Successful AI implementations rarely hinge on the unique innovation of a specific algorithm or data science technique. Those are important factors, but even more foundational to successful AI enablement are the core data operations and enabling platforms. These act as the fuel and chassis of the AI machine that a business must build and evolve for continued competitive advantage.
Here are the five foundational elements to be addressed to enable a successful transformation to an AI-empowered business:
1. Define an integration strategy for embedding AI and analytic insights into business operations
Successful digital transformations focus on evolving and optimizing business operations through the better use of data assets combined with modern technologies such as machine learning, AI, and robotics. These paradigm shifts result in the creation of new operating patterns rather than simply more efficient legacy operations. In this way, digital transformation represents the enterprise operations in the way the business wants to be run, rather than the way it has been running due to technical and operational limitations and barriers constraining it.
To go beyond siloed or single-use insights and fully benefit from AI and analytics, it must first be decided how the business desires/needs to function in the future. Determining your business transformation priorities then evaluating the advanced technology and data science options for addressing them is a key step towards maturing and evolving to a data-driven enterprise. This understanding will identify the type of AI and analytics that will be the most beneficial for your business and the technology required to accomplish it. Additional thoughts on overall data strategy can be found in the white paper “Defining a data strategy: An essential component of your digital transformation journey.”
2. Establish a holistic data and analytics platform
Selecting and configuring an integrated set of technologies to support data management and applied analytics is a complex challenge. Fortunately, solutions to such technical integration have matured in recent years into pre-built core platform components and best practices that can be accelerated and augmented further through value-added third party software and partner services.
Cloud-based modular platform environments bring together technical flexibility and financial elasticity with an ever-maturing technical set of capabilities, including interoperability across hybrid environments that include legacy on-premises deployments and geographical federation. In addition to open source components, such platforms include the option to integrate select native modules and commercial technology components for broader flexibility and a customizable architecture that can be deployed as prebuilt services for simpler adoption and integration.
The tools to support and enable AI integration into business operations are beginning to leverage the same capabilities they enable. For example, data pipeline tools are beginning to use machine learning (ML), metadata tools are using AI and ML to identify content and auto-generate the metadata on the fly, and user interfaces are embedding chatbot and digital assistant AI technology to guide end-users through the complexities of data science for accelerated insights. By adopting toolsets and platforms that have embedded AI and analytics in their core, the use and integration of AI into business operations will be more natural and accelerated across the enterprise community.
3. Know your data
Fully understanding the data your enterprise has access to may seem like a fundamental need when supporting operational reporting and analytics within the enterprise. Many organizations, however, stop with simple source systems listings and maybe some high-level business definitions and schemas.
Truly knowing your data includes a lineage-based view of where the data comes from and what business process it represents, what operations are performed on it prior to your access, what transformations are performed thereafter, the associated level of quality and, of course, the core “Vs” of big data; volume, velocity, variety, veracity and value.
Building an easily searchable, enterprise-wide data catalog of information is one of the first steps towards empowering the enterprise with data. Exposing the catalog to a crowdsourced editing model ensures richer content and wider adoption of such information across the enterprise.
4. Control and govern your data
Understanding the types of controls and governance your data needs is a natural extension of knowing your data. By reviewing the types of data and their business content with associated metadata, enterprises can align and define proper governance and compliance policies related to internal policies and to external standards such as HIPAA for healthcare, PCI DSS for secure payments, and PII and GDPR for data privacy.
It is also important that source data retains its original state integrity without over processing or over-filtering it. Aligning to the data pipeline workflow principles of “ingest, refine, consume” allows the same data to be leveraged efficiently for different uses with different policies and operational needs while ensuring security. Such controls can also be extended to support and define quality standards required for using the available data and to trigger any necessary control processes to correct or adjust for deviations in such standards.
You can safeguard proper policy compliance, improve ease of use and increase trust and adoption by the end user community by ensuring that governance controls are built into your data management operations from the start.
5. Simplify access to your data
To further expand the adoption of AI and analytics, it is important to simplify and automate data workflows and the use of analytical tools. Reducing manual process overhead can significantly improve time to market and quality of results. Providing clear and flexible governance allows enterprises to control such access without it becoming a barrier for use.
Self-service leads to rapid user community adoption and better integration of data and insights into business operations. By reducing the dependency on IT resources for complex data integration and preparation tools, average business users can interact with the data through simple common interfaces and receive results in simple and easily consumable formats.
Once these foundational elements are in place, organizations can take full advantage of the unique value proposition offered by advanced analytics and AI. And they can do so with the confidence that the resulting solutions are enterprise-grade in their scalability, security, quality and usability. It is this kind of confidence that leads to business user adoption and, in turn, successful digital transformation.