How to Reduce Lengthy Development Times in Data Processing?

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Welcome To Loginworks Softwares
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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.

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Big Data is commonly used by giants like Amazon,
Walmart, MacDonalds, 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.

IMPORTANCE OF REDUCING DEVELOPMENT TIME 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.
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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.

WAYS TO REDUCE DEVELOPMENT TIME IN DATA
PROCESSING

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.
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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 users query.
Sometimes, the information entered be web users
may be wrong.
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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.
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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.
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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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