What Is Data Processing?
Data processing is the exercise of collecting data then manipulating and translating it into information that’s accessible and meaningful. The benefit of this process is that it removes unnecessary bureaucracy, reduces clutter, and enhances efficiency when reading information.
The processing of data should be conducted by specialists (often Data Scientists) to ensure the data is processed accurately, so the information can be correctly interpreted.
While the term ‘data processing’ has been widespread since the 1950s, the action of processing data has been occurring for much longer than the popularisation of the term.
People have been gathering data, storing it, sorting it, processing it, analysing it, and presenting it for centuries. The abacus (a counting device) is one of the first examples of people processing data to draw conclusions and extract value from it, with this tool believed to have been invented in ancient Babylon in between 300 to 500 bc.
What’s really changed over the years is the amount of data we’re able to process — big data software is vastly more powerful than even the most impressive Babylonian abacuses.
Data processing begins with the collection of raw data, from sources such as data warehouses or lakes. It’s then converted into intelligible information. This delivers the context and structure required for businesses and public organisations to make relevant observations from the data when analysing it.
If the data hasn’t been processed correctly then this will undermine the end product or output, meaning that incorrect conclusions will be drawn from the information. Pending on what the processed data is being used for, the results of this could be significant.
Having explained what this is, why it’s important, and the reason it must be carried out correctly, we’ll detail the stages involved in data processing and give you the information you need to ensure your company processes data in the correct way.
This means that after reading this article, your business will be able to extract maximum value from the information at its disposal by taking decisions that benefit its bottom line, such as using user behaviour data to catch and address sales funnel drop-offs.
The 6 stages of data processing
Data processing is a serious task that needs to be done in the right way.
If data isn’t processed correctly then it may not provide the correct context and/or structure, meaning wrong decisions are taken based on the information. For example, it may be that data is processed for military purposes. If this is dealt with incorrectly then it might lead to attacks being launched in the wrong place and result in civilian casualties.
To ensure this is done properly, businesses should follow the six stages of data processing that we’ve listed and explained below.
1: Data collection
Step one is to collect the raw data, otherwise you’ll have nothing to process! The type of unrefined data you source has an enormous impact on how it’s interpreted and the outputs that come from it. This means you should always collect data from defined, accurate, and reliable sources, so the information leads to findings that are accurate and usable.
These are some examples of raw data you can collect:
- Monetary figures
- Website cookies
- User behaviour
2: Storing the data
Once you have the required data, you need to store the data in a safe and secure digital environment. This is to ensure it remains clean and unblemished, so it can be accurately analysed and presented.
You can store your data in one of the following places:
- Data lake: This is a centralised repository that aims to store large amounts of unstructured, semi-structured, and unstructured data.
- Data warehouse (DW): In this storage facility, data flows into a warehouse from relational databases or transactional systems. It may also be known as an enterprise data warehouse and can be from single or multiple sources.
- Data vault: This is a data modelling design pattern that’s used to create a warehouse for enterprise-level analytics. There are three different entities in a data vault —satellites, hubs, and links.
3: Sorting the data
Having collected and safely stored the data, you now need to begin the process of sorting and filtering the data. The purpose of this stage is to deliver accuracy and efficiency, which it does in two ways.
Firstly, it filters out any unnecessary information, so only data that’s relevant to the project is used and the results are accurate. Secondly, it brings order to the data, so those interpreting it are able to efficiently visualise and analyse it.
4: Processing of data
When you reach the point where you have sorted and filtered the data, you must then process the data. This is carried out by using machine learning algorithms, with the method you use depending on two things:
- The source of the data: Whether it has come from connected devices, data lakes, site cookies, or somewhere else.
- What you intend to use the data for: Is it for streamlining your operations, establishing patterns in user behaviour, or another purpose?
5: Analysing the data
You’re at the part of the process where you extract value from the data. This is achieved by using analytical and logical reasoning to systematically evaluate the data, delivering results and conclusions that you can present to your stakeholders .
There are four types of data analytics:
- Descriptive analytics: This concerns describing things that have occurred over time. It will be things such as whether one month’s revenue is higher than its predecessors, or if the number of visitors to a website has changed from one day to another.
