1. How data relates to action
How do you decide what is right for your organisation?
You call on your knowledge; the sum of your existing experience and training. You may seek the advice of your peers, employees or colleagues. Perhaps you also guided by intuition. These factors form your understanding and ultimately determine your action.
But what is missing from this process?
Every action has consequences, and these consequences can be quantified and recorded as data.
Unless it also incorporates this data, your understanding will always be fundamentally incomplete.
That all sounds fairly obvious. But many organisations still fail to use data effectively. Why?
The difficulty lies in the transformation of raw data into useful knowledge.
Intuition, advice and experience are human creations; they are expressed in language we can all understand; we can select or discard them based on relevance or quality to develop our understanding.
Data is different.
In its raw state, it cannot be readily incorporated into our understanding. Unlike intuition, advice or experience, raw data is inherently foreign to us.
Achieving a fuller understanding entails the transformation of this raw data into useful knowledge.
Ok, so how does this transformation take place?
It is both impractical and ineffective to process data manually. Anyone who has spent time poring over lists of figures will testify that the task is exhausting and time-consuming.
The insights derived from such endeavours are also fundamentally limited. Manual processing restricts the size of datasets and the potential for human error is unavoidable.
Software provides a far more efficient and reliable means to transform data into understanding, but off-the-shelf products are expensive and inherently generic in their functioning. They rarely offer insights specifically tailored to a particular organisation.
An ideal solution would be to take software already employed by your organisation and design a system in-house which caters precisely for your needs.
It turns out this is entirely possible.
It is called Excel development, and it is the key to unlocking and exploiting the deep insights contained in your organisation’s data.
It is also the engine driving the #ExcelRevolution.
2. Ignorant bliss
We are all aware that the Microsoft Office Suite has advanced capabilities that we use rarely if at all.
Excel is no different: most people make relatively limited use of its functions.
However, an important distinction exists between Excel and its co-applications: whereas the primary purpose of Word and PowerPoint is to present information, Excel is fundamentally an analytical tool.
An understandable and common mistake is to view all three Office applications as performing the same presentational function for different content: Word for written documents, PowerPoint for presentations and Excel for numerical data.
Employing this mindset – viewing Excel as kind of ‘Word but for numbers’ – actually encourages us to overlook its most powerful analytical functions.
This situation has serious implications. Our usage of Word or PowerPoint may be limited, but we are nevertheless aware of what is possible and are therefore able to develop our skills if necessary.
When it comes to Excel however, limitations in our skills are matched by limitations in our awareness: we are fundamentally unable to unlock the true power of Excel because we are not aware that this power exists.
At a personal level, this means we are probably handling data less effectively than we could be.
At an organisational level, we may also be wasting considerable sums of money on ill-suited off-the-shelf software when we could be developing more effective data analysis solutions with Excel.
At the level of the whole economy, a wide range of private and public organisations are missing out as they lose the competitive advantage to larger companies with more effective data analysis.
But are we really on the brink of an #ExcelRevolution?
3. The consequences of ignorant bliss
At this stage it is worth considering the impact of Excel’s functions – including those we weren’t even aware of.
In organisational terms, all Excel functions are not equal. Although a mastery of the basics can be very beneficial to individuals, substantial benefits at an organisational level really start to accrue when advanced Excel functions like vlookup and drop-down menus combine to enable the efficient and intuitive conversion of raw data to practical information.
We have used three graphics to communicate this idea.
The Excel Curve (p6) shows most people’s Excel proficiency is quite low.
The organisational benefits barchart (p7) shows that advanced Excel functions deliver dramatically higher benefit to the organisations than basic ones.
An intriguing picture (p8) emerges when we compare the two graphs: often Excel capabilities in an organisation drop off just at the point where organisation-level benefits really begin to rise.
It wouldn’t take much to move that Excel curve to the right. The rewards lie only just out of reach. And the really frustrating thing? Many people don’t even realise these rewards exist.
The Excel Curve (above) shows most people’s Excel proficiency is quite low.
The organisational benefits barchart (above) shows that advanced Excel functions deliver dramatically higher benefit to the organisations than basic ones.
