A comprehensive understanding of the data processing life cycle will help you succeed in many sectors, including finance, marketing, logistics, and government – plus quite a few more.
Of course, data projects vary from industry to industry and from organization to organization, so you can’t be expected to know everything, but they all follow a similar eight-step life cycle with which you can familiarize yourself.
Before we get into that, though, let’s first explore the question of:
What is data processing?
“Data processing” refers to the collection of data for analytic purposes – e.g., access to a CRM database containing protected information, sending promotional emails/surveys, or recording audio and video footage.
It covers a whole range of operations from creation, utilization, sharing, storage, and deletion. Let’s look at the different stages of data processing and how they are managed.
The data processing life cycle
1. Generation
The data processing life cycle actually starts prior to collection. For you to be able to collate it, the information must first be available for you to access.
Some data is generated transparently. In this case, consumers are asked to provide names, telephone numbers, email addresses, feedback, etc., which they must enter manually online.
But in today’s digital world, everything leaves a footprint, and browsing habits are really insightful for companies who are looking to create and display more targeted ads.
2. Collection
On average, a single person created 1.7MB of data every second in 2020. Times that by more than 7.5bn people and you’re looking at a global data usage of 2.5 quintillion (2.500.000.000.000.000.000) bytes a day. So, to say it’s a lot is a big understatement.
Needless to say, not all of this data is going to be relevant. Some organizations still take a scattergun approach to collecting data – gathering as much information as they can from every interaction and keeping it just in case – but you may find you get better results from being more selective. Concentrate your efforts on collecting the information that is most relevant to what you are looking at now.
You can do this by sending out forms and surveys, conducting interviews and market research, and analyzing how your customers interact with your brand.
3. Processing
Once this data has been collected, it will need processing in order for you to use and learn from it. But what exactly constitutes data processing, and how does this help?
Data processing is often a multi-faceted operation which can include stages such as:
- Data wrangling, the process of transforming raw data into another format (e.g., graphs, spreadsheets, etc.) to facilitate use.
- Data compression, the process by which data is shrunk in order to be stored more efficiently.
- Data encryption, the process of translating data into another form of code in order to keep it private and secure.
Not all data is digital, though. Paperwork can also contain personal data, and non-automated, non-digital processing is also covered by The General Data Protection Regulation (GDPR). Make sure, however you collect, process, and store your data, that you are only using it for purposes specified in line with EU directives.
4. Storage
Having collected and processed your data, you may then store it and keep it for as long as it’s still relevant. Commonly, organizations store this information on databases or in datasets, which are themselves stored on a server or in the cloud.
When considering your storage options, it is important to consider the safety and security of the data you are holding. Make sure it is backed up and protected from external threats.
5. Management
Data – or Database – Management isn’t so much a “stage” as a continual process that occurs throughout the data project lifecycle. It refers to how you organize and utilize your databases, which may change as you progress. Data management responsibilities may include storage, encryption, and tracking changes.
6. Analysis
In many ways, this is the most important step in the data processing lifecycle, because the whole point of collecting information is to learn from it. Through data analysis, you can gain an insight about the demographics and (e.g., spending/browsing) habits of your customers.
Data analysis is carried out by Business Analysts and Data Scientists using statistical modelling, algorithms, AI, data mining, machine learning, and Power BI. If you are numerically minded, this might be the perfect career choice for you.
Take a look at the role profiles and see how a master’s degree in Business Analytics & Data Science from the EU Business School can help you get there.
7. Visualization
Data visualization is all about how you represent your findings – typically through the use of graphs and charts. There are several useful free and subscription programs you can use to translate figures into graphics, including Microsoft Excel, Google Charts, and Tableau.
How you choose to depict your data will depend on the kinds of data you’re working with and what you’re trying to prove with it. Whether it’s in a pie chart, histogram, scatter plot, or an infographic, though, there’s no doubt that the information will be easier to read and interpret.
8. Interpretation
Interpretation is the final stage in the data processing lifecycle. This is the point at which you use your analysis and data visualization to draw conclusions from the information you have collected. You may simply want to present these findings to your team, but depending on what you have discovered, you may be able to use that data to make some changes to the way you operate. These changes will hopefully drive consumer engagement and sales.
Are you interested in developing your skills and pursuing a career in data science, management, or analytics? Check out our postgraduate courses in Digital Business, Cloud Computing, Information Systems, as well as this one in Business Analytics & Data Science.
