Business intelligence & Data Management | OpenTeQ

Business intelligence & Data Management | OpenTeQ

OpenTeQ Admin | Updated: Apr 17,2020
Business intelligence & Data Management | OpenTeQ

Data is the assets of every organization. The traditional Data services cost both: time and money to the companies, during the prepping and the cleaning of data. The business analysis suggests that to produce ad-hoc reports, every organization needs to be tech-savvy, or the companies need to wait for someone more knowledgeable to help the organization to fix the problems. Many companies are turning themselves to Business Intelligence to help themselves in business analysis. But most of the companies are facing the issues of Data management, due to the continuation of poor-quality data.

So if we think about Business Intelligence and Data management parallelly, we find that every organization there is a need that every organization must understand the impact of Data Management in Business Intelligence.


Business Intelligence is a set of processes which can convert raw data into meaningful information with the use of architecture and technologies, to drive profitable business solution. The data is converted into actionable knowledge and intelligence to impact the strategic, tactical and operational planning of the company. Business intelligence prefers historical data instead of assumptions to support fact-based decision making of the company.

The Business Intelligence tool is used to create dashboards, reports, graphs, charts and maps to help the users to understand the nature of business and provide detailed intelligence.


To implement BI, every organization needs to follow some mandatory steps. At the first step, the data is extracted or collected from the corporate database for the company, which could be spread through various and multiple systems.

The data is then cleaned. During the process table can be added to the data and data cubes can also be formed. The data is transformed into a data warehouse.

The Business Intelligence system can be used to create ad-hoc reports and to conduct business analysis


Business Intelligence depends upon good-quality of data. So, if we call data management to be the pipeline of Business Intelligence that won’t be wrong. The Business Intelligence needs filtered data extracted from various sources and which are also cleaned through multiple data management processes.

The quality of data presented in a reporting application can say a lot about the data management process. The companies have now realized that better and cleaned data can improve the return of Business Intelligence investments. According to a report of Forbes, enhanced data can enable business intelligence.

However, there is one more thing that you must know about data governance and management, and that is, there must be a healthy balance between flexibility and consistency. It is usually tough to maintain the ratio, but it is essential. The inconsistency in data can become a barrier in business intelligence success.

Data services are easier for the users or the companies who are not willing to get engrossed into explorative data discovery or complex data models. Data management has made it easier for companies who are willing to build self-service business intelligence.


We have partially understood the importance of data management in business intelligence. Now we must start to know data management from a closer perspective. Data management is a process of collecting, filtering and securely keeping data for future use. The goal of data management is to help organizations and individuals to make better decisions while taking cations and to maximize the benefits of the organization or the individual.

Now a days, a better data management strategy is becoming more critical than any other asset to create value. In today’s era, the companies need to have a data management system which can provide more efficiency to manage data across a diverse data tier. The data management platforms are being made to include databases, big data management systems and many more.


Though there are diverse challenges during the process of data management. But a well thought out and strategic planning can drive towards the best practices too. Though the best practices may vary based on the type of industry, the following best practices can help the organizations to address some of the common challenges:

Creating Discovery Layer: It is indeed a great challenge to identify a set of data from a vast database. The discover layer is thus creating over the data tier of every organization, which helps the analysts and the scientist to access and browse the data quickly when required.

Developing a data science environment: A data science environment enables the automation of the data transformation work. It helps in streamlining and evaluating the data models easier than before. The data science environment also eliminates the need for manual transformation and testing of the models.

Autonomous technology to maintain the performance level: The autonomous technology monitors the database always queries and optimizes the indexes. This helps the database to stay updated and highly responsive wherever the need be. It allows scientists to stay away from time taken database activities.

Use of standard query layer: The new technologies are helping the users to work with the database in a better and improved way. A typical layer helps the scientists and the data users to access the data without knowing the storage location of the data primarily. It also doesn’t require any manual transformation.


When the businesses have access to clean and well-managed data, the enterprises get empowered. The consistency of the data sources and the data recovery tools can rapidly transform the analytics done with the help of the data. Improved data management allows businesses to act on the market differently and where it is needed. Many times, the non-technical users may face the problem of missing the opportunity of keeping the proper insight at the correct place for the absence of authentic, reliable data sources.

Data management enables self-service Business intelligence which can quickly eliminate the confusion of the technical interferences and can cut the cost of data solution of any sort.

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