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4 Things to Know About Big Data Management

Concerns are mounting around the application development landscape and the lack of best practices surrounding the management of data powering of applications. Therefore, a number of organizations are forthcoming about adopting big data platforms that play an instrumental role in the management of data processes and tools.

Here are the four things that you need to know about big data management.

1. Organizations can Indulge in DIY Big Data Management

Business users today want to access data in its raw format instead of only handling it through a series of data stores that are operational, SAP data warehouses and data marts. They also want to scan and filter through the data sources to design and generate reports that cater to the needs of the business.

All of this is possible with big data platforms that support huge data sets in their original formats.

Business users can be more self-sufficient with their big data management in the following ways:

  • The platforms allow them to discover relevant data and pursue it independently
  • Access to data preparation tools will enable users to assemble the information gathered from multiple data sets and present it for evaluation

2. It is a Proven Data Model

Generally, traditional data models involved an approach of collecting and storing data that was later used for analysis and reporting through a predefined structured format. However, with the series of advancements in big data platforms, data management is now possible in both unstructured and structured data sets using a set of predefined data models that stores the data in its original, raw formats.

The advantage of this proven model is that the data can be adapted in multiple ways that best suit the needs of the business.

Having said that, there is the risk of conflicting interpretations and data inconsistency when managing huge data sets. This can be reduced with the help of systematic processes to document the business glossary, mapping the appropriate business terms to data elements and also maintaining an environment that encourages collaboration for analytics purposes.

3. Quality Lies in the Eye of the Beholder

In the traditional data management systems, the process of data cleansing and standardization was applied before storing the data in the predefined model. In the case of big data, this is not a requirement as the model stores data in their original, raw formats.

The advantage, no doubt, lies in the flexibility of data usage. However, it also means that the business user has to shoulder part of the responsibility in terms of applying the necessary data transformations. The user needs to ensure that there is no conflict when transforming the data sets that can be used for various purposes.

The users need to use methodologies that are able to capture data transformations without inconsistencies and maintain coherent data interpretations.

4. The World is Streaming in Real-time

In the past, the primary job of data was to serve and support analytical purposes that stemmed from within the business enterprise. The data, then, was also limited.

The scenario has completely changed today where massive volumes of data are being generated from devices that are streaming 24×7. Channels have multiplied and now there is a combination of data that is machine as well as human generated.

Therefore, any big data management strategy has to have the involvement of technology support that is able to scan, filter and select information that is meaningful for storage and future use.

Final Thoughts

The importance of big data platforms and the role it plays in big data management is evident. If your business is on the market to explore options for big data management, then be sure to get in touch with MDSap, a trusted SAP partner providing a range of big data solutions to clients in the Middle East.

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