An intriguing picture (above) emerges when we compare the two graphs: often Excel capabilities in an organisation drop off just at the point where organisation-level benefits really begin to rise.
At this point we can draw some important conclusions.
For many organisations the core problem is not a lack of technical ability, but a lack of awareness.
Once the potential of Excel is recognised, developing an organisation’s capabilities is therefore a lot easier than might be anticipated. This means that modest, incremental improvements in Excel capability can trigger exponential organisational benefits.
In the short term, these include saving money by replacing off-the-shelf software and improving data management with tailored in-house Excel development.
In the long term, handling data analysis in-house with Excel has a much more profound benefit: when empowered to perform their own data analysis, employees are exposed to a whole range of new ideas regarding their organisation; new ways of thinking are opened up which remained hidden while data was analysed ‘in the box’.
We’ve seen the potential for organisations to transform as their members explore the possibilities of Excel development to extract deep insight from their data.
4. The #ExcelRevolution
At this stage you may wonder why this isn’t already happening. If Excel development is so significant, why hasn’t this #ExcelRevolution already begun?
Lets take a look a some of the players we’re dealing with here.
First up: data specialists working for major corporations. These guys certainly know their way round Excel. They do great work and they are well paid for what is perceived to be a highly specialised skill set. So it is not really in their interest to propagate the notion that Excel development is actually a practical tool for non-specialists at all levels.
Next, software companies. As we have seen, the generic data analysis offered by their off-the-shelf products is achievable with applications developed in Excel. In fact, building applications in-house via Excel development projects actually improves data analysis by creating tailored tools and has the added fundamental advantage of empowering organisations to look outside the box and develop more creative strategies and solutions. Understandably, this is a reality that software companies aren’t particularly keen to promote.
So what about Microsoft itself? Surely it is missing a trick by not marketing Excel’s analytical functions?
Actually, Microsoft IS trying to get the word out – a glance at their website confirms that they offer materials specifically targeting analysis with Excel. But when was the last time you swung by the Microsoft Office website? Why indeed would you? If it really wanted to engage with organisations across the economy, Microsoft would have to invest a great deal to reach them. The fact is that for Microsoft, the major corporations have to be the key focus. It is here that the long-term software service contracts are signed which constitute the company’s main revenue stream.
So let’s look over the facts.
An established order has held sway over a set of resources which could be disseminated to the benefit of all kinds of organisations. When these resources are utilised the advantages are felt in both the long and short term at all levels of an organisation.
These organisations already have the ability to use these resources, they simply aren’t aware that they exist. And there are some influential players working to keep that knowledge out of the hands of the majority.
But given a nudge in the right direction, organisations can and do employ effective Excel development to unlock the insights hidden in their data, creating a sense of momentum that can transform individuals and organisations, and could ultimately contribute to a re-balancing of the economy away from big corporations and toward smaller organisations.
The #ExcelRevolution starts here.
Finding Things in Lists of Data in Excel
Finding and Counting Something – or Some Things – in Excel
You probably know how frustrating it is to be sat in front of a spreadsheet scrolling up and down a list trying to locate a piece of data. It is perhaps more frustrating to know that Excel has the formulae to help you do this kind of thing at the click-of-a-button.
This article will help you speed up this sort of time-consuming task by implementing counting formulae.
Example – Finding A Buyer’s Name in a Prospect List
We were recently asked to do a piece of Excel development work on this theme. The client had two lists of names: one of people who had purchased tickets for an event; the other a list of ‘prospects’, or people who had been contacted about the event. The client wanted to understand if a particular person had purchased a ticket, ie. if he or she appeared in both the prospect and buyer lists. There was also a need to understand how many people had purchased tickets and establish a ‘conversion’ rate (names changed in example).
Technique 1: Use Find and Select to Locate a Name
We recommend that you practise the following techniques yourself.
Download the ‘Finding Things in Lists of Data’ exercise file by clicking here.
The simplest and also most time-consuming way to locate something in a list is to use Excel’s ‘Find and Select’ facility. It is situated under the ‘Home’ tab on the far-right in Excel 2010.
