Data governance is a major lever for improving performance and accelerating business transformation. Today, data is a major corporate asset with very specific characteristics. It is easy to exchange, and its unit value is often low and/or ephemeral. What's more, data is easy to pirate, little known to users or beneficiaries, intangible, and sometimes difficult to locate, etc.
At iQo, we support Data Strategy through a better framework for exploiting data to open up new perspectives for value creation, both internally and in relation to customers and partners.
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Data governance: managing a strategic corporate asset
Data governance can become a lever for business performance and transformation, particularly through :
- better commercial targeting
- the design of innovative products and services and the challenge of the business model
- production system power-up and agility, thanks to continuous measurement and data exchange with the ecosystem (OpenData and Data Supply Chain)
- a Data For Good approach to reinforce CSR issues
- improving customer and partner relations/information
- improved company strategic management and decision-making through data analysis
- improving the employee experience
What can we expect from good data governance?
Data governance is first and foremost an organizational framework for organizing the creation, storage and management of data to guarantee its availability, consistency, usability, integrity and security. The aim is to manage data as a "product" that meets the needs of businesses, customers and partners.
At the same time, data governance must be sufficiently agile to be able to manage current evolutions, such as Big Data, Open Data etc., but also future revolutions, notably those linked to the convergence between Big Data and Artificial Intelligence.
6 principles for organizing data governance
1. Define a data governance framework
From the moment data becomes a source of revenue and/or performance, its exploitation must be monitored and controlled. The data governance framework in charge of this monitoring must guarantee that the business objectives of the data are met, and that data quality and security are beyond reproach.
This means taking several key dimensions into account:
- their reliability in terms of quality
- their integrity in terms of preservation
- their availability at all times and in all places
- their protection against unauthorized or fraudulent use
2. Adopt an adaptive approach to data governance
Depending on the complexity of activities and the level of mastery of data or analysis practices, data governance needs to adopt a style that can range from "command" to delegation / empowerment of data users and beneficiaries.
This choice is not unique to the company. It must be defined for each scope of use (functions or processes), re-evaluated over time, and adapted if necessary in an agile logic.
3. Promoting data and data science for business performance
The company's areas of differentiation and "pain points" provide fertile ground for developing and supporting the use of data science, in particular through :
- in-depth market and customer analysis
- product design/improvement
- process performance
- the Supply chain
- project management
4. Integrate data governance into existing governance structures
As we've seen, data is increasingly becoming a key business issue. It is added to other structuring subjects as an umpteenth subject that can lead to an umpteenth layer of data governance.
A major challenge is to integrate this governance as much as possible with existing governance structures, and to encourage acculturation so as not to give the impression of "adding a layer" to a steering system that needs to be agile.
5. A collaborative approach
Data is the fruit of a process, a cross-functional approach, which can involve the company's various internal functions, as well as partners and customers.
Data governance thus carries on its shoulders a challenge: defining objectives, standards, roles and responsibilities for data. This is a common challenge:
- performance management
- to quality management
- to knowledge management
6. Anticipating a Copernican revolution through data.
- The massive opening up of data. How can we conceive of data governance when companies are signing up en masse to "Data LinkedIn", a place where "data recruiters" bring economic players together to share, market and augment their data? What impact will this openness have on the business value of data? It will probably involve a strengthening of Data Lifecycle Management practices and, more generally, Information Lifecycle Management.
- The blurring of corporate boundaries in an increasingly interactive, co-decisional ecosystem: company & partners, company & customers, etc.
- Product / Data convergence. Already present in certain sectors, it will spread and involve a transformation of "data governance" into genuine "product management".
- Data / AI convergence. This convergence heralds a potentially vertiginous upheaval of the "people - methods - machines" triptych, with automation, self-adaptation and the empowerment of processes and certain decisions leading to a fusion between processes and data.
- The convergence of technological (IT, data, AI) and business governance. This will require the construction of new standards that are more open to the outside world, and a revolution in the company's governing and operational skills.
Data governance through strategic integration
At this point, it's clear that, because of its considerable potential, but also the challenges it represents for the company, it's imperative that the use of data feed into strategic corporate thinking.
This is all the more true given that its massive use can influence a company's business model, and therefore its strategic positioning. And current and future technological developments, which generate phenomenal quantities of data, will only reinforce this trend!
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