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Why Enterprise Data Collection Projects Need Data Management Systems During This Pandemic

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To keep pace with the data-driven world of modern commerce, businesses need to collect data about their customers, market, supply chains and more. While most organizations now understand the criticality of keeping up with data collection, they often lose track of the information once it’s stored on company servers, allowing useful information to aggregate into swamps of undifferentiated data that needs to be cleaned, enriched and sorted to be of any value.

 

As a result, almost every company will benefit from using smart data management systems. Read on to learn three important integrations that make information management valuable for data-driven businesses.

 

Connect to Machine Learning Models

Machine learning is the process by which a computer algorithm uses data to learn on its own, without human intervention. From this definition alone, it’s clear that the first and most important component of machine learning architecture is a robust data ingestion solution. More businesses than ever are interested in the prospect of developing better forecasting with machine learning models, but companies that can’t feed data to a model in real time, or close to it, won’t be able to harness the high-speed, accurate decision making that their competitors can.

 

Since almost every machine learning use case requires multiple data types, a pipelining data management system that processes numerous input types is usually the right solution. Data ingestion may be batched, or provided to the model on routine intervals, for applications where cost is a concern. True real-time data ingestion is more expensive but produces better results. Depending on the application, it may not be necessary to clean and sort the data before providing it to the model; machine learning algorithms looking for undefined correlations and groupings can use unsorted data sets for this process, called unsupervised learning. Companies that need to elucidate a particular relationship between two variables should use supervised learning for this process, which requires pre-organized data.

 

Connect to Long-Term Storage

Even in personal computing applications, few things are more crushing than to realize that a single catastrophic equipment failure can erase years of effort. Still, many businesses—even large ones—put off backups because they’re time-consuming or inconvenient. A robust data system responsible for providing information to human employees and automated forecasting models should always be connected to a long-term storage solution.

Although backing up to an onsite server with redundant architecture is a good start, businesses are still vulnerable to losing data due to physical damage from meteorological disasters or similar problems. Data is safer when organizations supplement onsite storage with a distributed cloud network backup, which uses redundant servers to spread data over several physical sites. Distributed storage is more common and more easily available than ever, so any company planning to invest in the benefits of big data should make smart backups a part of their strategy.

 

Connect to Convenient Outputs

Machines are capable of interpreting raw data, but humans benefit from visualization aids. Unfortunately, many basic spreadsheet and graphing tools are not capable of updating in real time, limiting their usefulness for companies that use machine learning tools or rely on processes that need to refresh frequently with new data, as in industrial applications that rely on sensor data for decision-making by humans.

 

Instead, companies that need rapid updates of real-time information should research how their data collection systems can connect to an API dashboard that outputs information in a visually friendly format. API, short for Application Program Interface, allows users of various different computing tools, from phones to desktops, to interact with a separate resource—in this case, the company data system. Whether they appear as dashboards, web services, or apps, APIs are at the heart of human-machine interfacing, and companies that intend to make data-driven decisions a part of their business need to ensure that their data management systems are connected to a robust API that allows users to understand data easily.

 

The Bottom Line

It’s no longer enough for businesses to collect data; to be competitive in contemporary markets and take advantage of new technological developments, organizations of all sizes need to invest in robust data management systems that integrate with a variety of applications.

 

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