DEV Community: Land Digital

Understanding technical debt: the what, why & when

Land Digital — Thu, 01 Aug 2024 10:16:28 +0000

Let’s talk about technical debt. We know, we know - we can sense your excitement from here…

You might have come across the term ‘technical debt’ if you’ve been involved in software projects, or maybe it’s just a phrase you’ve heard thrown around by your development team. To the ill-informed, this can be enough to set off alarm bells in your head - after all, debt rarely has the best of connotations, right?

But fear not; technical debt isn’t so much about finances, meaning you won’t have the loan sharks knocking and you don’t need to be securing your next round of investment on Dragons’ Den. So the question is, what does technical debt mean, and what impact can it have on your software projects?

There’s plenty of general discussion about what technical debt is, and the phrase is commonly used across the industry as a catchall covering everything from legacy code and bugs to missing documentation. Some think of it as an industry buzzword, while other definitions have become highly nuanced, and as a result it can be a challenge to articulate this in a way that’s clear and concise to a range of audiences - especially those team members and stakeholders from less technical backgrounds.

With all this in mind, it’s important to note that the information we cover here is primarily an opinion - but here’s what we understand technical debt to be, and how we approach it as a company when working on our digital projects.

So, what is technical debt?

When breaking down what technical debt is, it’s key to keep in mind from the get-go that not all debt is equal. Think of it like financial debt. An overdraft might have high interest rates associated with it, whereas a mortgage may have a lower rate but equate to a higher value over time, for example.

For those from non-technical backgrounds, understanding this concept and its implications is half the battle. It often sounds negative, especially to those focused on project management and delivery, but any and every project is likely to have some level of debt - it happens, and that’s OK! Just like a mortgage, this debt isn’t necessarily bad when managed correctly, and knowing about it means that you can make informed decisions.

To put it another way: *when adopted with the right know-how, technical debt is a deliberate and strategic tool. *

As any developer will tell you, it’s often the case that the code involved in a software project is worked on to a painstaking level of detail and attention. It’s not uncommon to have a comprehensive set of automated tests that might not even hit your production environment, for example, while the ‘quick fix’ that was meant to be in production for a few days or weeks ends up being left for years.

In these instances, it’s not always the debt incurred in the project that needs to be taken into account, but rather the interest rate (or, to extend the metaphor further, the overhead) that can occur in the not-so-distant future.

What do we mean by this?

Alright, enough with the finance talk - we feel like we’ve used more jargon than Martin Lewis! Let’s ditch the analogies and take a look at how this concept applies to a technical example.

So you have your proof of concept (POC) or minimum viable product (MVP) - nice work. Due to the nature of these deliverables and the time constraints placed upon your development team in getting your product shipped to receive user feedback, it’s likely (although not always a certainty) that some technical debt has been incurred. If the entire purpose of your POC is to determine the viability of taking the idea mainstream, this is no problemo.

However, technical debt isn’t a corner-cutting cheat code - it has consequences that must be considered in your approach. For example, if this POC is then built on and starts taking a different shape, this can mean that the decisions made in the early stages of the development cycle can have a knock-on effect on the process later down the line. The result? Future feature development can end up taking longer and costing more.

It’s all about finding that balance by weighing up the short-term benefits against the longer-term implications. How much longer is it going to take? How much more will this cost later down the line? And most importantly, how often does this happen (ie. how frequent is development in the area where the debt has been incurred)?

Incurring technical debt for a project that needs to be built quickly, if only to obtain user feedback and determine viability, is not a bad thing. After all, the delivery team can create a list of areas to come back to later and prioritise making the decisions that are more in-line with the overarching objectives of the project at that time. This approach is efficient and effective.

But in situations where technical debt has more substantial implications, a strategic balance must be achieved. For example, if your answers to the aforementioned questions were something to the tune of ‘a lot’, it’s not uncommon for this to spark the conversation of ‘starting again’, or using the data generated in your POC to inform the development of later versions of your product.

A good way to evaluate the impact of technical debt is to think of it as high interest or low interest (oops, looks like we weren’t quite done with the financial analogy after all). In other words, how much more will you be paying back in the future?

High-interest technical debt: this is often found in ageing software, and is typically incurred for a whole host of different reasons. But what’s the impact of it? Well, your team probably won’t want to work on it for starters, as it will likely take a significant amount of time for your team to work around years of legacy decisions that may no longer align with the current needs of the platform. And even when development has been completed, the likelihood of regressions or new bugs is still high. Of course, it’s best to avoid building up this high-interest technical debt, but in instances where it’s already been incurred, there is definitely value in taking the time to determine why those decisions were made. Don’t just accept it for what it is, as the high-interest technical debt presents a unique opportunity to obtain a greater understanding of the application as a whole
Low-interest technical debt: low-interest technical debt isn’t necessarily easier to address - code could still be complicated to deal with, for example. However, tackling this type of technical debt is often over quicker, or in an ideal scenario, can even be avoided entirely. It will typically have much smaller-scale implications, both in the number of people it affects and the impact it has on the project as a whole. Think of it this way: if your high-interest technical debt is taking out a loan with a whole lot of APR, then your low-interest accumulation is borrowing some money from a friend who might not even ask for it back!

