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Before relying on analytics for all or part of your strategic decision-making, it’s critical to implement suitable processes to ensure that data flows smoothly through all business departments while preserving its quality, accessibility, usability, and security using these tips. For data assets to be fully profitable for an organization, you must know how to select, collect, store, and use them effectively, especially as data is both abundant and easily lost.
You can start to do this by taking inventory of all the data present in the company, identifying its various sources (your management systems, digital platforms, social networks, marketing and advertising campaigns, etc.) and then defining the points of friction where there is a loss of value due to poor Data Quality. Keep in mind the “5 Vs”:
With the growth in the use of connected objects, the development of geolocation and the rise of analytics in digital marketing, the volume of data to be stored and processed has exploded in recent years. Determine the quantity of information held in your databases to guide your Data Management method, and do not hesitate to embrace minimization to be more efficient.
Data can be complex and diverse, as well as structured or unstructured (voice, transactional, digital analytics, textual, images, etc.). It can also come from a wide range of information systems. Capture it in different places, centralize it, and cross-check it to map all your data in an exhaustive way.
Because we live in an age of immediacy, personalization, and predictive marketing, we need to move increasingly quickly and proactively to meet customer needs. Choose high-performance software with powerful computing capabilities that is flexible and incorporates state-of-the-art machine learning. Audit your infrastructure to choose the most efficient tools, in line with your needs, and build a sound technical base.
This is one of the major objectives in data processing. The reliability of data collected and processed can be threatened in many ways: declarative errors (forms), the diversity of collection points, the actions of bots, malicious acts and other bugs, human errors, and more. There can also be numerous biases in the analysis. This is why it’s so important to carry out a diagnosis of the quality and accuracy of all your data.
The data you use must perfectly align with your organization’s business and marketing goals and create value for both the brand and your customers. In an environment with an overabundance of information, it is about being able to unify all your data, and only the data that is useful to you, and act on it swiftly to generate profit or knowledge.