Simply click ‘Find and Select’ and enter the value or word that you are looking for. In our case, we enter the name at the top of the ‘Confirmed Buyers’ list – ‘Beth Baker’. Then click ‘Find Next’ one or more times. Excel will show you each occurrence of the word or value. Cool!
We can see than ‘Beth Baker’ appears in both the prospect and buyer lists. We have ‘converted’ this person from prospect to buyer.
Technique 2: Use the =Countif() Formula to Count Occurrences of Name in a List
We can use Excel’s =Countif() Formula to tell us if a particular word or value appears in a row or column that we specify. So we can set up a formula to ask if a name in the buyer list also appears in the prospect list.
You can practise implementing the =Countif() formulae in the ‘Finding Things in Lists of Data’ exercise file. You can download the file by clicking here.
If we type “=countif(” into a cell (without the quotation marks), prompts will appear to help us complete the formula. Yes – Excel is trying to be helpful!
But what do these prompts mean? Remember, we want to find out if a name in the buyer list appears in the prospect list. In other words, we want to find out if a certain word or value appears in a certain row or column. In Excel language, we have to specify a ‘range’ and a ‘criteria’. This is what Excel is asking for.
The formula will look something like this: =COUNTIF(D17:D266,B17)
It should return a value of 1. It is telling us that ‘Beth Baker’ appears once in the prospect list. Neat!
Now, what if we were to implement =countif() formulae for all names in the buyer list?
Technique 3: Use Multiple =Countif() Formulae to Count Occurrences of Names in a List
Let’s recap. We have tried the ‘Find and Select’ facility which offers a simple but time-consuming solution.
We have implemented a single countif formula to tell us if a particular name in the buyer list appears in the prospect list; this provides instant feedback. So, let’s try to implement a countif formula for each name in the buyer list to give us the complete picture.
You can practise this approach in the ‘Finding Things in Lists of Data’ exercise file. You can download the file by clicking here.
Implement a =countif() formula next to one of the names to find if that name appears in the other list. For example, implement a =countif() formula to tell you if Beth Baker appears in the prospect list (above). Alternatively, create a formula to show whether names in the prospect list also appear in the buyer list – this is what we have done in the example in column G. Check it out!
Copy and paste formulae down the list – simply double-click in the bottom-right corner of the cell. Now, let’s try to add up all those ‘1’s to find our prospect-buyer conversion rate…
Technique 4: Add Summary Figures
Now we are in a position to calculate summary figures; in this case – prospect-buyer conversion rate. To calculate this, we need to know:
1. The total number of prospects
2. The number of prospects converted to buyers
You can practise this approach in the ‘Finding Things in Lists of Data’ exercise file. You can download the file by clicking here.
First, use a =counta() formula to count the number of prospects. This formula counts cells that are not blank (ie. that contain something) in a specified range; we need to remember to subtract unwanted entries (ie. the text in the rows at the top). So the formula will look something like: =COUNTA(D:D)-4
Next, use a simple addition formula to total up names that appear in both lists, or converted prospects. Remember, our =countif() formulae return a value of 1 where names appear in both lists – ie. for prospects who are converted to buyers. Use =sum() to add up those ‘1’s, and remember to select the correct range.
We can now add a simple division formula to divide converted prospects by total number of prospects. Implement % formatting (Home Tab, middle) to create a percentage. This is our conversion rate. Easy!
Summary: What we have learned and Why it is useful
This is an example of an analytical task that Excel can help us with. You have used intermediate formulae including =countif() and =counta() to execute the task and find the prospect-buyer conversion rate. Your boss is happy, your colleagues impressed!
But why is this kind of Excel technique so important to learn?
We believe that progress in Excel is not ‘linear’ – x hours study does not result in x units benefit. Rather, there is a group of techniques that – if you master – can dramatically accelerate your Excel learning, unlocking exponential benefits as you improve. =countif() is one of them, and the others are covered on this website.
Mastering these will allow you to approach data-intensive tasks with confidence.
This is the fundamental idea behind the #ExcelRevolution – that focused learning on certain Excel techniques can transform your analytical ability. This has knock-on effects on a company- or organisation-wide level, and even on the economy as a whole.
Read more about the #ExcelRevolution here. Or, follow us on Facebook and Twitter.