7 Critical Capabilities for Embedded Analytics

Posted September 12, 2019 by Ben, Senior Product Marketing Manager

Software and technology vendors serving a range of industries – from security to healthcare – are increasingly turning to embedded analytics as a way to deliver value-added services and applications to their customers.

Enterprises that embrace embedded analytics are changing the dynamics of business in every vertical market. The most disruptive companies in these industries will be those able to harness data as a core asset and use advanced analytics to predict and troubleshoot customer challenges, detect and mitigate security threats, or create entirely new features and applications that put customers on a faster path to better, data-driven decision making.

As a product manager, solutions architect, or application developer, you need to decide what type of database to embed in your application. When evaluating options and capabilities, make sure you select a database that can:

    • Manage huge data volumes: The selected database should provide limitless scale at minimal cost via a massive parallel processing, scale-out architecture. Today, the scale may be gigabytes or terabytes; tomorrow, petabytes and beyond.
    • Deliver fast analytics: Expectations are high; waiting for results is not acceptable. A software vendor should provide the load and query performance to ensure that analytics are timely and relevant. The solution should provide ways to optimize common queries without needing to constantly tune; and it should be flexible enough to run ad hoc queries, regardless of whether the data ingest is streamed, trickle-loaded, or batched.
    • Embed machine learning: With the volume and velocity of data combined with the goal of predictive and prescriptive analytics, embedded machine learning algorithms are critical. An analytics platform that iteratively learns from new data differentiates software solutions to a new level of intelligence.
    • Handle user-defined functions (UDx): Having an analytical platform interface that connects to hundreds of applications, data sources, ETL, and visualization modules is also critical. What it doesn’t connect to out of the box, the UDx can easily integrate. UDx is a clear differentiator for software vendors looking to add variety and improved data insight to their core offerings.
    • Support data scientists: The new breed of data scientists is using tools like Java, Python, and R to create predictive analytics. The underlying database should make it easier to leverage these programming languages to shift the paradigm from why something happened to what will happen, and ultimately how that outcome can be influenced. Data scientists will appreciate an analytics platform that delivers the tools to spot trends, uncover anomalies, and anticipate the unexpected.
    • Use highly advanced analytics: Many platforms can analyze a single table or a simple look-up, but very few can analyze dozens of tables, hundreds of data types, dimensions, and attributes from numerous sources over several years.
    • Require minimal administration: Enterprises have been conditioned to manually administer databases for optimal performance. The new paradigm is the analytics platform that tunes and manages backup and interface without heavy oversight.

Adopting a database that will support your embedded analytics and bring new value to your customer-facing applications is crucial. Businesses that embed Vertica stand out from the competition and deliver higher value to customers. Specifically designed for analytic workloads, Vertica’s blazing fast speed and performance, advanced analytics, ease of deployment, and support for data scientists make it tailor-made for embedding.

Interested in learning more? See how Nimble Storage, Cyberbit, Optimal Plus and other leading data-driven software and technology companies are bringing value-added applications and services to market, powered by Vertica.

Considering a NoSQL database as your analytics engine? Read this whitepaper from Eckerson Group to understand the pros and cons of embedding NoSQL and commercial analytics databases.