Garbage in, garbage out has long been the cry of the programmer facing an unintelligible report or other output. In the past, users have relied on IT to make sure that the information they were using to make their decisions was the right data. IT was responsible for the storage, security and accessibility of that data. Times, they are a-changing.
More Data Means Less IT Involvement
In the past, the volume of data was manageable by IT departments. They had the time and the resources to do the data governance for the business intelligence groups. But as “big data” becomes more and more a part of the business decision-making process, it is quickly becoming too much for IT departments alone to handle. This leaves business users with more responsibility for their data. And that is where data governance processes become essential to the enterprise.
What is Data Governance?
Today, most large scale organizations and enterprise businesses have data governance initiatives. A team composed of stakeholders like C-suite leadership, project managers, IT management and administrators work together to create standards and processes to maintain production data quality. Without proper governance, business users are not able to leverage their data in decision making processes. Users need to have confidence in their data and comply with a wide range of regulations. These issues can be properly addressed within an effective data governance initiative. There are several objectives to this:
- Ensuring data security
- Improving data compliance
- Improving overall data quality
- Creating baseline metrics
- Finding ways to add value to the organization from data
The tools for data governance are evolving. Traditionally, business stakeholders were excluded from the process because of the technical knowledge that was needed to manage their data. Now, tools are moving to a more user-friendly interface for the business customer. This allows more collaboration between business stakeholders and the technology groups that manage their data.
Governance and Data Analytics
Why does a data analytics provider need to consider governance? It all comes back to that “garbage in, garbage out” concept. Even the greatest analytics and reporting tool will not be able to make sense of data if it is not complete, clean and standardized. While data sampling techniques used in big data, machine learning and predictive analysis systems, today’s organizations still require clean and complete data when it comes to making data-driven business decisions.
Today, data governance is a large technology undertaking. This requires a collaboration between technology and business stakeholders to ensure data quality, one that gives end users more ownership of data, a better understanding of metrics and overall better data literacy. A data reporting tool that allows users to explore and visualize their data on the fly is an important way to help business users better understand their data and in the long run make better decisions. An analytic toolset should be chosen with the business users’ need in mind.
Big data is here to stay. As more data is uploaded to the servers of the business world, there will be an increasing need for data governance and collaboration between departments. Teamwork — and the right tools — are the only ways to ensure that organizations aren’t buried in their own data.
