Data Quality

Data Governance

Certifying data quality

Industries:

Finance & Insurance - Retail & FMCG - Transportation - Energy & Utility - Life Science - Industrial

Solutions:

Data Governance

Technologies:

Blindata

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

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...

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...