Analytics vision
This content is part of Tableau Blueprint – a maturity framework allowing you to zoom in and improve how your organisation uses data to drive impact. To begin your journey, take our assessment(Link opens in a new window).
It’s no secret we’re big fans of dashboards and vizzes (i.e., visualisations) here. But let’s get real; most customers invest in our platform to achieve business results, not just to create beautiful interactive charts. Bridging the gap between your analytics investment and business outcomes is easier said than done. To help you tackle this, we recommend setting an analytics vision. The Analytics Vision tab in the Tableau Blueprint Planner outlines questions you may want to take into account as you set your analytics vision.
Articulating an analytics vision is key to the success of your analytics investment and the foundation of a solid analytics strategy. Whether you decide to define an analytics vision at an organisation-wide level, a team level, or both, you’ll want to involve key executives early and ensure that high-level organisational goals sit at the heart of your analytics vision. This vision isn’t just about technology. This visioning process is about how your organisation can better meet its business goals and then align that vision with the right analytics capabilities to do the job.
When drafting your analytics vision, consider your organisation’s business goals, key performance indicators (KPIs) and strategic initiatives. Generally speaking, in addition to executive-level involvement we see two types of personas instrumental in delivering on the analytics strategy: business users and data professionals. Business users, those who use data for their work, must understand the dependencies and work in partnership with the data professional, whose work is data. Similarly, the data professional must understand the needs of executives and the business user in terms of what they need to know and how they want to consume data-driven insights in their workflow. Although their roles and responsibilities will be different under the analytics strategy, all three must work together to determine the best method for enabling data-driven decision-making across the organisation.
Below, we outline questions that will help you create an analytics vision. Feel free to keep your answers and ideas at a high level during this exercise, as you’ll get into specifics in later steps.
Think about the following questions as you draft your analytics vision statement.
| Question | Answer |
| What are our key strategic business goals? | |
| What are the business outcomes we’re striving for with these initiatives? | |
| What metrics and KPIs can help us track our progress on these business outcomes? | |
| Who needs access to these metrics and KPIs to make decisions that influence our business outcomes? | |
| When do these decision-makers need to see this data to make timely decisions? |
Next, think about your current state versus your desired future state.
| Question | Current state | Future state |
| Are business outcomes informed by data and analytics? If so, how? | ||
| How do business users and decision-makers access data? | ||
| What information do they have access to? | ||
| When do they get this information? | ||
| How do they act on this information? |
Use these answers to draft a clear analytics vision statement, outlining at a minimum what impact the use of data and analytics will have, who it will help and how it will do so.
We also recommend identifying the guiding principles that establish a framework for expected behaviour and decision-making to help your organisation build a data culture, prioritise analytics capabilities and realise your analytics vision. One principle we recommend is “we are iterative.” As mentioned earlier, tying in the use of data and analytics with your business goals takes time and effort. It’s worth pointing out that the process will always need refinement, and you may not be able to affect change in the entire organisation in one go. Being explicit about this will help set reasonable expectations so your stakeholders can better stay the course and keep a beginner’s mindset.
Example analytics vision: HR will serve accurate data to our business users within their workflows and preferred tools, enabling them to make timely and informed decisions to support our workforce.
Case Study: Setting a Vision for the Superstore HR Data and Analytics Team
In this and the next section (Business value), we’ll use a company’s human resources (HR) department as an illustrative example. We’ll call our fictional company Superstore.
Superstore HR aims to be more strategic about its use of data and analytics and has decided to leverage the Tableau Blueprint to help its efforts. After reading the Analytics vision section, the head of HR’s data and analytics team convenes a tiger team consisting of herself, several functional leaders and DEI leaders for a brainstorming session to draft an analytics vision statement. Like many other companies, employee attrition has increased recently and is top of mind for the team. During this exercise, the group decides to focus on one strategic business goal – to improve employee retention. They work through the sample questions together and come up with the following answers:
| Question | Answer |
| What are our key strategic business goals? | Improve employee retention. |
| What are the business outcomes we’re striving for with these initiatives? | • Reduce employee attrition by 25%. • Provide understandable and relevant insights, so 100% of functional leaders take action. |
| What metrics and KPIs can help us track our progress on these business outcomes? | • Employee retention rate • Employee satisfaction survey results • Individual Development Plan (IDP) completions • Check-ins between managers and individual contributors (ICs) |
| Who needs access to these metrics and KPIs to make decisions that influence our business outcomes? | Executives, operations, managers and individual contributors |
| When do these decision-makers need to see this data to make timely decisions? | • Check-ins between managers and ICs: quarterly • Employee retention rate: monthly • Employee satisfaction survey results: quarterly • IDP completions: - Executives need to see completion rates 1 week after each round of due dates - Operations staff need to see completion rates weekly starting from opening of IDPs - Managers need to see completion rates daily |
| Question | Current state | Future state |
| Are business outcomes informed by data and analytics? If so, how? | We assume so but can’t draw a straight line between data, action and outcomes. | Yes. We can tell the story of which business outcomes we achieved, which actions were taken, who took those actions and what data points they used. |
| How do business users and decision-makers access data? | The HR data and analytics teams push out reports via email. Business users and decision-makers have access to various dashboards published to Tableau Server. | We deliver the data they need within the tools they regularly use. They can access the data on demand through Slack and Tableau Server. |
| What information do they have access to? | The information that HR includes on reports and publishes to Tableau Server. | We’ll deliver the information needed to analyse progress towards business goals and making specific decisions. |
| When do they get this information? | On a cadence that the HR data and analytics team decides, or whenever they log in to Tableau Server and view the dashboards. | We’ll embed this information directly in their workflows. They can also request ad-hoc data, see it instantly and set up subscriptions to suit their personal preferences. |
| How do they act on this information? | We don’t know. There aren’t defined workflows for each role. | We’ve embedded the data in the tools they normally use, and the call to action is clear. They can click on a button or link to complete the appropriate next steps. |
After considering their answers, the group recognise that they currently consider data and analytics “analyst territory”, and decision-makers have limited options for accessing and acting on data. They realise that decision-makers could more easily act on data and insights if the HR data and analytics team were more intentional about what data they provide to these business users, and how, when and where they provide it. They draft the following analytics vision:
