AI
Governance
Quantyca Quality Inspection
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Overview

The success of companies is increasingly linked to an intensive use of data. The ‘data driven’, if not ‘algorithm driven’ approach seems to be winning. However, its success is closely linked to the availability of good quality data, where quality is defined as fitness for purpose. In particular, the DMbok identifies 6 dimensions of quality: Completeness, Uniqueness, Timeliness, Validity, Accuracy, Consistency, each of which must be analyzed and declined in relation to the context and needs of each individual company.

The continuous monitoring of ‘key’ data used in the definition of corporate strategies is a first point to ensure correct decisions. In the dynamic contexts that have characterized the market in recent years, being able to count on quality data is the key to reacting to change in a timely manner, exploiting the opportunities provided by new technologies and making them functional to achieving success.

Challenges

Here are the damage caused by poor data quality:

  • wrong decisions
  • misunderstandings
  • lack of confidence in data and processes
  • inefficiency in data management processes related to data remediation activities
  • potential sanctions
  • reputational damage
3%
of the data
in a business enterprise meets quality standards
Harvard Business Review
26%
of the time
spent in date cleansing
Anaconda

Solution

The approach to Data Quality involves two different perspectives, one closer to Business, which analyzes Data Quality issues, assesses their impacts and prioritizes the quality controls to be implemented, and a second one closer to IT, which deals with the implementation aspects, identifying the most appropriate points and methods for executing the controls, taking care of their development, testing them and releasing them into production.

The controls are carried out by local agents on the various components of the Data Platform, with varying logic and frequency. The outcome is collected and historicised within the Data Governance application, which sends notifications to the various owners. Data Quality can be approached as a stand-alone project, but is capable of developing strong synergies if it is conceived within the design of a broader Data Governance framework.

The complete route

1. Design
Definition of roles and priorities in data quality management. Implementation of a subset of quality controls, monitoring of the outcome of controls.
2. Land
Definition of the implementation plan of quality controls, evaluation of data quality issues on the business.
3. Optimize
Definition of quality improvement measures based on the evidence provided by the implemented controls. Measurement of the effect of the interventions.

Benefits

Greater confidence in data and processes
Positive impact on decision-making processes, in terms of quality and speed
Optimisation of resources dedicated to Data Management/Data Cleansing
Reduction of regulatory non-compliance risks
Events
Replay
Hybrid Event
IKN Utility Day 2021
Date and Time: 24/11/2021

Quantyca participated in IKN – Utility Day, the November Main Conference focusing on the map of digital, cultural and technological transformation of Italian Utilities. Francesco Gianferrari Pini, Co-Founder of Quantyca...

Replay
Video Talk
CDO – Chief Data Officer 2021
Date and Time: 30/06/2021

Quantyca took part in CDO 2021, the event organised by IKNItaly, with a talk by Andrea Gioia, CTO & Partner Quantyca, in which he addressed the challenges of IT in...

Resources

Blog
Free
07/07/2022

Il Data Mesh e il consumo self-service dei dati come prodotti

Blog
Free
14/04/2022

I principi di un moderno Data Management

Slide
Free
15/06/2021

Quality & Governance nell’evoluzione della Data Platform – IKN Utility Day

Video
Free
30/06/2021

Intervista ad Andrea Gioia, CTO & Partner Quantyca – IKN CDO 2021

Slide
Free
30/06/2021

Ripartire dai dati ponendo le integrazioni al centro della propria strategia – IKN CDO 2021

Blog
Free
11/05/2022

L’esigenza di governo nella gestione dei dati

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