Data Transformation: Paving the Way for AI Success in Your Organization
CRAIG PILKENTON
Vice President, Technical Delivery
Overview
Every organization has many customer-facing and business management sites, apps, and technology systems that are continuously outputting increasing amounts of data as they support business groups. Companies across many industries are exploring ways to leverage Artificial Intelligence (AI) to enhance their operations, increase productivity, and gain a competitive edge.
Many of these enterprises have focused multiple investments and funding over the years on digital transformations to improve their capabilities. Despite those efforts, organizations still may struggle to use that data for improving customer service, reducing costs, and optimizing the core processes that provide competitive advantages. In recent years, some of these transformations have been focused on AI initiatives, but many, if not all, won't succeed until a company’s data systems are properly prepared and mature.
What does this mean?
What does it mean to ready an organization's data for AI? As all tech leaders know, data is the fuel that powers AI. It is essential to ensure that data sets are accurate, complete, and reliable. Incomplete or insufficient data quality can lead to inaccurate AI models, which can have serious implications for the business.
While it may sound daunting on where to start, in most cases an initiative like this is no different than other data initiatives such as warehousing setups, data science workloads, or even unified reporting. When data is separated in unshared repositories, with insufficient documentation on what is stored within rather than organized to be used as fuel for larger initiatives, it can lead to failed initiatives. To help ensure data integrity, organizations should establish a data governance framework that includes data quality checks, data lineage tracking, and data access controls. Then, the most important piece is investing in data cleansing and normalization tools to ensure that the business data is consistent and error-free.
Why is this needed?
Without a consistent approach to developing, applying, and evolving organizational data structures, AI initiatives will only develop in a piecemeal and fragmented manner. They will always lack the underpinning of complete data that would allow the solution to be smart enough to make an impact for the organization. If moving forward while lacking these important steps being implemented, any AI results could be incomplete, or worse, give out erroneous answers.
Data maturity steps
To effectively utilize an organization's information for AI workloads, its data maturity journey must increase, or in some cases, begin. Data maturity is a measurement that validates the level at which a company makes the most out of their data and processes surrounding its capture and usage. To achieve a high level of data maturity in an organization, that data must be well-managed throughout the business and fully integrated into all decision-making and activities in a way that is both responsible and maximizes security.
To measure oneself, an organization should utilize a data maturity model or framework to ensure that they are making the most out of the data that they have. There are many different data maturity models available, and enterprises may change what they use as their needs evolve. In this case for AI workloads, Hylaine finds the Gartner Data Maturity Model first presented in 2008 helps define generic levels a company can use to obtain the best data maturity.
This model explains the levels clearly as well as what is needed to move to the next stage, which is especially useful when explaining the data policy of a company to employees for gathering buy-in. Each level builds upon the previous one and creates a minimum 'table stakes' for achieving success at a high level.
Looking Forward
Every enterprise has their important information, but all are at different data maturity levels which can affect the efficacy of any AI workloads tested. The higher an organization’s data maturity, the more trustworthy its AI capabilities and offerings will be, all by focusing on meaningful, mature data practices, and using them as a tool for growth.
In the journey towards using AI, nurturing data maturity is crucial. By prioritizing data privacy and security, we safeguard valuable information and earn the trust of our customers and stakeholders. Addressing data bias is equally vital, as it ensures fair and inclusive AI outcomes that resonate with accuracy and integrity. Through effective data governance, we lay the groundwork for informed decision-making, providing a solid framework for harnessing the power of data across the organization.
By fostering data maturity, we instill confidence in our AI endeavors. Armed with reliable data and unbiased AI models, we empower our teams to make data-driven decisions that unlock new opportunities, help navigate complexities, and drive sustainable growth. In embracing the potential of data, we move closer to AI excellence, where informed decisions guide us towards a prosperous future.
References
https://cloud.google.com/blog/transform/data-champions-digital-maturity-five-keys-bcg-research
https://hbr.org/2020/04/is-your-data-infrastructure-ready-for-ai
https://www.iandisoft.com/viewpoint/data-governance-ai-transformation-getting-your-data-ai-ready
