Data Entry and the Emerging Landscape of IoT

Data Entry and the Emerging Landscape of IoT

Published On March 06, 2018 -   by

The number of IoT (Internet of Things) devices is steadily increasing, creating a network of connections between people, the internet, and technology that is impacting businesses down to core functions.

IoT applications in business will only grow along with the amount of increasingly connected devices. Gartner predicts that the 8.4 billion connected things that were in use globally in 2017 will reach 20.4 billion by 2020.

All of these IoT devices are equipped with sensors that are transmitting massive amounts of data, and this affects how businesses manage their data and analytics processes. All industries and business models are making adjustments to manage streaming IoT data.

IoT devices are generating massive amounts of data, which demands a strategic approach to data entry to establish trusted, quality, reliable data throughout all operations and processes, starting with the foundation of data practices: data entry.

By adopting best data entry best practices, organizations can accurately read and interpret their IoT generated data, and implement decision-making strategies that are structured on a foundation of properly entered data.

The Connection Between Data Entry and IoT

IoT technology is improving the way in which organizations can monitor data in real-time, whether it’s reading data produced from consumers who are purchasing products via mobile channels, like smartphones and tablets, or points of data generation, like censored industrial machines or medical equipment.

Business’s IT infrastructures are also being impacted by IoT and must drive data solutions that accommodate the exponential growth of both connected devices and data storage within their company.

According to 84% of CEO’s in a study conducted by KPMG, data quality is a huge concern that plays a significant role in the ability to make sound decisions. Being able to accurately and capably collect data from potentially 20 billion connected devices shortly presents a massive business challenge. Organizations must cleanse, process, store, and analyze information properly for data to be of use, especially considering that IoT devices themselves are introducing errors in data quality.

Achieving Quality Data

IoT is enabling data processes to build better quality data from the start. To leverage the tide of IoT driven data, manual data entry practices must be augmented with maturing technologies.

IoT data needs to be uncovered, clean, and structured while addressing data challenges that arise from information that’s shared across different formats and devices that aren’t easily integrated. Because the majority of systems necessitate manual data entry, managing the process is crucial to avoid human error.

Doing so allows companies to reap the benefits of meaningful IoT data:

  • To inform decision making and aid problem-solving.
  • To seize potential for monetary savings and productivity.
  • To better address the customer experience.
  • To innovate across product development, research, and marketing.

Data Entry Enhanced by IoT

Integrating new IoT technologies and processes with existing data entry practices is key to enabling businesses to utilize IoT data to its full potential, and ensure it’s prepared for interpretation and application across all departments.

M2M: Data entry further boosts productivity and creates efficiencies with M2M (Machine to Machine) interactions that provides point-to-point communication between devices through wired networks. Adding M2M capabilities to traditional data entry helps minimize mistakes and enhances the effectiveness of IoT based data entry practices.

Edge Analytics: IoT will also solve a lot of challenges related to overtaxed central systems and massive streams of data regarding Edge Analytics. Data entry is performed at the sensor point, which prevents potential latency issues in transmitting data to businesses and saves costs regarding reducing quantities of data that need to be transferred across networks or stored in data pools.

Agile Stack: Given that IoT data resides across multiple relational and non-relational systems, companies need aggressive data prep and enrichment, flexible storage, and governance. An agile stack that begins with data entry creates the stepping stone of a data strategy.

Evolving Data Entry Practices

As IoT continues to mature, outsourcing data entry to qualified specialists becomes more imperative so businesses can deploy validated, useful data across departments and strategies to best advantage. Data Entry Outsourced (DEO) has command over the latest data entry techniques and technologies to help companies prepare for the evolving state of IoT.

– DataEntryOutsourced

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