Agile methods help companies respond to change, beat the competition and build high-quality products that customers want by aligning development work to business priorities. But what happens when an organization is unable to align strategy to execution or align its departments or teams due to internal silos? As organizations advance in agile maturity, so do their data analytic needs. Compiling and synchronizing the data of a few agile teams is significantly different than rolling up data across many teams or an entire portfolio.
Without a clear picture of how teams are delivering their work, organizations may be flying blind when it comes to understanding the delivery of their products or services.
See People, Time and Work in a Predictable Way
Every team, department, and organization relies on data to make important decisions that steer the business. What many organizations lack is a way to clearly understand, prioritize and utilize the data they have. From an agile practice perspective, that level of visibility should be established early on in a data reporting structure—project, initiative and work hierarchies—and with the proper agile software solution.
Setting data reporting standards helps teams understand how they are going to refer to, read out and analyze the data within their company. This type of data structuring promotes the standardization of data across teams, projects and work. But agreeing on a data infrastructure is only one part of data analysis. All of your organization, team and work-task information still needs a centralized system of record for proper utilization. Without a centralized database or agile software solution, data management becomes an extremely manual task. By establishing a data schema for your team-level, day-to-day work within a centralized system of record, all teams can start down a path of easier data accessibility, rollups, comprehension and insight.
But, this type of reporting is not the end all, be all for most organizations. To truly have a full and comprehensive read-out of agile work, teams need to be able to slice their data any way they want to accurately reflect the work of the organization as a whole.
How Does Data Discipline Lend Itself to Predictive Insights and Planning?
Formatting data in an organized fashion has to be the first step in understanding the work that is happening across an organization. Without this first step, agile metrics become a tedious task of manual data rollups and missed opportunities. The hodge-podge of data becomes nearly unusable outside of each individual team, as the organization does not know how to compare health, status, risks, dependencies, cadence or velocity team to team.
Getting an organization grounded on common data reporting practices has many benefits, such as:
Everyone is Aligned to the Priorities
With all teams seeing the work produced across an organization, teams keep focused on the most important tasks, align on dependencies and ensure they meet important delivery timelines. It also prevents unsanctioned “work for the sake of work” from happening, as all eyes can see the work in the system and how it impacts the overall business goals.
Highly Prioritized Backlog
Not only are teams able to align on the priorities, but they align on the prioritization of the work needed to meet and deliver on the business goals.
Better Time Management
With a common data management structure built into your agile software, time management, velocity and cadence planning become much easier. Leadership can see which teams are successfully delivering all their work during each sprint, as well as how much work they can deliver over time. This sense of time tracking and management gives executives a glimpse at predictive delivery. With predictive delivery comes more predicative revenue.
Consistent KPIs
If you ask several individuals what their idea of success is, you will get a different answer from each person. Looking at agile metrics is very much the same. If teams are given free rein to set up their own data structure and reporting, all reports and dashboards will be different. This makes rolling up the progress, status and health of initiatives time consuming and painful. With a governed data structure prebuilt in an agile software solution, all teams and departments within the organization can see the same type of reports and metrics all in one place. These teams then have the ability to analyze their data using standard reporting metrics from standard reports or dashboards.
Transparency
While no one wants to think that a project can go awry, it does happen. And it is much better to make course corrections as early as you know there is an issue, as opposed to waiting until it is too late. Giving all parts of an organization a centralized place to review progress in a standardized way promotes overall success.
The Ultimate Goal: Build a Better Plan
Standardized agile performance metrics that stem from an ordered data-schema empower organizational productivity, predictability, quality and responsiveness. With the insight that is gained from this level of data consistency—historical, real-time and health details—the pump is primed for deeper insights and advanced data manipulation.
The Future is Predictive Planning and Big Data
The old adage, “You only get out what you put in,” also holds true for how an organization uses its data. This is why that critical first step of organizing and housing your agile work in a similar structure in a centralized system is key. Without this simple alignment, advanced metrics and analysis are nearly impossible. And it is those advanced metrics that are the key to truly predictive planning. The future of planning lies in customized data reporting and manipulation with big data analysis. With big data, disparate pieces of information can be analyzed, effectively slicing and dicing the metrics any way an organization needs. Reports can be created to fit the shape of the organization, roles and teams that need them–how they need them. To do this, organizations need to input the data into a centralized system first! Analysis is only as good as the data inputting allows.
