In the current environment, informed decisions are being made on reliable data
Reliable data are not being made on what people have a gut a feel for or what they think but, you know, we hear every day, that they’re there waiting for the data to come through.
They’re pouring over that data, they need good quality data and that’s what they’re basing all of those decisions on and looking to make those real informed decisions in the current environment.
Challenge #1
Finding the exact question to ask.
One of the major issues that data scientist face that we’ve come across is finding the exact question to ask that they need to analyse.
They’re working on a daily basis with management and executives who really want some insight into their business and how it’s performing, and that making decisions, whether it’s policies decisions or investment decisions for the future.
And so what they need to do and work with the management is to define the exact question that they need an answer to so that they can then go off and find the data that they need to find, and then come back with the answer to what you were looking for very quickly.
Challenge #2
Finding the data they need within their organisation.
A data scientist has the question that they need to answer, the next bit is actually trying to find the data that can support the work to try and find out the answer. And so there are generally two problems with this.
There’s the bit about, does the data exist within their organisation? And so often is the case of some of it exists and some of it doesn’t, so they need to go out and try and source that extra data that they need. But that’s probably in some parts, the easier part of it.
One of the major issues is that the data may exist within their organisation, but it’s just really hard to find who has that data and how they can access it. And so, in our experience, when we talk to data scientists, they’re spending quite a lot of the time that they’ve been given to answer the question in actually finding the data, sourcing it and putting it in a way that they can start to do their analysis.
Challenge #3
Data quality.
As a data scientist, you know the questions that you need to find an answer to, you managed to source the data that you need to support your analysis work, but then the other big problem is the reliability of the data that you’re actually looking at. There might be a few different issues around that.
It could:
- Just be as the general quality of it and the validity of that data.
- There are multiple versions within your organisation that exists for that data.
- There could be different ways and methodologies that data is being collected that change over time.
- The sourcing and finding of the data.
Another big challenge for data scientists is that they spend quite a lot of time consolidating and cleansing data to get it to a point where they can actually do their analysis work. Our experience has found that data scientists or data analysts can spend up to two thirds to three-quarters of their time just fixing the data quality issues so that they can get some good quality analysis.
The way we’ve helped some organisations around the data quality issues is to have a look at what are the issues with the data, what causes it and look at fixing those. So sometimes they might be a process issue or a technology issue and those sorts of things, but, from a data scientist perspective, what they need to try and avoid is repeatedly cleansing that data.
So if we can get a method across that puts that data somewhere so that it is cleansed and it is accessible to them that way, if they need to rerun that analysis or, or add new data, they’re not starting from scratch again each and every single time to go through and cleanse and consolidate all the data that they need.
Challenge #4
The need to be a good communicator.
Another challenge for data scientists is how they project the analysis that they’ve done to the management and executives.
What we find is, you get started as data scientists and people doing analytical work are very good technically, and you know, they’re very smart, intelligent people, but the term you might hear quite often is about the data telling a story.
And so there’s not just the technical ability that they need to have, but it’s also in some ways almost, communication and sort of marketing so that they can project the confidence in what they’ve put forward and that they can tell their story in a fairly succinct way that cuts across any technical jargon about what they did or what that may mean.
Often it’s about putting the picture in and telling the story in a very simple way that builds the confidence of management and executives that they’re getting the right advice from their data scientists.
A challenge for a lot of people with technical abilities is about being a really good communicator, sort of natural communicator, and until you can build that confidence and that trust.
Challenge #5
Their findings and analysis are not taken on board.
The other challenge is actually decision-makers liking the answer that they’ve been given. So quite often, data scientists will look at all the data that’s there and come up with their theories or hypothesis around all of that.
And perhaps the answer that they found might not be to everyone’s liking, there might be some fairly hard truths in that. And so again, part of the thing about being a data scientist is being that good communicator. They need to be able to project that confidence and really show how the data is telling the story and how they’ve come to the conclusions that they have come up with.
Lots of people do make decisions based on what their gut feel and that sort of things. So it’s a little bit of a balancing act to help the management and executives to show them that, “Yes, do you think it might be this, but you know, the data is telling us a very different story and that’s what you should be basing your decisions on.”
What’s Next?
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