Thursday, July 18, 2019

6 Key Data Management Principles For Analytics Usage

  Anonymous       Thursday, July 18, 2019
Data holds the key to improve the performance of modern enterprises and analytics helps to find that key in large volumes of information. Businesses need to know the main data management principles for analytics to ensure that the input provided by the solutions is useful. An incorrect inference can easily lead a company in the wrong direction and cause great damage. You cannot hope to access effective business intelligence without accurate and consistent information. In order to prepare better data for analytics, it is essential that the correct system to manage the assets is established. Laying down clear protocols will streamline the entire assessment process and provide more in-depth knowledge. Before you hire a data management services company to create an information management program, it will be pertinent to go through these fundamental management concepts for using analytics.

1. Provide Easy Access To Data Elements

Analysts and data scientists want access to as much information as possible. This is because the larger the volumes, the better the predictors will be. However, consolidating all the assets from disparate sources, structures, and formats can be difficult. Enterprises must implement a system with native access capabilities which will simplify acquiring information from varied sources. This will improve the organization’s data preparation efforts and make sure that the analytics users get easy access to more and more accurate data.

2. Use Advanced Techniques To Run The Analytics Program

The main objective of using analytics is to identify trends and patterns which can act as pointers for the future. However, it is essential that the entire evaluation is done rapidly to facilitate quick decision-making. Enterprises must use advanced techniques to run their program so that helpful output can be generated. A system with frequency analysis capabilities will help identify oddities that would have been missed by traditional tools. Correlation is another vital aspect wherein an analytical model is devised after determining the most ideal combination of elements. Using advanced technologies will help you optimize your analytics program to the fullest.

3. Define Clear Ownership Of Data Assets

One of the key data management principles for analytics is to define clear ownership of various data assets. This can be a confusing topic for many organizations. An analytics application can source elements from various places to find a common strain in the entire information. Each item can be relayed to multiple users and undergo a transformation as well. How will that change be recorded and informed to other unrelated users? Assigning ownership of the assets to appropriate entities will be helpful in keeping track of all the elements at all times. This will also eliminate any confusion which can arise when the item develops an error or is used in an incorrect manner.

4. Scrub The Data To Ensure Consistency

Inaccurate information will lead to incorrect analyses and affect the quality of the final decision-making. Organizations need to scrub their data assets to make sure that the analytics program receives accurate elements. In order to cleanse the items, corporations first, need to have a proper understanding of the assets. They must use profiling and correction techniques to remove anomalies from the datasets. It will also be necessary to standardize data formats and identify and remove duplicate items from the database.

5. Create A Governance Framework For Analytics 

It is impossible to successfully run an information management initiative without en effective governance mechanism. The data being accessed by analytics solutions is of a sensitive nature. It contains personal information related to individuals like clients and critical financial details of business operations. It is necessary to keep a close watch on who accesses the elements. A failure to regulate the management process can have serious legal repercussions also. Governance will help create a level-based permission system and establish rules for data access.

6. Ask The Correct Questions

Analytics tools cannot provide valuable insight by themselves. It is the human users who identify the elements for building a predictive model. Enterprises cannot expect to draw helpful inferences from accurate information assets if the analysts are looking in the wrong places. Its is essential that the users know the questions they need to ask to get helpful answers.


The application of these data management principles for analytics will help in preparing accurate information for technological solutions. This will help in extracting optimum value out of the analytics program.

Thanks for reading 6 Key Data Management Principles For Analytics Usage

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