For companies that can master it, artificial intelligence (AI) promises cost savings, competitive advantage and a foothold in future business.But while the adoption rate of AI continue to rise, investment levels are often out of sync with monetary returns. To be successful in AI, you need the right data architecture. This article tells you how.
Currently, only 26% of AI initiatives Go into widespread production with the organization. Unfortunately, this means that many companies spend a lot of time on AI deployments without seeing any tangible return on investment.
All businesses should operate like tech businesses
At the same time, in a world where every business must operate like a technology company to stay ahead, technology teams, engineering and IT leaders are under increasing pressure to harness data for growth.first As cloud storage spending increases, businesses looking to improve efficiency and maximize ROI for data that is expensive to store. But unfortunately, they don’t have enough time.
To meet this need for quick results, the schema of cartographic data cannot be spread out for months without a clear goal. At the same time, focusing on standard data cleaning or business intelligence (BI) reporting is backwards.
Technology leaders must build a data architecture with AI as the primary goal.
Otherwise, they’ll find themselves upgrading it later. In today’s enterprise, data architecture should target clear outcomes that include AI applications with clear end-user benefits. This is critical to preparing your business for future success, even if you are (yet) ready for AI.
Home from scratch?Start with data best practices
Data architecture requires knowledge. There are a lot of tools out there, and how you put them together depends on your business and what you need to accomplish. The starting point is always a literature review to see what has worked for similar businesses, as well as to gain insight into the tool you are considering and its use case.
Microsoft has a great repository of data models, as well as many publications on data best practices. There are also some excellent books that can help you develop a more strategic and business-like approach to data architecture.
prediction machine By Ajay Agarwal, Joshua Gans, and Avi Goldfarb Great for understanding AI at a more fundamental level and providing functional information on how to use AI and data to operate effectively.Finally, for more experienced engineers and technologists, I recommend Design data-intensive applications by Martin Klapman. This book will provide you with the latest thinking in the field and provide practical advice on how to build data applications, architectures, and strategies.
The three foundations of a successful data architecture
Several basic principles will help you design a data architecture that can power AI applications that deliver ROI. Consider the following as basic points of comparison when creating, formatting, and organizing data:
Building towards a goal:
When building and evolving your data architecture, always focusing on your target business outcomes is a fundamental rule. In particular, I recommend looking at your company’s short-term goals and adjusting 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 use data to get there. It doesn’t have to be intimidating: break down larger goals into smaller ones and work toward them.
Design for rapid value creation:
While defining a clear goal is essential, the final solution must always be flexible enough to adapt to changing business needs. For example, small projects can become multi-channel and you need to build with that in mind. Fixed modeling and fixed rules just create more work.
Any architecture you design should be able to accommodate more data as it becomes available, and leverage that data to achieve your ultimate business goals. I also recommend automating as much as possible. This will help you quickly and repeatedly generate valuable business impact through your data strategy over time.
For example, if you know you need to provide monthly reports, automate this process from the start. That way, you’ll only spend time on it for the first month. From there, the impact will always be effective and positive.
Know how to test success:
To stay on track, knowing whether your data architecture is functioning effectively is critical. Data architecture comes into play when it can (1) support AI and (2) make relevant data available to every employee in the business. Keeping a close eye on these safeguards will help ensure your data strategy is fit for purpose and fit for the future.
The future of data architecture: innovations you should know about
While these key principles are a great starting point for tech leads and teams, it’s also important not to stick to one way of doing things. Otherwise, companies risk missing opportunities that could deliver more long-term value. Instead, technology leaders must continually incorporate new technologies coming to market that can improve their work and lead to better outcomes for their business:
We are already seeing innovations that make treatment more cost-effective. This is critical because many advanced technologies being developed require such high levels of computing power that they exist only in theory. Neural networks are a good example. But as the required level of computing power becomes more feasible, we will be able to use more sophisticated methods to solve problems.
For example, data scientists must train every machine learning model. But in the future, it will be possible to create models that can train other models. Of course, this is still just a theory, but as processing power becomes more readily available, we’re sure to see an acceleration in innovation like this.
Furthermore, when it comes to applications or software that can shorten the time to value from AI, we are now at a stage where most of the available technologies can only do one thing well. The tools needed to generate AI, such as storage, machine learning providers, API deployment, and quality control, are not bundled.
Companies today risk wasting valuable time simply figuring out what tools they need and how to integrate them. But the technology is emerging to help address multiple data architecture use cases, as well as the specialized databases that power AI applications.
These more bundled offerings will help companies bring AI into production faster. This is similar to what we’ve seen in fintech. The companies first focused on being the best in one core competency before eventually merging to create a bundled solution.
Data marts and data warehouses:
Going forward, it is safe to predict that the data lake will be the most significant investment in AI and data stacks for any organization. A data lake will help organizations understand predictions and how best to execute on that information. I see data storage becoming more and more valuable in the future.
Marts provides the same data to every team in the company in an easy-to-understand format. For example, marketing and finance teams will see the same data in metrics they are familiar with and, more importantly, in a format they can use. The next generation of data marts will be more than just dimensions, facts and hierarchies. Not only do they slice information, but they also support department-specific decision-making.
As technology continues to evolve, businesses must stay current or they will be left behind. This means technology leaders stay connected to their teams and empower them to bring new innovations to the table.
Even as a company’s data architecture and AI applications become more powerful, it’s important to take the time to experiment, learn, and (eventually) innovate.
Image Credits: Polina Zimmerman; Pixels; Thanks!