Data Standardization Is Among Key Themes At This Year’s Adobe Summit
After a multiple-year hiatus due to the pandemic, this year’s much-anticipated Adobe Summit came with quite a few announcements and new offerings from the hosting company. For many attendees, this was one of their first major conferences since the pandemic. And from the floor, you could feel the general excitement to reconnect.
The event’s theme focused on delivering and maintaining exceptional client services through personalized experiences. It was surprising to see Adobe announce Firefly on the first day, as it might’ve been something traditionally released at Adobe Max in October. There was a major emphasis around the data used in generative AI for Adobe – whether it is commercially safe for marketers, and how Adobe is making it a mission to become the trusted partner for brands with regard to generative AI.
Key themes from Adobe’s announcements during the Summit prove that first-party data and data standardization will be the focus and a critical next step for organizations who are looking to benefit from key innovations along the content supply chain such as AI, data clean rooms, and new collaborative platforms.
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The Growing Content Supply Chain
A generous portion of the product experience has moved online – significantly altering the way consumers make buying decisions and experience products. Modern brands are experiencing an unprecedented consumer demand to create more and more high-quality content, and quicker.
For this reason, the content supply chain model has become a necessity for brands to keep up with demand. Described as an outline of the content production process used to plan, create, manage, and route content to desired channels and audiences more efficiently, it’s oftentimes used to expedite these processes.
With content being created at a rapid pace, a new challenge is developing for brands – specifically analytics, operations and AdOps teams. With more assets comes the increased need to categorize and standardize associated data, which is compounded by the accelerated pace of content production.
The need for a consistent marketing taxonomy and data standards is even greater now given this industry trend.
More Assets, More Data
To address this new trend of growing assets and growing data, organizations may assume that a solution lies in a more rigorous process for data standardization. This is exactly right, but the strategy behind the process is important to consider.
What does this process look like? How scalable is it? These are just some of the questions that business leaders need to be asking and thinking about. What we have seen is that there is one reality we can’t ignore, and that is that human error is highly probable and likely when it comes to data entry and data standardization processes. People are highly prone to make small, yet sometimes high-impact errors when handling large volumes of data. People can also get impatient with the process, and can circumvent procedures such as adding in appropriate tags and inputting data into the right columns.
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This challenge of human error has adverse effects on the content supply chain. Increasingly more amounts of content being created and published ultimately leads to more data being gathered. Without an evolved data governance system to go along with the growing amount of collected data, erroneous categorizations can have disastrous and time-consuming effects down the chain.
Ensuring that data has integrity at the start adds value to the organization’s drawn conclusions and eliminates the need for manual, retrospective corrections.
Avoiding and fixing “messy” data within the content supply chain – and across the organization – begins with a comprehensive data governance system. When data standards are defined, applied, and connected across datasets and touchpoints within the organization, the content supply chain then has the metadata it needs to produce formidable assets.
The value added by new taxonomies can be leveraged by the entirety of the organization, not just within the specific team or supply chain in question. The key here is implementing data standards at the source, as opposed to sorting through the results further downstream. People may be a part of the problem, but they’re also a source for the solution. Successfully integrating a taxonomic approach to measurement across teams and metadata allows companies to better understand – and improve – their data’s performance and function.
The Benefits of Standardizing Data at the Source
As increasingly more content is being produced, adopting first-party data strategies has organizations reconsidering their existing tech stacks and data organization. Data standardization being a prominent topic at this year’s Adobe Summit further demonstrates that better organizational and taxonomic tactics are top of mind for many industry executives.
A lack of data taxonomy in the content supply chain will deliver inconsistent naming conventions, which will inevitably waste content and dilute the effects of creative efforts. With data standards applied, the discovery, automation, personalization and analysis processes all become noticeably easier – resulting in the ability to use content as another measure of optimization to drive better experiences and ROI.
For example, companies looking to overhaul their digital marketing data management process can successfully do so by shifting their mindset from “data is the problem” to “data is the solution.” Rolling out data standards across marketing strategies can enforce consistency, quickly improve campaign tracking across external and internal campaigns, and allow for quickly-pulled reports, conclusive insights, and strategic recommendations for leadership.
Implementing a new data standards practice into an organization from the start of a dataset’s journey is likely to make previously siloed processes more efficient, and the primary purposes of supporting outside tech vendors obsolete. This is not to say they’d all be unnecessary, but rather, their functions may benefit from a reconfiguration to better suit the new needs of the organization.
A Democratized Data Future
What does the future of data look like? The ongoing trend within the industry is to arrive at a place where employees all across an organization, regardless of their technological prowess, will be comfortable and confident interacting with data. The best way to remove the drama from marketing metadata and to ensure everyone is on the same page is to unite employees under one data taxonomy system.
The democratization and the standardization of data go hand in hand. Data that is easier to comprehend and conceptualize will allow for interest to grow and capabilities to flourish. Not only that, data with integrity is also consistent, whole, reliable, and valuable from beginning to end. It is safely and securely stored, and remains standardized through any modifications, transfers, or erasures. In sum, data standards and concerns around creating privacy-forward means of efficient data gathering are on the rise across the industry, as is evident from this year’s Adobe Summit. And, with more and more people beginning to dip their toes into the data pool, now is the time to have your organization join in.
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