[SSB#12] Get your data working for you.. I mean REALLY working for you!
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Welcome to the 12th edition of my newsletter "Supersonic Business", a fortnightly publication by me, Laura Landmark, with a focus on business growth, value and performance management, in other words, all things that make a business better.
Thanks for being here! P.S. Was this email forwarded to you? Get your own!
Sandnes, Norway, Saturday morning, 8th February 2020 🌄
"Supersonic Business" is sent from me, a Management Accountant, to those that have a leading position in SME's (small/medium businesses). The vision for the newsletter is to share information, tips and tricks to build healthy, thriving businesses from the 'inside out', it is written from the finance and administration perspective.
I hope you enjoy [SSB#12], write to me on firstname.lastname@example.org if you have any feedback or questions.
After passing my Microsoft PowerBI exams a few weeks ago, I set to experiment on our own data, just for fun! I worked to get Mantle's data into a fast, fresh & user-friendly format in the Microsoft cloud. The first job was to actually get hold of the data (harnessing it), then to build the data model (organizing it), then to do the fun stuff which is making the dashboards filled with intuitive graphs, charts & number cards, etc.
This is the world I live in every day, but when you are working on your own data, it gets really exciting as you can relate directly to the numbers that start popping into their various boxes on the dashboards. Maybe that sounds geeky, but it is a head-rush!
We all know, that in the digital age data is the fuel that not only runs our engines run but also gives us our 'edge', ie our competitive advantage. We need to make the most of our data to keep our businesses buoyant. But how many businesses are ready to take that advantage and to really start making their data useful?
how many businesses are ready to take that advantage and to really start making their data useful?
I would like to share some tips on what you should be thinking about, and would encourage everyone to get started. Building dashboards is super fun if you like that sort of thing, but it can be difficult to get hold of the data and to actually mold it into something 'workable'. We can certainly help you with that bit (the back-end boring bit!) so that you have a good foundation to start from, then you can literally go wild. Your imagination is the only limitation!
Here is a flavor of some of the things you should be considering
Step 1 - Get hold of your data, and make sure it is secure.
The data could be coming from a database, via an API, or from a flat-file (or other). It is important to make sure that when you start opening up the data pipelines that you have considered where the data is coming from (ie how you are physically going to get hold of it), and how to keep it secure.
Data security is a critical consideration and we can help you with this if you have questions on the best and most elegant way of creating & securing those channels between your different data sources. Breaking down data silos is an art in itself, and not for the faint-hearted (not for me either, my fantastic technical colleagues do that bit for me!).
Step 2 - Consider the quality of your data, is it 'clean' and logical?
We often find that when companies start working with their data, they fall straight into a state of denial. They don't believe that some of the results could be true and start arguing with the numbers. There are usually a couple of things at play here. Either the logic in the data model is incorrect, or a filter is wrong... OR, the underlying data is actually bad quality.
A typical example is that costs have been booked to the wrong department or project, making the results look really bad for that particular manager. Once this is exposed for all the players to see, intercompany squabbling can break loose. The important thing here is to go back to the source and re-train the people or machines that are booking the transactions to make sure they verify the correct cost center's (GIGO!).
Do this and the quality will improve over time. Our usual rule is not to try and go back and fix history, but to make improvements for the future.
Step 3 - What data do you even have, where is it, and what do you want to do with it?
Self-service Business Intelligence is all well and good, but it can be overwhelming too. When the flood gates of data, previously hidden from view bubbling away in their own little silos, are suddenly blown wide, it's hard to know where to start.
It can be a good idea to take a step back from the machines and consider what you are aiming for. What are the drivers of the business? who owns the data? do you have a logical flow of data? and a clear definition of which systems are the master? and are the systems integrated effectively (or at all?)? , and what do you need to know from the data? There are lots of considerations, things that you most likely have not spent time thinking too much about before.
An example of optimizing routines is that we want to avoid double punching into systems. This creates a high risk for user error, we need to streamline the routines and procedures before we can expect to get really good quality data out. Think about it like the difference of turning on the taps and getting a trickle of muddy brown water vs a blast of good clean refreshing water. The clean refreshing water depends on a good source and clean pipes.
Next steps -
So this week, I really encourage you to take steps towards getting your data working for you, think about what is going on behind the scenes, as it is the backstage stuff that will make the difference between a mega-hit or a mega flop on a dashboard.
