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.

Best Tools for IoT Data Processing

Many people think the Internet of things (IoT) is a futuristic phenomenon. However, it is already in use today.

IoT allows you to connect the physical world to the internet. As we speak, at least one of the following is connected to the internet: your refrigerator, manufacturing equipment, security cameras, or AC unit.

The reality is that any device that can be powered on can be a part of the IoT. It is gaining massive usage every day, and according to research firm IDC, IoT spending was $674 billion in 2017.

The IoT is expected to reach a whopping $1.1 trillion by 2021. In 2014, Cisco estimated the net worth of internet of things to be $19 trillion. Nicholas Negroponte, the co-founder of MIT Media Lab and author of being digital, said, “When we talk about the Internet of Things, it’s not just putting RFID tags on some dumb thing so we smart people know where that dumb thing is. It’s about embedding intelligence so things become smarter and do more than they were proposed to do.

The use and benefit of IoT cut across various industries, including manufacturing, supply chain, agriculture, healthcare, and energy. It is used daily by these industries to increase productivity, efficiency, and transparency.

The Internet of Things is everywhere

IoT Data Systems

One of the numerous challenges with IoT data system is the sheer volume of data that flows every minute. Parker Trewin, Senior Director of Content and Communications at Aria Systems, said, “With emerging IoT technologies collecting terabytes of personal data…” people are generating data at a high rate, in 2010, the world generated over 1ZB of data (1,000,000,000,000,000 megabytes.) That’s a lot of zeros.

The Challenges of IoT

It is important to note that every stage of IoT is filled with challenges. Chris Murphy, Editor at Information Week said “One of the myths about the Internet of Things is that companies have all the data they need, but their real challenge is making sense of it. In reality, the cost of collecting some kinds of data remains too high, the quality of the data isn’t always good enough, and it remains difficult to integrate multiple data sources.

You need to get the best out of your IoT solutions, either as a data analyst or a business owner. There are numerous challenges in every phase of the IoT process.

The first step to mitigating these problems is ensuring that the data processing system you are using is top-notch. We’ve explored a number of IoT systems. The following are our best tools for IoT data processing based on user satisfaction. To know what is required of IoT systems, it is important you understand the IoT process.

The IoT Process

The IoT Process

The IoT process starts with acquiring data from devices and sensors.

The next step is storing raw data that is sent from devices, cleaning the data by removing errors, incomplete or inaccurate records. Clean data moves to the transforming systems, where it is manipulated and transformed from one structure to the other to produce results. These results need to be stored and should be retrievable at any time.

The processes described above can be grouped into three major stages:

  1. The data is moved from sensors or devices to the cloud through suitable connectivity,
  2. Data processing,
  3. Results or output.

The result or output can be an email, image, notification, chart, or video (but not exclusively.)

Let’s explore some great IoT tools for data processing. There are numerous IoT platforms out there.

This list is not exhaustive and it is not in any order.

1. Salesforce IoT Cloud

Salesforce IoT Cloud is a cloud platform owned by Salesforce.com. It is powered by thunder. The Salesforce team says thunder is a “massively scalable real-time event processing engine.

This IoT tool is designed to handle enormous data received from devices, sensors, websites, customers, apps, and partners connected to the Cloud. It can also initiate a response in real-time.

For instance, it can automatically regulate your home if it becomes too cold or too hot, it can notify you of a break-in and send videos or pictures of the culprit.

The beauty of the Salesforce IoT Cloud is you do not have to be tech-savvy to use it. Salesforce launched its IoT Cloud in 2015 and has been soaring high ever since.

2. AWS IoT Core

AWS IoT Core is a product from Amazon Web Services. It supports HTTP, MQTT, and WebSockets, making it compatible with several industry-standard devices and sensors.

According to the AWS IoT Core team, it can support billions of devices, trillions of messages, and keep track of your connected devices. It is very compatible with other AWS services such as Amazon Machine Learning, AWS CloudTrail, Amazon Kinesis, and more. It is also known to reduce bandwidth usage.

The AWS IoT team boasts of its tight security and how it can process received data and act automatically.

Furthermore, your device doesn’t have to be online all the time. AWS IoT Core stores the last information received from your device, it stores and shows its last status and updates automatically once the device reconnects.

3. Oracle IoT

Oracle has several IoT platforms, like Asset Monitoring Cloud, it gives real-time data on asset health and usability, notify and predict asset failure.

Oracle IoT Production Monitoring is used in the manufacturing industry. It gives real-time data on your equipment, factories, production system, and products. It is particularly effective in reducing product defects.

Oracle Stream Analytics is an in-memory technology that carries out analytic manipulation on a continuous influx of massive data. It accepts data from IoT sensors, POS devices, ATMs, social media, and more. It can be accessed as a service in Oracle Cloud or installed in local systems.

