“Knowledge is having the right answers
Intelligence is asking the right questions
Wisdom is knowing when to ask the right questions”
Professor Richard Feynman (American Physicist)
The Pursuit of Knowledge was the title of the session we delivered this week at Essex County Council’s fantastic LearnFest. LearnFest is an annual event that brings together expert speakers from a variety of backgrounds that share their views on ways of working, and professional and personal development.
Our team wanted to highlight the ‘ways of the modern analyst’.
There were two underlying themes:
1 – Analysis is NOT something done by a sole person sitting in a dark room crunching numbers. It is very much a collaborative process, and one that requires stakeholder involvement from start to finish.
2 – Questions are the key to everything! Setting good research questions, asking questions of data, questioning outputs, and questioning actions. And linking back to our first theme – those questions need to be asked together.
Our Data & Analytics strategy has the strapline – Data is Everyone’s Business. And whilst we strongly believe in that ethos, when planning our session, we were predominantly expecting data-users to attend; analysts and researchers that use data on a regular basis.
We were surprised and elated to have attendees from all corners of the organisation attend, all of whom saw a session about ‘data’ and didn’t go running for the hills screaming. Public Health leads, Housing Strategy Delivery Managers, Senior Auditors, Commissioners for Skills Development, Procurement Managers, Wellbeing Leads, Analysts of all shape and size, and many, many more! And the session was well received. One jubilant participant even gave a glowing review of “it was alright” which, when you’re talking to non-analysts about data and data ethics, is the equivalent of a 5-star review!
Part of our session itself highlighted the importance of having diversity of thought throughout data projects. It’s important that service expertise, practitioner experience, subject matter expertise, anecdote, user experience, are all part of the intelligence base to compliment research and analysis. So here we were with an incredibly diverse range of skills, each with their own reason for being interested in working with data and data analysts.
Maybe we’ve said ‘Data is Everyone’s Business’ so many times that people have bought into it! Or maybe its because events of the last few years have accelerated the use of data and brought analytics and data science into the spotlight. Either way, we don’t believe we would have had such a large and varied audience 2-3 years ago, and I’m interpreting this as a first victory in our quest to raise the understanding of the value data can bring.
In our pursuit to increase data literacy we often spend time considering what we want to tell our colleagues, and in reflecting on this session and the importance of good questions what I thought was valuable was what this eclectic mix of attendees asked us.
Data itself was at the forefront of questions – with folk asking about data availability, data integrity, and data quality. I’ve spoken before about the four V’s of data (Volume, Variety, Velocity, and Veracity) and we still have our work cut out ensuring that everyone from different backgrounds that want to use data know how to access it. Many were unaware of the magnitude of data we have available to support our varied work.
But it was clear that ‘veracity’, the reliability and integrity of data, was also a priority for our attendees which was reassuring to hear. The old analogy of ‘rubbish in rubbish out’ reared its head, and its important that as we start to make use of all varieties of data we have well established data standards that our colleagues understand and enable.
The span of our own team’s work came up. What services do we support?
Operational models of analytics teams, including our own, have changed a lot in the last few years. Five years ago most analytics teams were de-centralised, and aligned directly to specific services. However, most have gone through a process of moving to centralised models (such as a ‘centre of excellence’ or ‘hub and spoke’ model). What this now means is that our team support all services and functions across the organisation. As someone in the team, this is my favoured set-up. It facilitates greater opportunities to draw upon the plethora of skills from like-minded people, and enables anyone to work across different areas resulting in a great variation of work from one week to the next.
For people outside of our own team though this can sometimes be confusing. We need to ensure our team’s offer is visible to everyone across the organisation.
We were asked about the sensitive subject of ‘machine learning algorithms’ – always a provocative topic. I’ll never pass on the opportunity to bust out one of my catchphrases “analytics is to augment human decision making never replace it” – but what was apparent within this discussion was the growing understanding of data science and data science methodologies. I barely needed to spend more than 30 seconds explaining what it was and how we use it (as an additional source of intelligence).
I’ll chalk this up as another victory of our data literacy efforts.
And finally, there were several questions about ‘what we do’ to ensure we incorporate service voice into our intelligence suite.
Which is a nice one to end on. This was very much a key reason for us hosting the session in the first place. We see the value of data, but even more so we see the value of data AND commissioning intelligence. Data and analytics is only a small part of the puzzle.
The most thorough evidence base needs all of this, and everyone pulling together in the same direction. I started by saying ‘analytics is not something done by a sole person sitting in a dark room’. Modern analysis is collaborative. From the very beginning to the very end. It is participatory and collective. To that end, we are all analysts.
If you want to know more (or would like us to repeat the session with you) then please get in touch.