The 2000s represented a mass migration to digital data. If you were at a big company during this time, part of your time was probably spent on basic data entry – inputting data from paper forms or digitizing documents through scanning.
Today, thanks to that digital revolution, we’re spoiled by the ability to drop data into spreadsheets or pull up searchable file archives. And although it seems like only a few years ago that hand-written data went the way of the dinosaur, we’re actually on the cusp of another transformative evolution in data management. Next stop: automated data management.
The Data Management Journey So Far…
To understand the next step in data management, it’s worth looking back to how we got to where we are today. Early data management practices provide insight into the direction is headed for data automation. As they say, sometimes you have to know where you’ve been to understand where you’re going. Let’s start with the age before computers when notes were handwritten or typewritten and organized by folder or binder.
Data was sequestered in this early stage of data collection and management. If you needed to know the answer to something, you could usually find it by looking in the right binder or file cabinet. But therein lies the problem. Business data was primarily a reference tool. Organizations viewed data as informational, not actionable.
Fueling this outlook was a lack of truth and consistency in data. While businesses had the means to collect and organize it, they weren’t able to aggregate data or cross-examine it at scale. An even more significant hindrance was the fact that data was not a real-time resource.
One of the best examples of old-school data management is the Sears-Roebuck catalog. In 1925, the catalog had an astronomical circulation rate of 7.2 million. And while the Sears-Roebuck corporation was able to coordinate the mailing of millions of catalogs to subscribers, there was no realistic way to track who was ordering, other than to dig through invoices and packing slips, pairing order numbers to subscribers. Targeted marketing at the subscriber level? Impossible.
For Sears-Roebuck and every other company of the age, data may have been ordered and organized, but it was hardly mobile.
The Rise of Computation and Data Validation
With the advent of computers came the availability and mobilization of data. As businesses took their old hard copies and digitized them, they also began to create digital-first processes. The result was a consolidation of data. Entire file cabinets fit into spreadsheets. Archives of binders and folders were easily organized with naming conventions on a hard drive. Data not only was housed in one place, but it was also accessible with just a few clicks.
Consolidation opened up opportunities for broader data collection and analysis. Suddenly, it wasn’t overwhelming to compare data from multiple sources. So, the more data, the better! That led to everything from new tracking metrics and KPIs for companies to user-generated data from customers themselves in digital submission forms. In a few short years, businesses started to see the opportunities inherent in data as a source of truth. Not only was it accessible, but it was also plentiful.
That takes us to today, and the age of cloud computing. After years of practice setting up systems to collect and consolidate it, our focus today is data management and mobilization. Businesses are looking at data to provide answers. So, they’re taking steps to aggregate, organize and cleanse it to ensure data becomes an absolute source of truth. Today, data lives in the cloud and is accessible by many, used to power decision-making and strategy at every level. That’s the democratization of data.
The Next Evolution in Data Management
With the digital infrastructure already built out and the migration to cloud management in full swing, businesses are well on their way toward the next step forward in mobilizing data: automation. Companies are already customizing APIs to extract data and deposit it into data lakes or to warehouse it. The next evolution is connecting ordered, organized data to applications so that end users can make the most of that rich information.
The concept of data automation as a form of management is relatively novel and coincides with the rise in machine learning. Programs take seconds to do what might take people hours and weeks – namely, aggregating, cleansing, ordering, and organizing data. By writing rules and algorithms to govern data management practices, data engineers are engaged in simple automations that keep their data fresh and accessible.
But automation doesn’t stop at data preparation. It also extends to mobilization. For example, through an integration platform as a service (iPaaS) like the industry-leading Boomi AtomSphere Platform, companies can connect applications, programs, and other resources to their data lakes and data warehouses, from on-premises, to the cloud, to the edge. That way, they can pull (or push) data seamlessly across barriers.
When square pegs start to fit round holes and data moves unencumbered from storage to application, a new level of possibilities becomes apparent. With new advances in artificial intelligence (AI) and machine learning (ML), it’s not unreasonable to expect companies will soon have the capability to automate entire data-driven processes from the point of collection to a specific method of execution – without human intervention.
The key to embracing automation to its fullest extent is breaking down data mobility barriers and establishing the systems that manage data accordingly as it passes from application to application, process to process.
The Evolution of Data Management Shows Understanding
As businesses rely more and more on data, demand heightens for better management systems and practices. It speaks volumes of how valuable data is when we look at the sophisticated jumps from simply filing and referencing data to mobilizing it across organizations, so it can power decision-making at every level.
As we usher in the age of data management automation, it’s even more likely that the ways we use data will expand. Even small businesses are learning the powerful innovation capabilities offered by good data preparation and mobilization. With powerful software like the Boomi AtomSphere Platform, which includes Boomi DataHub for master data management, it’s getting easier for these businesses to make that data work for them more efficiently, and with less demand for oversight.
To learn more about optimizing data management for the new age, read our ebook: “Avoid the Digital Transformation Failure Trap“