While there is a lot of talk about the opportunities presented by better use of data, it is not always easy for an organisation to actually extract value from it. Implementing a “Data Intelligence” approach within your organisation is a major project, which entails considering many aspects of the technology. If you want to adopt a data analysis process or, ultimately, use artificial intelligence or machine learning throughout your organisation, you first need to be familiar with your data and have full control over it.
Prerequisites for a data intelligence approach
For several years, POST has been investing in data intelligence to meet its own needs and those of its customers. Based on our Data Analytics platform, we are exploring new use cases to create value through data. Above all, we have acquired real experience of the issues involved in a data-driven approach that achieves its objectives.
More and more organisations want to be able to use data analysis tools, develop algorithms or even use artificial intelligence solutions. However, all too often they have not yet acquired the level of maturity required to implement such approaches. The fact is that most organisations do not have sufficient knowledge of the data available to them, this data is not organised, and its quality is not guaranteed.
Knowing and controlling your data
These are the foundations of any data intelligence approach. Before seeking to deploy new use cases, it is essential that the organisation acquires the necessary maturity to pursue its goals. Today, building on the expertise they have acquired, POST's teams are helping organisations to achieve their objectives, enabling them to gain the maturity they need to get the most out of their data.
In this regard, data intelligence, or data science, is more of a journey than a technological solution. To extract value from your data, first and foremost you need to know it well, gain full control over it, and establish a framework and governance system to ensure that it is used effectively.
The organisation must therefore start by drawing up an inventory of the data at its disposal. This is the “data discovery” stage. In this phase, the aim is to check the quality of the data, define its potential value and start to organise it.
Implementing governance
With this in mind, the organisation will establish a governance structure, setting out the procedures for collecting, preserving and using the data. Many regulations, such as the GDPR, impose restrictions on the preservation and use of personal data. A series of measures must therefore be adopted at company level and taken up at governance level to ensure that the data collected is used in accordance with the law. This involves, for example, defining retention periods and establishing procedures to ensure that the data is deleted once the preservation period has expired.
Guaranteeing data quality is essential. So much so that a decision taken on the basis of erroneous or incomplete data can be risky or even counter-productive.
Through governance, roles can be established to ensure that data is properly managed, such as that of data owner or data steward.
Exploring the data
With proper governance and organisation of its data in place, the company can gradually explore this data, see how it can be enriched and try to identify patterns. This is what is known as “data mining”. Data analysis, for example, enables us to identify certain behaviours and anticipate specific events, with a view to making particular decisions. One often-cited example is where a company detects which customers are likely to change suppliers in order to take action and convince them to stay, through targeted special offers for instance, and to combat customer attrition.
How gaining in maturity can take you further
These concepts, which allow an organisation to gradually gain in maturity, can be implemented using tools such as the POST data analytics platform. Making this change will enable it to capture more and more data from a variety of sources, and to process, enhance and employ it in its development through a range of use cases.
Depending on the sector, a variety of uses can be envisaged: in marketing, to target campaigns more effectively; in commerce, to propose personalised offers; in healthcare, to better understand the causes of illness or improve diagnosis, etc.
Developing a genuine data culture
The use of artificial intelligence tools and solutions to enhance the value of data requires a thorough command of the information.
The challenge is to develop a company-wide data culture that is shared by all employees. This won’t happen overnight. Ultimately, the challenge is to ensure that every piece of data produced can be made usable and valuable.
This is what we contribute to, alongside our customers, by raising awareness and offering training. We develop a roadmap with them based on their goals, enabling them to acquire the maturity they need for their project and set up a governance structure specific to their business sector.
