Artificial intelligence (AI) holds the potential for cost savings, a competitive edge, and a place in the future of business for organizations that can grasp it. However, even if the rate of AI adoption increases, the degree of investment is frequently out of step with financial rewards. Only 26% of AI initiatives are widely implemented within a firm. As a result, many businesses invest a lot of effort in AI deployments without seeing any real results.
Meanwhile, technical teams and engineering and IT leaders are under increasing pressure to use data for commercial growth in a world where every business must operate like a digital company to remain competitive. Businesses are eager to optimize productivity and maximize ROI from data that is expensive to retain, especially as spending on cloud storage rises. Time is not something they can afford.
To satisfy this requirement for quick outcomes, data architecture for mapping cannot continue for months on end with no clear end in sight. Focusing on routine data cleaning or Business Intelligence (BI) reporting is also backward.
Instead, tech pioneers must create data architecture with AI at the center of their goals. If you do something else, they’ll have to retrofit it later. Data design in modern enterprises should be geared toward a specific goal, including AI applications that benefit users. Even if you aren’t (now) ready for AI, this is crucial to positioning your company for future success.
Beginning from Nothing? Start by using best practices.
Data architecture calls for expertise. There are many tools available, and how you combine them depends on your business and what you want to accomplish. Always begin with a literature review to see what previous similar businesses have found to be successful, followed by a thorough investigation of the tools you’re evaluating and their potential applications.
In addition to a wealth of literature on effective data practices, Microsoft maintains a good resource for data models. A more strategic, business-oriented approach to data architecture can be developed with the aid of some available books.
A Successful Data Architecture Must Follow These Principles
You may create a data architecture that can support AI applications that provide ROI by adhering to a few key criteria. Consider the following as compass points you can use to gauge your progress when creating, structuring, and to organize data:
The golden rule is to always focus on the business objective you are trying to achieve while you construct your data architecture. I advise looking at your company’s short-term objectives and adjusting your data approach accordingly. Find ways to leverage data to support your business strategy, such as if your goal is to generate $30M in revenue by year’s end. Breaking the bigger goal into smaller goals and working toward them will make it less intimidating.
The result must always be fluid enough to respond to shifting company needs, even though having a clear target is essential. You must build with the possibility that small initiatives will expand to include multiple channels in mind. Fixed modeling and regulations will only produce additional labor in the long run. Any architecture you create should be able to accommodate more data as it becomes available and use that data to further the most recent objectives of your business.
Additionally, I advise automating as much as you can. This will enable you to swiftly and repeatedly positively impact your business over time with your data strategy. For instance, automate reporting delivery from the beginning if you know you must do it each month. In this manner, the first month will be the only time you devote to it. The effect will then be constantly effective and favorable.
It’s crucial to understand how to determine whether your data architecture is operating efficiently to stay on the right road. Data architecture is effective when it can assist AI and provide useful, pertinent data to every individual within the company. Your data strategy will be fitter for purpose and future-proof if you adhere to these boundaries.
The Future of Data Architecture: New Developments to Be Aware Of
While these guiding principles are a fantastic beginning for technical leaders and teams, it’s also crucial to avoid becoming accustomed to a particular method of operation. Otherwise, companies risk passing on chances that could result in even higher value. Tech leaders need to be up to date on the latest innovations that can improve their work and produce better results for their companies:
We are already witnessing advances that reduce the cost of processing. This is crucial because many cutting-edge technologies currently under development require so much computing power that they are merely theoretical. One such example is neural networks. We will have access to increasingly sophisticated problem-solving methods as the requisite degree of computer power becomes more attainable.
Looking further into the future, it seems safe to say that data lakes will surpass all other investments in the AI and data stack for all businesses. Organizations will benefit from using data lakes to understand better forecasts and how to implement them.
Businesses must keep up with technological advancements if they don’t want to fall behind and be left behind. That requires tech executives to stay in touch with their teams and give them a chance to provide fresh ideas. Making time for experimentation, learning, and (in the end) innovation is crucial, even as a company’s data architecture and AI applications become more solid.
