There are numerous challenges IT professionals and data scientists encounter when striving for accurate and efficient data capture. But unfortunately, many of these challenges don’t always get the level of attention and discussion they deserve, despite having a significant impact on data capture.
Here are three data capture challenges familiar to IT professionals and data scientists.
Increasing Sophistication and Variety of Data Capture
Today, there are more sources for data than ever, ranging from oil and gas firms exploring data under the ocean to space exploration relying on satellites in orbit. From the depths of the sea to outer space, data collections are occurring. Naturally, such a variety of information presents a challenge for data architecture, driving the development of new data centers with varying types of data. The result is a new edge computing environment that’s fit for capturing, storing and analyzing copious amounts of data. The impact these data centers will have relates to changes in computing infrastructures, ranging from GPUs to flash storage and IoT networks.
The abundance of data and data sources prompts a new approach to data architecture, as centralized data becomes less of a reality. Since many businesses have adequate infrastructure in place that embraces a centralized data hub, there’s a reluctance to transition to a new computing infrastructure with different centers that are more able to capture, store and process varying types of data.
Cloud computing presents a solution for some to scale the infrastructure with efficiency by outsourcing hardware abstraction options to achieve data timeliness, distribution and access. Still, many industries are reluctant to shift their technology and methods in favor of something that feels anticipatory.
The Role of Manual Data Entry
In an industry like the medical field, there’s enduring familiarity with manually entering data like medical history, orders and diagnostics. A more efficient approach involves data capturing automation to ease manual data entry. Manual entry can be an obstacle in leveraging complete data, though specific industries grow accustomed to the manual approach — hence why some sectors overlook data capturing automation and its importance.
Issues still arise in manual data entry comparative to automation, since automation is relying on uniform variables and fields. If someone accidentally misspells a word or phrase during manual data entry, the protocol for data analysis may have a significantly detrimental impact regarding accuracy and usability. Although some still swear by manual data entry as playing a pivotal role in data capture, it’s clear that data capturing automation will play an influential role in the future, even if some are reluctant to adapt.
Personal Data Collection
Privacy is always a sensitive issue, impacting everyone involved in data capturing. How far is too far when it comes to collecting personal data? It’s a question that initiatives like the GDPR aim to address. As a result, personal data collection is a sensitive topic, with businesses knowing that collecting personal data can allow them to personalize the consumer experience, leading to better conversions.
In the medical industry, healthcare organizations continue to collect patient data through monitoring devices, though recent initiatives that strive for better protection against personal data collection are likely to prompt a shift in how personal data collection is permitted. Although digital platforms and increasing technological sophistication can help accommodate efficient and accurate data capture, there are many challenges that come along with this increasing sophistication.
Additionally, the role of manual data entry is becoming anachronistic, despite many still clinging to the manual process as pivotal. Personal data collection remains an uncomfortable topic in the field of data collection, as data professionals juggle the high value of this data with privacy concerns.
About the Author
Contributed by: Kayla Matthews, a technology writer and blogger covering big data topics for websites like Productivity Bytes, CloudTweaks, SandHill and VMblog.
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