## How Big Data transformed Decision Making

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# How Big Data transformed Decision Making

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The increase in the volume of data and the improvements in the analysing methods, have occurred in parallel. At around the same time, the organizations have been mingling up the data more easily with the decision-making process. However, the increased amount of both traditional and non-traditional data is overloading the companies, as their frameworks are not versatile enough to accommodate large volume of data of various categories. This has resulted in the inevitable. These companies are now dealing with the gaps between data acquisition and implementation.

A series of surveys have revealed that organizations are struggling with two polarizing principles-

1. The attempts to attain agility overnight
2. Involving all stakeholders in their processes.

This has given rise to debates over the centralization and decentralization approach, which has resulted in serious conflicts. In reality, customers and clients want more agility in their work and on the other side, employees and partners seek empowerment. So it is advisable that the companies encourage an optimized mix in their processes.

To resolve this, companies appended “data-driven decision-making” principle. Post integration, it is found that the companies are enjoying 6% more productivity than the organizations that are relying on conventions. Decision making is playing a pivotal role in this age of Big Data. Given below are some of the ways in which Big Data has been found to be useful-

1. Collaborative Decision-making Application

Organizations have now shifted from a single decision maker to a multiple decision makers environment, where people can work both asynchronously and in a united manner. Hence, these new decision-making processes require well-connected decision-making models and technologies.

1. Dynamic-Temporal-Spatial Applications

With the introduction of  Big Data and the abundant amount of information available on the internet, the dynamic decision-making process is getting more

complex and nonlinear. Therefore, there has been a growing need for Large Scale Spatial-Temporal Decision-Making (LSSTDM) tool which is capable of handling the enormous, multi-directional, multi-source data and information.

1. Big Data Analytics and Logistics in Supply Chain Management (SCM)

In today’s digital economy,  big data analytics has opened the path for new tools and techniques to ease decision making in logistics and SCM. According to existing literature, decisions are made using traditional approaches. In today’s Big Data era, social media, online data collection and big data analysis have given six potential and more accurate decisions, which can provide an opportunity to transfuse the supply chain into the integrated supply network to supply adaptive tracking using RFID, to evaluate risk and mitigate it with the use of GPS tracking and to upgrade demand-driven operations.

1. Crisis Management, Risky and Critical Applications

If you have to understand the risk environment within the natural crisis management, it requires access to key information which is to be implemented during the crisis.

Big Data Decision factors

Data is valued for a person or an organization, and is also known as information. To work on that data, decision-making is an important context which is completely based on the decision quality. For Big Data decisions, the following factors are important:

• Relevance

Data is relevant to a decision if that data is already present with the decision-maker and the outcomes are distinguished from each other. It can be decided according to the tests carried out on the data. The first test should be to find out whether the data item is relevant to that category of the decision or not. The second test will be- checking the relevance of the data-item adopted value to that particular decision. So this is the first decision factor which affects the decisions by taking data into consideration.

• Meaning

Within a positive framework, each data item should possess a proper “meaning” of the domain and the meaning of the values in the domain as well. However, the definition of the ‘meaning’ in the data-items from the data collections is-

1. Never defined explicitly
2. Defined ambiguously
3. Can change over time, without recording the changes and the dates on which they took effect.
• Transparency

While taking a decision for an organization, it is important to understand, how the decision mechanism works and how it can be applied to data in order to achieve the desired goal.

These Big Data decision factors if applied properly can define the steps to achieve organizational goals within a short time.

### KnowledgeHut

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KnowledgeHut is a fast growing Management Consulting and Training firm that is a source of Intelligent Information support for businesses and professionals across the globe.

Website : https://www.knowledgehut.com