In my last blog, we talked about challenges of creating a successful data analytics strategy. In this blog, we’ll identify 5 key focus areas for reliable data analytics delivery in your midsize organization.
Tenet 1: Commitment to Defining Expected Results
If a business is unable to articulate how critical metrics are calculated, it is a telltale sign of unclear expectations. There is a misconception that “requirements gathering” alone mitigates this problem. Requirements gathering sessions don’t always force stakeholders to dive into data nuances and business rules. Consequently, completing a requirements gathering session can give a false sense of security that the solution is entirely defined.
What are “expected results?”
Gathering “expected results” refers to the deliberate analysis and documentation of what the end product is supposed to show in critical use cases. For example, “What is the expected gross profits by division in Q3 2016?” The purpose of this exercise is to uncover gaps in the requirements documentation – before it happens in user acceptance testing (UAT).
What can this solve?
Acknowledging these gaps sooner in the process will allow for one of two things:
- Your team can course correct and potentially still deliver within the target time and budget
- Your team can communicate impacts to timeline and budget. In this scenario, the sponsor is given an opportunity to make an actionable decision based on scope, budget, or timeline.
A failure to uncover gaps in requirements can lead to unsuccessful UAT sessions, wasted build hours, late schedules, and missed opportunities for project sponsors to mitigate risks.
Tenet 2: Advanced Technical Expertise
Users of different skill levels can now take advantage of evolving, user friendly analytics tools. However, there is an immense difference between an enterprise analytics solution and a novice ad hoc data experiment.
To build a robust analytics solution that can strategically scale over time, technical expertise is a must. An experienced architect will plan sophisticated architectures, optimize ingestion and data management, foster development standards, enforce a rigorous test strategy, and ensure long-term maintainability. Building the wrong technical solution will waste considerable dollars and time in the long run.
Tenet 3: Effective Project Management
The skill of a project manager is critical to the success of a data analytics project. There is a notable difference between monitoring and managing a project. Only monitoring tasks and checking off boxes in a project plan can lead to catastrophe: Project dependencies may not be managed properly, resulting in budget and timeline overages, where project sponsors have minimal opportunity to course correct.
A successful project manager will have a keen ability to manage risks (even before a project sponsor knows there is a problem). They connect dots in delivery, while communicating not only difficult messages, but also proactive options to steer the project back to a desirable state. They take full accountability of the project successes and challenges.
Data analytics projects can only be successful if expectations are proactively managed, not just monitored.
Tenet 4: Organizational Change Management
Is the perfect data analytics solution still valuable if an organization never uses it?
Organizations that invest time in user adoption ensure their technology investments are truly realized. Organizational change management is a deliberate set of processes, activities, and deliverables to answer the following questions:
- How will we market the new technology and get employees excited about the end product?
- What is the training/enablement strategy and what resources are available to employees before go-live?
Becoming a data-driven business requires changes throughout the organization. But there’s one big problem – people often don’t like change!
Tenet 5: Data Governance
Data Analytics is rarely a single project or investment, but rather an evolution alongside a growing business. Data Governance is a formalized structure of people and processes that help foster effective data use in an organization. Data Governance includes the management of data quality, integrity, availability, security, performance, and priorities.