For the love of data – Are you Tableau?

August 2021 when I came back from my 22 months of full-time travel, I had to find something I could be on fire for. A mission, a vision that I could identify with. One that would remain a worthy companion for life. Well guess what, Data is everywhere, every single day we live, we create more data just by being.

Hence the question for me – how can data be used to facilitate change? Can I use this understanding to help people become better at what they do? Can helping people see and understand their data move them closer to their goals?

November 2021 – For the love of Data, I became Tableau. Like anything you start newly, you need to understand the what, the why, the where, the when and the how. You need to ask a lot of questions and you need to speak the language.

I had to learn some key terms and understand what it meant to me in my position and what it could mean for my future customer. Terms like data sources, data literacy, data management, data quality, usability, ROI of data, all play vital roles in the journey of becoming data driven in decision making.?

Let’s start at the beginning:

What & Why – Enabling companies to use insights from their data to realise business outcomes i.e., growth, change, stability i.e., become Data Driven in their decision making.

Where & When – which departments, which roles, which processes ideally could benefit from leveraging insights and when exactly, where is the data stored? – answers to these questions help determine the how and moves you closer to a successful outcome with impact.

How - How should these insights be accessed, processed and consumed? Are the insights required in the flow of work for it to have the desired impact? Are the insights required as a foundation for planning and execution? Are the insights used to gauge performance and effectiveness of processes & projects? Who requires these different insights & how often?

If we start by asking these questions:

• Who could benefit from being data literate in the organisation?
• What percentage of the organisation requires which data skills/ literacy level?
• What impact would this have on the individuals work and towards the business goal based on their role?

With this you can lay a foundation for leveraging data across your organisation that can constantly adapt to changes in strategy and priorities.

Data literacy is the ability to derive meaningful information from data, just as literacy in general is the ability to derive information from the written word (TechTarget)

Data literacy – Possessing the skills necessary to understand, explore, use, make decisions with, and communicate using data. (Forrester)

Gartner defines data literacy as the ability to read, write and communicate data in context — with an understanding of the data sources and constructs, analytical methods and techniques applied — and the ability to describe the use-case application and resulting business value or outcome.

Not everyone in a company needs to have the length and breath of data literacy as defined by Gartner. However, being able to read, write and communicate data in context is definitely an aspect that would benefit an organisation if a larger percentage of its workforce had this basis.

Since joining Tableau, I have asked myself what exactly does being data literate mean to me in my role as individual contributor. How can I leverage insights to work differently as a Sales person. From what different departments do I require data to have my questions answered? How would the insights have to be surfaced for it be relevant for my work?

Questions that I personally ask data regarding my sales territory include

• What is the average deal size?
• What is the average deal cycle?
• How many touch points with how many contacts are required to close deals on the average?
• What is our market penetration by segment and industry?
• What method of engagement has proven to be more successful in my territory when it comes to new business and existing customers?
• How do the different quarters affect the close rate?
• When, Why and How often have we lost to another provider?
• When did the last interaction occur and what was it about?
• What is the general sentiment towards the subject?
• On which channels + locations are my prospects most active? Am I there?
• Are there other companies with similar profiles who have engaged are services recently?
• Which companies should I focus on?

Now all these questions are great questions that can help inform my sales approach for my territory but for the questions to be answered, the data must be created in a manner that allows it to be accessed, processed and re-proposed.

In others words Data Quality and Data Management. The famous garbage in garbage out stays true in this example. What format must the data creation take for it to facilitate effective data processing and communication. How recent must this data be?

Data Quality measures of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.

Data Management is the process of ingesting, storing, organising and maintaining the data created and collected by an organisation.

• Is there a defined process for creating data at your organisation?
• Have the necessary data points required to optimise performance been defined and communicated?
• Do you measure the accuracy and adherence to the data quality guidelines in your organisation?
• Are these guidelines implemented at the level of data origination?

The next aspect is, am I able to ask these questions in way that the time consumed in getting the answers doesn’t outweigh the benefit? If the time invest is higher than the assumed benefit then I would most likely go with an educated guess based on past experience.

Two things:

• The impact of me leveraging such insights to plan my year and sales approach must be measurable and comparable
• The how & where involved for me to leverage these insights has to improve my work in terms of expected outcome and results.

These two things would help drive adoption of leveraging insights to plan and execute.

Leading to the next question,

is it more effective considering my job profile for me to be able to ask these questions directly of my data, for me to know the exact data sources and the analytical methods that need to be applied to get the right insights?

or is it more effective for the required insights to be surfaced in the right application with possibility for me to drill down into further details if necessary.

If I am required to do both? Do I have the necessary data skills to perform both? Or do I need to up-skill ?

“Data skills – The techniques used to extrapolate meaning from and communicate discoveries with data. Basic data skills include data literacy and basic data analysis abilities. Advanced skills include data science, AI, machine learning, and advanced analysis techniques.” Forrester.

If I have learnt anything in the past months, it is that the answers to these questions will differ for every company but the building blocks and key terms more less remain the same.

This where Tableau meets you wherever you are. We do not dictate your infrastructure (Data Sources + Storage), We don’t require specific level of literacy to empower different profiles to leverage insights, we do not limit where you can surface the insights.

What we do, is to help everyone at your organisation see and understand data regardless of their data literacy level focusing on delivering the insights where and when it matters the most, fostering a data culture that leads to becoming data driven in your decision making.

Hence the question - Are you Tableau?

This is my journey and understanding of data and the data value proposition so far. It’s a learning journey, the more I interact with organisations, subject matter experts, service providers the more I learn and I can’t wait for more. I would love to hear from you – what is your relationship to data and what works the best for you and why??