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Alation 2018 Predictions: AI, Machine Learning, Cloud Lock-in, Microservices, Data Lakes and More

VMblog Predictions 2018

Industry executives and experts share their predictions for 2018.  Read them in this 10th annual series exclusive.

Contributed by Satyen Sangani, Ken Hoang, David Crawford and Aaron Kalb of Alation

AI, Machine Learning, Cloud Lock-in, Microservices, Data Lakes and More

2017 was an evolutionary year for data analytics, with "big data" waning as a term, the rise of self-service analytics and, ultimately, machine learning (ML) and artificial intelligence (AI) taking center stage. With 2018 right around the corner, the conversations on ML and AI are now shifting to focus on how practical and useful these technologies will really be for the business. Here's what executives at data cataloging company Alation see coming in the data analytics space for 2018:


Satyen Sangani, co-founder and CEO of Alation:

  • Fear of Cloud Lock-in Will Result in Cloud Sprawl: As CIOs try to diversify investment in their compute providers, inclusive of their own on-premise capabilities, the diversification will result in data, services and algorithms spreading across multiple clouds. Finding information or code within a single cloud is tough enough. The data silos built from multiple clouds will be deep and far apart, pushing the cost of management onto humans that need to understand the infrastructure.
  • Microservices Will Result in Macro-confusion: With the proliferation of containers and microservices, the cost of software creation, deployment and infrastructure will further decrease. Which services exist? How are they being used? How do we know if the service is deprecated? Who/what else is using the service?
  • Buyers Bias from Buying Dumbed-Down Data Interfaces to Smartening Up Their Workforce: With "simple" business intelligence (BI) and pretty dashboards having been the talk of the BI landscape, organizations are coming to grips with the fact that they still can't trust their data. At scale, with the vast variety, complexity and volume of data, traditional governance methods are failing to get trusted answers to data consumers. Consequently, organizations will shift away from simplistic dashboards toward teaching people to be more data literate, with the best interfaces helping with this challenge.


Ken Hoang, VP Strategy and Alliances at Alation

  • Super Hubs Emerge in the Enterprise to Enable Contextual Services, Delivering More Impactful ML: Deployments of large data hubs over the last 25 years (e.g., data warehouses, master data management, data lakes, Salesforce and ERP) resulted in more data silos that are not easily understood, related or shared. A hub of hubs will bring the ability to relate assets across these hubs, enabling Context-as-a-Service. This in turn will drive more relevant and powerful predictive insights to enable faster and better operational business results.
  • AI Will Finally Enable the Enterprise to Relate Unstructured and Structured Data: Semantic processing at scale can finally extract relevance from documents and tie it to structured data assets, bringing a true, 360-degree view into an enterprise's customers, partners, products and other key assets.
  • Data Lakes Will Need to Demonstrate Business Value or Die: The new dumping ground of data - data lakes - has gone through experimental deployments over the last few years, and will start to be shut down unless they prove that they can deliver value. The hallmark for a successful data lake will be having an enterprise catalog that brings information discovery, AI and information stewarding together to deliver new insights to the business.


David Crawford, Director of Software Engineering at Alation

  • Data Analysts Begin to Reap the Benefits of AI: While "data analyst" seems like a job ripe for automation (isn't that what computers do well?), the advancements in AI lead to efficient assistants rather than replacements. We're getting closer to a place where data analysts leverage AI for pattern matching and conducting closed environment analysis. Soon, the job of analysts will be to point the AI to the right questions to be analyzed and to decide how to interpret the results in the real world.


Aaron Kalb, Co-founder and Head of Product at Alation

  • Autonomous and Inscrutable AIs Will Drive Up Demand for Data Quality: Organizations are increasingly taking humans out-of-the-loop and empowering AIs to make actual decisions, including how to price flights, stock shelves or even triage ER patients. At the same time, researchers are finding that "black box" deep learning algorithms - which, once trained, can't be tweaked nor even understood by humans - are the most effective for many problems. Since these algorithms are "garbage in, garbage out," with the results of garbage-output becoming ever more consequential, high-quality training data will become a coveted resource - like oil for the information age. The sharpest human minds in tech may even shift their attention from creating algorithms to feeding those algorithms the best data diet.

From cloud sprawl and the emergence of super hubs, to data lakes realizing business value and ML/AI changing the sector as whole, 2018 is looking to be a turning point for data analytics as we see the true impact of these technologies on the enterprise.


Published Friday, December 15, 2017 7:14 AM by David Marshall
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