We at Mantle are dedicated to making your data show-worthy and work with our customers at every stage of the journey. It doesn't need to be difficult if you have the right team on board, getting a few simple dashboards, ie your low hanging fruit can be done relatively quickly, so don't be shy about taking that first step on the data journey.
Warning (prepare to get hooked!)
If you want to make better faster decisions, you need to use your data for all its worth, but warning, it can become addictive, once you get your first taste, you will be guaranteed to want more!
Microsoft PowerBI is just one of the tools we regularly use to help our customers get value from their data, we have other wonderful tools in our suitcase, so we always look for the right tool for the job. PM me if you want to know more.
Today's newsletter is all about getting value from your data, tapping into your full potential by supersizing your insights, and keeping ahead of the crowd. One of our major key's to success is tapping into the value locked up in our data.
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The data dashboards that help La Liga clubs evolve
All industries can benefit from knowing more about their business, football included. The attached article shows how the clubs in Spanish professional football are using data to drive intelligent decisions.
The clubs have now been given access to a new suite of dashboards that they can use to drill into important metrics, giving them a snapshot of their market position as well as where they can find new growth opportunities.
Read the full article here
5 Ways to Use Business Intelligence Tools to Stay Competitive in 2020
Here is an article highlighting specific ways you can get your data working for you.
Whether you use it to understand what has contributed to your past successes, or to understand your customers better, or to make decisions about the future, for example, what stock to buy (or not to buy), data holds the answers.
data-driven decision-making can help you to turn information into profit. Ryan Ayers
Read the full article here
What Are Analytics and What Makes Them So Effective?
Analytics is a foreign word for many, this article shows why it is important to embrace analytics.
Analytics is really all about using the insights generated by your data to create wealth for your company. The important thing is this needs to be automated. None of us have the time or money to spend on doing this manually, especially when the business intelligence tools can do it 1000's of times quicker, and 100's times more accurately than we humans can!
Once you put the machines to work you can grow to the moon and back without having to add expensive human resources into the company. I am not suggesting that companies should be only run by machines at the expense of all humans, but in this day and age, if we want to remain competitive, and stay in the game, we need to keep a lean back office.
read the full article here
Business Intelligence (BI) is a well used and well-loved buzzword, but what actually is it?
How has everything I have written above fit into this definition? The video below is relatively old now, but I have shown it in many arena's, as I think it does a great job at explaining.
The goal of business intelligence is to get the right information (whatever that might be), to the right people (whoever they may be), at the right time (wherever they may be), so that they can make better decisions, faster.
The idea is simple, the reality is not always so simple since data can be tricky to find, catch and mold into meaningful actionable information.
Performance Management (often called Business Performance Management BPM, or Corporate Performance Management CPM, or Enterprise Performance Management EPM),
Predictive Modelling and a lot more.
In other words, Business Intelligence is not just one thing, it is an umbrella term.
Armed with this insightful information businesses are able to understand a lot more about their businesses, this enables them to grasp opportunities (quickly), and navigate around threats, plus enhance strengths and improve on their weaknesses. Just like driving a car with an intuitive dashboard, an up to date GPS, and mirrors, it helps the journey go a lot more smoothly. Imagine driving without them?!
Did you know?
According to an IBM Study poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.
This apparently costs the US almost 20% of the nation's entire GDP. Specifically, the cost is of inferior data.
In fairness, the study was addressing Big Ginormous data, but imagine if there was an opportunity to add 20% onto our top or bottom line by making better use of our data, what difference could that make?
"Ignoring data is a costly mistake"
Words of wisdom
Books on the bedside table this week.
I have to read this book at least once a year. Gay Hendricks totally nails it when he describes the upper limiting problem that we all inherently have.
Have you ever found yourself having a lovely day, and then all of a sudden some dark or worrying thought or feeling starts trying to nudge its way in. Read this book and find out why and how to deal with it, its fascinating.
Top Tip -
Make things in business simple and uncomplicated
"Less is more"!
Always end with a thanks!
Something to watch
Have a peek at my last video where I talk to Kenneth Flaglien of Visma software about the order to cash process where he explains how to ensure you get 30% of your money on time. A lifesaver for a scaling business.
Thanks for reading & see you next time
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