I recently had the pleasure of meeting with the Reltio Community for an “Ask Me Anything” discussion, which covered a variety of topics ranging from the origin of the company to improving data quality.
Reflecting back on this conversation, it is hard to believe that it has been over a decade since I formed Reltio. The challenges that I saw back then–which led to the birth of Reltio–have only grown in scope, fueled especially by the COVID-19 pandemic. Globally, companies continue to increase technology budgets as digital becomes the primary way most interact with their customers, how their employees work and how to reach vendors. And the rise of data is only just beginning as it is expected that data creation will increase exponentially in the coming years.
The three trends that led me to form Reltio have all accelerated over the last few years, and are driving increased adoption of modern MDM solutions, are:
- App proliferation. The number of applications at most large enterprises has grown significantly across every single function from marketing to finance to sales to human resources. Today, the average enterprise has 464 custom applications– a 22% increase in just four years, according to McAfee. And those apps generate loads of data, every minute of every day, posing a massive and growing challenge for companies seeking to have a consistent view of their customers, operations and analytics, among other things.
- Digital transformation. Every company is going “digital first,” a trend that accelerated during the pandemic. Every company must have a digital orientation for its business. Without that, it's a question of survivability. It's not even about growth, it's survivability.
- Cloud adoption. Back in 2011, there was only one cloud provider, AWS. At that point in time, it was starting to become clear that if you have to manage data, first of all, it will continue to grow. So you need systems that can scale horizontally or can continue to add more data. Today, 80% of enterprises are adopting a cloud-first strategy. Not only are our customers adopting a cloud-first strategy--they are fast embracing a multi-cloud reality.
Democratizing Data Governance
Beyond some of the macro trends driving modern MDM adoption, community members frequently ask me about the underlying issues that companies encounter when considering or implementing it. How data governance and MDM work together has become a familiar theme. Having secure and trusted high-quality data is critical for smooth operations and for accelerating growth. We seek to put data into the hands of more users because the more eyeballs that can be on the data, the better the quality and the governance of that data. Otherwise, it's in a black box and nobody has a clear understanding of what the governance policies and practices are. Bringing more users with the data together drives better outcomes.
Many of our customers started with a highly centralized data governance model. It worked in very controlled environments, but we are now seeing a trend towards a more distributed governance model. By centralizing the data, they're able to distribute the governance of that information into a single data repository.
Data Quality - There is a Method to the Madness
With the proliferation of apps has come an avalanche of data–and errors leading to records that can stifle growth. Most companies have inaccurate records which primarily result from human error. About three-quarters of business leaders in an Experian survey reported that their bottom line is impacted by inaccurate and incomplete data.
There is an important sequence we follow to improve data quality. Most data consumption occurs within specific applications. And most of those applications don't have good data quality capabilities or the ability to prevent duplicates from getting created. Instead of trying to rewire each system on day one, however, I think the sequence that works best is to centralize information. Instead of reaching across 50 different applications and managing quality in each one separately, you have to get to a shared understanding of the data quality. In other words, don't try to solve the quality problem in all the peripheral applications. Centralize that information, get better insights into where you stand on the data quality paradigm, and then, with that centralized information you can push the enhanced quality out and reduce the noise at the edges.
Building a Real-Time Operating System for Data
The motivation for me a decade ago was helping solve a big problem that only got bigger. What has become increasingly clear for most businesses is that today’s data will help drive the decisions that are made tomorrow. The core data that businesses run on include customer information, product information, supplier information, asset information, and employee information. Given the fragmented state of enterprise data, there is a dire need for a central system that will govern, manage, aggregate, unify, and provide a single source of truth for these data domains. Applications will continue to create and consume data, but they will not be governors or owners of data. Creating a real-time operating system for data that supports businesses for decades to come is essential to remain competitive in today’s landscape. Data has become every organization’s most valuable asset – its heartbeat.