As our world continues to be even more interconnected and data-driven, there is a profound impact on public sector organizations. This impact is driving these organizations to be even more focused on operating with insight, building analytics capability, and developing talent to enhance mission outcomes and service delivery.
Shortly before the COVID-19 pandemic disrupted the focus and operations of state and local governments across the county, the National Association of State CIOs (NASCIO), and the Public Technology Forum (PTI), delivered their 2020 State and Local Tech Forecast. This forecast covered topics like cybersecurity, digital government, the evolving role of the state CIO, cloud services, and data analytics. The topic of data analytics has been in the top 10 of priorities for state CIOs since 2016 and has several sub-components that include: data governance, data architecture, business intelligence, and predictive analytics. These represent challenges for CIOs and the emerging role of the government chief data officer and the ability to address these challenges and others around data have a significant bearing on course of effective digital transformation within state and local governments.
Analytics Ranked Important
According to an analytics survey conducted by Johns Hopkins University, analytics are important to government — with 82% of respondents saying analytics were “Dominant”, “Of Significant Importance”, or “Equal to Other Factors” in the most significant decisions made by their agency. But even with the importance placed on analytics, organizations spend the most time on the manual gathering of data (23%), versus analyzing (8%), communicating insights (14%), or acting on it (10%). This points to the importance of streamlining and automating the processes around data collection, analysis, and reporting.
And while the adoption of artificial intelligence (AI) and machine learning (ML) capabilities receive the most attention, there is still inconsistent implementation and success. A recent NASCIO CIO survey identified that 65% of the states see AI, ML, and automation as a priority, yet only 25% indicate that actual deployment was in place or plan.
42% of the state CIOs surveyed that their data is not organized in a manner that would be suitable for use in AI or ML.
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.
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.
Black box: Inputs and operations are not visible to the user/inability to show its reasoning.
Clear box: Ability to view decision-making in real-time.
While AI and ML can be trained to perform many tasks without humans in the loop, these systems can often operate in a black box fashion, leaving it unclear as to how these machine-based decisions are made. The converse of this black box approach is a clear box approach that enables insight into how machine learning makes predictions.
The Alteryx APA Platform is built specifically with the concept of being human-centered, augmenting human capability regardless of one’s technical acumen. In other words, with Alteryx, everyone can participate and benefit from a collaborative advanced-analytics environment. Even those who are not proficient in R or Python or able to write their own models can take advantage of geospatial, predictive, and ML-based analytic capabilities to collaborate, innovate, and solve. Specifically, with the Alteryx Intelligence Suite, an assisted modeling capability provides documented “clear box” approach to understanding how predictive models interpret and analyze data, giving users of the platform a level of insight and confidence into the results that machine learning models are producing.
Maintain Transparency and Testing
Another key factor in deploying responsible AI is the requirement to maintain accountability and transparency for iterations, versions, and changes made to models. The Alteryx APA Platform is built upon enabling the open documentation of workflows and models. This includes understanding the source, quality, and lineage of data, and the certification and reliability of deployed models and the flexibility to share insights across multiple reporting, visualization, and BI platforms. With the Alteryx APA Platform, organizations have a unified platform to create actionable insights that can be shared and help propel outcomes.
One of the significant promises of AI and ML is the ability to harness and drive insights within unstructured data, from determining express sentiment in a social media post to modeling topics within textual-based information. Within the Alteryx Intelligence Suite, users get the ability to leverage automated building blocks that deliver capabilities for working with semi-structured and unstructured data through OCR recognition, sentiment analysis, and topic modeling. With these elevated capabilities, unstructured data agencies within the IC are going to have better access to actionable insights.
Mitigate Undesired Bias and Ensure Objectivity
A guiding principle for responsible AI deployment that needs to be considered is time. When analytic teams are slowed down by manual, mundane, and repetitive processes, they can become stressed, overworked, or behind schedule. The Alteryx APA Platform enables your data teams to automate basic data gathering, cleaning, joining, and analysis functions.
The more time spent prepping data for advanced analytics, AI, or ML, the less time spent dedicated to the due diligence needed to ensure responsible analysis, including understanding possible inherent bias contained in data or previous analyses.
In a recent blog by Alan Jacobson, Chief Data and Analytics Officer, Alteryx, he writes that, “When leveraging artificial intelligence and advanced analytic methods, we must be careful that inputs don’t bias the outcomes. In many cases, models are built based on historical data, and if these data include biases, they can propagate into future decision-making. In one now-famous ‘AI fail’ example, a tech company looking to automatically perform initial resume screening built a model based on the characteristics of historically ‘high-achieving’ employees. However, the inputs were flawed: the tech industry is heavily male-dominated, and their high-achieving employees were thus more likely to be male as a percentage.” As Jacobson explains, “Artificial intelligence doesn’t make moral judgements. It is not inherently biased, but historical data and the creators of the model could be.”
With a strong foundation and ethical framework for responsible AI and an Analytic Process Automation capability, state and local organizations will be well-positioned to build the actionable insights needed to accelerate mission outcomes, elevate service delivery, and meet the critical needs of people.
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Discover how the convergence of analytics, data science, and process automation is accelerating successful digital transformation and fueling business outcomes in “The Essential Guide to Analytic Process Automation”.