10 Key Challenges of Data Processing – Loginworks Softwares

Data processing and management are essentially about the 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

  1. Collection of data is the foremost step. A few types of collections include census, sample surveys, and administrative by-products.
  2. Preparation follows next that involves the manipulation of data for processing and analysis.
  3. 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.
  4. Processing is when the data undergoes various techniques of manipulation.
  5. Output and interpretation of the processed data which is done by the user for the acquirement of meaningful information.
  6. Storage of the data and information for further use.

The six steps of the Data Processing Cycle: collection, preparation, input, processing, and output

However, data processing comes with its own set of challenges. Elucidated below are ten key issues that stakeholders face with data processing.

1. Breach of Privacy and Security Is a Big Issue for Stakeholders

A breach of privacy is the loss of, or unauthentic access or disclosure of,  confidential personal 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 the breach, 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 a secure connection throughout the entire cycle of data processing from collection to storage.

2. The Anonymity of Every Stakeholder Is Almost Impossible

Data anonymization is a kind of data sanitization that ensures the protection of the 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 to whom the data attributes 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 simple. However, private presence and identity theft on the internet can be prevented with more stringent encryption and security measures.

3. 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.

4. 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.

Electronical discovery reference model

5. Shortage of Talent for Data Processing

Business organizations often lack talent, and as such, stakeholders face grave issues in data processing and data analytics.

Data analytics and data processing is a complex field of computations and becomes even more intricate when deep learning, machine learning, and other components of artificial intelligence are employed to process and analyze the data.

The intricateness 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.

As a result, it will become 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.

Current analytics talent does not meet the demand, there is a shortage of 1.5 M big data experts

6. 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 the surging demand for 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 organization’s 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 that are continuously transforming.

7. 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 are 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.

8. Organizational Resistance to Data Processing

Data processing methods are advanced and require technical 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 a 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:

  • Insufficiency in organizational alignment
  • Shortage in the adoption of mid-level management and lack of understanding
  • Lack of human capital and financial resources
  • Organizational resistance

To enable organizations to capitalize on the myriads of opportunities offered by the subjectively huge volumes of data to data processing and analytics, organizations need to bring about a cultural shift and do everything differently.

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.

9. 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 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 is a variety of tools and techniques available in the digital world for the integration of a huge amount of data, 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.

A visualization of how data integration works

10. Timely Generation of Insights is Almost an Impossibility

Organizations aren’t 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 with the statistics revealed by the survey conducted by NewVantage Partners, the most primary objectives of organizations include:

  • Decreasing the organizational expense through the introduction of cost-effective operations
  • Establishment of a new organizational culture powered by data and decision-making
  • Creation of novel opportunities for innovation in technology and disruption
  • Acceleration of the speed of deployment of services
  • Introduction of new products and services

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 the 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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