Successful AI requires the right data architecture

For companies that hope to master artificial intelligence, artificial intelligence (AI) is expected to save costs, gain competitive advantages, and gain a firm foothold in the future business battlefield. But while the rate of AI adoption continues to rise, the level of investment is often not proportional to the returns. There are many keys to success in artificial intelligence, including the right data architecture.

#Currently, only 26% of AI initiatives are widely put into production by organizations. Unfortunately, this means that many companies spend a lot of time on AI deployment without getting actual ROI.

All companies must behave like a technology company

At the same time, in a world where every company must behave like a technology company to stay ahead, technology teams, engineering and There is increasing pressure on IT leaders to leverage data for business growth. Especially as cloud storage spending increases, enterprises want to improve efficiency and maximize the ROI of storing more expensive data. But unfortunately, they didn't have enough time.

In order to meet the demand for fast results, mapping data architecture can no longer continue without a clear goal. Technology leaders must build data architecture with AI as the primary goal.

If they don't, they'll find themselves reworking it later to fix it. In today's business, data architecture should move toward a clear outcome—and that outcome should include AI applications with clear benefits to end users. This is key to setting your business up for future success.

Three Essential Elements of a Successful Data Architecture

Several core principles will help you design a data architecture that can support AI applications that deliver ROI. As you build, format, and organize your data, consider the following points as a guide to check in with yourself:

Work toward a goal

When building and developing your data architecture, Always focusing on business results is the fundamental rule. It’s especially recommended to review the company’s near-term goals and adjust your data strategy accordingly.

For example, if your business strategy is to achieve $30 million in revenue by the end of the year, figure out how to leverage data to drive that goal. Break down more important goals into smaller goals and then work toward those goals.

Design to create value quickly

While setting clear goals is key, the final solution must always be agile enough to adapt to changing business needs. For example, a small-scale project may grow into a multi-channel project, and you need to take this into account when building. Fixed modeling and fixed rules just create more work.

Any architecture designed should be able to accommodate more of the data available and leverage it to achieve the company's latest goals. Automate as much as possible. This will quickly and iteratively leverage data strategies to create valuable business impact.

For example, if you know you need to submit monthly reports, automate the process from the beginning. This way, you will only spend some time on this process in the first month. The resulting impact will continue to be effective and positive.

Know How to Test Success

To stay on the right track, you must know whether your data architecture is performing effectively. Data architecture works when it can both support AI operations and deliver usable, relevant data to every employee in the business. Keeping an eye on these will help ensure your data strategy is fit for purpose and into the future.

As technology continues to evolve, businesses must keep up or be left behind. This means tech leaders staying connected to their teams and allowing them to bring new innovations to the boardroom for discussion.

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