To weed out poor-quality data and prevent it from affecting their simulations, companies need to have the right management tools in place.
The sheer volume of inconsistent, inaccurate, or incomplete data makes it difficult to use them as reliable sources for consistency of reporting. digital twins. Companies cannot run the risk of having incorrect information that would make any simulation operation unusable. And, in a context of sobriety, wasting a precious budget on adopting a flawed strategy that relies on uncertain outcomes would be sheer nonsense.
Poor data is the Achilles heel of digital twins
Second forecasts provided by IDC in its Global DataSphere report, the volume of data generated per year is projected to more than double between 2021 and 2026. In other words, unless the necessary measures are taken, the situation is bound to get worse, because as the amount of data increases, its quality tends to decrease.
For the burgeoning digital twin market, this trend is dire. In fact, this technology is spreading and becoming essential for all kinds of sectors, manufacturing industry, construction, urban planning, environmental observation, transportation and health, etc. Thanks to their ability to replicate, measure, supervise, predict, test and simulate in real time, digital twins have already made a name for themselves and their influence is set to grow.
However, their growth is closely linked to data quality; creating accurate simulations of real-world scenarios depends on it. Otherwise, this innovation promising and the resulting opportunities will be nipped in the bud. The underlying AI and machine learning algorithms should be designed using good quality data. Otherwise, companies will inherit a completely broken system that incorrectly flags anomalies or makes random predictions. While value added digital twins is precisely to allow companies to save money, improper use would lead to the opposite: additional costs and loss of time.
The 5 steps to clean data for digital twins
The modern architectures and platforms made available to companies allow data to be improved in order to make them reliable and avoid inaccuracies. The main goal is to avoid silos to cover all sources of contextual data, integrate this data and thereby promote better decision making. Optimizing data quality is an iterative five-step process.
- Integrate data sources from a variety of systems with virtualization data and sources in real time
- Profile data to discover and analyze where it needs to be corrected or improved
- Manually troubleshoot to resolve the issues identified in the previous steps
- Automate data cleansing and deduplication using templates and rules
- Monitor data in real time and set KPIs to understand data trends
It is also possible to create a “firewall” by filtering data based on its quality. There’s nothing worse for a business than allowing poor quality data back into its systems. This “firewall” can provide real-time error detection and correction to protect the digital twin and ensure that all captured data meets defined quality levels.
With the growing importance of the decisions companies want to make using simulations based on digital twins, data quality must be at the heart of their thinking. Thus they can avoid compromising the lives of patients, slowing down a production process, delaying trains or sending engineers into the field to perform unnecessary maintenance work. The fundamental importance of data quality should be obvious, especially when artificial intelligence/ machine learning they are trained and used to make crucial decisions. Any other approach would be suicidal.