Data has become an essential part of life. This is true in nearly all aspects, especially our professional lives. It is usual for today’s professionals to interact with data in one way or another. It fuels decision-making as much as it does innovation and creativity and allows us to experiment with new ideas while still meeting customer needs. The biggest drawback to the increased data use, of course, is that all of that information has to come from somewhere, be analyzed somehow, and then stored somewhere before eventually being deleted securely. How do we create business environments where data is simple to access and easy to understand without putting people’s safety and personal information at risk? Business intelligence is the solution, paired with a healthy dose of data governance.
In this article, we’ll explore business intelligence and data governance, what they mean on their own, and why the combination of them is so important in today’s business landscape.
Table of Contents
Business intelligence (BI) is a set of technologies and strategies enterprises use to take business information, analyze it, and transform it into insights that businesses can use to make tactical and strategic business decisions. This tool allows professionals to access and analyze raw data sets and present easy-to-understand analytical results in reports, dashboards, summaries, maps, charts, and graphics to provide upper management with detailed intelligence about the business and its operations. BI strategies are widely considered among the most important tools available to businesses of all sizes.
Reporting tools are the most widely used of all the business intelligence solutions on the market. Dashboards give BI professionals an easy way to collect, analyze, interpret, and present findings while streamlining the effort that non-BI professionals must expend to understand the implications of those findings. The goal with BI tools is always to make it easier to understand even large raw data sets so that professionals can make informed decisions even without a data analysis background.
Consider a company that wants to improve its shipping capabilities. BI tools can be used to determine where problems are popping up during the inventory collection and packaging process to explain delayed shipment departure and arrival to customers. Even beyond analyzing the stock-to-shipment process, BI capabilities can help businesses determine which items are most likely to be delayed as well as other related issues. Once this information has been collected and analyzed, business leaders can streamline the collection, packaging, and shipping processes to meet their goals.
In this way, BI can be used as more than a metric of business performance elements like reduced costs and improved sales. It can be used to enhance productivity and increase efficiency in business processes on all levels. Tightening operations without negatively impacting employees can improve profit, too – often without requiring enterprises to spend additional money on new employees or machinery.
With that said, BI tools can also be used to keep an eye on routine business concerns such as:
BI is often considered a subgenre of business analytics. While both tools analyze data, BI is focused on descriptive data collection, analytics, knowledge management, and data storage more than raw data collection. In other words, it takes the data that was collected and initially analyzed by business analysis and further interprets it to make it easier for other professionals to understand.
Long-term management of data is just as important as initial data collection and analysis. The term “data governance” refers to an approach to data management at every stage of its lifecycle, from acquisition to disposal. Everything you do to keep data usable, available, private, accurate, and secure is part of your data governance strategy. The term is used to describe the standards surrounding how data is disposed, processed, stored, and gathered – all of which can vary from business to business.
Good data governance offers businesses a variety of benefits:
The past decade has seen many changes in many industries around the world. Safety regulations are increasing just about everywhere, for example, from food handling jobs to education and everything in between. The most sweeping changes have undoubtedly been in regard to the way we collect and store data. Data governance strategies make it easier to maintain even vast amounts of data as regulations change and different measures need to be met to ensure compliance in the future.
Data governance also helps businesses manage their resources more efficiently by allowing them to eliminate data duplication. Data duplication is a big issue in some businesses – particularly those that use information silos – so any strategies that can cut down on overbuying and, by extension, over-maintaining are a good way to control costs.
Timelier decision-making is a fairly self-explanatory benefit that allows users throughout the organization to find and use the most up-to-date data for marketing, product design and improvement, and customer service. Quicker and more accurate decisions build trust with consumers, too, as does complying with both external and internal data policies.
Finally, data governance plays a big role in risk management. Businesses can more easily gate data behind authorization requirements as needed to ensure that personnel have access to the data they need, but nothing beyond that. This lowers the risk of data being used improperly while still giving personnel the ability to use information to make their jobs easier and their work more accurate.
