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10 Key Considerations for Big Data Integration

Posted by Preetisha Dayal

Jun 21, 2018 11:30:00 PM

The data has managed to change the world and especially the business field with the daily requirement while it is formulating for the future world of analysis, big data, and real-time operations. For this to accomplish, it is important to think about and improving the data management, skills, teams, and infrastructures.

Big Data Integration
How to deal with Big Data Integration? 

Data integration plays a drastic role in the processing, collecting and moving data whether it is old or new. You can say that the data integration is a vivacious fragment of the answer. Big Data is used for the complex and large data-sets in which the applications that were used earlier are not adequate. However, doing data integration on such a huge set is a complex process. However, no matter what type of application is used, it is important to have accuracy as that will result in the better decision making.

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Here are the few key points that much be considered while dealing with the big data integration.

Read More: 10 Reasons why Big Data Insights is the need of the hour

1. Less is always More

You all might have heard that more is better in the terms of data. Well, that is not the case every time. The more can be complex, messy and if the larger amount of data is irrelevant then it will hard to get the accurate result and give out false signals. However, the more data also showcases that the error standard will be minimal to zero which will give out a better and relevant model. There is a chance that you will be able to see the new trends and irregularities that helps in the communication and processing of the most relevant information comes with the more data only instead of some random sample of data set.

Also Read: 21 Statistics and Predictions on the future of Big Data

2. Reuse the Data

Data is equal to the raw material for the companies. You don’t have to worry that it will expire after a single use as it is simply not the case. There are so many unexpected ways in which you can reuse the data and can provide can find the solutions to several problems and that too in the different time period. There are historical data servers that show the exact use of this type. The old data is re-purposed and reused in an indescribable manner to the traditional collector. Hence, reuse the data by thinking out of the box and never think that it is waste of time. Every way will make you reach somewhere.

data as an asset

Also Read: 6 Powerful Big Data Models to decipher powerful insights

3. Treat Data as an Asset

Data has its own importance from the traditional way of running a business to the modern form. It is a type of resource that contains real and assessable values. Your data will help you in making future decisions and will eventually help with your productivity growth. Hence, if you want to take timely decisions then, it is essential for you to collect the real-time data accurately one mistake can cost you a lot of money. Therefore, make sure to manage and secure the data in a way through which you can obtain it whenever you want to.

 Also Read: 12 Key Considerations while choosing for Big Data Technology stack

4. Difference between Correlation and Causation

There is a difference between causation and correlation. If you are experiencing an increase in the sales then knowing the reason can only come handy but it can’t help you to plan out the strategies to improve the sales in future as well. Correlation can be recognized as a more cheap and quick way rather than the root causes. Big Data helps in determining the type of variable that will be best for the future sales as it can exactly tell us the requirements of a customer. With the help of this, a business can increase or decrease the value to maximize the sales and output value. The big data makes us less worried about the reason (which can be out of control) and helps in focusing on the correlation through which better business performance can be predicted.

value of data

Also Read: 5 Competitive Advantages of Big Data in Business

5. The Use determines the Value of Data

The use and reuse of data determine the value of the big data. The value usually determined when a number of data-sets are combined together in order to determine the solution for the queries. However, with the help of big data, the sum of data holds less value than the answer as it determines the actual business production.

Also Read: 7 Steps to create a successful Big Data strategy

6. Risk Factor Attached

There are so many things in the big data that increases the risk factor of the big data. The data is a powerful asset for business which makes the vision towards the future easily that is predictable and more powerful due to the power of the power. To make things more clearly, the analysis actually depends on the numbers and the ways user interpret them. However, results can be unfair, algorithms mis-analyzed and misleading numbers. Therefore, it is crucial to justify the risks with the help of the understanding of problem and limitations of big data.

Also Read: Attribution Modeling: How is gets the best out of Data

7. Underestimating Data Quality

The quality of a data is considered as a significant thing. In any organization, the analysis can be ruined due to the poor quality of data-set. However, for big data, the data quality is damaged due to the semi-structured and unstructured data used for the data set integration. For the data processing, it is important to have the quality data or improved data that can give proper result. If this step is not followed then it is possible that the output will be twisted which can impact in the business analytical system in a negative way.

Also Read: 4 Ways how Big Data Automation is changing Data science

8. Improper Contextual Data

The contextualization of data is the fundamental logic behind executing the text analytics and processing context data. If the contextualization is avoided then it is possible that the proceeded data will have a lot of inaccuracy and will produce twisted analytical results.

complex data
9. Not Understanding the Complexity of Data

Big data is formed with the many layers that are combined with the complexity which can’t be determined by simple inspection. These type of complexity are in the data due to the format, structure, metadata, and content. If you don’t understand each and every complexity of the model that is used for the statistical, text mining and mathematical analysis then the result will be hazy.

Also Read: 8 Keys to make the most of your Big Data

10. Embrace New Data tools and practices for Data Preparation

There are so many names to explain the concept of data preparation, it is used as a data munging, data wrangling, DI light, and data blending. Irrespective of the name, you can prepare the data by using different tools like profiling, integration, analytics, exploration, and visualization.

Data preparation for integration is the technique that is used by data scientists to determine the proper result while working on the source. The data is also analyzed that is the essential part of the data integration modernization process. The analytics and data scientist teams work to determine the valued chunks of the vision behind the data that is hidden. One such example is outliers that is the indication used for the new customer section. There are many modern preparation techniques and tools required due to their flexible, fast and easy-to-understand behavior. They also make the data exploration easy and motivate people to do so.

Also Read: 5 Open Source Big Data tools

There are many approaches to the modernization of data integration process.

  • You can integrate data for your own services such as storing data directly in your database, data hub or vaults that can be hosted by relational databases, Hadoop, and file systems.
  • You can rely on your own tools that can make it easy to present your business-friendly data view. It also opens up the preparation and self-service access.

While working towards the data integration modernization, it is essential to work more on the data capturing and processing the new data set with the help of multiple tools and at a fast speed. This will take your attention to the old methods in which you can make few adjustments and DI solutions.

Also, there its own purpose of capturing data-set in the original format. It can help in preparing the report, analysis the data, and set the operation correctly. With the advanced technology, there are so many ways through which this could be easily achieved at the desired speed. These changes can give you the more accurate result that can help to determine the proper business strategy and productivity.

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