- Building a Data Analytics Model
- How Data Analytics Works in Asset Management?
- Importance of Data Analytics in Asset Management
- Reduced asset maintenance costs
- Optimization of field force
- Improved customer satisfaction
- Improved safety & compliance system
- Use Cases of Data Analytics in Asset Management
- Data Science in Investment Management
- Predictive Analytics in Asset Management
- Data Scientist for Asset Management
- Topics in Data Analytics
“Effective predictive maintenance programme can result in a savings of 8-12 per cent over a programme simply utilizing preventive maintenance strategies.” – Mario Montag, CEO Predikto
Data analytics is the use of past historical events to inform future actions based on current data. While data is the goldmine that can be used to better the performance of a company, descriptive analytics is the way the objective can be achieved.
It encompasses a range of disciplines from machine learning, artificial intelligence, big data, statistics and modelling and simulations and is applied in most sectors including asset management. Data analytics extracts and completely exploits data sets, therefore provides in-depth and rich insight.
In asset management, data analytics is applied in:
- Early warning of assent condition and failure
- Early warning of process anomalies and process optimization
Building a Data Analytics Model
Tangible knowledge and hindsight are key ingredients of developing data analytics models. These models require tons of data and sophisticated software to develop. Thanks to advancements in technology, there is rich data software in the market. One way of developing data analytics models is the Cross-Industry Standard Process of Data Mining (CRISP-DM), invented in 1996 by Daimler Chrysler.
|Stage||Task and Objectives|
|Defining Objective||Define a business objective. This will provide you with the scope of your project|
|Preparing Data||Train your model using historical data. The data should be cleaned and prepared. Good quality guarantees a good model|
|Sampling Your Data||Have two sets of data for training and testing.|
|Building the Model||Study your data, run algorithms and compare outputs|
|Deploying the Model||Coordinate with other departments when deploying the model. Monitor your model update it and continue improving it.|
How Data Analytics Works in Asset Management?
A software program creates a model and predicts the output value based on the data at hand. It further raises an alarm whenever it detects an anomaly. The main purpose is to create an early warning so that corrective action can be taken before a failure.
The model identifies the important action to be taken and the asset in the picture. It then creates a communication between the Data source and database which is either cloud-based or site-based hence the need for fast internet connections. Data sources can include; equipment control systems data, equipment sensor readings and computerized maintenance management system data et cetera.
Once an anomaly is detected by advanced pattern recognition, experienced personnel assess the threat and justifies the alarm whether it’s within the acceptable terms. In this case, machine learning is implemented in its early stages.
Dashboards provided by the software show an indication of process health together with an expected life until failure.
Data analytics is used in these two broad methods:
- Cloud-based – a company subscribes for at least 6 months. In this scenario, the company outsources everything from software, hardware and technical team.
- Site-based – a company purchases the hardware and software and they are installed within the company premise with the help of the software supplier.
Power generation and distribution companies have implemented the use of Data Analytics, coming up with their data analytics in partnership with software firms. Companies like Exelon in the USA have reported savings of up to $4.6 M per year.
Importance of Data Analytics in Asset Management
Reduced asset maintenance costs
Data analytics provides a laser-sharp prediction of failures and outcomes. This will help avoid regulatory penalties, collateral damage and loss of revenue. This obliterates the overhead of unforeseen asset maintenance and lessens fixed costs.
Optimization of field force
Unexpected re-allocation of field-force teams can be avoided by having a proactive system that periodically monitors the health of field assets.
Improved customer satisfaction
Managing asset risks is one way of ensuring customer satisfaction, avoiding unexpected failures and maintaining valuable assets before a misfire. By use of data analytics huge losses can be avoided. Existing systems need to be upgraded with models that can help control and asset failures.
Improved safety & compliance system
With the availability of predictive insights, utilities can address possible safety risks. They can act pro-actively and mitigate safety risks.
Use Cases of Data Analytics in Asset Management
Tons of data points are being generated and consumed every minute across the investment lifecycle. This presents an opportunity for investment platforms to beat their competition. With better data, it gives them a competitive edge; they can produce better products and features.
Artificial intelligence and machine learning play a key role in investment management. With historical data, data analytics can help investment platforms to predict the future by anticipating demand, predict life events, predict and manage prospects, expose the best ventures for business growth and anticipate demand.
Platform data assets will grow every day, investing in advanced analytics will make models more accurate. This guarantees better service from investment advisers, personalized investor experience and better funding due to accurate insight that only investors can access.
Asset performance management
18% of assets use preventive maintenance with increasingly failing with time, the remaining 82% fail randomly. Data analytics uses real-time equipment to process data and can foretell possible failure. With accuracy it predicts failure and minimal false positives, thanks to big data and analytics.
