Reaching data and AI maturity: the key to unlocking business value
While many companies across a range of industries have placed artificial intelligence (AI) and machine learning (ML) at the heart of their growth strategy, most do not feel they are in a position to successfully harness its power. The major reason for this is because many Big Data projects lack a mature approach to getting the best out of AI and ML deployments.
According to a 2021 Databricks and MIT Technology Review Insights survey, companies’ most important business objectives for their enterprise data strategy over the next two years are expanding sales and service channels ( cited by 45 percent of respondents), better operational efficiency (43 percent) and improving innovation and reducing time to market (42 percent). It’s great to have these objectives, but are businesses equipped to execute them? According to Gartner, 85 percent of big data projects fail, and according to the MIT Report only 13 percent of companies excel at implementing their data strategy with measurable results. When asking “low-achievers” (organizations having difficulties with their data strategy initiatives) what their main barriers are, the feedback highlighted limited scalability of their data management platform, difficulties in facilitating collaboration and slow processing of large data volumes. It’s clear that many organizations face challenges in scale, speed and collaboration in all areas of data exploitation.
To generate actionable insights from data, often in real-time, organizations have to take a joined-up approach that delivers the desired business results from AI and ML deployments. They need staff to be data literate, to tackle the fear of the unknown and to really streamline their processes. Here are three ways that businesses can gain data and AI maturity.
When paying attention to the foundations of strong data management and focusing on building an architecture that “democratizes” data, companies are much more likely to succeed and see measurable results. Managing enterprise data is highly complex and organizations need to remove the burden of legacy systems and a variety of tools, as well as data silos – unless they can be integrated or isolated. These combined issues impact organizations’ data platforms and the ML models that they support by reducing the speed and scale required to deliver the expected business results. The right foundational architecture should reduce data duplication, increase ease of access to relevant data, enable the processing of large amounts of data to be processed at high speeds, and improve overall data quality.
UPS’s delivery optimization project aims to shorten each delivery driver’s route by one mile per day with an expected annual gain of over £36 million. This is a good example of how companies can use data to break and rebuild the system. It relies on several maturity criteria: a modern and scalable proven infrastructure, the continued support at every level from management, despite failures and delays. Furthermore, it relies on the desire to obtain rich, instantaneous and accurate data via GPS on transactions, points of locations, vehicles and even drivers where necessary.
As part of enterprise transformation, data and AI maturity is also about the democratization of analytics, ML and AI capabilities to help business users make informed decisions. This requires a strong data culture through collaboration and cutting-edge technology to enable the use of data to improve decision-making and its respective impact.
