Organizations everywhere want to benefit from the power of artificial intelligence (AI) and machine learning (ML). Data organization is essential to success.
Most organizations have such unorganized data that their analytics capabilities are fractured. A lack of data standards and best practices, and difficulty finding and accessing the right data across teams leads to siloed efforts and negative consequences.
Without organized data—ensured by data governance—AI and ML models will fail.
Business units, departments, and individual teams often act autonomously to fulfill their duties without communicating across all teams. Frequently, data engineering work is duplicated because data teams lack awareness of each others’ work. And without standards, each team may address data engineering challenges in completely different ways, creating more work later, even though the data itself is considered “clean.”
It is very inefficient to redo data engineering efforts on the same challenges from team to team. When these teams do work together, all the data they prepared separately must be re-engineered. Data science projects that could have been completed in weeks now take months—or even years—to get the data reorganized correctly across domains before significant insights can be leveraged.
Often analytics can only be successfully conducted within individual teams or departments. Those teams may discover a lot of value from data insights for their particular needs, but the full picture is missing. For executive leaders seeking to make the best decisions for the company, this poses a real problem. They are limited to independent team insights without the full context. When data doesn’t cross team boundaries, it becomes much harder to make well-informed business decisions.
Data science is a powerful tool by which organizations can assess business health, understand customer habits, and find opportunities for improvement. However, successfully monitoring the health and progress of your organization requires organized data systems and agreed-upon best practices with data. Without these, teams are less able to connect and work together, and data modeling experts end up spending more time engineering data before they can begin their actual work.
When data elements across teams appear the same but are defined differently, personnel struggle to interpret insights. Without dataset discoverability—making data easy to find and understand—valuable datasets are remade or can even go unused. AI and ML projects take years to perfect because so much time is required to understand and re-engineer gigantic amounts of data.
All organizations struggle with data management. Aligning your data through a data governance strategy tailored to your organization will set you right. Most organizations shy away from beginning a serious data governance initiative because it takes coordination, time, and change management. It may not be the most glamorous work, but it is incredibly critical—especially in today’s technology-driven world.
After the heavy lifting of data governance is accomplished, all data initiatives immediately and organically become empowered.
Data governance is a group effort. It’s something the executive leadership team and experts from all over the organization need to champion. By contributing their domain expertise, they can guide the process of clearly defining unique data elements and aligning those elements with the ones common across all teams.
Data engineering teams will then be able to take those definitions and implement automated transformations in the data everyone agrees on and would need to accomplish anyway. Data governance points personnel towards the correct access requests and security requirements depending on the data of interest.
Data is incredibly valuable but only when its organized and ready to use. The same goes for energy. It’s great when you have the oil field, but you start making it valuable when you organize the extraction and distribution of the oil to be used for energy. The same goes with data insights. You can have all the data in the world, but unless it’s high quality (cleaned, transformed, and consumable), it isn’t worth much. But as soon as it is, you can energize your organization with valuable insights.
Data governance creates data quality standards that must be met before a dataset or stream of data can be considered usable for analytics groups. These datasets could be thought of as data products that teams can leverage for everything from product enhancements to customer profiles.
With data governance in place, executive leaders can much more easily retrieve insights into any part of the business they wish. Real-time dashboards can forecast the sales of different products as new ones roll out. Understanding a specific customer group to offer them a better user experience or product becomes much easier since the data is already organized, findable, and usable.
Instead of taking a year or two to understand your customer preferences, you could have a thorough report ready much more quickly. Organized data makes it much easier to get ahead of other organizations who haven’t started data governance. You’ll be able to pull off data projects more smoothly and quickly and be further ahead of the curve with an AI-ready infrastructure.
Imagine if all the nations operating at international airports ignored international standards and stuck to their own ways of doing things. Airports would be total chaos, and travelers would constantly be confused trying to navigate different laws and languages.
Instead, the guidelines and procedures (that it took effort to hammer out) inform and empower travelers from around the world. Baggage and ticketing systems are standardized, making the traveling experience easier and smoother. Regardless of native language, flyers can successfully navigate large airports because translations are abundant. And behind the scenes, airport personnel are acting across borders to ensure safety and security and manage schedules.
Modern airports are large ecosystems of data, and they work so well because they’ve implemented systems that govern the industry. Through data governance, you can guide your teams to work better together and achieve goals quicker. While the early stages of data governance involve difficult work—including change management and aligning multiple team leaders on a unified plan—those initial efforts lead to fantastic results later.
In two upcoming blogs, we will explore:
Many organizations overlook organized data, but implementing data governance at your organization puts you ahead. As AI and machine learning models evolve the way we operate, you want to be sure your organization is ready for it.
