Adopting an Agile Data Approach: Tips and Challenges

Adopting an Agile Data Approach: Tips and Challenges

Big data is constantly growing and changing, and businesses need to adapt accordingly. To adapt successfully, businesses need agile data management processes. Data agility refers to the ability to adapt to the changing nature of big data successfully. The changing nature of big data reflects the dynamic nature of business. Companies constantly have to re-evaluate and modify strategies according to changing business realities. The situation represents a big shift from when companies could afford to stick with a particular strategy for some time.

Data agility can be likened to physical agility. Physical agility enables you to become swifter in movements, avoid injuries, and respond to rapidly changing situations quickly. Similarly, data agility reflects how well a company is managing the big data it is collecting. This is much more than simply collecting and analyzing data. This is about using the data so that you are able to respond to change quickly and appropriately, which is a constant in today’s business world.

Data agility enables companies to strategize based on insights and to evaluate and modify strategies in real time or as close to real time as possible. For example, instead of structuring data into static schemas, agile data analysis practices can help analysts explore different approaches to the structuring of data so that analysts find more ways of discovering the value hidden in data. This approach is known as schema-on-read. Another example of data agility is the retaining of unused data. Many businesses discard or neglect unused data, a practice that might deprive them of valuable, unearthed insights. The low price points of the Hadoop File System enables companies to retain the unused data.

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The following tips can help you adopt data agility and make it work:

Assess the condition of data. First, your data must be ready to be used in agile mode. So begin by assessing the condition of your data. For example, if there is data duplication, too much junk, or outdated data, or if the quantity of data is not significant enough, you must address these issues before embarking on data agility adoption drive.

Draw from all data sources. You need to document all types of customer data that your business produces from all sources, internal or external. You also need to analyze what types of data you are collecting or not collecting. After that, deploy and unify all the data in a customer data platform so that all data are available centrally, in real time. That platform should be the place where all data gets analyzed and you should be accessing the platform all the time.

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Real-time actions. For organizations adopting the data agility model, the old way of data collection and analysis is out. With the old model, data would be collected, stored, and analyzed, and then insights were collected. The entire process would take a lot of time. Now, organizations need to respond to frequent feedback and changes, and take actions almost instantly. The luxury of taking weeks or months to gather and normalize data into a data warehouse is a thing of the past.

Foster a culture of agility. Fostering a culture of agility is as important as having updated tools and processes.;

 



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