Solving For Your Asset Performance Management Data Problem

Do you want the data of your assets (equipment) to work for you or against you?  

In the digital world of industrial operations, your assets should not be just monitored but should be generating enough data for the could application to consume to help you make data-driven decisions every second. But there is still a question: Are you really making the most of your assets?  

IoT data has been an invaluable asset to businesses and more businesses are preferring data-driven decision making to improve their operation’s productivity. For asset-intensive businesses, extracting data, data analytics in IIoT solution, and making the most of it isn’t optional, it’s critical. From reduced downtime to failure forecasting, it all depends on the IoT data that is interpreted. Because your asset data could do what your competitors can’t. Yet, many businesses are trapped in the chaos of fragmented data and noisy signals.

Having an asset management solution built on the foundations of advanced Artificial intelligence and Machine Learning algorithms will help you gain insights on the broader picture of your assets and impact critical decisions.

The Data Challenges Holding You Back

When it comes to Asset Performance Management (APM), effective utilization of data can have a larger impact on operational efficiency and business growth. Since a robust data management practice is the backbone of APM, it should never be ignored. You must have the plan for encompassing processes and technologies that ensures data security, accuracy, accessibility, and usability. Missing on these might pose multiple challenges such as compromised data integrity, increased cost, as well as unnecessary resource consumption.

a. Data collection at scale: Imagine you have a business that has a fleet of 30,000 assets remotely deployed across multiple regions and facilities. These assets speak in different languages—some speak via MQTT, some uses CoAP to HTTP and others speak differently.  You can’t risk drowning in disconnected streams, right? This is where you need a promising data ingestion strategy. Using an IoT middleware that has protocol agnostic integration, you can standardize data ingestion and streamline the data collection process.

b. Compromised data quality: Poor quality data is of no use. The first and foremost step to a successful data management strategy is to improve the data quality. Not having it could have a substantial impact on decision making. Incomplete data or inaccurate data will lead to ineffective operational insights and therefore you end up compromising on the information regarding your assets, further impacting customer experience.  

Way forward? Automate data validation at the ingestion points and use AI advanced models to fix anomalies.

c. Data silos: According to Forrester, businesses that break down data silos have seen 70% better data-driven decision-making outcomes.  

For an effective APM, you need to get rid of data silos and move towards IoT data management. It usually occurs when different systems store data separately. Lacking interconnectedness among these systems and the recurring data could potentially impact your APM efforts.  What you can do in this case is, adopt a centralized data management system that helps in cross-system collaboration. Utilize cloud-based data lakes to break down data silos.

d. Data privacy and security: With the increasing inclination towards data-driven technologies, data privacy and security are becoming even more essential. The potential risks such as data breaches, unauthorized access, misuse of sensitive information, and data leaks can damage your APM efforts. The best possible way out of all this is restricting access control, encrypting sensitive data, and regularly conducting security audits that can help in enhancing data protection. The next steps are following compliance GDPR and CCPA along with robust security protocols. Multi-factor authentication and Role-based access controls can further help protecting critical data.

e. Poor Data Analysis: Data, if not read or analysed, stands as just volume with no value. Analysing large datasets in real time demands advanced methodologies and techniques. Since real time data impacts actionable insights, it is eminent to analyse data as soon as you receive it. Employ machine learning algorithms, identify data patterns, and predict data trends to maximize the full potential.

Combat the Data Problem

a. Predicting asset failure: Data can help in predicting asset failure. Use the data to perform predictive analytics to forecast failure of assets and ensure time to action for multiple assets. Leverage prescriptive analytics to measure asset performance. Identify gaps and voids or false positives using by collecting and analysing the data.  

b. Knowing the condition of your assets: You can’t move on to the next steps if you can’t see the condition of the assets deployed at your customer’s end. Businesses that deal with multiple assets need to continuously track asset performance metrics. Using data analytics and data management algorithms, they can detect the delta of how their assets performed then and now, if their normal behavior is changed, if there are trigger alerts which enables them to not just prevent failures but also enable maintenance schedules. This helps in reducing downtime, extending asset lifespan, and maximizing the potential of your assets.  

c. Asset utilization: Data analytics provides an insight on asset performance and its utilization, which further helps in optimizing asset usage. By analyzing the data on asset utilization, businesses can identify the assets that are underutilized or idle and then make data-driven decisions about redeployment or disposal. By doing this, you are identifying bottlenecks, inefficiencies, or any possible incorrect patterns of equipment failure. Not just that, you are also identifying optimization opportunities and enhancing overall operational performance.

Solving your APM Data Hitch

You won’t be solving the data problem only by collecting, it should be the right mix of collecting, analyzing and then making the right calls.  

One fine grain that separates the laggards from the leaders is owning their data strategy—not those who just know that they should have the data incoming. With an IoT Middleware like Flex83, businesses are already building and owning custom Asset Performance Management solutions that not just integrate but is scale-ready and is backed by real-time data analytics and predictive analytics.