Now that we understand data governance and BI individually better, let’s talk about how they intersect. BI tools aim to make it easier to use data to drive decisions and strategies at every level of an organization. Data governance matters because it makes it easier for enterprises to benefit from their data assets. Without effective data governance, BI tools would be far less effective and yield less accurate and less meaningful insights.
The good news is that data governance and BI go hand-in-hand in more ways than one. In addition to data governance making BI more important and effective to businesses, BI makes it easier to develop and maintain a solid data governance strategy. The hallmark of any great BI strategy is the ability to effectively collect, analyze effectively, and store data, after all – all of which are important parts of data governance policies.
It is almost only possible to get the most out of BI tools with an excellent and effective data governance strategy. Data governance matters in the BI community because it is necessary for data security and analysis.
More specifically, there are five ways that business intelligence professionals use data governance within their role:
The next sections of this article will explore each of these in more detail.
It is difficult to overstate the importance of reliable quality in a business. No matter what industry you’re in, the ability to produce results you can count on is crucial to success. That doesn’t mean that it’s something that just happens overnight. The most successful organizations in your market have likely put a lot of thought and time into their quality assurance and have created a sense of trust that consumers naturally appreciate.
You can do that, too. And the most important tools at your disposal in doing so are data and the systems in place to analyze, interpret, manage, and store it. BI professionals excel in this area, especially when they have effective data governance strategies to pull from. Remember, consistent quality isn’t a fluke. Decision makers spend time analyzing data to ensure that their business is running the best it possibly can, and data governance ensures that the data they need is stored properly, safely, and accessibly.
Once you’ve moved past the data discovery process, your next task is to use the information you have uncovered. This is sometimes easier said than done, especially when the data is extensive, contains sensitive information, and requires enhanced security. Data governance can be used to facilitate integration and collaboration between team members. A marketing team, for example, could quickly work with a sales team by sharing customer insights and data. The data discovery process and other skills necessary to improve business intelligence can be achieved by completing an online Doctorate of Business Administration in Business Intelligence, such as the one offered by Marymount University. This degree can improve your business expertise and position you as an agile leader.
By integrating data use into everyday work processes, organizations can promote mutually beneficial relationships between departments even without direct contact. Sometimes, updating shared data with the latest insights and analysis is enough to improve the accuracy of each team’s work, with no lengthy meetings needed.
As you know, data must be collected securely, used securely, stored securely, and disposed of securely. This is true for all data, but especially for that which might include personal information or sensitive business numbers. Data governance can secure that information by serving as a “best practice” example. Professionals with clearly identified and explained steps are more likely to take the time to actively manage their data, and data governance strategies actively offer just that.
When used together, data governance and BI make it easier to remain in compliance with seemingly ever-changing regulations. Instead of just storing data, BI professionals use data governance policies to make it easy for everyone to understand how their data is collected, used, stored, and deleted. When regulatory changes come down the pipeline, these policies make compliance surprisingly simple.
Professionals who understand how something is currently done are more prepared to alter their routines to meet specific goals than people with no concrete data management steps to follow.
When you think about managing data, you might envision a simple spreadsheet or hard drive and consider the process fairly straightforward. While the assumption is understandable, it also could not be further from the truth. Data management requires flexibility within structured environments as well as secure user permissions that take multiple devices and access points into account. Data lifecycle management is the process of managing data from the second it is created to the moment it is deleted.
BI professionals use data governance to define their data lifecycle management procedures. More specifically, data lifecycle management contains the following stages:
Good data governance plans have steps and guidelines in place to ensure data integrity and security at each of the above stages. BI professionals use those guidelines every time they interact with data so that the risk of misuse remains as low as possible.
While this article explains the basics of data governance and business intelligence strategies, there is much left to learn. The field of data governance is a growing industry and promises to continue expanding in the future as well.
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