Companies continue to struggle with the promise of data and effectively utilizing it throughout an organization. In 2019, organizations must identify if staff have the right tools, talent, ability and resources to effectively deploy data within their organization. Data governance will remain paramount as organizations must ensure they are providing safe, governed, timely, accurate, fast and valuable data that provides the right insights. Companies that do not take advantage of data, deriving true business insights, will be left behind in our ever-changing and fast-moving digital economy. Companies that leverage data to get closer to customers will gain the upper hand, becoming models of success other organizations will seek to emulate.
Most organizations today know that data can help them run their business more effectively while increasing their connection with customers. Nonetheless, while most organizations understand the great promise and inherent benefits of data, they also underestimate the immense effort to fully deploy data-solutions and to become a data-driven company. There are so many different data “stacks” that have different benefits and failings, and something few vendors will tell you is how do you know which one is right for your organization? There are different approaches on how to bring all your data from silos, mixed-legacy-systems, federated locations, into a coherent platform that serves all your business needs and users.
In a recent study, Computer Economics found that AI is the last priority for enterprise investment spending. How can that be when AI, data and analytics are some of the hottest trends in technology today? Most organizations do not have their data platforms, stacks, infrastructure, policies, and institutional knowledge in place to start viable AI products and services. In order to truly reap the benefits of AI investments, organizations need to first invest in foundational aspects of their data ecosystem. This includes significant enhancements to their data crunching power by leveraging existing and newly acquired data wranglers who are able to normalize, cleanse, and transform their date into a usable asset. DataOps must then put data into play in zero-latency, highly-secure, compliant and scalable solutions, services and products. Unfortunately, many companies rush into AI before all the requisite data skills, knowledge and capabilities are in place. As a result, most companies have the desire, but lack in ability and capability to see their aspirations come to life.
Getting AI into play is only one example of data being pivotal and yet difficult for organizations to fully deploy. Automating supply chains, pricing optimization, operational efficiencies, customer insights and competitive intelligence are other enterprise functions that data can truly help with but require highly functioning data science teams. All of this points to the strategic importance of acquiring and continually upskilling the data talent organizations need to implement the right AI initiatives that will have a positive impact on the business. The challenge is not just data complexity. In order to adequately address these challenges, organization need to also invest in building capable teams to exploit the current state of data technologies.
