Data Quality Intuitively comes along with iNeighbour Analytics
The quality of data is a prerequisite for a meaningful data analytics deployment. Characteristics of Data Quality are accuracy, completeness, consistency, uniqueness, and timeliness. Without Data Quality, data analytics process might paint a false picture and risking the companies to make wrong decision.
The few elements contribute to Data Quality:
Accuracy: a company has to cross-check data with the source. If there is no data lineage, the accuracy is always questionable.
Completeness: It doesn’t mean all data source, but it must adhere to the business requirements, especially those data is used for KPI measurement.
Consistency: If data is flowed to multiple applications, it must have the same properties and not conflicting each other. For example, if the date format is DDMMYYYY, make sure it should be the same across all applications.
Validity: It should follow the business rules and parameters. For example, if the amount rounds up after 2 decimals, then it should.
Uniqueness: There must be no duplication of data.
Timelines: Data must be available when promised.
There are quite some methods suggested by data experts to improve Data Quality, among them: Data Profiling, Data Standardization, Data Geocoding, Data Matching and Linking, and Data Quality Monitoring.
To ensure Data Quality is kept, organizations have to maintain a Data Life Cycle, which involves the processes:
1. Find data through root cause analysis
2. Investigate data
3. Find potential causes
4. Perform root cause analysis
5. Apply correction
6. Monitor by continuous improvement monitoring
7. Sustain by applying fixes on sources or closest to source
Hence, to achieve Data Driven Organization is easier said than done. Since data itself generates further data, especially when raw data go into applications generate and regenerate much meaningful data, solution providers who have the capability to provide data analytics services should be the better choices for companies that take data seriously and have the plan for data transformation.
Technological advancements that consolidate all activities into a single super app in recent years are aiding residential property management in its digital transformation journey. TimeTec is the prominent smart community solution provider capable of fulfilling the most sophisticated residential property management needs through its iNeighbour super app, and now furthering the journey on data transformation.
iNeighbour Analytics is available as an optional module for the iNeighbour super app, allowing JMB/JMC/MC/RA/Developers to continue the data journey, saving costs and time without the hassle and need for extensive data lifecycle measures mentioned earlier to maintain data quality. Data quality flows intuitively in a lineage form along with the applications, providing readily prepared templates for easy visualization and benchmark references.
iNeighbour Analytics offers instantaneous data crunching and data visualization, providing a 360° view on:
• Units and user analytics
• Occupancy rates
• Accounting analytics
• Outstanding debt analytics
• Facility booking analytics
• Visitors analytics
• Patrol management analytics
• E-info, e-Form analytics
• Maintenance analytics
• Parking analytics
• Feedback/Inquiry analytics
• Admin KPI
• Many more
The Benefits:
- Increase unit owners' and tenants' satisfaction
- Detect and remedy blind spots easily
- Better accounting control
- More accurate advice on tenants and residents
- Enhance neighborhood security
- Review and improve property management
- Informed decision-making driven by data
- Boost property value
Interested in learning more about iNeighbour Analytics and iNeighbour? Request your free demo of the iNeighbour Residential Property Management solution now.
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