POWER TO THE PEOPLE
A growing scarcity of specialized skills, the need to activate data as close to the business as possible – plus powerful AI and automation tools – are all driving the unstoppable self- service data revolution
Time to fight the central power! Within a true Technology Business, everyone is part data scientist, part data engineer. Activation of data happens best in the closest proximity to the business, at the very edges of central IT and data departments. But the right skills are becoming ever rarer, and secure, high-quality access to the right data is just as difficult to find. AI and automation bring easy-to-use, self-service tools that provide the power of activating data to more people. It offloads the pressure on central delivery, deals with scarcity, and democratizes access and use of data. Something to push through the barricades for.
Mukesh Jain Expert in Residence
- Within a Technology Business, data needs to be accessed and used – activated – near or right within the business; a Capgemini Research Institute publication shows that true “data masters” put a strong focus on data democratization.
- Data democratization requires powerful self-service tools that decrease dependency on central, scarce skills and technology, although they will just as well increase central productivity.
- Self-service tools increasingly offer natural language and other “low-” or “no-code” automated and augmented ways to access data and turn it into intelligence, analytics, and even AI – making the accessing of data a much more inclusive activity.
- These tools can only work on an industrialized, highly automated, AI-augmented platform to find and access data – from an accessible marketplace front end, all the way up to secure, enterprise-scale, factory-style data delivery.
- Individuals can also become active participants in producing and “marketing” their data for others – inside and outside the organization – both for enterprise performance objectives and for the greater societal good.
- A European bank standardized and automated their client’s asset allocation insights on Microsoft PowerBI, making them available as self-service to both investment advisors and their clients. This created higher engagement and client satisfaction.
- A manufacturing company empowered its business users with self-service procurement insights, demand sensing, and supplier risk assessment solutions. This allowed business users to drive their inventory management more successfully.
- A bank’s marketing department identified a surprisingly interesting wealth management segment using a plain “AutoML” studio, with other business users building algorithmic models that reduced loan defaults in microfinance by 5%.
- The Damp Busters project provides Bristol citizens with sensors that gather temperature and humidity data, to understand damp conditions. Through citizen-generated data, more inhabitants are actively involved in helping to solve their city’s challenges.
- More cost-effective, faster production of high-quality BI, analytics, and AI results, both near or within the business and from a central delivery function.
- Better and faster access for the business to more relevant data from various internal and external sources increases the delivered value from data.
- Speedier availability of new insights to the business, improving responsiveness and adaptability.
- Increasing cultural and practical awareness on the business side of activating data into insights, algorithms, and AI for their business objectives.
- Addressing the rapidly growing scarcity of specialized resources in data engineering, data science, and data visualization.
- Freeing up time for specialized data scientists and data engineers to work on the highest priority models and business outcomes and breakthrough innovations.
- Data marketplaces: AWS, Snowflake, DAWEX, 890 by Capgemini
- Self-service BI and analytics: AWS QuickSight, Tableau, Microsoft Power BI, Qlik, SAS Visual Analytics, Dataiku, Saagie, Google, TIBCO, 890 by Capgemini
- AutoML: DataRobot, Google, H2O.ai, Microsoft, AutoKeras, Databricks, Feedzai, Kortical, Oracle, TransmogrifAI, IBM, AWS
- MLOps: Dataiku, Amazon Sagemaker, Azure Synapse, 890 by Capgemini