Master Your ML/AI Success with Enterprise Data Management
Advanced analytics, in particular analytics that takes advantage of Machine Learning and Artificial Intelligence (ML/AI), have become established mainstream business initiatives. Several reports and surveys published by confirm the rapid growth in ML and AI projects at enterprises of all sizes and in all industries. A Gartner global survey of CIOs found that AI implementations grew by 270% in the prior four-year period. According to Forbes , 93% of executives expect to get some value from AI investments. Algorithmia’s “State of 2020 Machine Learning” survey found that budgets for ML initiatives are growing by 25% annually, with Banking, Manufacturing and IT industries having the largest growth.
Businesses are leveraging ML and AI for many different capabilities – at their core, these technologies allow businesses to uncover deeper insights, make better business predictions, and take actions on these predictions. Some of the business use cases for ML/AI that we have most commonly seen in our work with clients are:
generating customer insights
internal process automation to reduce costs
Across industries, ML and AI not only provide competitive advantages but have become must-have capabilities that are necessary to remain viable and competitive. Due to the rapid decline in the cost of ML/AI platforms and technologies, the ROI for ML/AI initiatives has reached a level making it more actionable to an increasing number of businesses.
Challenges
Despite this tremendous growth, many businesses have faced significant challenges in fulfilling the high expectations of ML/AI and actually realizing business value. In the Forbes report, 65% of executives reported that they are not yet seeing the expected value from their AI investments. In a TransUnion survey of finance, risk and marketing executives, 76% indicated that one of their biggest challenges was the data cleansing and prep work required to derive the expected value. Based on our experience with multiple ML engagements across a range of industries, one of the most significant challenges on ML projects is the poor quality of the data. In fact, 80% of time on ML/AI projects is spent on data understanding and preparation – cleaning poor quality data, determining how to fill data gaps, blending data from different sources, standardizing data definitions across various data sets, and other data prep activities.
This illustrates the lack of maturity in Enterprise Data Management which is quite common in most organizations. Enterprise Data Management (EDM) is the discipline which strives to continually increase the overall data maturity of an organization. This includes capabilities of data governance, master data management (MDM), data quality, metadata management, data engineering, data security and data risk management. EDM maturity is important not only for general reporting needs, but particularly for ML/AI needs as well.
In many ML/AI projects, poor input data leads to less insightful ML/AI models, which result in limited business value! Some of the key impacts of this are highlighted below.
