The Future of Data & Analytics: Reengineering Decision Making & Adapting for Change PT. II
By Daria Chadwick | Part II of III
In Part I, we explored some of the major challenges currently facing organizations around becoming more agile and data-driven. In Part II, we’ll be taking a closer look at what the experts recommend, and what today’s data & analytics capabilities should look like based on these recommendations.
Becoming Truly Data-Driven: What The Experts Recommend
At Gartner's Data & Analytics Summit, we heard Gareth Herschel, Research VP @ Gartner, indicate during his keynote presentation that organizations needed to focus on change, adaptability, and data-driven decision-making to become successful. More specifically, Herschel said that organizations need to:
Find agents of change.
Build adaptive systems that will evolve as technology advances.
Find ways to extend the influence of data and analytics to all employees, embedding data and analytics into every decision.
We heard this as: “Equip change agents with an adaptable solution that can generate and deploy high-quality, actionable insight at scale.”
Process Tempo has already taken significant steps in building this adaptive, flexible solution for change agents. But in anticipation of the release of PT 3.7, we challenged ourselves to go further.
Guided by the notions that we needed our data & analytics capabilities to support composable architectures, value streams, and unpredictable change, and that we needed to provide even more outstanding support from a bottom-up approach that fits into how people and organizations function, we set ourselves three major goals:
Rethink Self-Service Capabilities Around Data;
Build Modular, Business-Orientated, Analytics Experiences;
Eliminate Business Process Debt + Achieve Operational Excellence.
Goal #1: Rethink Self-Service Capabilities
Self-service analytics is a form of business intelligence in which line-of-business professionals are enabled and encouraged to perform queries and generate reports on their own, with nominal IT support. The emphasis falls on the enablement and encouragement of business professionals to ask their questions, get answers, and generate reports and valuable insights.
Unfortunately, self-service hasn’t lived up to the hype in recent years, meaning it’s beyond time for a paradigm shift in the way organizations approach it.
From our perspective, we wanted our self-service, actionable analytics capabilities to encourage more people to use data, break existing bottlenecks in the system, and maximize data value.
When we investigated why self-service initiatives weren’t delivering for organizations, we found some commonalities.
We found that when data self-service initiatives were not backed by a degree of centralized management—in the form of unified data models and agreed-upon definitions and measures—the resulting profusion of reports and dashboards would lead to conflicting and overlapping versions of the truth.
In addition, we saw significant dependencies on others with specialized skills to generate insight, causing bottlenecks that slowed progress for business-facing users or shut down progress entirely if these skillsets weren’t readily available.
Further compounding self-service challenges is the presence of “defensive” data approaches that lock down data access and usage. We found that, even when data became available after navigating bureaucracy and restrictions and had been repurposed by skilled personnel into actionable data, it still took too much time and effort to translate into actionable insight for business users.
To counter these issues, we developed the ability for users to tap into an advanced yet simplistic self-service function within the platform. This function leverages unified data models, creates single versions of the truth, and eliminates dependencies on others to obtain insight. It frees up time for skilled individuals to focus on move-the-needle projects while empowering business users to securely work with accurate data and present it in the format they need to tell their data stories. Finally, it allows the transition of actionable data to actionable insight to occur much faster.
See it in action:
Goal #2: Build Modular, Business-Orientated, Analytics Experiences
If data-driven decision-making is to occur as frequently, effectively, and efficiently as possible for all stakeholders, we found it imperative to develop centralized, tailored analytics experiences for users that are capable of supporting all of their different data projects and involvements. We opted to incorporate a data hub strategy as a way for users to better organize and standardize their data projects.
We also needed to ensure that these efforts could be supported when expanded to a team, department, or organizational level. We found this to be critical in helping improve and support the collaboration levels and shared best practices that are so often required within large, federated organizations.
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Goal #3: Eliminate Business Process Debt + Achieve Operational Excellence
Business process debt represents inefficiencies in business processes. Ideally, a strong relationship between business and IT can guide the efficient resolution of business process debt. The result yields satisfactory debt resolution, improved technology choice, innovation, and process improvement.
However, in many organizations, the relationship between business and IT is not strong or barely exists, leading to poor or little resolution. Often, issues can proliferate as make-shift approaches take hold.
We worked to relieve difficulties around business process debt by building a technology-driven, process-improvement function directly within the platform. The function helps improve the relationship between business & IT by reducing dependencies and making it easy to implement efficient resolution of business process debt.
We designed this capability to ensure that data involved in those improved business processes was kept secure and managed. Additionally, we recognized the importance of being able to directly upgrade measurements of success to ensure improvements are achieved and sustained. Finally, we built this function to help support leaders infuse business processes with their new or existing value streams.
If these capabilities interest you, watch the video below:
In Part III, we’ll be explaining how to apply value stream management techniques to a real-world use case using these new capabilities.
Process Tempo is an Intelligent Data Platform built on industry-leading graph technology. The no-code, collaborative data science, data engineering, and data analytics platform simplifies complex data environments, empowering people, processes, and technologies to work together across the enterprise. The secure, governed, high-performance environment delivers actionable insight rapidly to all stakeholders, helping to accelerate the delivery of quality, data-driven decision-making and improve business outcomes at scale. Schedule a discovery session