In the realm of Artificial Intelligence (AI) and digital twin solutions, data is the lifeblood that fuels innovation and drives decision-making.
However, the importance of data goes beyond its mere existence. The quality, diversity, and accessibility of this data are equally crucial.
Surprisingly, data preparation, a process that ensures these aspects, can consume up to 80% of a data project’s time. This is where the concept of Data as a Service (DaaS) comes into play.
What is Data as a Service (DaaS)?
Data as a Service (DaaS) is a cloud-based strategy that enables data teams to access high-quality, engineered data from anywhere, anytime. A DaaS provider takes care of the data infrastructure services including data collection, cleansing, validation, contextualization, and engineering, so that the data team can focus on using the data for their applications.
DaaS providers manage the data, ensuring its quality, security, and accessibility, thereby freeing users from the complexities of data management. This model is particularly beneficial for businesses that need to access large volumes of data but do not have the resources or expertise to manage it themselves.
This model allows for scalability, as businesses can increase or decrease their data consumption based on their needs.
DaaS also promotes data democratization, as it allows data to be accessed by multiple users across different departments or organizations, fostering collaboration and innovation. Furthermore, DaaS providers often offer analytics tools and services that help users make sense of the data, turning raw data into actionable insights.
Top Big Data Preparation Challenges
Data preparation is a complex process, fraught with challenges.
Some of them are:
- Unstructured data: Raw data can be messy and inaccurate (caused by say, a sensor getting stuck), missing (due to network issues or human issues), ambiguous (one PLC’s data tags in one plant are named differently from another PLC’s data tags in another plant), and out of order (due to how the data pipeline works).
- High-volume data: Raw data can be of extremely high volume. Some AI solutions aspire to process 150,000 data tags but how do you check the fidelity of all the data tags? In most cases, that’s close to impossible.
- Data at high speed: Raw data can be high speed. Vibration sensors, for example, can run at 12KHz in a 3-axis, generating 150kB/s data in real time. That’s too fast for most software.
- Disparate data: An AI or digital twin solution needs to work on different types of data: sensor data, machine data, log data, measurement data, configuration data, and etc. All these data have different physical meanings, speeds, properties, and etc.
- Data from various sources: Raw data comes from different sources. Sources can include physical sensors or machines, databases, log files, spreadsheets, and more. Each of these includes multiple formats and protocols, and therefore automating this data collection is a daunting task.
- Data from different locations: Raw data comes from different locations. These locations can include production plants, private cloud, public cloud, and software services.
- Each of these can have different data security requirements depending on not only the requirements of the individual plants, company, and geographical location (think US vs. EU).
- Edge data collection: When it comes to collecting data from the edge, there is a set of challenges on its own. Think about setting up edge gateways, deployment and orchestration, long-term reliability, minimizing data consumption on cellular if needed, and etc.
- Constant requirement changes: Data requirements change frequently. As AI and digital solutions get deployed, data requirements will inevitably change. An inflexible system will take weeks to make any changes, which is not fast enough to meet iteration requirements.
The Solution: Industrial Data as a Services
Industrial Data as a Service (IDaaS) emerges as a viable solution to these challenges. It allows organizations to shift their focus from data preparation to data science, their core competency, and key value proposition.
Managed data services, like those offered by Prescient, specialize in data preparation. They understand how to work with messy, unstructured industrial data and have expertise in edge data acquisition systems and distributed low-code data frameworks. You can expect:
Big Data and Knowledge Management with Digital Twin solution
The intersection of big data management and knowledge management is where IDaaS truly shines.
By providing a comprehensive ecosystem of open-source software for big data management, IDaaS enables organizations to effectively handle the volume, velocity, and variety of data.
This not only enhances the efficiency of data preparation but also facilitates the extraction of actionable insights from the data.
Big Data as a Service
Big data as a service (BDaaS) is another aspect of IDaaS that leverages the synergy between big data and AI.
By providing scalable and reliable data processing and analytics capabilities, BDaaS enables effective AI.
Enterprise big data solutions, as part of BDaaS, can handle the massive amounts of data required for AI applications, thereby accelerating the development and deployment of AI solutions.
Data Analytics as a Service
Data analytics as a service (DAaaS), a component of IDaaS, offers a compelling value proposition.
It provides organizations with access to sophisticated analytics tools and techniques without the need for significant upfront investment.
This not only reduces the burden of data preparation but also enables organizations to derive meaningful insights from their data, driving informed decision-making and strategic planning.
In conclusion, Industrial Data as a Service (IDaaS) offers a comprehensive solution to the challenges of data preparation in AI and digital twin projects.
By shifting the focus from data preparation to data science, and leveraging the power of big data management, open-source software, managed data services, and data analytics, IDaaS enables organizations to unlock the full potential of their data, thereby driving innovation and growth.
Many companies, including Global 1000 companies, would take up to 6 months to prepare data for each deployment, or 2 years to set up production-level edge data pipelines. Get there faster with our digital twin solution for IDaaS.
Book a demo to learn more about how Industrial Data as a Service can help you to accelerate most of your AI and digital twin projects.