Industry executives and experts share their predictions for 2019.  Read them in this 11th annual VMblog.com series exclusive.

Contributed by Nima Negahban, CTO and co-founder and Daniel Raskin, CMO, Kinetica

Informing The Enterprise Through Data

Enterprises are having a hard time keeping up with the growing volume and speed of data, creating new challenges in integration, security, performance, management, and governance. Traditional analytics platforms are unable to manage the growing volume of data nor can they achieve providing actionable, real-time insights. As enterprises continue to face such difficulties, Kinetica has seen a need for GPU-powered data platforms and better enterprise data practices. Here is what executives at Kinetica see coming in the enterprise data space for 2019:

2019 Predictions from Nima Negahban, CTO and co-founder, Kinetica - enterprise data practices, AI/ML

Demand for smart analytical applications redefine enterprise data practices: Enterprises are in a race to become data-powered businesses yet only a small fraction of the value of advanced analytics has been unlocked. In 2019, there will be high-demand for new innovations around smart analytical applications that are driven by real-time interactions, embedded analytics and AI. The business requirements for these applications will be a forcing function for enterprise data teams to evolve their traditional big data architecture and implement a new active analytics tier for building intelligent, data-powered applications for real-time decisioning.

The rise of the data engineer brings AI to the forefront within the enterprise: Last year was the year of the data scientist. Enterprises focused heavily on hiring and empowering data scientists to create advanced analytics and machine learning models. 2019 is the year of the data engineer. Data engineers will find themselves in high demand - they specialize in translating the work of data scientists into hardened, data-driven software solutions for the business. This involves creating in-depth AI development, testing, devops and auditing processes that enable a company to incorporate AI and data pipelines at scale across the enterprise.

Human and machine learning form symbiotic relationship to drive real-time business decisions: In 2019, the world of AI and analytics will need to converge in order to drive more meaningful business decisions. This will require a common approach for combining historical batch analytics, streaming analytics, location intelligence, graph analytics, and artificial intelligence in a single platform for complex analysis. The end result is a new model for combining ad-hoc analysis and machine learning to provide better insight faster than ever before.

2019 Predictions from Daniel Raskin, CMO of Kinetica - AI, marketing

In 2019, AI Becomes a Marketer's Best Friend - Woof!: In 2019, incorporating AI will be an essential part of the marketing strategy. Trained models around predictive analytics, sentiment analysis, programmatic advertising, to name a few, will revolutionize how marketers automate more aspects of the marketing pipeline and develop highly targeted Account Based Marketing (ABM) strategies. This will require investment in new technologies but will also lower custom acquisition costs by making marketing dollars more effective.

Real-Time Data Makes Every Day Christmas: For years, enterprises have been talking about the importance of omnichannel marketing and having a 360 degree view of the customer. However, the promise of a unified customer has been evasive. New advances in real-time customer analytics, location-based intelligence and GPU compute power will change this. Marketers will be able to continuously assess complex customer data, and automate complex queries with millisecond response times, to serve up real-time advertisements, coupons, and promotions to customers in situ.

The Rise of the Marketing Data Scientist: For years, marketing has been shifting from a qualitative discipline to a much more quantifiable discipline. Marketing Data Science will become an essential element of every marketing team. The Marketing Data Scientist will be focused on deriving detailed insight about customer behavior and producing reliable predictive and prescriptive insights based on complex data models and machine learning. These models will evolve from historical analysis into real-time applications that transform how products are delivered to customers. For example, if the demand for rain boots spikes in usually sunny San Diego when unusual precipitation is in the forecast, marketing data scientists can build active analytical applications that trigger more product to be sent from warehouses in Nevada to stores in San Diego, instead of routing them to Portland as planned.

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About the Authors

Nima Negahban, CTO and co-founder

Nima is the Chief Technology Officer, original developer and software architect of the Kinetica platform. Leveraging his unique insight into data processing, he established the core vision and goal of the Kinetica platform. Nima leads Kinetica's technical strategy and roadmap development while also managing the engineering team. He has developed innovative big data systems across a wide spectrum of market sectors, ranging from biotechnology to high-speed trading systems using GPUs, as Lead Architect and Engineer with The Real Deal, Digital Sports, Equipoise Imaging, and Synergetic Data Systems. Early in his career, Nima was a Senior Consultant with Booz Allen Hamilton. Nima holds a B.S. in Computer Science from the University of Maryland.

Daniel Raskin, CMO