5 Steps To Accelerate AI through data architecture !

The COVID-19 pandemic only increased digital and AI acceleration. Higher investments were made in AI to keep up-to-date with the emerging need for digital acceleration.

Despite such digital advancements and the urgent need for modernisation, only a few brands have been able to shift entirely on a foundational level. We have observed that many financial institutions are still running on disparate data models. They are yet to integrate crucial data into their architecture.

This has led to complex data quality issues, hindering AI development processes and the final delivery of novel capabilities. Though indeed, making substantial tech changes (in record time) is not easy, it is not often the root of the issue.

Growth Jockey’s team has observed that in at least eight out of 10 cases, the problem lies with the complexity of the process the company adopts, not the technology itself. Conventional data architecture is often seen as the culprit, paralysing progress and making organisations limp against a rapidly evolving backdrop.

Thankfully, there is good news. Companies can break past this gridlock by accelerating modernisation through tactics adopted by their successful counterparts.

Listed below are the five steps to accelerate your AI through data architecture.

1. Have a Solid Blueprint in Place

The days are far back when the only way to make progress in AI accelerating was by starting from scratch. Today’s tech leaders can build a modern data architecture with the help of a reference architecture that fulfils the needs of agility, innovation, and flexibility.

Growth Jockey’s team has observed the benefits of reference data architecture. Teams can road-test a plethora of data transformation use cases across industry verticals. The process helps reduce costs for traditional uses of AI and helps reuse new AI initiatives.

Tech leaders no longer need to spend exorbitant amounts on building architecture design. The same is taken care of by the blueprint. Your organisation can utilise the time spent building the architecture from scratch to align offerings with stakeholder needs.

Though CIOs must review progress regularly, this process helps re-focus efforts to the right places and gain immediate business impact.

2. Scale after Building a Minimum Viable Product

Growth Jockey’s team has found that most organisations view the stages of the AI ladder as a waterfall project. They may start from scratch by mapping out every phase separately right until the end and only then return to tackle each step.

Most organisations’ data analysis process is defined by such an approach to the extent that it’s become their sinking sand. Our team recommends taking the use-case approach instead to see tangible results faster.

The use-case approach works like this – tech leaders develop a minimum viable product that can deliver the desired components of a particular use case. Then based on user feedback, adjustments are made to the final product.

This approach helps organisations to save in terms of development costs and reduces the time-to-market. This approach also enables faster access to data, allowing the company to personalise offerings faster across multiple marketing channels. Once all the components for a use case are developed, the company can start focusing on expanding capabilities and scaling.

3. Make Your Company Ready for the Change to Come

One thing that is all too common with companies starving for change is allowing secondary concerns to take the place of legitimate ones. We mean that most companies spend a significant chunk of their time analysing the risks, weighing the trade-offs, and focusing on business output.

However, this might make the process to accelerate Artificial Intelligence much more time-consuming. This is because legacy solutions do not match modern data architecture regarding cost savings or business performance.

Moreover, Growth Jockey encourages companies to abandon this road because legacy solutions do not help achieve maximum business potential. It would be better if tech leaders educated their teams on the need to let go of legacy technologies, not hold onto them.

For instance – CIOs can conduct mandatory technology courses for the company’s business managers to improve tech literacy and decision-making. The course can offer work context and train the team on modern data architecture and its benefits.

Finally, business leaders should also redirect efforts toward their legacy stack, especially the data-as-a-service section. This will help reduce the time-to-market and business complexities that make data analysis and management challenging.

4. Focus Heavily on Data Engineering

One of the most crucial steps to accelerate AI for modernisation is to have an integrated team of data experts to help develop and implement modern data architecture. Achieving this is only possible when the right cultural and structural elements are in place.

What does this mean from the organisation’s viewpoint? Growth Jockey’s team believes a massive reorientation is on the horizon, especially toward a platform and data model. There will likely be two kinds of teams involved –

· The data platform team consists of data architects, engineers, modellers, and stewards, mainly those involved in developing and operating the data architecture.

· The next team consists of the data product team, including business analysts, translators, and data scientists. These professionals are involved in business-driven uses cases of AI, like campaign management.

Speaking of the right cultural elements, these are mainly directed at bettering the talent acquisition process to ensure that engineers can learn and grow well. Our team would encourage companies to make the following efforts –

· Offering career paths to engineers that are well-defined and grounded

· Assessing levels of expertise through a pragmatic approach

· Setting up a culture that promotes consistent tech learning

· Encouraging the development of engineering skills such as coding

5. Use DataOps to Automate Deployment

Given the crucial role of data architecture in AI, it is natural that completely transforming the data architecture along with data pipelines and models is a highly cumbersome process. Growth Jockey’s experts have observed teams spending a significant amount of time loading processes, extracting them, and transforming them even after architectural changes.

This time can be utilised elsewhere if only teams would seek the help of DataOps, which takes a DeveOps approach to the data. This tool is structured into continuous deployment phases so that all low-value activities can be taken care of.

This way, engineers can dedicate their time solely to building relevant codes, thereby making the production process faster and easier. In some cases, the time taken for deployment could be reduced from a few weeks to hours.

On a Final Note

Adopting and migration are no longer cumbersome with most technologies already on the cloud. This is primarily why the difference between laggards and leaders depends on the company’s ability to evolve its data architecture through proper data analysis.

Companies that fall behind in this race will be those that fail to switch rapidly. Their progress in AI accelerating is at risk of getting derailed. The five steps mentioned above can enable organisations to create enough inertia needed to build value and master the changes already taking place.

So, it’s about time companies avail of every opportunity to build a robust architecture that makes AI acceleration a breeze, which Growth Jockey provides.

At Growth Jockey, we are committed to building customised solutions that effectively address the critical challenges faced by our clients across diverse industries. Regardless of the size of your company, whether it's a small-scale enterprise or a large corporation, you can now optimise your IT architecture. Take the decisive step towards unlocking the next level of growth for your brand by contacting us today!