In many cases, these states have not completed an assessment of their data to determine how usable, accessible, and clean it is to effectively leverage AI, ML, and automation capabilities which are foundational to successful digital transformation.
An additional barrier that many state and local government organizations face in trying to digitally transform and become more digitally enabled relate to a lack of higher-level data skills and lack of a focus on upskilling existing resources. In the Johns Hopkins study, staffing was identified as the biggest hurdle — in fact attracting/retaining staff was named the biggest challenge, with more than 74% of respondents indicating that their agency did not have adequate resources or the analytics capability needed. With deepening budget constraints, most state and local government organizations will not be able to make their way out of a deep resource gap, which means the ability to upskill talent will become even more critical.
All these challenges have a profound impact on the ability for an organization to digitally transform. This journey towards digital transformation is not just the development of a singular capability, instead we should think of it as the convergence of three key pillars: data, process, and people. Unfortunately, with most organizations, there is often a lack of alignment and we see all three of these components existing in disconnected silos.
As a result:
- The access to data needed for analysis is slow and analytics are limited.
- Many processes around data and analytics are manual and not fully optimized.
- The efforts of people can become disjointed, and the effect of being bogged down in many manual processes leads to employees who are not engaged in building their skills.
When these are not aligned, digital transformation fails, and this is what stands in the way of delivering successful accelerated outcomes for the programs, people, and communities that depend on state and local governments.
In response to these challenges, a new category of analytics has emerged — Analytic Process Automation (APA), which is helping public sector organizations execute critical missions with actionable insights. APA is a platform software capability that is swiftly differentiating itself by accelerating the rate at which organizations can make critical, data-driven decisions.
APA is the convergence of data, process, and people.
First, it’s how we automate the many levels of process in our organizations — everything from simple data acquisition and transformation, to enriching, analyzing, and delivering actionable insights. By freeing up significant resources trapped in these processes, we get to redeploy domain specialists to focus on new, innovative, or high value work. That is the spark for cultural change to build a digital-ready workforce.
Through these unified analytic platforms, methods that have historically required a high level of skill can now be executed by any level of data worker regardless of technical acumen thanks to low-code or no-code assisted building blocks that can construct models with transparency and make upskilling or reskilling easier.
And finally, none of this would be possible without transformative thinking about the data itself. We go into this process with the hard-won understanding that technology does not deliver value in the change process alone — it’s the data that provides the actionable insights in your high-performing analytics culture.
That is the Alteryx Analytic Process Automation (APA) Platform™ advantage — being able to align these core components of any digital transformation program within a unified analytics, data science, and process automation platform that leads to faster and more successful business outcomes.
Understand Goals and Risks
Any successful endeavor — from house building to digital transformation — requires the establishment of a strong foundation, and this holds true when deploying advanced analytics, AI, ML, and automation. According to a report by Gartner, a key factor for any successful AI deployment is a strong level of data management and analytical maturity since there is a high dependency on reliable, high-quality data.
However, this is dependent on having an understanding of your current analytic situation. For instance, do you know your strengths and weaknesses when it comes to analytics? Are your processes hostage to legacy systems (i.e., spreadsheets), technology, data silos, or team alignment? If these are areas of concern, then some focus needs to be paid to building an analytics culture that focuses on breaking down traditional barriers between data scientists, IT, citizen data scientists, analysts, and domain experts. The emergence of unified analytic platforms, like the Alteryx APA Platform, is helping organizations overcome these barriers to creating a strong analytics culture.
APA enables organizations to democratize data, automate processes, and upskill resources.
Self-service analytic platforms like the Alteryx APA Platform include drag-and-drop capabilities, allowing you to deploy geospatial analysis, natural language processing, and predictive analytics into repeatable workflows. This gives your data science teams more time to focus on building and deploying AI and ML models and address any risks across the lifecycle of these models. The Alteryx APA Platform enables organizations to democratize data, automate processes, and upskill resources with enhanced analytic capabilities, creating a natural and robust foundation for the responsible use of AI, ML, analytics, and related data.
Incorporate Human Judgment and Accountability
One key principle in any responsible AI framework is the concept of keeping humans in the loop, incorporating human judgment and accountability, and informing decisions appropriately. Since the deployment of AI, there has been a significant delineation between “black box” and "clear box” AI.