Outsource Data Processing Services

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Welcome To Loginworks Softwares
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10 Key Challenges of Data Processing

• Data processing and management are essentially
  about extraction of useful information from data.
  This extraction involves the process of data
  mining which has a wide variety of approaches,
  techniques, and tools. Data processing refers to
  the conversion of raw data to consequential
  information. It follows a particular cycle in
  which raw data is fed to the system and
  information is obtained as output after thorough
  processing and analysis. The life cycle of data
  processing involves a number of different steps.
• Steps of the Data Processing Cycle
• Collection of data is the foremost step. A few
  types of collections include census, sample
  surveys, and administrative by-products.
• Preparation follows next that involves
  manipulation of data for processing and analysis.
• Input is the activity where the authenticated
  data is converted into a form that is
  machine-readable and can be processed by the
  computer system.
• Processing is when the data undergoes various
  techniques of manipulation.
• Output and interpretation of the processed data
  which is done by the user for the acquirement of
  meaningful information.
• Storage of the data and information for further
  use.

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Data Processing
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Breach of Privacy and Security is a Big Issue for
Stakeholders

• A breach of privacy is the loss or unauthentic
  access or disclosure of personal confidential
  data. A few common threats to privacy occur when
  personal information gets stolen, mistakenly
  shared or lost. Breach of privacy can also occur
  due to operational breakdowns and business
  failures. Breach of privacy occurs for
  stakeholders when business ventures employ weak
  and flawed security measures. Even though the
  hackers are legally liable for the acts of breach
  but still prevention of the security breach is
  the most crucial responsibility of the data
  processing system and organization. To combat the
  breach of privacy risk in data processing, the
  organization needs to invest in high-quality
  antivirus and antimalware software and use the
  strictest methods of encryption to ensure secure
  connection from the throughout the entire cycle
  of data processing from collection to storage.

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The Anonymity of Every Stakeholder is Almost
Impossible

• Data anonymization is a kind of data sanitization
  that ensures the protection of privacy of data in
  the cycle of data processing. It involves the
  process of encryption of data or removal of
  personally identifiable data from public datasets
  so that the users whom the data attribute to can
  remain securely anonymous. Re-identification of
  stakeholders with the employment of anonymous
  data in public datasets is a possibility now.
  However, with a computer and internet connection
  re-identification has become quite easy and
  personal information is no longer confidential.
  This re-identification might not be a simple but
  private presence and identity theft on the
  internet can be done away with it with more
  stringent encryption and security measures.

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Analytics Is Not Completely Accurate

• Analysis of data processing is not entirely
  accurate and this is a big problem for
  stakeholders. In the data processing cycle, there
  is a colossal volume of data involved and as such
  a huge amount of error creeps into the analyzed
  information with only a marginal flaw that might
  occur in the processing stage of data. This
  becomes a huge issue for stakeholders. What needs
  to be done by organizations is to work on 100
  accuracy of data processing and use trusted tools
  and techniques of analytics that will guarantee
  maximized accuracy.

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Problems of Stakeholders with E-Discovery

• E-discovery is the search of digital data to be
  used as evidence in legal proceedings in court.
  Even the government and the court can order for
  e-discovery in the form of ethical hacking to
  facilitate the search for critical evidence.
  However, owing to the fact that data processing
  involves a huge amount of data in databases,
  e-discovery is extremely difficult and so is in
  compliance with legal requirements and
  restrictions. Besides, e-discovery is highly
  expensive. This poses to be a grave issue for
  stakeholders. However, organizations can
  negotiate the costs of e-discovery and eliminate
  the issue encountered by stakeholders for the
  same.

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Shortage of Talent for Data Processing

• Business organizations often lack in talent and
  as such stakeholders face grave issues in data
  processing and data analytics. Data analytics and
  data processing is a complex field of computation
  and becomes even more intricate when deep
  learning, machine learning and other components
  of artificial intelligence are employed to
  process and analyze the data. This puts a
  colossal and growing demand for data scientists
  who are skilled in a wide range of fields owing
  to the fact that the job of data processing and
  analytics are heavily multi-disciplinary. This
  demand will continue to grow with increasing
  avenues of data processing using advanced
  analytics and artificially intelligent methods.
  This becomes a serious issue for stakeholders as
  organizations find it extremely difficult to look
  for and recruit data scientists and thus face a
  grave loss in the data processing sector.

