In the modern marketplace, data is everywhere. On social media, in online shopping, all over web browsers, and mobile apps, and the list continues. Data is everywhere. In turn, companies and organizations are using data at a higher capacity than ever before. Enterprise organizations rely on a massive series of technologies and softwares to create their technology stack. Within this stack, there needs to be avenues through which data can travel, transform, and sync between different systems and platforms. This is a common issue that organizations and SaaS providers are trying to tackle all over the world.
There are a variety of reasons, scenarios, and contexts in which it makes sense for one system to share or synchronize data with another. In fact, many organizations are looking for ways to consolidate all of the data collected on any single consumer into a centralized and unified consumer profile. These profiles can then be leveraged in marketing, advertising, and sales efforts. Consumer profiles also help organizations create and deliver a customized consumer experience which is extremely important in the modern economic landscape.
Data in the Modern Economy
Data is extremely valuable in today’s day and age. Organizations buy and sell data like they do stocks. However, raw consumer data by itself is only so helpful. So, it isn’t just the consumer data that is valuable. It’s the analysis, understanding, and activation of that data that really shoulders the weight when it comes to consumer insights and company actions.
In approaching data transfers and data analysis though, the accuracy of that consumer data is even more important. No matter how good the analysis is, if there’s bad data on the input side of things, bad data will certainly be the result. This emphasizes the necessity of accurate and effective data transfer and data sharing techniques.
There are a variety of strategies that organizations can implement in order to move, transform, and share data between the various platforms and technologies they rely on, and each has their own suite of pros and cons. Some strategies like ELT and ETL are specifically designed for data transfer from cloud-based systems to a data warehouse. These techniques are also equipped with features designed to handle data transformation and modeling.
Other popular data transfer technologies like iPaaS, on the other hand, are more lightweight and are designed to simply move data between various cloud-based services. Where you’re trying to move your data will dictate which strategy is best for you and your organization.
Understanding the Consumer
When it comes down to it, all of these different data-mining and data-transfer technologies are designed with the same goal in mind; helping organizations better understand the consumers that make up their audience base.
This is true at every level. Whether it’s making use of a data warehouse as a CDP in order to unify the consumer data collected by disparate marketing systems, or API integration to better capture live-consumer data, this information is used to create more effective and more personalized marketing campaigns.
Understanding the modern consumer and what they find important is incredibly helpful in connecting with consumers on a more human level.
Creative and Personalized Marketing
Personalized marketing and creative content marketing are two of the dominant marketing practices in the digital age. There are very accessible examples of this, like Amazon and Netflix, for instance.
Amazon, the behemoth online retailer and enterprise organization does an incredible job of personalizing product suggestions on their shopping pages. These suggestions are based on previous consumer actions, events, and patterns. This is all consumer data captured and utilized by the Amazon tech stack.
Netflix is another great example of a personally curated experience. The way that suggestions are delivered on what to watch next showcases a level of personalization that other organizations strive to achieve.
Keeping Data Fresh
None of this could be possible without the proper and regular logging of data progress, though. Almost every developer has to learn the importance of logging data progress the hard way. In other words, it only takes losing 10 hours of work one time to remember to regularly implement saves and to log data progress. Logging can also help users understand the progress of long-running syncs, to keep everyone on the same page.
Just like with any big job, it is vital to continually log data progress as you perform the operation.
Wrapping up on Logging Data Progress
There’s no worse feeling in the world than suddenly realizing you’ve just lost hours and hours of progress. Keeping proper logs on data progress is an absolute best-practice and should become a routine habit. With data serving as many roles as it does in a modern organization, it is more important than ever that teams have refreshed, accurate, and current data from which to work.