How to Reduce Lengthy Development Times in Data Processing?

Big Data Analytics involves the capture and analysis of vast amounts of real-time data through social media and other online activities of customers. Data in millions is collected via OCR, ICR, OMR, MICR, Smart cards, Magnetic strip cards, voice recognition, and web data inputs. The sheer volume, the variety, the speed at which data comes in for analysis, and the accuracy of the input data, makes Data Analytics a very complex process.

Big Data is commonly used by giants like Amazon, Walmart, MacDonald’s, and other business enterprises to track customer behavior and preferences so that they can plan their marketing strategies for maximum impact. There are millions of web users entering data every second. So it becomes very crucial to know how to reduce development times in data processing.


Sending the right information to the right person at the right time is the fundamental law of a good marketing strategy. So if your clients want to reach their target customers in a timely manner your Data Analytics tool has to foolproof and nearly 100% accurate. So your data processing model must be designed for speed as well as accuracy as it collects data and analyzes it.

Ideally, your data architecture must be capable of top-speed exchange of information. It must also be capable of handling high-speed analysis of streamed data into what is relevant to the context and what is not. The process of capture, analysis, transformation, and loading with the help of Data Analytics tools should be an instantaneous process to ensure customer engagement and profits for your client.


As a business enterprise expands and increases its customer base the volume of data entry increases exponentially for that organization. So when you dealing with similar situations of millions of business organization, naturally the volume of real-time streamed data is unimaginably large. Your Data Analytics tool should be updated and tweaked to ensure that there is no room for error even with the ever-increasing processing load and an influx of new variables that might have to be analyzed.

Some of the ways to reduce lengthy development times in data processing are:

Reduce Data Redundancy

Your data model must be capable of reducing the redundancies. It should be capable of eliminating irrelevant information (not in context) so that the result is produced quickly and accurately.

Carry Out Audit

Occasionally you must take sample data and test the speed and accuracy of your DA tool. The audits will alert you if it is producing wrong results despite the correctness of the input data. It will also give you an indication whether the processing speed is good enough to keep the web user involved and fruitfully engaged.

Validate Data

Besides speed and accuracy, it is also important that the analytical output is unique, consistent, complete, and relevant to the web user’s query. Sometimes, the information entered be web users may be wrong.

In other words, your data analysis tool must be capable of detecting any inherent defects in the input information so that it is not used during the processing of data. Using defective data will corrupt the output as well. If your model is capable of identifying and ignoring such data, then you will not have to spend more time on reworking on the data model. Also, the processing speed will increase considerably.

Conduct Error Analysis

Testing to check if your data model itself has defects is a vital step to reduce development time in data processing. The error analysis should be carried out in two ways:

  • Cluster analysis: Add a parameter to your data model so that it is capable of narrowing down the usual sources of errors. You have to use the inaccurate data set itself as a test sample in order to arrive at the source of that error.
  • Event analysis: This is a slightly more complex problem. You have to track which events are causing the error in data collection. Once the events are identified you can update your data model to prevent future incidences in error from the root itself.

Avoid Over-Fitting

Regularization of the test sample to a pre-defined level of data-concentration will prevent over-fitting and will reduce lengthy development times in data processing. For effective regularization, you need a testing parameter in the test sample itself so the sample can modify itself to prevent over-fitting of data. The algorithm will be complex initially, but it will not require frequent updating because it will be ‘mutating’ to prevent errors and give more accurate outputs.

Reduce the Iterations

Small and highly focused iterations will produce more accurate results. This is also another method to prevent over-fitting of data. And it is also a very effective way of troubleshooting any problems.

Add New Features

As new types of data are entered by web users it is essential that you add new features to your data model for accurate analysis. If you do not update your data model there is a strong possibility that it will treat this new information as dirty and will not analyze it. Whereas, in fact, it might be relevant and pertinent information and should have been included as ‘good data’.

To summarize, millions of little bits of data are entered into online forms, social media networks, business networks, website feedback forms, etc. Business Intelligence and Data Analytics deal with these pieces of information to analyze the data for the desired output. If your algorithm is designed to reduce redundancy by collecting only relevant data the processing time is considerably reduced.

Also, over-fitting of data should be avoided and the data tool must be capable of solving this problem on its own via regularization. Thirdly, periodic audits will help you to monitor your data model so that it works at its peak efficiency. Fourthly, you must add new parameters for analysis to keep abreast with the ever-changing categories of relevant input data.

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