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Restricted Technical Capacities for Data
Processing

• Despite the sky-high technological advancement in
  the development of better and faster data
  processors for the enhancement of computational
  abilities, technical capacities are being
  constantly challenged by a surging demand of even
  faster data processors that will be able to
  process huge volumes of data. This has become an
  issue for stakeholders as technological
  development takes a toll on the organizations
  wallet. For startup ventures and small and medium
  scale businesses, technological development is
  synonymous with large and unaffordable sums of
  financial capital. On the other hand, AI
  algorithms are complex and require thousands of
  complicated calculations and more powerful
  processors for the implementation of data
  processing using AI driven analytics and methods.
  This is a serious challenge that stakeholders
  encounter as now businesses find it difficult to
  secure data from hundreds of non-relational
  databases which are constantly transforming.

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Interpretation Might Become an Issue of Ethics
for Stakeholders

• Interpretation of information after data
  processing might become an issue of ethics for
  stakeholders. The foremost reason behind data
  processing and analytics is to reach a decision
  on the basis of the information obtained after
  the data is processed. However, it is anything
  but prudent to rely on digital data alone and not
  concern oneself with the impact that the decision
  might have on the people and the environment.
  This poses to be a significant ethical issue for
  stakeholders who prefer to be concerned with data
  processing alone. It is important for everyone to
  be aware of the fact that data processing and
  analytics is done to be of help to everyone. The
  decision-making needs to be based on how they
  will affect the ones involved from the grass root
  level to the top and not solely on numbers and
  figures in a spreadsheet.

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8. Organizational Resistance to Data Processing
Data processing methods are advanced and require
technological as well as cultural transformations
to which most organizations tend to resist.
Organizational resistance is a major impediment
for stakeholders. In a survey conducted by New
Vantage Partners on a large number of corporate
firms, the statistics revealed that about 86
percent of the firms were dedicated to the
creation of data-driven and intelligent culture
but only 37 percent of the firms had actually
been successful. This huge disparity between what
the companies are driving at and the outcomes
attributed to four different reasons.

1. Insufficiency in organizational alignment

2. Shortage in the adoption of mid-level
management and lack of understanding

3. Lack of human capital and financial resources

4. Organizational resistance
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To enable organizations to capitalize on the
myriads of opportunities offered by the
subjective huge volumes of data to data
processing and analytics, organizations need to
bring about a cultural shift and do everything
differently. But such changes are tremendously
painstaking for large-scale organizations to
implement thus making organizational resistance a
major impediment for stakeholders. The report
suggests that to bring about any improvement in
the decision making capability of the company,
the organizational HRs needs to recruit and
invest in human capital that understands the
opportunities and challenges and knows how to act
on them.
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Integration of the Data for Data Processing

• The colossal volumes of data secured for data
  processing are obtained from a huge number of
  data sources. Integration of the disparate
  sources of data proves to be a challenge for the
  stakeholders of an organization. The large volume
  of data is secured from enterprise applications,
  email systems, social media streams,
  documentation created by the employees of the
  organization and so on. Combination of all the
  data and reconciliation of the same to build
  reports can be tremendously difficult. Even
  though there are a variety of tool and techniques
  available in the digital world for the
  integration of the huge amount of data, yet
  several organizations claim that they are yet to
  solve the problems of data integration. However,
  the organizations are resorting to new and
  advanced technological solutions for the
  combination of data. As per the statistics of the
  survey conducted by the IDG, about 89 percent of
  the firms surveyed have disclosed that they are
  planning to purchase integration technologies
  next to data processing and analytics software.

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Timely Generation of Insights is Almost
Impossibility

Organizations arent necessarily seeking to store
the data after data processing and analytics. It
is the goal of every organization to analyze the
data and make a powerful decision on the basis of
the data. In accordance to the statistics
revealed by the survey conducted by NewVantage
Partners, the most primary objectives of
organizations include-

1. Decreasing the organizational expense through
the introduction of cost-effective operations

2. Establishment of a new organizational culture
powered by data and decision-making

3. Creation of novel opportunities for innovation
in technology and disruption

4. Acceleration of the speed of deployment of
services

5. Introduction of new products and services
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All of these objectives can augment any
organization but that depends on one and only
condition and that is a timely extraction of
information after data processing and analysis of
big data and then acting on that information to
reach sound decisions. To achieve speed in
information extraction and decision-making
organizations are seeking a new generation of
ETL, technologies and analytics tools and methods
to bring about a dramatic reduction in the time
taken in the generation of reports. Stakeholders
of different companies are investing in software
and technologies with capabilities of real-time
analytics that will enable them to respond to
fluctuating market trends instantaneously.

As a bottom line, the challenges of data
processing are many, but every day a new solution
is getting added to the immense world of data and
information. All that it takes for stakeholders
is to invest in resourceful and rich human
capital and then drive the organization to
success.
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