With the advent of generative AI and large language models, the importance of data  is at its highest point. There are unlimited use cases that can be leveraged using AI and generative AI but data remains at the core of everything and to realize the value of generative AI you need to have AI ready data.

To enable AI ready data and bringing the culture of  innovation; each and every organization needs to become a data-driven enterprise. And to achieve this, you need to focus on 4 main areas –

·         Data Governance

·         Data Modernization

·         Compliance, Ethics and Trust

·         Data Literacy and Change Management

Now let’s deep dive into each of the focus areas to understand how these can help in achieving the goal of AI ready data.

Data Governance:-

Data governance as a practice has been there for ages and it's not a new thing, but it still has been the most ignored area. In most of the organizations data governance exist in pockets, sometimes in a Federated environment, sometimes centrally used and sometimes exist in hybrid mode. But to realize the end to end potential of data governance you need to understand the maturity of the organization and then implement the desired model. The most important aspect of data governance is identifying the right roles and responsibilities, implementing the right processes and having the right enablement through technology, supported with right tooling. The Processes, Technology and People aspect, when combined together, provide the right mix and triangulation to achieve the correct data governance.

The two main areas under data governance are Data Quality and Master Data Management; and these are much needed to set the right data foundation for any organization. And the maturity for Master Mata Management and Data Quality can be achieved through the right Target Operating Model which brings in everyone together from the executive leadership to the team that build and implement the business metadata, the technical metadata and define the KPI’s to provide single view of truth.

Data Modernization:-

Data modernization is a very new element and practice in the field of data supporting digital transformation. With all organizations looking to move to cloud, scaling the value through data, providing right data at the right time to the right person and with the advent of AI & generative AI the use cases are becoming more complex thus we need new solutions to solve the new problems. With the new age platforms like Microsoft fabric, Databricks and Snowflake, which provides end to end and one stop shop solution for the  data value chain right from the data integration coupled with data quality, data profiling, metadata management, traceability, privacy capabilities. They also make it easy to manage data where you can create bronze silver and  gold areas of data where different users and consumers of data can access information in real time and also use it for downstream AI applications.

These days platforms like Databricks and Snowflake comes with its own governance capability where they can trace the data,  track the metadata and provide catalogue of data which makes data governance easy to be implemented thus supporting readiness of data for AI.

Another great innovation which is happening in this regards is using Data as a Product supported through Datamesh Architecture. A data product is a Reusable data assets that are designed fit-for-purpose based on intended business value.

Compliance, Ethics and Privacy:-

As there are multiple regulations in practice or in pipeline for AI and data across the world like European union’s GDPR which is general data protection regulation, European Union Artificial Intelligence act, America’s CCPA which is California Consumer Privacy Act, and plethora of other regulations makes it important that you have right data management and governance in place. Embedding right security, data access and control measures is very important as cyber threats are increasing day by day. Also, when you use the data to train your AI and generative AI large language models; it is highly important to use the right data to remove bias, strengthen privacy, improve resilience and increase robustness of the models. Thus end to end data security measure implementation and compliance is the need of the hour and should not be ignored.

As the consumer, user, producer and distributor of AI applications and data; it is a moral duty of an organization and an individual to adhere to the compliance requirements and build in Responsible AI and security by design. This will also come under the umbrella of responsible business and support the goals of sustainability by providing right environment to include fair treatment of people, protect information and provide foundation for AI for good.

Data literacy and Change Management:-

The most ignored and the underrated topic is of change management and providing data literacy in an organization to bring in the culture of data-driven enterprise. No matter how sophisticated the use of AI and data is in your organization, unless you provide the right training to the right people, at the right time and bringing the culture of data literacy all big and small projects will fail eventually. It is very important to have right roles and responsibilities as well as right understanding to use data and tools and technologies as enablers within an organization to be successful; and this is only possible when individuals and teams are aware of what they can achieve through the right use of data, what does it mean for them and for the organization in terms of strategic goals and ultimately, how those goals are tying up to their data requirements!

Thus, it is highly important to have the right target operating model consisting of change champions who are tying up strategic goals of the organization with team goals supported with write data and technology knowledge and working together to improve the ways of working with the right processes, people and technology.

Change management is the first thing in an organization that needs sponsorship from the executive committee and should be pushed down from the top to the bottom consistently with the most important aspect – communication, which needs to be clear, simple and effective. This can be achieved through right  incentive program which can motivate individuals and group of people to achieve a common goal of making the organization successful.

In conclusion, I would like to say that AI ready data is not that hard to achieve provided, there is right sponsorship, methodology and discipline in place. To achieve a successful digital transformation, cloud and data modernization and data-driven culture you need to have right standards controls and policies in place. To achieve optimum value through data and AI, provide right employee and customer experience with increased financial performance and achieving your sustainability goals in parallel- YOU NEED AI READY DATA.

All views are mine and mine only.