Informatica Unleashes AI, Brand, Cloud, and Data-Driven Disruption at Informatica World 2017

New Informatica Brand for New Informatica Aspirations
New Informatica Brand for New Informatica Aspirations
New Informatica Brand for New Informatica Aspirations

Amalgam Insights (AI) recently attended Informatica World 2017, where executives, partners, and customers provided backing for Informatica’s ability to support “The Disruptive Power of Data,” (an Informatica-trademarked phrase) as well as its positioning as the Enterprise Cloud Data Management leader.

This show provided a variety of positioning points that demonstrated a change in positioning, ranging from a rebranding and repositioning led by CMO Sally Jenkins as the “hottest pre-IPO company” to the hosting of the event by legendary reporter and Executive Editor of Recode, Kara Swisher. Jenkins set the stage with a core Amalgam Insights belief that IT must be transformational for digital disruption to succeed. A core challenge that businesses face is that IT is asked to be functional and worry about simply keeping the lights and services on at a time when technology and data management challenges are growing exponentially.

As an industry observer, my focus was on how Informatica planned to publicly support customers on an ongoing basis through intended packaging and forward-facing roadmap. From that perspective, this summary will focus on the guidance that CEO Anil Chakravarthy and Chief Product Amit Walia provided to the audience during the executive keynote.

In kicking off Informatica World, CEO Anil Chakravarthy walked the 2300+ attendees through the three generations of data-driven market disruption:

IW2017 Generational Data Disruption
IW2017 Generational Data Disruption

1) Data used in specific business applications: the introduction of custom enterprise applications that we all started to see in the 1980s and 1990s with the emergence of applications to replace both paper and spreadsheets.

2) Data used to support enterprise-wide business processes: the integration of applications associated with bringing ERP, CRM, supply chain management, knowledge management, and other enterprise applications together into an integrated suite.

3) Data powers digital transformation: the current generation of data where businesses realize that the proprietary and operational data that they have been collecting, archiving, and often deleting as digital exhaust can now be used to deeply understand customers, benchmark specific business tasks, and serve as new sources of revenue.

As a starting point, this set of generational definitions made sense and also immediately pointed out how the majority of data management and integration solutions were currently focused on this second generation. From AI’s perspective, this positioning was a good starting point for Informatica to describe its suite of capabilities.

With this “3.0” emergence of data, Informatica made the case that it was increasingly important to have a supplier capable of supporting Enterprise Cloud Data Management across six different areas:

IW2017 Enterprise Cloud Data Management
6 Key Categories for Enterprise Cloud Data Management

  • Data Integration
  • Big Data Management
  • Cloud Data Management
  • Data Quality
  • Master Data Management
  • Data Security

Data management is now a foundational enabler for digital integration. In light of this, Amalgam Insights believes that this positioning is important. Separating the tactical aspects of data quality and data integration from the corporate aspects of data governance and data security and then further distancing those tasks from the business data definitions of Big Data management, Cloud Data management, and Master Data Management is a long-term recipe for disaster. The result of this disaggregated approach will be that enterprises build a new set of silos for the IT departments of the future.

AI believes that Informatica’s established leadership stance in each of these markets also provides launching points to additional markets over time. For instance, Big Data management leads to potential expansion into key Big Data sources such as the Internet of Things, video, and content management. Cloud Data could leave to support of cloud brokerages or multi-cloud resource management. Master Data Management will increasingly require the machine-learning aided automation of ontologies and taxonomies that will accelerate business mergers, talent recruitment, and value-based business processes where companies seek to effectively articulate and support the highest-value use cases associated with their current capabilities.

In addition. Chakravarthy pointed out how Informatica had shifted its entire portfolio to a subscription pricing model, an announcement that was accentuated in April 2017 when Informatica announced Informatica Cloud solutions on the AWS marketplace.

AI’s perspective is that this consumption-based pricing is increasingly important both to support ad-hoc data management needs and to provide evergreen support and upgrades for the ongoing needs of Informatica clients. In particular, AI believes that providing PowerCenter as a usage-based purchase is an important step forward because PowerCenter is fundamentally a consumption-based service at this point.

To provide greater detail, Amit Walia later provided guidance on Informatica’s view of five key imperatives for data management, which AI found useful in contextualizing Informatica’s view of the future:

Five Imperatives for Modern Data Management
Five Imperatives for Modern Data Management

  • Leveraging existing investments
  • Bridge to hybrid clouds
  • Futureproof your business
  • Pricing flexibility
  • Innovation at Enterprise scale

The first three bullet points are key in that enterprises have invested in foundational IT over the past 30+ years, including the collection of massive data sets and defining technology-enabled business processes. That on-premises-based and foundational data can be used to benchmark and optimize the present, but it first must be unlocked. AI believes that, to go forward, businesses must look back at their “legacy” data and learn from the time stamping, metadata-based ontologies, and semantic contextualization that a generation of workers has already defined for enterprise data and processes. There is no reason to rediscover the past when enterprises have already literally invested billions of dollars in technology and employee time in creating this data.

