Getting started with data analytics in your business — 3 areas to think about.
The problem
Whilst I’ve been involved in the data analytics space for over 10 years, there is one thing that has always been there in terms of an organisational brick wall when it comes to projects and that is weighing up the benefits that will be gained by undertaking an analytics transformation. Whether you’re going from zero to hero or you already have some sort of analytics in your business, there are ways that you can improve your current lot in (business) life by bringing in new technologies.
Having been a seller of software and consulting services in the past I know that it’s a no-brainer from my side of the fence but having been a customer (and even now getting ‘sold’ on certain technologies) I know it’s not such an easy sell. Customers have day to day tasks that could be improved with new technologies but often don’t see the benefit of how a change could be worth it.
In this article I break down how those changes could be worth it by looking at 3 areas where technologies can be used to help companies and end users and show you how bringing in the right tools can be a win, win, win (for users, company and software sellers/vendors).
What are the areas that companies can benefit from by bringing in new tools?
Whether its data analytics software or robotic process automation tools or others, there are 3 key areas that tools like this can help improve company performance; reducing costs, increasing revenues, and lowering risks. In this section we break down how projects should be run to help companies better understand the benefits that they can gain in each
Reducing costs
Tools like analytics software are not just useful for creating fancy looking, interactive reports. In fact, they often don’t have to look fancy at all. One of the key aspects to using tools like this is in how they can help automate manual processes that users often have to go through to achieve results. If a company has a report where an analyst takes 2 hours to put together each week using Excel then a good way to measure this is by attributing a monetary value to it. If that analyst is paid $100k a year and we apply (very low) overheads of 20% we get total yearly cost of $120k. Assuming 4 weeks of holidays we get 48 weeks of work and divide that by 5 (work days) and 7.5 (work hours) we get $66.7 per hour that the analyst costs the business. 2 times $66.7 does not seem like much but doing that task weekly is $6,403 for the year.
That’s just 1 task. If that task could be fully automated or have a significant amount of its time reduced then it really starts to add up. Even if we automated just 2 similar tasks for that employee and automation reduced their workload by 80% then that’s still $10k of costs saved. Multiply this by numerous analysts all doing the same tasks (let’s say there are 5 of them) then this is $25k of savings. It adds up as you do more of this. Here’s a breakdown of the above in Excel.
It’s not just the monetary savings that could be had here but its also the time savings. All that extra time now gives people an ability to focus on other tasks like getting in front of their customers more.
Automation really can have a price attached to it.
Increasing Revenue
The bigger the company the more data they seem to have but it can sometimes become too much. I’ve seen many an analyst have to switch between one software and another (often exporting data out of them) just to get further insights (by joining and using VLOOKUP functions in Excel). The process that businesses (and their employees) usually go through to look for these hidden gems can be time consuming, especially if the process has not been automated and also if the answers are not easily apparent because they’re sitting in tables of information.
Firstly, the process of moving from software to software, back and forth and processing information along the way (with Excel or maybe even just in your head) can be time consuming. Worse yet, is if you do this process each time you need to get those answers.
Secondly, it’s also about pattern recognition. How do you mean? Well, humans are great at interpreting patterns but if you put a table of information in front of someone it can look as confusing as a crossword puzzle (where the answers are there but it takes time to find it).
So how to solve the above would be through analytics tools. These don’t need to be dashboards but they should be visual and they should be as automated as possible. Pulling in all sorts of information and giving end users a console which automatically takes care of their manual work they used to do is one thing, making it highly visual so that trends and patterns can energy is another. Combining the 2 should make insights more apparent.
An example of how multiple datasets can be brought together can be seen in APRA data as I mentioned in these 2 articles:
- “A visual analysis of the “APRA — MySuper Heatmap” dataset
- Further adventures in superannuation land
If an analyst has to look into data that sits across multiple Excel tables and spreadsheets to come to some sort of insight or conclusion, it can take time if they just use traditional, non-analytics/dashboard methods.
By having these converted into an interactive and visual dashboard, insights could become far more apparent and analysis that can be used to find revenue generating opportunities can come far sooner
Lowering Risks
“there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know.” — Donald Rumsfeld
Whilst it sounds silly when you say it, there is something profound about what Donald Rumsfeld said many years ago whilst serving as Secretary of Defense under US President George Bush.
Because there are risks to doing business, we need information systems that can help us parse all of this information that’s out there and do so in as automated a way as possible.
We could do this by having access to all sorts of news feeds and information that we pay for as well as information that we get for free (e.g. government statistics, demographic information). But if these all sit in silos then it will be hard to use.
Analytics tools can be used to help piece these bits of information together. Often times businesses might have a team of data scientists using R and Python to do some sort of analysis of risk but these insights need to come out of those tools and into more general dashboard and analytics tools so that more people can find use from them and luckily, as time has past, analytics tools have ways of integrating data science analysis into their more mainstream analysis.
The outcome of having tools that help you take care of this is that you stand a far better chance of seeing when certain market or customer behaviours start falling outside of your standard thresholds and are more likely to be able to do something about this than if that information was not so easily available
How to get started?
Okay, so the numbers add up but still, how does this apply to me or how do I get started? It’s not always easy or clear to be honest. Every business out there might not see benefit in each of the above examples as these might not be specific to each business situation. But if we could apply each of these situations to something bespoke to your business then there might be some light bulbs that go off in your heads. Imagine the time savings, new opportunities and ways that you can stand out from the competition if you could do any of the above things, let alone combine them.
The best way to get started is to have a conversation with experts to see what can be done for you.
I wouldn’t even recommend starting with something big in terms of projects unless you’ve already done this sort of thing before. Large projects have larger risks and unless you’re a large corporate (or even if you are) you don’t want to carry risk like that without guarantees.
A great way to get started is small scale. A small project that has characteristics of a larger one in terms of tools used, expertise required, outcomes expected (and other factors) is a great way to get started. It’s akin to the lean startup methodology of failing fast by doing smaller projects and testing them with the market (your market could be internal staff only for example). This way you either create a good project and take it further or you fail quickly, learn and have something that did not cost you much in terms of what didn’t work.
Which area do I tackle first?
One of the things I’ve learnt is that when you’re selling an analytics solution, the ability for it to reduce costs (or even risk) is not as enticing as the ability to increase revenue. However, reducing costs is the area that has the easiest to see mathematics when it comes to adding up the costs versus benefits. It’s the one area that has an identifiable metric that can be easily measured.
Perhaps that’s what makes it a good area to start to think about if the other 2 forces (higher revenue, reduced risk) are not so appealing to a business.
Conclusion
Whatever way you cut it, there are many benefits that can be gained by getting started in doing something with data analytics tools. Especially now that many businesses are working from home or having to make do with less resources (employees or otherwise).
If any of you are wondering what to do in this space or even curious as to how this could apply to you then don’t hesitate to contact one of us at Prosperity Advisers to see how we could help.
