10 Big Data Do's and Don'ts
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Loading... - Solange Ruiz
- 03 Aug 2022
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Big data is used and applied across multiple business domains as data analytics, artificial intelligence and machine learning continue to become part of the mainstream. Big data analytics can extract the real value out of this wealth of data, and this data can be structured, unstructured or semi-structured.
The emergence of social media has given rise to many new opportunities to collect data about customer behavior. Here are some examples:
Clickstream data comes from website interactions such as mouse clicks and webpage scrolling.
Social business sites are online communities of customers who are willing to share information about their buying behavior.
Sensors provide data about customers' physical environments, such as temperature, humidity, and traffic patterns.
The insights gained from data analytics can help organizations in their decision making process. But the real benefit of big data is achieved only if it is managed in a proper way. Organizations can avoid becoming lost in the big data space by ensuring they identify the starting point with simple use cases and implement it to check the output quickly.
The first step before starting any big data initiative is proper planning. An organization must clearly know the purpose of the project. They should also identify what value they want to extract and how it is going to impact business decisions. The most promising area should be chosen to start with.
Here, in this article, we will explore some of the Do's and Don'ts of big data initiatives.
1. Do know the purpose and the starting point
The purpose of data collection and identifying the starting point is very crucial for the success of any big data project. To start with, the objective should be to identify the most promising use cases for the business. It will help the organization to identify the components for those use cases.
After this, a proper planning should be done to apply Bigdata techniques to these uses cases and extract valuable insight for the business growth. The priority of execution should depend upon the factors like:
Cost of implementation.
Anticipated impact on the business.
Length of time required to launch.
Speed of implementation.
Organizations should always start with a simple and easy to implement application as a pilot project.
2. Do evaluate data licenses properly
Data is the fuel for any big data and analytics projects. So, it is very important to protect your data from misuse. Proper licensing terms and conditions should be in place before granting data access to any vendor or third party user. The data license should clearly mention the following basic points. There will be lots of other critical parameters also in the license agreement.
Who is going to use the data?
What data will be accessible?
How the data will be used?
If there is any failure in licensing, the resulting data loss and misuse will have an undeniably negative impact on the business.
3. Do allow data democratization
Data democratization can be defined as a continuous process, where everyone in an organization is able to access the data. The people in an organization should be comfortable working with the data and expressing their opinion confidently.
Data democratization helps organizations to become more agile and take data-informed business decisions. This can be achieved by establishing a proper process. First, the data should be accessible to all the layers, irrespective of organizational structure. Second, a single source of truth (referred to as "the Golden Source") should be established after validating the data. Third, everyone should be allowed to check the data and give their input. Fourth, the new ideas can be tested by taking calculated risks. If the new idea is successful, then the organizations can move forward, otherwise is can be considered a lesson learnt.
4. Do build a collaborative culture
In the game of big data, mutual collaboration among different departments and groups in an organization is very important. A big data initiative can only be successful when a proper organizational culture is built across all the layers, irrespective of their roles and responsibilities.
The management of an organization should have a clear vision for the future and they must encourage new ideas. All the employees and their departments should be allowed to find opportunities and build proof of concepts to validate it. There should not be any politics to blame and stop the game. It is always a learning process, which must be accepted equally for both the success and failure. Read More...
