Enterprise-level data is constantly growing and developing—and organizations are starting to recognize the value in collecting it. But when it comes to actually leveraging that data as an asset, enterprises are faced with several unique challenges. We often refer to the three Vs of Big Data–volume, velocity and variety. But many are facing a much bigger problem in the variety of their data than they are in the volume or velocity.
Data variety refers to discrepancies in the way data is collected across an organization–in different formats, across different business units, and with differing organizational structures. All of this adds up to a major hindrance when it comes to an organization’s ability to fully leverage its own data to driven business-defining results. Add to that the fact that these same companies are competing against “Cloud natives” like Amazon and Google, and they’re discovering that their siloed data and the old approach to collecting and analyzing it can’t keep up.
Why is Data Variety Different?
Data Variety is a bit more complex than the traditional approaches, such as Master Data Management (MDM) and Extract, Transform & Load (ETL) can keep up with. While efficient at a small scale, the variety of data, the speed at which data is acquired, and the need to quickly and cost effectively analyze that data starts illuminating the shortcomings of these original approaches.
Data variety is messy—and it’s only getting messier as enterprises grow and expand—and it stands in the way of organizations seeking to leverage data as an asset. The reason for this? Independent Business Units (IBUs), aka data silos.
The old way of keeping data separated and siloed in IBUs makes agile decisions easy, because you avoid any need for decisions to go through CEOs and a team of execs. Too many cooks in the kitchen bogs things down, so it makes sense (on the surface) to keep things separated. Unfortunately, this is terrible for data because data silos are rarely compatible with one another across the entire organization. While the old approach could parse through chunks of data, there was still a great deal that went untouched because enterprises almost never realized the extent of their data silos and the limitations they present until they’re well into a data integration or analytics project.
This isn’t just a one time problem. It’s a problem that grows every year that a company ignores its enterprise data debt (EDD). More and more data is acquired, making it increasingly difficult for enterprises to tackle that Big Variety challenge.
How to Embrace Data Variety
Data variety doesn’t have to be a hindrance for the enterprise, though. Enterprises need a quick, efficient way to make decisions based on hundreds of thousands of datasets—even those stored across different regions, continents and business units. Tackling Big Variety no longer has to be an obstacle to leveraging data as an asset. With the integration of human-guided machine learning, enterprises are given the scalability needed not just to tackle Big Volume and Big Velocity, but most importantly, Big Variety.
In many corporations, data analysts are deployed to pull data from a variety of sources in order to answer a specific question. Once the data is collected, they perform integration on the resulting datasets. This means a data analyst’s valuable time is spent integrating and cleaning data before they can even start doing the key portion of their job title—analyzing.
Enter: human-guided machine learning. With this type of supervised machine learning, your enterprise can manage the mapping, integration and transformation of many datasets into a common data model, all in a scalable way. Machine learning helps teams reduce the time to add new sources of data, manage many data sources and improve the quality of the data by allowing your own subject matter experts to do more.
Simply, in order to utilize this approach, subject matter experts accelerate the learning process by teaching the technology—all in real time. It’s a system of checks and balances where the technology looks to humans for training and validation. The best part is that models are constantly learning as you use the data, so they get smarter over time as the data gets cleaner. This means it also require less and less human interaction the smarter the technology gets. Now, adding new data sources is easier, and structured data is more agile. This saves time and money in getting critical business data into more hands and creates more use cases.
To learn more about how your organization can embrace data variety to deliver business-driving analytics, download our latest ebook below. Or to learn more about how Tamr helps enterprises with data variety challenges, schedule a demo.