The Need for an Intelligent Data Platform

In this paper, I will review information’s importance to business, connect data architecture to business success, define data maturity and discuss how to architect information and improve data maturity efficiently with an Intelligent Data Platform.

The Informatica Intelligent Data Platform (IDP) is an integrated end-to-end data management platform to spur data maturity and enable business initiatives with the right data at the right time. IDP also aims to decrease complexity by providing a unified platform for enterprise data, connectivity, metadata, and operations. This brings the entire realm of data management under a single umbrella.

Link to paper.

Benchmarking Enterprise Streaming Data and Message Queuing Platforms

This category of data is known by several names: streaming, messaging, live feeds, real-time, event-driven, and so on. This type of data needs special attention, because delayed processing can and will negatively affect its value—a sudden price change, a critical threshold met, an anomaly detected, a sensor reading changing rapidly, an outlier in a log file—all can be of immense value to a decision maker, but only if he or she is alerted in time to affect the outcome.

We will introduce and demonstrate a method for an organization to assess and benchmark—for their own current and future uses and workloads—the technologies currently available. We will begin by reviewing the landscape of streaming data and message queueing technology. They are alike in purpose—process massive amounts of streaming data generated from social media, logging systems, clickstreams, Internet-of-Things devices, and so forth. However, they also have a few distinctions, strengths, and weaknesses.

Link to paper (fee).

Moving the Enterprise Analytical Database – A Guide For Enterprises: Strategies And Options To Modernizing Data Architecture and the Data Warehouse

The benefits of modern data architecture are as follows:

  1. It ensures the ability of the data analysis function of the organization to actually do analysis rather than restrict it to data hunting and preparation almost exclusively.
  2. It provides the ability to maneuver as an organization in the modern era of information competition with consistent, connected data sets with every data set playing a mindful and appropriate role.
  3. It enables a company to measure and improve the business with timely key performance indicators, such as streamlining your supply chain or opening up new markets with new products and services supported by technology built for analytics.

This paper will help an organization understand the value of modernizing its data architecture and how to frame a modernization effort that delivers analysis capabilities, diverse yet connected data, and key performance measures.

Link to paper (fee)

Transitioning from PostgreSQL to an Analytical Database for Higher Performance and Massive Scale

In today’s data driven world, where effective decisions are based on a company’s ability to access information in seconds or minutes rather than hours or days, selecting the right analytical database platform is critical.

Read this McKnight white paper to learn:

  • Which criteria to consider for an analytical database
  • The process for transitioning away from PostgreSQL
  • Transition success stories from Etsy, TravelBird and Nimble Storage

Link to paper.

Sector Roadmap: Unstructured Data Management 2017

This Sector Roadmap is focused on unstructured data management tool selection for multiple uses across the enterprise. We eliminated any products that may have been well-positioned and viable for limited or non-analytical uses, such as log file management, but deficient in other areas. Our selected use cases are designed for high relevance for years to come and so the products we chose needed to match all these uses. In general, we recommend that an enterprise only pursue an unstructured data management tool capable of addressing a majority or all of that enterprises’ use cases.

In this Sector Roadmap, vendor solutions are evaluated over five Disruption Vectors: query operations, search capabilities, deployment options, data management features, and schema requirements.

Link to report (fee).

Sector Roadmap: Modern Enterprise Grade Data Integration 2017

This Sector Roadmap is focused on data integration (DI) selection for multiple/general purposes across the enterprise.

Vendor solutions are evaluated over six Disruption Vectors: SaaS Applications Connectivity, Use of Artificial Intelligence, Conversion from any format to any format, Intuitive and Programming Time Efficient, Strength in DevOps and Shared Metadata across data platforms.


Link to report (fee).

Sector Roadmap: Modern Master Data Management 2017

This Sector Roadmap is focused on master data management (MDM) selection for multiple data domains across the enterprise. In this Sector Roadmap, vendor solutions are evaluated over seven Disruption Vectors: cloud offerings, collaborative data management, going beyond traditional hierarchies, big data integration, machine learning-enabled, APIs and data-as-a-service, and onboard analytics.mdm

Link to report (fee).

Vertica Predictive Maintenance Testing Trial

This is NOT a white paper (except that there is documentation) but rather it’s a test drive for predictive maintenance – something on the minds of many these days –  we built.

Experience how Vertica enables you to store in near real time sensor data from multiple cooling towers across the USA and predict equipment failure ahead of time to provide continuity of service. In this AWS Test Drive, we will create an instance of the Vertica Cluster and generate readings from multiple cooling towers in real time that are stored in Vertica. The test drive also includes a web based dashboard that interacts with Vertica to leverage machine learning algorithm such as logistic regression to predict risk of failure to prevent down-time. You will have 4 hours to play, query and analyze the dataset.

Contact us for the “Vertica Predictive Maintenance Testing Trial”.

Moving to a Software-as-a-Service Model

This is a series of 4 blog posts.

If you’re a software vendor moving to a SaaS business model either by creating new product lines (from scratch or by adding cloud characteristics to existing products) or converting an existing product portfolio, the transition to a SaaS model will impact every aspect of the company right down to the company’s DNA.

In these posts, William addresses the top four considerations for choosing the database in the move. The database selection is critical and acts as a catalyst for all other technology decisions. The database needs to support both the immediate requirements as well as future, unspecified and unknown requirements. Ideally the DBMS selection should be one of the first technology decisions made for the move.

Link to posts.

Moving Analytic Workloads to the Cloud: A Transition Guide

Recent trends in information management see companies shifting their focus to, or entertaining a notion for a first-time use of, a cloud-based solution for their data warehouse and analytic environment. In the past, the only clear choice for most organizations has been on-premises data solutions —oftentimes using an appliance-based platform. However, the costs of scale are gnawing away at the notion that this remains the best approach for some or all of a company’s analytical needs.

According to market research, through 2020, spending on cloud-based Big Data Analytics technology will grow 4.5x faster than spending for on-premises solutions.  Due to the economics and functionality, use of the cloud should now be a given in most database selections. The factors driving data projects to the cloud are many.

Additionally, the multitudinous architectures made possible by hybrid cloud make the question no longer “Cloud, yes or no?” but “How much?” and “How can we get started?” This paper will reflect on the top decision points in determining what depth to move into the cloud and what you need to do in order to be successful in the move. This could be a move of an existing analytical workload or the move of the organization to the cloud for the first time. It’s “everything but the product selection.”

Link to report (GigaOM membership or fee required for full report).