Data engineering Data science Insights
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Data challenges across companies

After more than ten years of research and working on data projects with clients (who made their journey to the new digital era) our experts noticed some recurrent pain points and challenges in the areas of data access, usage, and governance. Here below you can find some of the challenges that we encounter trough pour experiences:

How do I install an effective data governance strategy or a data-centric culture in my company?

To instore or build a data culture in a department and/or organization you have to let the data talk itself and trust the steering of the analytical statistics it generates. Being successful as a data-driven entity requires open access to data to use and process it. One of the most important aspects of creating a data-centric culture is data lineage.

To put data at the centre of a business, security models must be able to track data throughout the organisation. But how do you track the lineage of your data? You also have to make sure that your data usage is compliant and follows the GDPR rules.

How do I decrease my time to market of my POCs?

Support all the data usages, including services, BI reporting, and data analytics.  If you want to create value from your data you have to :

    • feed models and applications developed upon the data architecture.
    • integrate your available data into the service organisation, while guaranteeing the consistency of the data all along the process
    • ensure the real-time capability of your data processing.

How do I increase the efficiency in the production of your data science models?

Standardise and industrialise your data science models. Once all the data science work is done, another step needs to be taken to reap the benefits of data-driven decision-making. The models must be integrated with the IT systems to consume the new data and return insights to decision-makers. To complete your industrialization of your data science model you will need to answer the following questions:

    • How do you plan to automate the data ingestion?
    • How will you retrain the models?
    • How do you share the model’s insights with the whole organization?

Conclusion

These challenges have been known for years, across industries, use cases, and departments. We have observed that time-to-market, cost and feasibility of your data science projects all depend on your processes, people… and tools.

To help solve those challenges in a fast and easy way, Sabri Skhiri designed a Data Architecture Vision (DAV).  It led to the creation of Digazu, a spinoff that emerged from EURA NOVA’s solution incubator, and its data platforms:

digazu-data-flow