Innovations in Analytics
To say that we are creating data like never before is an understatement – more than 1 billion persons use Facebook every day, about 1 trillion photos are taken annually, and approximately 1.4 billion smartphones are shipped every year (Bernard Marr, Forbes, 2015). All this data needs to be analyzed by large corporations in order to understand consumer trends and sentiments, and that is where technologies such as analytics and big data come in. In addition, while customer insight is the largest beneficiary of analytics, it is not the only significant big data application; other use cases for this technology include operational analytics, fraud detection and new product creation.
It is not surprising then that the big data market is expected to grow at phenomenal rates going forward. As per IDC, the big data technology and services market is expected to grow at a CAGR of 23.1% over 5 years and reach $48.6 billion in 2019, with all the major big data segments – infrastructure (the largest), software, and services expected to grow at CAGRs of 20%+ each (IDC, 2015). As per Frost and Sullivan, the big data market will generate revenues of $122 billion by 2025, with global data traffic crossing 100 zettabytes annually (Frost and Sullivan, 2014).
Innovations in Analytics
Given the potential of big data and analytics, it’s no surprise that a number of proactive organizations (apart from Amazon and Google) have already found innovative uses of big data and analytics in their operations.
While AT&T has been optimizing the customer experience and networks using big data, GE has created a ‘digital thread’ to integrate information silos in factories and use machine learning to create new manufacturing efficiencies. While retailers are using analytics to analyze weather patterns and in turn using the information to stock specific outlet supplies, industrial companies such as John Deere are using IOT sensors to undertake preventive maintenance of machinery, with the emphasis now on monitoring soil and weather as well to improve overall agricultural efficiencies.
Pharma companies are using big data to reduce research work time, effort and costs; and life insurers are reducing lab costs by predicting which consumers will require blood tests by correlating user responses with external data. Financial companies are using analytics for reducing risks, meeting regulatory objectives, determining pricing in real time and even for advisory services (robo-advisors).
While automotive companies are using big data in concepts such as the connected car, the real estate sector is using analytics for checking building structure strength, for building management, and as we are all aware, in ‘smart homes’. Companies across industries are using analytics in various ways, including for increasing loyalty, reducing attrition rates and finding new cross-selling opportunities.
In fact, analytics is now going mainstream – NFL teams are using data analysis to predict star athlete’s injuries during a football season, recording companies are using predictive analytics to estimate which band will rock the charts tomorrow, and even police departments are using analytics technology for better policing and reporting.
The Future of Big Data
Already in use and driving innovation today, big data is expected to have an even bigger impact in the future. For example: smart machines will likely complement employees more; visual data discovery tools, face recognition and cognitive technology will evolve to become more mainstream; machine learning and real-time streaming insights will improve automated decision making abilities; automated data and content preparation, data analysis, data monetization and memory-optimized technology will likely show greater growth; algorithms will be available directly instead of being created; and connected homes will reach the next level with more standardization and better connectivity. The possibilities associated with big data evolution are virtually endless.
Big Data Best Practices & Suggestions
Though big data and analytics have immense potential, it is important to understand that the technology is not a ‘magic wand’. In fact as per Gartner, through 2017, 60% of big data projects will fail to undergo full implementation and will need to be abandoned altogether (Gartner, 2015).
Big data, though a revolutionary technology, still requires a key set of essentials for it to be successful – which includes having the right technology, people and ongoing, strong collaboration between business and IT. Thus, state of the art analytic tools and clean data are essential for successful analytic implementations, as is focusing on the softer aspects of analytics. These aspects include transforming the company culture to be more data driven, hiring the right talent, having the confidence to fund analytic investments and to trust data, and to drive big data usage across the entire organization.
Given the potential of analytics, it is essential that companies start incorporating this technology in different areas, and couple it with optimum processes and support to derive full benefits.