almaz monitoring

Adaptive machine learning real-time data

quality monitoring of corporate reporting,

business and technological processes

No active monitoring of data quality

Data

Warehouse

5% of data becomes null

Incorrect financial reports

Incorrect operational

reports

Anti-target

marketing

Why revenue goes

down while traffic is

unchanged?

I can’t trust

these

reports!I don’t need these!

Example of streaming data

Normal behavior

Abnormality detected: no peaks

Almaz Monitoring

Abnormal behavior is detected!

System administrator

There was not enough lubricant!

Old lubricant worked just a month

and probably was defected.

Production Manager

Good job, guys!

If the turbine broke, It could cost us

$250 000

13:21

13:22

13:23

Business needs

Financial and operational reports

must have trusted data

Corporate data layer must be

verified before it is used by other

systems of data scientists

Operational KPI’es must be

monitored in real-time and there

should be immediate reaction to

any abnormal behavior before it

leads to substantial damage

Tasks Solution Benefits

Constant autonomous data quality

monitoring of corporate reporting,

business and technological

processes

Instant alarms on occur of any

statistically significant deviation.

Notifications to mobile devices

Self learning system that can

adapt to the data and user

behavior. The system that does

not need any expert or data

scientist and start working right

out of the box.

Trusted data

Revenue growth by minimizing

downtimes

No more important business

decisions on inaccurate data

Objective data quality control by

machine. Eliminated “human

factor”.

Prediction of potential

mulfuntions

Machine Learning Quality Control

Spikes Gaps

Outliers Symmetric

deviations

Change Points

Instant detection of significant

abnormality of real-world data flows

Seasonality trends

Weekend or holidays

Daily intervals

Outliers

Non-cleansed data

Other particular business specifics

Visual

Identification of abrupt changes in the

generative parameters of sequential data.

Major algorithms are adopted to enterprise

data

• Moving average

• Bayesian

• Autocorrelation

• Regressions

• CUSUM

• Shewhart

Adaptive Machine Learning

Accurate changepoint detection

Adaptive Machine Learning

Data Quality Monitoring at

Enterprise scale

Financial reports

Analytical aggregates and views

Real time data flows

Operational reporting

Big Data streams and storage

Enterprise scale

High Level Schema

Data connectors Control and

Visualization

module (web ui)

Notification

Engine

Trello

E-mail

SMS

… and many

more

… and many

more

Integration

layer

Adaptive Machine

Learning Module

A high-

throughput

distributed

messaging

system

MLlib

User Interface

Create new monitoring

System allows to drag&drop KPI’es and ‘group by’ fields

System scans the data and automatically detects the type of fields and their specifics (discrete/

continuous, numeric / string, dictionary lookups etc.)

Visualize Abnormal Behavior

Trello: Notifications and Issue resolution workflow

Convenient issue tracking and resolution workflow

Push notifications on mobile devices

Competitors

New Generation of Data Quality

Adaptive machine learning real-time data quality monitoring of corporate reporting, business and technological processes

Artificial Intelligence and Adaptive machine learning

Complete autonomy

Big Data Real-time monitoring and notifications

Classical Data Quality Monitoring

Convenient tools of data visualization and data

analysis for data scientists

Rules templates. Experts create validation rules and

refresh them after data trends changes

Monitoring and notifications

Informatica Proactive Monitoring for Data Quality

SAS® Data Quality

InfoSphere Information Server for Data Quality

Trillium Software Data Quality

SAP Data Quality Management

Thank you!

Vladimir BakhovCell-phone: +7 (905) 716 54 46E-mail: vbakhov@inleksys.ru

Resident of Skolkovo Innovation Center