TIME-SERIES DATA RELIABILITY &
OBSERVABILITY PLATFORM

Tame unreliable data and reduce overall data downtime with Timeseer.aI.

 

Timeseer.AI is already being trusted by leading data teams of 10+ Fortune 5000 companies.

 

// Why?

“We don't need better models, we need better data.”

— Andrew NG

“It is crucial to a grip on unreliable data before it's impacting your operations. It only cost 1$ to detect data downtime where it would have cost 10$ to fix the problem and it would balloon to 100$ of financial impact when it hits you operational.”

— Quote Source

// Core Platform components

LOREM IPSUM DOLOR SIT
AMET CONSECTETUR

 

#1 DATA RELIABILITY SCORING AND PROFILING

  • 30+ built in quality metrics that express the overall health of the underlying time series.

  • Support to define your own custom metrics.

  • Metrics as icons: variance drift, broken correlations, stale data, missing values, persist anomalies, IQR anomaly, ...

#2 DATA MONITORING & OBSERVABILITY

  • Scan data proactively at scale & check quality

  • Segment data & create overview of data downtime issues.

  • Define data SLAs and define quality gates

  • Collaborate on issues to improve Overall Data Effectiveness

#3 DATA QUALITY OPTIMIZATION, AUGMENTATION AND CLEAINING

  • Impute missing values

  • Filter out artefacts

  • Keep data volumes manageable while avoiding information loss

  • Align and transform data from different series

  • Define your own augmentation logic

Figure: Before (non equidistant) stream / AFTER data matrix.

#4 DATA CONNECTIVITY AND UNIFORMIZATION

Lower the burden of data integration and easily map to one uniform time series data model.

 

// Integrations

TIME-SERIES OPS SEAMLESSLY INTEGRATED IN THE MODERN DATA STACK

// Benefits per role

LOREM IPSUM DOLOR SIT
AMET CONSECTETUR

 

Data scientist

  • Reduce time spent on data preparation and cleaning
  • Understand which data can be trusted for modelling purposes
  • Make manual, repetitive, reactive cleansing tasks more scalable and proactive job
  • Analyse data in context
  • Be aware of data complexity (eg. Non-stationarity) issues that are relevant for modelling choices
  • Make data preparation work reusable across projects and teams

Data engineer (IT)

  • Simplify integration of time series data into your infrastructure
  • Integrate data quality checks into your data pipelines
  • Shift data cleaning and preparation steps upstream
  • Feed analytics apps with relevant context data
  • Get alerted when data downtime occurs

Automation engineer (OT)

  • Safeguard a healthy data environment
  • Guarantee the desired data quality service level for your OT data
  • Get alerts for potential sensor failures

Citizen Data Scientist

  • Understand which data can be trusted in your analytics work
  • Start your analytics journey by looking into relevant data quality events

Data Officer

  • Define and monitor data SLAs to safeguard data hygiene
  • Set up a collaborative approach to examine and resolve data quality issues
  • Understand data downtime and overall data effectiveness
  • See where data quality issues impact decision making

WANT TO SEE TIMESEER IN ACTION?

Find out how timeseer can help your company to get a grip on data downtime