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2016 Top Ten Trends for Big Data
January 19, 2016


In the beginning of 2016, let’s explore ten big data trends so that we can better prepare ourselves and apply big data for our business, work and life effectively in the time ahead.


  1. Cognitive and Graph Analytics – Making Better Decision

People and machine will join forces to make better decisions. With big data analytics embedded in our processes, automated decisions will get into our daily works and lives. The advancement on Cognitive Analytics, which is a hybrid of machine learning, natural language processing and advanced analytics infrastructure, will take this to next stage. It will become disruptive to many of us and our businesses. Unstructured data will drive the adoption of graph database and graph analytics. It will strengthen our capability to analyze the relationships in the network of networks in the virtual world. Algorithms for predictive and prescriptive use will be gradually adopted in more traditional industries as their top management start knowing it more and recognizing big data is a new normal for competitions in these days.


  1. Intelligence-as-a-Service – Moving Big Data into Mainstream

Data scientists are in high demand. Even large enterprises cannot hire or keep data science talents as they want. SMEs are in an even worse position in this talent war. A new business model called “Intelligence-as-a-Service” (IaaS) might help this challenge. Companies can outsource their whole data analysis process to external companies with data science expertise. The IaaS company will simply makes decisions for their clients by taking into account all possible variables and handle the data correctness, compliance and security issues. They will charge the clients by subscription or Pay-as-You-Go(PAYG) scheme. Data Brokerage is another type of new middleman. It proactively collects data and sell to clients.


  1. New Big Data Roles– Making Big Data Work in Organizations

Companies, which are getting serious about big data, will set up new roles to drive it in their organizations. Chief Data Officer (CDO), Chief Analytics Officer (CAO) and Chief Privacy Officer (CPO) are just some of those new roles. Apart from new positions, these companies will also set up Analytics Center of Excellence (ACoE) to drive their business-aligned big data strategy. It will also integrate data silos, democratize data across their enterprises and build a lasting data-driven culture.


  1. Big Data Enters Back office – Democratizing Data to All Departments

Big data will no longer be proprietary to just marketing or risk departments of organizations.   HR Analytics, Accounting Analytics, Tax Analytics, Operation Analytics, and so on, are having more adoptions in enterprises. Smart leaders will leverage on their experience learned from marketing analytics and replicate their success in their back office. Optimization and efficiency gains from cross-organizational analytics will lead to a large divide from their industry rivals with partial application of big data in their organizations.


  1. Visuals and Storytelling – Enhancing Big Data Adoption

A successful data scientist has to be a great story journalist and teller as they have to communicate effectively with business leaders to gain buy-in for their findings from big data analytics and convince their bosses to invest resources and take action to test their hypotheses. They also need to visualize their logics and findings effectively by making use of relevant tools and empower their users to make use of these visual tools to understand and find out answers themselves.


  1. Key Stakeholders – Building a Healthy Big Data Ecosystem

Just business leaders, data scientists and data experts are not going to dig out the pearl from big data and sustain their success in the long run. In order to enable big data have a healthy growth, we need a number of key stakeholders to be present. Government’s open data policies and initiatives to support big data development are important. Big data specific startups and startups in other technological areas are the sources of innovations and applications of big data. Universities will need to nurture more data scientists with not just statistical expertise but also business domain and communications capabilities. Big data technology and platform providers also need to work alone with research teams in universities to come up with practical and advanced big data breakthroughs.


  1. IoT and Data Streaming – Managing a Double-Edged Sword

Internet of Things (IoT) will provide a huge amount of useful contextual data to feed our algorithms. Streaming data is another great source of data for our algorithms. Adding all these will empower us to make the right offer at the right place in real time to our target customers. However, IoT and data streaming also create tough technical and security issues for us to resolve. Data-Lake-as-a-Service, Data Virtualization, Fog Computing and even Blockchain are some of the advanced technologies to resolve these challenges.


  1. Big Data Becomes a Corporate Asset – Attracting Investors

The value of data, proven models and algorithms will be quantified and will be counted as other assets in internal and external transactions. Executives especially from the finance side will take big data more seriously.


  1. Gamification and Self Service – Everyone Can Use and Love Big Data

Startups and innovators will bring in their experience on gamification to make big data easy and fun to use. People will use big data analytics at work and daily lives without knowing they are using it.


  1. Big Data Security and Data Breach – Triggering Consumers Backlash

Last but not least, big data helps find new ways to detect data breach as it looks for not a rare event but an accumulation of events in context. However, big data applications plus pure data governance within organizations will provide opportunities for data breaches. When consumers know how powerful big data is and how much their behaviors and even identities are being exposed, they will respond and demand for data privacy protection. Data governance, laws on data privacy laws, consumers’ sense of data protection, benefits offered to consumers based on analytics, and so on, are some of the key variables that are required to manage well in order to overcome the consumers backlash.


In sum, big data is disruptive to our business, work and life, and it is irreversible and inevitable trend. By knowing where they are going to will help us embrace them better, monetize big data more smartly and manage the downside of this new technology more effectively. Welcome to the big data journey and let’s have a great ride.