The benefits of proactive maintenance is evident. This includes asset longevity, safety, maintenance control and impressive uptime. Experts advocate for proactive maintenance with analytics.
Data analytics can aid in predicting the needs and activities of the customers. By categorizing customers, tailoring offers and engagements, fund managers can use this to increase revenues and achieve better results.
Data-driven asset management
Assets operators and asset owners use predictive failure platforms to effectively make data-driven decisions to predict asset failure and successfully manage assets.
Data analytics provide insight to gauge the main reasons which contribute to asset performance challenges. It can combine and process information from various datasets and uses algorithms to analyze performance data. This enables asset owners and operators to accurately model failure of assets, thus, sufficient maintenance and early warnings of issues.
Data Science in Investment Management
The use cases of data science are growing with each passing day and making it the apt choice for effective investment management too. The data science concepts are used across the investment industry for taking data-driven risk management decisions. Risk, being the key factor for determining the investment proportion, therefore it becomes important to channelize effort towards mitigating risk and developing healthy investment plans. The key advantages of data –drive investment management are:
- Analyzing the financial market to value investment assets and establishing an accurate investment portfolio in real-time.
- Visualizing the anomalies to identify hidden potential risk factors.
- Conducting AI-driven market research to support investment plans and boost capital return.
- Facilitates framing the right strategies to gain a competitive advantage in the industry.
The algorithms of data science and analytics are the prominent tools to execute investment queries and render an interactive ecosystem to understand every tit and bit of the capital and other markets. With the help of data science and suitable prediction, the analysts are able to develop a new level of precision on several business grounds and help managers create progressive portfolios. As a result, data science plays a crucial role in streamlining the investment choices towards the common goal of framing high investment returns.
Predictive Analytics in Asset Management
Assets are the strength of every business entity only if managed and maintained with great precision. Thus, to support a healthy asset management process predictive analytics is considered a reliable tool. Predictive-based asset management facilitates assessing the condition of the equipment to understand and identify the remediation need. With the help of predictive asset management the organization can obtain required information at the right time”. As a result Predictive Based Maintenance (PDM) closely monitors all the problematic conditions in the equipment and provides space for utilities to focus on improving operations.
Predictive-based asset management serves the need of all types of equipment in the industries. The key benefits of predictive data analytics are as follows:
- It allows organizations to plan, schedule, and replace overhead assets.
- PDM boosts the reliability and performance of the assets in the longer run.
- It also promotes employee safety by establishing a healthy work condition.
- Moreover, it reduces the cost arising out of outage restoration and unplanned repairs.
Data Scientist for Asset Management
The data scientist is the key performing member in the asset management team and is responsible for supporting the availability and reliability of the asset. Such person in charge is both technically and analytically trained to prepare the development model, testing model, ensure data validation and integrity which contribute immensely towards the daily organizational operations.
The core responsibilities of data scientists for effective assets mentioned are mentioned below.
- They are the primary contributor in defining the project scope, developing the technical aspect, and executing the algorithm of root cause analysis.
- The personnel also focus on mining data to support asset performance and develop suitable project guidelines.
- The data scientist is responsible for translating business requirements into the success factor by considering the technical and data aspects of the process.
- The person also ensures extracting, transforming, and loading all data source systems to build and monitor asset performance for ideal response time.
- A data specialist communicates priorities, operational impacts of the project and optimizes them with the organizational expectations.
- The specialist maintains the daily administrative functions of the organization by scheduling processes, monitoring assets, and measuring administrative performance.
- Ensures active collaboration with vendors for technology and software which empathize on improving the internal control and process investigation.
Data analytics is obviously the future of business and embracing it early is wise. As AI grows with technology so does data analytics. Asset management will improve in future and companies that use analytics will be a step ahead of the rest.
Topics in Data Analytics
- Advanced Data Analytics
- Clinical Analytics
- Credit Risk Analytics
- Cyber Risk Analytics
- Data Analytics for Customer Behavior and Customer Experience
- Data Analytics for Customer Journey
- Data Analytics for Fraud Detection
- Data Analytics for Human Resources
- Data Analytics for Logistics and Supply Management
- Data Analytics for Risk Management
- Data Analytics for Talent Acquisition and Management
- Data Analytics in Asset Management
- Data Analytics in Digital Marketing
- Data Analytics in Healthcare – Use Cases, Metrics, Techniques, Companies and More
- Data Analytics in Manufacturing
- Data Analytics in Pharmaceutical Industry
- Descriptive Analytics – Definition, Types, Examples, and More
- Digital Analytics
- Financial Data Analytics
- Financial Risk Analytics
- HealthCare Claim Analytics
- Insurance Risk Analytics
- Insurance Risk Analytics
- Population Health Analytics
- Portfolio Risk Analytics
- Revenue Cycle Analytics
- Risk Assessment Analytics