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For the second year in a row, Databricks recently partnered with MIT Technology Review Insights to survey 600 CIOs, CTOs, and CDOs from large enterprises. The key result: CxOs and boards recognize that their organizations’ ability to generate actionable insights from data, often in real-time, is of the highest strategic importance. All respondents agreed that companies must view AI adoption as mission critical in order to succeed. But without effective data strategies, businesses miss massive opportunities to better understand their customers, offer high-value products, and streamline operations.
With such a significant link between effective AI strategies and strong data, not using the right AI tools or neglecting to leverage AI in the most effective ways can foil even the best-laid data plans. Here are four resolutions to make your data strategy pay off this year.
1. Reassess your data architecture
Most executives (72 percent) say that data, both fragmented and with poor quality, is likely to be the biggest issue when aspiring to achieve AI goals. The only way to better prepare for these challenges is to invest in a flexible data and computing architecture, like a lakehouse, that embraces open standards and can scale to meet the changing needs of the business. By creating a lakehouse, a company gives every employee the ability to access and employ data and artificial intelligence to make better business decisions. Many organizations that implement a lakehouse as their key data strategy are seeing lightning-speed data insights with horizontally scalable data-engineering pipelines. Walgreens specifically shared that a lakehouse enabled smarter algorithms and generated new types of reporting that help people understand the supply chain and store labor and productivity, patient vaccine scheduling, and prescription pickup processes.
2. Build your tech stack in the multi-cloud
Many data and technology leaders believe it’s not enough to think about the cloud in the singular sense — instead, they think about building a multi-cloud environment. As the adoption of cloud-based technology grows, many look for solutions that can move across major clouds (such as those from AWS, Azure, and Google Cloud). In our survey, 78 percent of executives agreed that a multi-cloud approach ensures the most flexible foundation possible for AI development. It offers organizations easy integrations when bringing on new solutions or businesses that use other cloud providers, flexibility to run workloads anywhere, and the assurance that they can comply with regulations down the road. Organizations that adopt a multi-cloud approach can also create new revenue opportunities and enhance customer experiences.
“Without effective data strategies, businesses miss massive opportunities to better understand their customers, offer high-value products, and streamline operations.”
3. Invest in low/no code
Low- and no-code approaches are opening new pathways to innovation and lowering the barrier to entry for people who want to get quick insights from their data. Given how competitive it is to find the right tech talent in today’s hiring market, low- and no-code tools are key to relieving some of the pressure on data teams, empowering less technical teams to build models – even with just a basic understanding of machine learning. No-code platforms make it possible to leverage AI without hiring expensive developers and data scientists, which means smaller businesses can more easily harness its power. Columbia Sportwear embraced this resolution and has seen more business units using the platform in a self-service manner that was not possible before. This has sped up the time for insights for all groups.
4. Embrace opensource AI and open standards
Open-source data lakehouses are quickly becoming the standard for how the most innovative companies handle their data and AI. It prevents teams from building tricky solutions in-house from scratch, which eats up resources. In our survey, 50 percent of respondents said that open-source standards and open-data formats were at the top of their dream tech stack list. Open source usually comes at little to no cost and, more importantly, it’s tried and true — it’s a community effort, and solutions have been adopted and vetted by many, which equates to fewer headaches for your IT team down the road. On top of this, the commitment to open data standards fuels the open-source community, which helps create a large talent pool of data experts who are better equipped to move use cases to production. It’s easy to get overwhelmed with your new year’s resolutions – especially the hard ones. Organizations must continue to stay inspired when they think about their data strategy. Our survey showed that a smart data strategy will ultimately provide better data value.
Stay the course to be prepared for a happy new year!
REASSESS YOUR DATA ARCHITECTURE
Invest in a flexible data and computing architecture, like a lakehouse.
BUILD YOUR TECH STACK IN THE MULTI-CLOUD
A multi-cloud approach ensures the most flexible possible foundation for AI development.
INVEST IN LOW/NO CODE
Leverage AI without hiring expensive developers and data scientists.
EMBRACE OPEN – SOURCE AI AND OPEN STANDARDS
Take advantage of an open-source community and talent pool.
Capgemini’s Innovation publication, Data-powered Innovation Review | Wave 5 features 19 such articles crafted by leading Capgemini and partner experts, about looking beyond the usual surroundings and be inspired by new ways to elevate data & AI. Explore the articles on serendipity, data like poker, circular economy, or data mesh. In addition, several articles are in collaboration with key technology partners such AWS, Denodo, Databricks and Dataiku.
You can also find all previous editions here.
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