Q&A with President Alok Prasad on 2017: A Year of Growth and Innovation

Posted by Kirk Newell on Feb 28, 2018 4:56:00 PM
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2017 was a banner year for both graph-based technology and Cambridge Semantics. We recently sat down with Alok Prasad, President of Cambridge Semantics, to review the past year as well as see what to expect in 2018.

Q: Alok, can you talk about your general view of the past year. Did it meet or exceed your expectations?

Alok_PrasadA: 2017 was a key foundational year for us from several perspectives. We doubled our client base, quadrupled our salesforce, reinvested in our technology, and delivered a new product – Anzo®, our integrated offering for doing diverse data management.

Current solutions in the market are fragmented and don’t offer what’s needed from the customer perspective.  If you are dealing with harmonizing and bringing data from diverse sources, you want to be able to say where it came from, is it the most recent information, do I trust it - those are important considerations for enterprises. If you’re not using the right information for your analytics, it’s just “garbage in / garbage out.”

With an integrated platform like Anzo, you can create a knowledge graph of what you have and then give users self-service capabilities to do discovery and analytics, creating a semantic layer across your different sources. It also features a streamlined user interface which was built to appeal to all the functions and roles in an organization. This is a single platform that can be used by IT, data scientists, business analysts and business users.

Q: What effect has Cambridge Semantics’ leadership in graph-based analytics had on the market?

A: The market has become a lot more real – the demand is accelerating for knowledge graphs that can link and harmonize data for analytics. Many industry pundits are declaring 2018 as “The Year of the Graph,” and we agree.

The reason people are choosing Anzo is because they want increased self-data discovery and self-service. Previously, if you didn’t use graph, you designed the systems and the entire infrastructure to answer specific questions. Smart Data Lakes with knowledge graphs give you the ability to do discovery and explore the data on a self-service basis with an assurance of proper data governance and provenance. It gives businesses a dramatic increase in productivity and a reduction of inefficiencies in securing meaningful insights. As data volumes grow, the need for self-service with the right amount and type of data is becoming very important. Our capabilities such as Data Catalogs, Graphmarts and Data Layers are significantly helping our users.

Also, in many industries such as financial services and life sciences, there’s a growing trend of adoption of semantic industry standards which Anzo enables. There are many tools where you can aggregate and analyze information, but they are not based on open industry standards, so you get locked in to a specific vendor. That’s another contributor to our growth - people want to do data analytics based on open standards. These open standards are being set up by industry groups such as FIBO in the financial services industry, FHIR in healthcare, or CDISC in pharma. Users want to be using these standards for managing and analyzing data in a self-service manner. Anzo supports these standards and allow companies to build solutions using these standards.

Q: Any other reasons the market is embracing graph-based solutions?

A: Machine learning is coming, artificial intelligence (AI) is coming.  As you look at AI and machine learning, you need a data fabric that can help discover and feed these systems. Our Semantic Layer with knowledge graphs provides that data fabric for easy discovery and understanding of the data and enables the ability to provide curated data to AI and machine learning tools. Our graph-based infrastructure also provides a data fabric for embedding text processing, AI and machine learning tools. By embedding these tools in our Semantic Layer, companies can take advantage of our entity linking, data lineage, security and other such capabilities. Companies are recognizing that traditional approaches in data analytics can no longer stay apace with new technology and the growth of data. Graph-based approaches are delivering better results.

There is also the growth in data catalogs, where people can just understand what information is there. We not only provide the data catalog but we allow you to do interactive analytics at huge scales with diverse information. We leverage graph models to describe the data in business contexts and capture all the different types of data required, spanning all enterprise data sources and all data sets stored in Anzo. Users can easily browse and discover data sets of interest and understand their context in a secure and governed environment.

Q: What is your forecast for 2018?

A: We’re already seeing broader adoption and market demand outside of our core industries, including government, oil and gas, and material sciences, and we’re even seeing adoption internationally.

Last year we saw companies deploying a Semantic Layer for managing and analyzing data in specific use case areas such as scientific data discovery, safety, customer 360, and fraud analytics, and we started seeing enterprise-wide deployments for on-demand data discovery and data management. We see these and other use cases becoming more widely deployed with tens of thousands of users actively using the systems.

We expect increased usage of our system with third party BI, AI and machine learning systems. Because of our Semantic Layer and knowledge graphs, we see more users in companies using third-party AI and machine learning systems to analyze their data. Net-net, we see much faster adoption of Semantic Layers and knowledge graphs.

We see ‘The Year of the Graph’ happening with Amazon announcing Amazon Neptune.  We are also a partner for managing and analyzing data across Amazon Neptune deployments and other sources.

We see the graph market maturing. We see different graph solutions being used for different use cases. Graph OLTP systems such as Amazon Neptune, Neo4j, Stardog and others will be used for learning more about a specific entity and analytics around the entity and relationship with other entities. Meanwhile, our recently announced AnzoGraph graph OLAP offering will be used to provide aggregate analytics across all similar entities and other such analytics.

We are looking forward to a very exciting year. We want to thank all our customers, partners, employees and investors for all their support. Thank you!


Tags: Big Data, Graph, Machine Learning, Artificial Intelligence, Smart Data Lake

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