To cope with the vast volumes of data they interact with, enterprises are adopting means that allow them to make use of data in different business operations. Business intelligence (BI) includes technologies, best practices, and tools that help enterprises make sense of data to optimize their operations. However, business intelligence alone is not enough.
Business intelligence is supported by data management — the processes and procedures that facilitate an enterprise’s control over its data. Data management and business intelligence increasingly overlap to improve the data-driven decision-making of an enterprise.
Tying BI and Data Management Together
The effectiveness of BI is determined by the quality of an organization’s data management processes. Great data management depends on how well an organization approaches data storage, performance, security, and compliance. The outcome is amplified by business intelligence. As such, with poor data management, you can expect inconclusive business intelligence reports. Business intelligence and data management work together to improve data quality and mitigate data management challenges.
Loop of improvement
Good data management gives business intelligence analysts a chance to enhance data management processes where needed. Improving data management processes leads to more effective business intelligence. Data is the winner in this cycle of improvement, as leveraging these management processes produces better quality data.
Business intelligence, through integrated feedback solutions, allows reporting in a way that’s both easy to understand and actionable to those accountable for data management practices. This spares data analysts from having to continually tackle the same reporting problems.
Let’s look at how the gears driving BI and data management dovetail to allow enterprises to establish a comprehensive, competitive edge.
Also read: Top Data Management Platforms & Systems 2021
The Role of Business Intelligence
Business intelligence converts stored raw data into data assets. It aims to improve decision making and overall efficiency of the enterprise. Business intelligence also aims to enhance an enterprise’s awareness of new opportunities. Here are some of the ways it provides value:
- Decision making. The vast amounts of data available to enterprises can be used for strategic decision-making. Business intelligence transforms this data into actionable insights.
- Competitive advantage. If an enterprise can efficiently make sense of its data, it can make decisions much faster. Enterprises can confidently take action, allowing them to move faster than competitors.
- Operations. Business intelligence helps to optimize enterprise operations, employing data to report on the status of projects and departments as well as to measure performance and establish benchmarks.
- Wealth of information. Business intelligence centralizes and gives enterprises total oversight over their data, providing insight on how to best optimize storage capacity.
- Compliance. Having oversight over your storage makes it easier to observe data storage laws and regulations. You get to verify that data is correct, up-to-date as well as stored in the right place.
Data Management Tools
As the data needs of enterprises continue to grow alongside the cost of storing that data, data protection laws and regulations are also evolving. To stay abreast of changes, enterprises require a data management strategy.
Data management is the process of taking in, storing, and regulating data that an enterprise has access to. IT teams ensure that data is maintained. The result of efficient data management is better decision making.
The value of data management
- Optimizing storage. Proper data and storage management gives users adequate storage capacity. Enterprises can become more selective about what they store. This avoids redundancy, bloated storage costs, and reduced performance.
- Oversight over storage systems. Administrators have oversight over storage resources in a centralized manner. Controlling data and storage systems is simplified.
- Anticipating changing data needs. It is much easier to anticipate fluctuating data and storage needs. Organizations can easily plan for greater or fewer data storage needs, effectively taking a future-proof approach to their storage needs.
- Performance. With increased control over data and storage, there is an improvement in efficiency among all interacting systems. Storage is optimized, wastage is mitigated, future needs of data are catered for. This result is increased performance.
- Security. Having central oversight over data provides administrators with awareness of the types of data stored, the security requirements associated with each type of data as well as storage systems, possible points of breaches and attacks, and allows for the development of security measures and protocols.
- Compliance. Having a grip on where your data resides; having the right type of data stored in the correct place ensures that legal obligations associated with the data are met.
Also read: 5 Storage Needs of Modern Data Centers
Working Together in Storage Infrastructures
Enterprise storage also benefits from the combination of good data management and business intelligence practices.
Improved data quality
A combination of BI and data management improves the overall data warehousing model of an organization. Proper data storage and management put in place protocols and standards to ensure that quality data is available. It also offers better understanding of the data resources at the organization’s disposal.
Ease of querying data
Good data management ensures that business intelligence tools have access to the correct data sources. Business intelligence seamlessly integrates with well-managed data sources. Querying data from storage becomes simpler thanks to data management and BI.
Democratization of data
The right combination of data management and BI not only establishes but also eases data stewardship, which traditionally is the responsibility of administrators and BI teams. It is now easier for non-technologists to share roles, allowing them access to data as well as giving them an understanding of reports from BI tools. The simplified access to data improves the efficiency of all systems involved, including storage. There are no bottlenecks to accessing data from storage sources by depending on traditional data stewards. This results in the democratization of data management and business intelligence.
Good data management standards, coupled with the capability of BI allow enterprises to be more selective about what data they store. Actionable insights on data supported by good management practices give clarity on what data is most valuable and important.
Automated Data Storage
Traditional business intelligence and data management practices are struggling to keep up with the increasing volume and complexity of data. Furthermore, unstructured data is shaping the future of these two concepts. Organizations are increasingly dependent on unstructured data. As a result, this data will power BI, data management software, and machine learning.
To also handle unstructured data at scale, augmented analytics is on the horizon. It promises to simplify, enhance and shorten the BI process through the use of machine learning and artificial intelligence. These intelligent techniques are expected to automate data preparation, identifying insights, and reporting.
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