What is Data Processing?
Data is perhaps the greatest single asset a business owns. It’s why processing data is so crucial to the continuing success and growth of any organisation. By collecting and ‘translating’ business data using data processing, organisations gain the ability to identify emerging trends, spot issues – and discover new opportunities.
What data processing means
Data is everywhere, covering all aspects of a company’s operations from customer preferences and supplier performance to the efficacy of internal workflows and processes. However, without data processing, any raw data is rendered worthless as it lacks any context or meaning. And in this era of Big Data, that problem only increases. The net result? Any data insights that could inform future business strategies and improve efficiency and profitability are hidden away in a database.
Data processing can take vast quantities of data and, with the expertise of data scientists or data engineers, collect, organise and store it before being presented to company stakeholders in a coherent format via documents, tables or graphs. Suddenly, by processing data, Big Data stops being overwhelming, even indecipherable. Instead, it is transformed into an invaluable asset that supercharges business decision making.
How to begin processing data effectively
Successfully processing data means adopting a methodical six-step approach. Here’s how it breaks down:
Step 1: Data collection
The most important part of processing data, you collect data from sources that are reliable, accurate and of the highest quality. Such data is typically stored in official data lakes and data warehouses, ready to begin on the data processing journey. Get collection sources wrong however and the old adage ‘garbage in, garbage out’ becomes an expensive – and probable – outcome for any data processing venture.
Step 2: Data cleaning
Even high quality collected data can have multiple issues from errors and duplications to incomplete or incorrect entries. The data cleaning phase gives you the opportunity to identify any errant data and resolve any issues so you have the best possible data sets available for data processing.
Step 3: Data input
The cleansed data can now be entered into a data processing solution such as a customer relationship management or enterprise resource planning via a number of different input sources. This process enables the data to be ‘translated’ into a format that the solution can understand.
Step 4: Data processing
Artificial intelligence and machine learning algorithms work through the data, collating and organising the datasets based on your criteria. Data processing criteria can focus on any aspect of company operations from customer behaviours to tracking advertising and marketing outcomes.
Step 5: Data output
The resulting data is outputted, ready for non-data analysts to identify and interpret data findings. These insights can then be presented to stakeholders in formats that are understandable such as graphs, infographics, text, and more. Remember, any outputs from data processing can be fed back into the system and processed to continue providing invaluable – and updated – intel.
Step 6: Data storage
Processing data means any data can be saved so it can be quickly retrieved for future reference. Importantly, data must be stored in adherence with any local data protection regulations such as the General Data Protection Regulation. This ensures you not only benefit from the data’s insights and analysis – but can also demonstrate to regulators that your data is protected and compliant at all times.
What are the 4 types of data processing?
Different tasks and applications often require data to be processed in different ways to access insights. There are four main types of data processing:
Batch data processing
Data is collected into batches before being assigned a time to be processed, typically during non-peak hours. This is an optimum approach for processing large volumes of data relating to end-of-day reporting and payroll systems.
Real-time data processing
As soon as data is received, it is processed and gives instant results. Typically, such data sets are small like those generated by online transactions and instant messaging.
Online data processing
Online Analytical data Processing
Designed for business intelligence and trend analysis, Online Analytical Processing (OLAP) is optimised to ‘interrogate’ data sets through analytical questioning before reporting back with insights.
Why cloud computing is essential to data processing
It all comes down to Big Data – those huge volumes that, if not organised and sorted correctly, can become a problem, not a solution for a business. While legacy systems have struggled to manage such vast data sets, cloud computing has stepped in and changed the data processing landscape forever.
Enterprise can now centralise all its disparate systems on a cloud-based data server, one capable of managing the massive workloads created by Big Data. This gives your data experts the platform and tools they need to glean vital intel and insights using a technology that is faster, more scalable and less costly than existing legacy solutions. Also, processing data means you can introduce comprehensive data lifecycle management systems as well as robust data backup processes.
OVHcloud and Data Processing
As well as supplying a wide range of storage solutions, OVHcloud offers best-in-class machine learning solutions plus data analytics services designed to process your datasets with minimal effort, all while helping to create actionable insights for better business management and growth.
