How is your data management strategy crafted to boost your analytics and AI goals? Any key successes or hurdles?
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Data & AI Practice Lead2 months ago
Our data management strategy has been carefully defined and we are now in the execution phase. The strategy focuses on ensuring the availability of high-quality data, which is crucial for advancing our analytics and AI initiatives. Through robust data governance frameworks and proper data cataloging and lineage, we maintain the accuracy and consistency of our data, enabling well-informed decision-making.A key component of our strategy is maintaining data privacy and security. We have established strong measures and continuously update our protocols to protect sensitive information, ensuring compliance with data privacy regulations and safeguarding our data from potential threats.
Scalability is another essential aspect of our strategy. We have built a flexible data infrastructure that can accommodate growing data needs and enhance our processing capabilities, allowing us to manage large volumes of data efficiently and support complex analytics and AI models.
We are also focused on increasing efficiency through automation. By automating repetitive data processes and leveraging machine learning for predictive analytics, we reduce manual errors and accelerate data processing, enabling us to derive insights more quickly and accurately.
Despite our progress, we recognise the challenges in clearly defining and rolling out roles and responsibilities across the data management team. We are addressing this by developing a detailed organisational structure and providing comprehensive training programs. Another challenge is accurately estimating the cost of data management initiatives and demonstrating a clear return on investment (ROI). To overcome this, we are conducting thorough cost-benefit analyses and using pilot projects to validate our assumptions and demonstrate value.
1. Organization Goal
2. How we should be managing the data : Acquire , Store , Clean, Enrich , Actionize and integrate into organization processes and System.
3. How the planned roadmap of Data strategy impacts - People , Processes and System of the organization.
4. Areas of Influence : Increasing Visibility around organization Key metrics, Data driven decision making , Automation , Orchestration and Simplification of processes.
With the above Data strategy pillars being defined to boost Analytics & AI goal remember the following three important aspect:
1. The work product of your team is to be used by someone else inside the organization to move their Vision. Hence are you aligned with your "Analytics & AI" Product goal with the user goal? This alignment is must & your top priority .
2. "Analytics & AI" Product in an enterprise always includes change in existing ecosystem , hence what is your "Change Management" Strategy .
3. Co-creation. Dont work in Silos. Identify from your user group who will be working during the Journey, that person is your "Functional Product Manager".
4. Continuous Communication : Effective & Continuous communication is a must for Data Leader.
If the data leader is taking care of above four points: Alignment , Change Management , Co-creation & Communication , it will lead success if not taken care of it will lead to hurdles as well.