As we look at 2018 and predict changes on the way, they coalesce under a singular point of view: this year is all about bringing intelligence to our data-science processes. Last year we created dashboards and started asking how to turn data into real insights that we could act on; this year we’ll see changes that enable us to build and deploy intelligent applications.
Digitizing the Financial-Services Industry
The hottest topic in financial news this year—and most of last year—is Bitcoin. With the CBOE and CME launching Bitcoin futures in December 2017, the currency has sparked many academic conversations, but we’ll see practical repercussions beyond rampant cryptocurrency speculation. The year 2018 will be when blockchain goes mainstream.
Blockchain technology isn’t new, but Bitcoin’s recent turn as a potentially credible currency is increasing awareness and interest in blockchain’s other applications. Blockchain, like any other technology, must be weighed on its relative merits; just because a solution can be implemented using blockchain doesn’t mean blockchain is necessary or the best approach. For example, immutability is a crucial characteristic of the technology, but new regulations such as the EU’s GDPR create challenges with regard to privacy and ability to be forgotten.
The side effect of the blockchain boom is faster digitization of the financial-services industry. This industry has traditionally been among the slowest to adopt new technologies and processes owing to data-security concerns and regulatory issues. The rise of cryptocurrencies, blockchain technology and new regulations such as PSD2 are forcing banks to innovate through digital automation and enhanced customer experience. Even with artificial intelligence and other proven innovation drivers, banks cite siloed data and confusion over ownership of emerging technologies as hindrances. Bitcoin will push the entire industry to confront these issues sooner than later.
Cloud Going Serverless
The benefits of cloud are now widely acknowledged and the technology adopted, but serverless cloud presents an even greater opportunity for efficiency and elasticity. Owing to idle time in today’s cloud instances, some estimate going serverless yields a 5–10x efficiency gain.
We’ll see serverless-cloud adoption in 2018 for several reasons. For example, serverless cloud lends itself to the deployment of smart applications and analysis of IoT data, as these tasks consume resources only when they request them. As smart applications grow in number and the data that IoT devices generate increases, the responsiveness a serverless cloud architecture enables will drive adoption.
Additionally, we’ll see serverless-cloud adoption because of its impact on business costs. Going serverless reduces overhead costs and the headcount needed to manage a cloud infrastructure. This newfound efficiency allows companies to repurpose headcount to focus on business innovation instead of infrastructure maintenance.
Automating Data Science
The final way that 2018 will herald intelligent solutions is through the automation of data science across industries. This year, artificial intelligence will move from novelty to need as companies integrate it directly into platforms, automating data analysis in a new way.
Businesses are evolving beyond business intelligence, where management insights derive from analytics dashboards and reports. While those insights certainly inform better decision making, they involve some inefficiency; reports must be generated and insights must be analyzed and discussed before decisions can be made. The shift we’re already seeing, and that we’ll see more of throughout 2018, is to intelligent automation. By embedding intelligence into business processes, optimizations can be made automatically, drastically increasing efficiency.
This idea of embedded intelligence extends beyond reporting into every industry data science and analytics touch. Quantitative funds in financial services are driven by machine learning, health care is employing data science in genomics and Amazon’s Alexa as well as other consumer products are using artificial intelligence to sell more and create personal experiences.
The data-science trends we’ll see in 2018 are pushing the industry even further, moving from processes supported and supplemented by intelligence, insights and analytics to processes that are inherently intelligent, generating insights and implementing them. Are you ready to keep up?
About the Author
Tripp Smith is chief technology officer of Clarity Insights. Tripp joined Clarity in 2011 to lead Clarity Labs, building tools, teams and strategies that apply big data analytics to solve real business problems for clients. During this period, Clarity has grown nearly 20x to become the largest independent consultancy in the U.S. focused solely on data analytics. His teams’ efforts have generated multi-million-dollar revenues at Fortune 1000 companies in industries such as health care, technology, finance and consumer products for the world’s most innovative companies. Before joining Clarity Insights, Tripp consulted with Knightsbridge Solutions, acquired by Hewlett-Packard, and developed analytics SaaS products at the Advisory Board Company.