 83% of companies have already invested in Big Data or are considering to do so in the future, which is an almost 20% increase compared to Gartner’s 2013 results. Not only has Big Data become even bigger, but the focus has shifted, as well. In regard to Big Data, 2014 mostly marked the year for the Internet of Things (IoT) movement, overcoming data silos by facilitating the flow of data within a company, and focusing on bigger, faster and always on data centers as well as utilizing the Big Data found in collaboration networks and in apps. While all of these advancements are still relevant in 2015, Accenture finds them to have developed to a further degree.

IoT brought the data collection process off computers and into the real-world. Everyday objects are becoming increasingly connected, acquiring individual preferences and habits in depth. With this highly contextual data, companies are increasing the quality of a user’s experience by offering more personalized services. 2015, therefore, is no longer impressed by IoT but focused on IoMe (Internet of Me). Wearables, SmartHomes and connected cars alike, a more data-driven personalization is becoming the standard in all environments, both offline and online.

This not only offers customers a better overall experience with any given product, but companies are also given new channels of data collection, which is particularly relevant for the less tech-driven companies. For example, fashion designers Ralph Lauren, Diesel and Guess are getting to know their customers better with new tech-supported wearables. What this means for Big Data: IoM is enabling companies to provide personalized services by exploring new data channels.

These new channels are primarily possible, due to the fact that more and more companies are becoming more acquainted with hardware – not just traditional hardware but intelligent hardware, as well. Because sensors are more sensitive and less expensive, IoT possibilities are being utilized and in turn customers are being provided with more meaningful outcomes. This embodies 2015’s development towards an outcome economy. Thereby, the user’s perception of this outcome is of great importance. Feedback loops are implemented to obtain real value out of the data and improve the user’s experience. Brands like Verizon and Best Buy were able to seize such opportunities for improvements by combining customer feedback with other useful data, like sales trends. What this means for Big Data: circular data collection processes are creating better outcomes by focusing on revised data.

Valuable data, however, isn’t just acquired through key collection phases. Instead, Big Data in 2015 is simply predicted. Even though simple might be an exaggerated wording, intelligent systems, that sense the contextual metadata, comprehend this data and use experience, statistics or rules to perform further actions, are becoming more vital in a corporation’s strategic process. An example for this year’s interest in predictive data is retargeting. Marketing has been an area of interest for Big Data since its birth. With the advancements in ad displaying algorithms, window shoppers can be detected and a prediction can be made as to an ideal time to retarget the potential customer. What this means for Big Data: real value is being tapped by using predictive data.

It isn’t necessary for a single company to do it all. Big Data isn’t a general assessment of user data, but instead specific insight into an individual’s experience on all levels. Given the high specificity, 2015 shows a large shift towards digital business platforms. In this context platforms aren’t defined as a technical necessity but as strategic partnerships. By utilizing technologies such as easily shared Clouds or APIs, organizations are able to collaborate more easily and reap the benefits collectively. Power-couples are popping up all across the board. Philips and Salesforce as well as Apple and IBM are just a few examples of enterprises that realize their individual success is dependent on the success of their digital ecosystem. This creates a win/win/win for the cooperative companies and customers while simultaneously driving innovation. Instagram is also showing a further development in this direction by implementing a Shop Now-API onto its app. What this means for Big Data: digital ecosystems are optimizing results by utilizing shared data.

The final trend for 2015 assessed by Accenture isn’t focused on end-user Big Data but on how Big Data can be beneficial in the workplace. With advances in M2C technology, communication is becoming more natural and increasingly intuitive. For instance, many companies are investing in virtual assistants, similar to Microsoft’s Cortana, Apple’s Siri or Google Now. After Facebook’s acquisition of wit.ai, it’s been anticipated that these automated helping-hands will be integrated into the social network. This anticipation might soon be a reality, with their internally named Moneypenny for the messenger app. Amazon also recently launched their Amazon Echo with long range voice detection to deploy the assistant without having to speak into a phone. This concept can be easily transferred to the workplace. With strategically placed sensors, companies can be told when to shut down machines, when to expect high work flows and how to adjust schedules accordingly or even what to do when unsafe conditions are anticipated.

What this means for Big Data: company-specific analytics can be improved by implementing an automated data-analyzing workforce. To recap 2015 primarily incorporates a shift towards IoMe in an outcome economy with intelligent data analysis systems, strategic alliances, and an automated workforce. In regard to Big Data, focus lies on revised, predictive, and shared data gathered through different channels and analyzed by an automated staff. A shift is also apparent in the Big Data definition. It’s not the amount of data that makes it big, instead Big Data is used in a big way.

(image credit: KamiPhuc)

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