Oracle Edge Analytics is used by different industries, including industrial automation, appliance management, transportation, telemetry, healthcare, smart retail vending machines, and more.

4. Particle IoT

Particle IoT pride itself as the all-in-one IoT platform. It handles a large volume of data and ensures secure communication by devices.

Its platform is user-friendly and can be used by anyone. It can be integrated with other platforms like Microsoft Azure, Google Cloud, or any IoT that supports REST API.

It integrates hardware, software, and connectivity. It processes complex data and automates responses. OptiRTC, Incorporated used a Particle platform to analyze and monitor their smart drainage system.

5. Predix

Predix calls itself the OS of the industrial internet. It is a platform for creating, deploying, and maintaining apps for industrial machinery. It securely connects machines, receives data, conducts analytics, and provides feedback. It is said to make any machine an intelligent asset.

It provides data management for predictive analytics of machines and helps avoid downtime. It is also available on mobile devices to help you monitor your industrial assets on the go. It can be used by developers, data scientists, or control engineers.

6. SQLstream

SQLstream offers easy integration for Kafka, Kinesis, and other stream users and analyses data in real time. It is easy to use, and it can analyze and trigger actions with results. It offers real-time continuous machine learning.

SQLstream offers data wrangling, data enrichment, streaming analytics, continuous egress, streaming ingestion and dashboard to visualise your data.

7. Unidots

Unidots started as an engineering service firm in 2012. It specialized in hardware and software solutions. The Unidots IoT platform offers data collection, analysis, and visualization tools. It seamlessly connects hardware, devices and sensors to the cloud.

Unidots is compatible with systems that use REST API. It is compatible with Microsoft Azure as well.

It also offers customized solutions. There are multiple tutorials available for people who are new to the IoT and Unidots’ platform.

8. AWS IoT Analytics

AWS is another IoT platform from Amazon Web Services. It collects a large amount of data from devices and stores them. You can run complex analytics to reveal or answer the query you input.

A unique feature of AWS IoT Analytics is it cleans and filters data received from sensors. It enriches the data and runs analytics. You can run queries using the inbuilt SQL query engine. Using AWS IoT Analytics, you can know which users are most likely to stop using their wearable devices.

9. Azure Stream Analytics

Azure Stream Analytics is a product of Microsoft, it integrates with Azure IoT Hub and Azure IoT Suite. It features real-time analytics on data from devices and has real-time analytical intelligence. It processes data from devices and displays results with Power BI.

10. Ayla Insights

This IoT platform is a product of Ayla Network. Ayla Insights is a powerful tool that fully integrates business intelligence and analytics. Its target markets are manufacturers and service providers.

It allows organizations to see how their products are being used. It requires no extra software to function – it is a fully integrated system.

11. Watson IOT Platform

IBM Watson uses cognitive computing to give its users deep insights into their data. Watson allows users to receive data from devices, run complex analytics, and produces great visuals. It is a cloud-hosted service, you’d connect and register your devices.

It allows its users to securely receive data from devices and sensors connected to the Cloud.

12. Cisco IoT Cloud

A platform owned by Cisco. Its target markets are manufacturing, energy, transportation, smart cities, government, healthcare, and more. It obtains data from sensors, stores, and performs complex analytics.

13. Google Cloud IoT

Google Cloud IoT

Google Cloud IoT offer fully managed IoT services. It is a fully integrated platform where you simply connect your device, manage it, get solutions to complex problems, and visualize your data in real-time. It also gives room to make operational changes. It can automate responses or allow you to take action as needed.

Google uses Cloud IoT Core to obtain data from devices. This data is stored on Cloud Pub/Sub. Google BigQuery allows for quick queries and insights. Cloud Machine Learning Engine runs advanced analytics as well as machine learning. Google Data Studio publishes the result on its rich dashboard.

This platform works well with Android, it also supports devices from Intel and Microchip. Its target market includes manufacturing, utilities, smart transportation, oil and gas, and more.

14. Autodesk Fusion Connect

Autodesk Fusion Connect is an IoT solution from Autodesk. It claims to be the leading enterprise-focused IoT platform. It allows two-way communication with devices in the field. This allows the user to monitor, analyze, and remotely controlling their devices. It also gives information on device maintenance, which ensures lower downtime.

The platform is easy to use and allow just about anyone to configure their devices, control them and build custom connectivity solutions for machine-to-machine (M2M).

15. SAP Analytics Cloud

SAP Analytics offers its users with cloud connection, real-time analytics, ad-hoc queries, and collaboration tools. It uses in-memory technology from SAP HANA. It uses machine learning to make predictions and future trends.

In conclusion, the most beneficial features in any IoT platform are user-friendliness, connectivity, real-time updates, data processing, and visuals or notifications. Before choosing a platform to go with considering its usability. As an entrepreneur, it is also important you consider cost and computing power. However, the IoT platforms and devices are becoming cheaper.