Over the past few years, there has been a renewed focus on the strategic value data represents to the federal government. The implementation of a Federal Data Strategy and the move to appoint chief data officers (CDO/CDAO) in every agency (both on the civilian and DOD sides) has been welcomed by most. However, simply adopting a new strategy and appointing responsibility is still a long way from seeing significant growth of an analytics culture taking hold equally across the federal government.
In my view, there are two primary obstacles that impede many agencies from fully embracing a culture of analytics:
- There is widespread agreement that analytics — or more specifically the insights derived from analytics — is a strategic component that drives significant business and operational value in both the public and private sectors. However, many organizations are flooded with data and their teams are struggling to identify what is most important and the best actions to take. As more and more data is made available and the multiple formats and complexity of data increases, there are going to be more variables and barriers that overburdened data and analytics teams are going to struggle with, if spreadsheets, manual processes, and disjointed tools continue to be the status quo.
- The second major barrier is the lack of employees with the skills necessary to fully harness the strategic value of data. A report by Gartner states that while many organizations look to embrace digital transformation and better integrate the use of data science, machine learning, and artificial intelligence into their processes, they simply lack the data science expertise. The challenge then for many organizations is how to democratize data, automate analytic processes, and upskill their existing resources to take advantage of actionable insight.
Fortunately, in the face of these challenges there is a convergence happening between data, process, and people that will enable organizations of all sizes to succeed in the data economy and accelerate digital transformation. People, processes, and data are an organization’s richest assets, and when these three assets are addressed together, transformative outcomes are realized.
To capitalize on this convergence, organizations need a unified analytics platform approach that provides access to requisite and relevant data while automating business processes and fostering the rapid upskilling of people within an organization. These platforms provide comprehensive automation capabilities across the continuum of diverse data access, diagnostic, predictive, prescriptive analytics, augmented AI and machine learning, and business process automation. Most importantly, they foster deep engagement of the workforce to enable the rapid upskilling of people.
At Alteryx, we have taken a leading role in developing a category called Analytic Process Automation (APA) which is changing organizational culture to empower more people and organizations with actionable insights. More importantly, by focusing on enabling every level of data worker and not just highly trained data scientists, organizations can build and enhance self-service analytic capability with the resources they have on hand. The expansion of self-service insights and the ability for people to learn analytics skills at their own pace are having a profound impact on organizational cultures and workforce skillsets.
Analytic Process Automation
“Data analytics has become a requirement for leaders across industries, given the need to understand the state of the business today and where to head next,” said Libby Duane Adams, co-founder and chief customer officer at Alteryx. “Analytic Process Automation is helping to expand a new insights-driven culture in which executives can no longer rely on old methods of gut instincts alone.”
Analytic Process Automation (APA) enables leaders to upskill employees with technology that “essentially creates a workflow for analytics and problem-solving,” said Duane Adams. “By upskilling people to handle the entire analytic process — rather than being an expert in one or two technologies and then handing it to someone else who is an expert in a different piece of technology — it’s allowing those individuals to own the process from beginning to end.”
Today’s integrated self-service analytics and data science solutions have proven to simplify and broaden the accessibility of data, analytics, and data science to everyone without needing specialized skillsets and even apply AI and machine learning without having to be an expert or coder. They enable advanced diagnostic, predictive, and prescriptive analytics, machine learning, and AI via a .simple self-service experience that engages the workforce and accelerates achieving a culture of analytics. In short, what APA enables is the augmentation of analytics capability.
According to Gartner, 60% of respondents to their Data and Analytics Summit poll believe augmented analytics will have a high or transformational impact on their ability to scale the value of analytics in their organization.
The objectives of Analytic Process Automation are also very much aligned with the Federal Data Strategy’s outline of Enterprise Data Governance that calls for policies that democratize data through greater access, use and augmentation, improved commercialization, innovation and public use, and insights that elevate decision making and accountability. Additionally, the Federal Data Strategy calls out four specific cross-cutting drivers of change which are policy, people, process, and platform. These core objectives and drivers found in the Federal Data Strategy are directly supported by an APA approach to analytics.
In working with different federal agencies, we have already seen how an APA approach has improved transparency, increased efficiency, elevated service delivery, and operational insights.
In one case, an APA approach using Natural Language Processing has automated the review of award description information, reducing the time it takes to find errors, streamlining the processing time, and increasing transparency by providing more complete descriptions on what contracts are awarded for.
In another case, the assessment of storm damage was better automated with geo-spatial and predictive analytics that reduced the need for physical inspection. Reducing the need for manual inspection sped up the evaluation process and made the recovery process more efficient for the people and communities impacted.
At a defense-related financial agency, an APA approach was utilized to connect to eight different accounting systems, two different reporting systems, and reconcile all financial reporting functions automatically. With hundreds of input files and the requirement to generate over 300 deliverables for quarterly audit purposes, the agency was able to utilize APA to automate routine analysis and processes, improving user productivity, and enhance data quality and transparency by reducing the reliance on manual inputs and create faster speed to insight leading to quicker decision making.
The question is not if APA will become a fundamental element and enabler of creating analytics culture in government, the question will be why wouldn’t government agencies embrace an APA approach sooner rather than later?
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