Where Should Organizations Start Data Strategy, AI Strategy, or Concurrent Data and AI Strategies
Data Strategy is by all means at the heart of digital strategy, while AI Strategy is like one vein carrying blood (data) from that heart. So, an AI strategy without a foundational data strategy can not realize its return on investment and it will be more of a hindrance than help in today’s digital battles, as it is going to inherit the common challenges of today’s siloed and heterogeneous platforms which results in poor internal and external integration capabilities, lack of data consistency, validity, completeness, timeliness and integrability, weakness of decision-making capabilities and henceforth fragile operational capabilities. So, the resultant AI product will be no better off.
Therefore, Organizations should start their digital mission by embarking on the data strategy by establishing and developing the potentially governed, managed, and operable foundational data platform with an elastic and open architecture (Lambda Architecture). The open and elastic architecture of such a data platform will realize its potential value from the following perspectives :
Integeration : The data platform is going to be the single source of truth for all internal and external integrations adhering to governance , security and compliance schemes. The norm of integration will be reliant on a unified data model (Canonical Data Model) and catalog (Enterprise Data Catalog) avoiding the technical complexities of APIs and ESBs maintenance and operations.
Operation : The data platform can be a trigger for operational excellence initiatives that employs the Robotic Process Automation (RPA) , which guarantees the organization operational excellence.
Decision-Making: Decisions will be timely and accurate, as it will be based on accurate and consistent information not just a veneer of quality hiding lots of inconsistencies and anomalies.
Advanced Analytics: Having built the data platform , organizations can embark on advanced analytics products like ML , AI and RPA on accurate , consistent and panoramic view of the enterprise exemplified in their data platform.
Cost Efficiency: since the data platform will render the organization a data-driven , so IT strategy will be driven by the fit-for-purpose technologies and platforms which will result in cost effectiveness enterprise wide.
Strategic Alignment : having a data platform in place will benefit the organization in a manner that strategy and business operations will be aligned and concurrent through the established KPIs and performance management schemes , as it will minimize the previously reported gaps between them.
Parallel with the Data platform development , Organizations can concurrently apply some AI use-cases to realize the ROI of foundational data platform development.
In conclusion , Organizations should embark on the initiative of establishing the data platform where all the governance , management and operations can be maintained. Also , organizations can implement some core AI use-cases during the establishment of the data platform to guarantee the added-value of the data platform , which can surely scale up to more and more AI use-cases. So , starting with an AI strategy overriding the overarching data strategy is not recommended at all if organizations would like to render themselves a data-driven or even an AI-driven organizations.