Pricing flexibility can also be thought of as “value flexibility,” a core practice that AI looks at from a financial management and pricing perspective. Value-based pricing, a topic AI has previously covered in a primer, must ultimately be defined based on the core use of technologies and services as either consumption-based services or as value-based packaged products. Based on the portfolio of products that Informatica provides, AI believes that Informatica must provide a nuanced range of pricing options from the per-hour approach that works best for data integration to the per-user approach needed for data quality and cleansing tasks to value-based pricing approaches for newer artificial intelligence and Master Data Management capabilities that can greatly accelerate business execution.

Finally, innovation at enterprise scale is highly dependent on a data strategy. One way to consider the scale of innovation is to consider how often startups and technology businesses stall out somewhere in the order of magnitude of $10 million in annual revenue. This is not necessarily due to a lack of service and product quality, but the inability to support ongoing data and personnel management at scale. By providing a consistent set of data definitions and management tools to employees, companies can maintain effective business definitions of products, services, workforce, contracts, components, revenue events, and performance obligations without having to depend on tribal knowledge across the organization to maintain basic business operations.

Walia also went in-depth into the importance of broad-based enterprise unified metadata across all data sources as a core value proposition for Informatica Enterprise Information Catalog as part of the intelligent Data Platform. In a previous incarnation, AI wrote about the importance of metadata as a powerful force for doing good and the tools to unleash this value are finally coming into place.

This metadata capability was announced as a precursor to Informatica’s launch of CLAIRE, a unified metadata intelligence engine embedded into Informatica Intelligent Data Platform. Because of the depth of this announcement, AI will be tackling CLAIRE in a separate post detailing how this engine differs from traditional metadata management approaches and evaluating its technology approach.

As a starting point, AI notes that CLAIRE’s combination of data clustering, semantic domain discovery, entity discovery, ontological mapping, data structure parsing, and anomaly detection represent an important core set of capabilities for unlocking the value of business data and amalgamates a variety of technologies that have been brought to market as standalone capabilities by a variety of startups and established enterprise application companies.

At Informatica World, AI saw that Informatica’s focus remained strong on supporting the role that data plays in supporting accretive and profitable business disruption. In general, AI notes that even enterprises that consider themselves to be data-savvy are still at an early adopter phase of effectively monetizing legacy operational data stores because they are still supporting legacy requests for data and analytics. Informatica’s continued positioning of the evolving Intelligent Data Platform is important both in expanding Informatica’s brand beyond PowerCenter and towards the product information, master data management, data security, and emerging data governance capabilities associated with the recent Diaku acquisition.

AI saw Informatica World 2017 as an important event both for Informatica to place stakes in the ground on the future of enterprise data and to define its role as a data management platform to support the current era of business disruption. In light of the themes of this event, AI makes the following recommendations to enterprises exploring data integration, management, security, and governance solutions:

1) Explore Informatica’s subscription pricing options across its entire portfolio. Informatica has typically had a reputation of having expensive and CapEx-heavy solutions based on traditional PowerCenter purchase models. However, with Informatica’s pursuit both of cloud-based solutions and of subscription pricing, enterprises should be able to develop a head-to-head and apples-to-apples comparison of Informatica’s products to existing cloud integration, management, and governance solutions.

2) Consider how both legacy and emerging cloud data environments will be supported in a holistic fashion across data cataloging, quality, governance, security, integration, management, and metadata definitions. All of these capabilities must come together or else businesses risk setting up a new set of silos that will impede the basic operations of a data-driven business. Digital Transformation is not just a one-time commitment to creating a data-supported business process, but a fundamental change in treating all business data as an ongoing asset that must be available and contextualized for employees to use, augment, productize, and analyze. If employees don’t trust and understand their data, the ongoing analytics and application support don’t matter. Garbage In, Garbage Out is still true in a Big Data Cloud world.

3) Translate IT into a department focused on transformational change. Fundamentally, this means that IT must support the services that drive value, not the commoditized services that are poorly differentiated. 20 years ago, IT had to focus on data center management and asset-based security methods. Today, IT must think about how to radically expand this approach to include new sources and accept that not every device can be fully secured, leading to a data-centric and service-specific approach to IT. As businesses identify new capabilities that must be supported and traditional IT is subsumed into a set of apps and established APIs, IT must transform. To do so, all data will need to be considered as both analytic and business relevant across all areas of IT. Any part of IT that does not understand data will become increasingly irrelevant. This means that every part of IT needs to be an active part of the corporate data management strategy, including networking, telecom, business process management, and application-specific personnel.