- Diagnostic analytics: The focus here is on understanding the reason an event has occurred. It needs a much broader set of data and it needs a hypothesis (such as “does the Olympic games increase sales of running shoes?”) that you seek to prove or disprove.
- Predictive analytics: This type of analysis addresses events that are believed to be set for occurrence in the immediate future. It seeks to answer questions concerning things like the weather, for example: “how much hotter will this year’s summer be than last year’s?”
- Prescriptive analytics: The distinguishing factor in this type of analysis is that there is a plan of action. For instance, a company may seek a plan for how to deal with the impact an increase of 5 degrees in temperature may have on its operations. By considering all the factors relevant to this, the data analysis determines the optimal approach to take in the event of this occurring.
6: Presenting the data
The final part of data processing is to present your findings. To make the demonstration clear and intelligible, your data will be represented in one or more of the following ways:
- Plain text files: This is the simplest way of representing data, with the information being presented as Word, GDoc, or notepad files.
- Spreadsheets and tables: A multifunctional way of presenting data, this displays the information in columns and rows. The data can be interpreted in a range of ways, with sorting, filtering, and ordering all possible.
- Charts and graphs: Using this approach makes it easy for your viewers to make sense of complex data, as numbers can be visualised.
- Images, maps, or vectors: If you’re displaying spatial data or geographical information then you may decide to choose this method of presentation. It’s ideal for data that’s regional, national, continental, or international.
The key is that your company’s stakeholders and team members are able to understand the conclusions drawn from the data, so select the format(s) that will be best for the people who will review your results.
From data processing to data analysis
Developments in technology (particularly the cloud) mean that we’re now in a reality where countless businesses and public institutions are benefiting from data processing. These organisations use software to collect information that reveals associations, patterns, and trends. To arrive at this outcome, they’ll follow five steps:
- Determining the questions and goals
- Collecting the data they require
- Wrangling the data
- Establishing the data analysis approach
- Interpreting their results
The last of these steps is where your organisation analyses the data it has collected. The goal of this is to deliver valuable information, provide and support conclusions, and aid decision-making for a variety of purposes.
There are many different examples of data analysis, both in professional and personal environments.
In the first instance, a private or public organisation may analyse data it holds about its users in order to deliver a more personalised service. For example, a customer’s past purchases may be assessed and this information could be used by companies to create bespoke offers for them.
In the second instance, you might review a range of different companies that offer the same product and make a data-driven decision on which one to take by assessing the features against the cost.
Data mining is a specific data analysis technique. It’s focused on the use of knowledge discovery and statistical modelling, with this being carried out for predictive (as opposed to descriptive) purposes.
What’s the future of data processing?
Data processing has benefited enormously from advances in computer technology, as machines have become quicker and more powerful. Greater amounts of data can be collected, with analysis able to be carried out at a faster and deeper rate.
So, what’s next? While it’s impossible to make definitive statements on where data processing will go, these are a few things it would be unsurprising to see in the future:
- Big data will get even BIGGER
- Continuation of cloud migration
- Machine learning will enhance observations
- Data Scientists will be in enormous demand
- Privacy will continue to be a huge concern
- Fast data and actionable data will be the new big data
It’s the perhaps last of these predictions that will be the most important in the future of data processing. Whereas big data generally relies on NoSQL and Hadoop databases to analyse data in batches, fast data is all about processing in real-time streams. It means that organisations can take instant decisions and act immediately once they receive data.
The rise of fast data means even more real-time interactions for users, something that’s becoming ever more important in the definition of what a good user-experience is.
Conclusion: follow the six stages of data processing at all times
In this article, we’ve covered all the key points about data processing. We’ve told you that it has been practised for centuries but the name has only been in popular usage since the 1950s. We’ve explained that it’s a hugely valuable task where data is collected, stored, sorted, processed, analysed, and presented.
It will be clear to you now that the conclusions revealed through data processing are used by countess organisations in both the public and private sector. Indeed, people even use them in their personal lives regularly without even realising it — if you’ve used a comparison site then you’re benefiting from data processing.
You’ll also understand that the future offers many possibilities for data processing, with technology allowing for more data to be processed and at a much faster rate.
So, now that you know what it is and it’s six stages, make sure that your organisation is carrying data processing out in the right way. Because failing to do so will lead you to draw incorrect conclusions from the data at your disposal, something that could have a severe impact upon your organisation.