To help explain the need to better manage Enterprise Data Operations and Orchestration, we'll start by surveying the scope of the challenge.
The challenge is simple enough to see. 1 Business now operates within a rising flood of data, pouring in through every customer and partner interaction. Informed decisions are being made faster than ever before. And, you can either 2 move at the speed of these decisions. Or, be washed away in the flood.
Digital transformation is about competing and winning against digital disruption. 1 The fact is that some newer companies were born to treat their data operations as a platform, providing those companies significant agility compared to the patchwork of legacy systems, points tools, and glue code, that populate some traditional enterprises. 2 By putting data first, pervasively collecting and using objective metrics, digital natives gain quciker insights than when reports must be commissioned on a localized, ad hoc basis, after the need has already emerged. 3 Because their analytical processes arise from a common platform, automation is pervasive, and data usage can be actively promoted and measured. Other companies just keep adding engineers. 4 The result of this digital transformation is a seamless data fabric, deployed to whatever infrastructure makes sense, yet driven by a common skill set. The alternative, in other environments, is a fragmented infrastructure which itself divides the teams across various, fragmented skill sets.
It's like death by 1,000 paper cuts, when you try to do it all by hand. 1 Just ingestion from all your systems involves change data capture, parallelization, and tracking slowly changing dimensions to support historical analysis, not to mention type conversion from various systems to a common standard. 2 Along the way you're merging and synchronizing from all the various data schema which have evolved over time throughout your enterprise, while at the same time tracking that history, so you can report trends over time. 3 Then with all this data brought to a common lake, the real work begins, building pipelines to transform all this data to the forms you need for reporting, analytics, machine learning, and more. Pipelines often built differently by different teams, potentially with different tools, creating long term management complexity. 4 The end point of these transformations, of course, are the models and cubes you must design to support your reports and analytics. 5 All of which must be tuned in various ways to run at speed. 6 And, all this while cataloging, tracking, and managing who gets access to which parts of all this data, in which states, to support industry and regulatory compliance. 7 Then there's all the operational complexity under the hood. How are systems scaling to meet load requirements. How are your pipelines and models being migrated out of development and into production? How are these operations being monitored. Are you able to pause and restart your transformations, and retry from a known state if they fail? When you dial all this out to the broadest view, it's a bit astounding that anyone can make all this work at all.
It all seemed so simple, back in the day. 1 Over the past several decades, business operations have come to be defined by Enterprise Resource Planning, coupled with rigid, old style data warehouses. As online commerce has grown, new types of Operational Data Store have come into play, along with Customer Relationship Management systems. 2 Each of these were backed by their own particular data silos, some of these internal, but more commonly external in recent years, up in the cloud. 3 Over time it became clear that the built in analytical and reporting capabilities of any single system were insufficient, because data is spread across many systems and silos. 4 So, a whole new tooling ecosystem emerged to extract, transform, and load data from one system to another, supporting growing needs for Business Intelligence Reporting and Enterprise Analytics. Of course, these tools all came with their own specialized requirements, forcing the creation of specialized coding and service teams, from which 5 it became normal to expect 6 to 12 month cycles to implement new cross-system use cases. Yes, things generally eventually worked, but the custom coding was slow, the teams were expensive, and the resulting systems were often locked onto monolithic hardware platforms, unable to take advantage of cluster-scale technologies.
Meanwhile, the use cases just kept proliferating. With so many different systems in play, Customer 360 views have become a core requirement. But it doesn't end there, 1 From Robotics Processes all the way out to leading edge 2 Artificial Intelligence and Machine Learning needs, new data requirements keep growing their way onto the enterprise data management stack. 3 The scaling pressure of these needs brought Hadoop into the market and into the Enterprise, followed by Spark, both bringing along a whole zoo full of open source and vendor specific point tools, requiring entirely new skill sets and along with their specialized service and coding teams. Then, as the unsustainable cost of maintaining scalable hardware on premise became clear 4 big data tooling began moving into the cloud, with teams generally choosing either Team Yellow or 5 Team Blue, as appropriate to their skills, but then, by doing so, also potentially locking companies into those cloud vendors, because of all the additional specialized skills needed to work within each of the clouds. And yes, again ... it all works, sort of, eventually 6 but it can also result in a bit of hairball, when viewed from above. And, you still have slow, expensive projects, with some team somewhere custom coding each use case using the latest design patterns for whatever is the current point tooling in use, plus a whole new world of security and governance issues.
Wouldn't it be nice to streamline your systems into one common fabric? To have all your data ingestion, transformation, optimization, orchestration, operation, export, governance, and replication processes ... happening through a common user interface, 1 standardizing and automating all the repetitive underlying processes, and eliminating the need to run a private software factory. 2 Particularly if you can run it all over your same tested and trusted clusters, but through an abstraction layer allowing the underlying, integrated technology to evolve over time with minimal impact, and allowing you to swap cloud vendors through an automated migration process, should business requirements ever create the need. 3 That's Enterprise Data Operations and Orchestration.
So, what does 1 EDO2 mean? Enterprise data operations and orchestration 2 refers to a set of systems and processes enabling business to organize and govern data drawn from many different sources, and prepare that data for delivery to disparate analytic and operational applications. 3 Said differently, it's a platform for transforming traditional enterprises into modern digital competitors ready to do their own disrupting.
So, what does that mean in practce?? 1 Speed, allowing you to quickly onboard new data sources and implement new use cases, because your teams are configuring standardized processes, rather than custom coding to their latest convention. 2 Self service, putting no-code tooling into your analyst's hands, extending the power and scaling the capabilities of your talent pool. 3 Governance, providing you the standardized end to end data time-based lineage and secure domain control necessary to meet industry and regulatory demands. 4 Freedom, giving you a seamless abstraction layer enabling underlying technologies and vendors to evolve, without disrupting your data platform. 5 And, extensibility, letting you adaptably integrate with any external application able to maintain a data connection.
Infoworks delivers you an Agile Data Platform, supporting 1 petabytes of data streaming through thousands of configurable, no-code pipelines, 2 supporting thousands of applications and analytical processes, with 3 operational agility to leverage the productivity of your talent pools, leading to 4 rapid insights into your enterprise wide data, and 5 all the bottom line goodness available to those who swim the currents without getting dashed upon the rocks.
So, what have you learned? 1 legacy data integration tools lack the features needed to manage the hockey-stick growth in data loads, 2 while the code-centric complexity of newer tooling is strangling their own potential. 3 Much greater agility is needed to achieve the potential of enterprise-sized multi-sourced data lakes. 4 This agility is achieved by building or buying a common platform to ingest, transform, optimize, orchestrate, operate, export, govern, and replicate all this data and supporting technology, with little or no need for custom code. 5 By having the abstraction layer needed so you can port and operate across any major distributed data infrastructure, and 6 to port and operate on whatever on premise or cloud environment your changing business demands may require, 7 all while integrating with any in-house or third party application able to maintain a standard data connection.
That's Infoworks. Come on back, and we'll keep diving deeper.