That’s not to say you’ll always have complete control over the amount or type of technical debt you incur. In fact, in some instances it’s all but unavoidable. For example, there is a technical debt incurred over time from any packages, framework, or language usage. To build software, you’re going to be using at least one of these (and in modern web development, likely all of them), and this means you’re guaranteed to begin building up technical debt from the off.

That’s because, to stay secure and get access to new features, these will all need to be upgraded regularly, sufficiently maintained, and given consistent close attention - and this all equates to a certain degree of technical debt.

How much? Well, while this may start as low interest and seemingly low priority, it can quickly become the biggest pain point for new development or even the maintenance, bug fixing, or security of the system. So, this kind of debt should be paid down regularly through consistent updates and maintenance on any software system to avoid it becoming high interest. You’d be surprised at just how quickly this can happen, too - because this debt is inherent when you take on building a software, it builds up even with no changes to the system. So ignore it at your own peril!

Using the debt figure to inform decisions

As we hope we’ve established by now, incurring technical debt can be strategic - and for this reason, it should play a major role in decision making.

Technical debt needs to be acknowledged by all stakeholders in a project and should form a key part of the context to your decision making. It’s absolutely vital that everyone understands the costs both in the short-term and long-term, so that a decision can be as informed as possible. This isn’t so that you can pin the blame on someone, but instead to ensure you have that all-important context as to why decisions were made when reflecting further down the line.

When making project decisions, as a rule of thumb, ‘good’ technical debt should be considered as those low-interest accumulations that are normally incurred to meet business deadlines. This might be some low-priority optimisations, for example, that would result in small improvements to the overall code quality and potentially spur minor performance improvements in sprints, and have minimal cost to the project unless being actively developed.

We consider ‘bad’ technical debt, on the other hand, to be built-up in instances where the delivery team will take much longer to develop new features, and upgrades send chills down the spines of all those involved (and if you think we’re being dramatic, just ask your tech teams!). The resulting impact is that revenue opportunities are often missed due to the lack of agility, and it’s often tough to build strong business cases due to the time and costs involved with implementing more substantial change.

This can quickly have a domino effect, too. Prolonged time and budget estimates due to high-interest technical debt will quickly become accepted as the norm, which significantly reduces the desire to improve and drive change because it simply becomes too much effort.

Don’t be fooled into thinking you can just start afresh, either. In fact, this almost always presents a whole host of new hurdles to overcome, which are often far tougher to clear than when tackling non-technical debt head on.

The benchmark for starting again due to technical debt needs to be high and is not a decision that should be taken lightly, which only highlights just how important it is to get your decisions right the first time around. Ensure your approach is considered and well-informed to give you a great strategic advantage when delivering on a project, but always ensure you make each decision with both awareness and caution.

No-code tools: optimising business operations with the next iteration of no-code

Land Digital — Tue, 23 Jul 2024 12:39:31 +0000

The Commodores famously said they were ‘easy like a Sunday morning’, but when it comes to building applications to manage your business operations, it can often feel more like The Bangles’ ‘Manic Mondays’.

Our point is that building internal apps has historically been no easy ride. It has required an in-depth understanding of digital best practices, and significant experience honing the necessary skills using cutting-edge techniques and technologies.

That’s until no-code came to town.

No-code is an evolutionary approach to software development that requires very few programming skills in order to build functional applications.

Now we know what you’re thinking: this isn’t exactly breaking news. No-code has been a hot topic of conversation over the last few years as the market has matured and platforms have begun providing a lot more features and functionality than they have historically. Remember that even popular long-standing platforms like WordPress could be considered no-code when used with certain themes.

So why are we still talking about no-code today? Surely in the age of mind-blowing breakthroughs like artificial intelligence and machine learning, no-code is old news…right?

Far from it!

In fact, the next iteration of no-code available in the market today has the power to democratise the technology landscape and make tools available to anyone. And spoiler alert: AI actually has a big role to play in that…

What does the next iteration of no-code look like?
OK, so it’s safe to say that ‘democratising the technology landscape’ certainly sounds pretty impressive, but what does this actually mean?

To answer this, first we need to establish what these tools are.

Let’s start with traditional no-code. These no-code tools are effectively a UI or visual interface that allows the user to create a visual map of how your application should operate, look and feel. They’re considerably more straightforward to implement than your traditional developer tools, as they remove obstacles like specialist programming languages and syntax in favour of a more visual and logical approach. This empowers individuals without traditional programming skills to better understand how to build and deliver apps, and in this sense, these tools democratise development by lowering the barriers to entry.

But while this all might sound smooth sailing, it’s vital to understand one thing: the majority of these platforms still have a learning curve, meaning you can make mistakes just as easily as you can with traditional approaches to coding. And so to utilise no-code most effectively, tools should be harnessed by someone with enough technical competency and understanding to optimise use and maximise output. You have been warned!

So what about the next iteration of these tools?

Well, although it might not be the first thing that springs to mind when you think of ‘no-code’, we now have the ability to create tools that support our daily professional (and personal) lives using an approach we’re all pretty competent and confident in already. That’s right: we can now use the power of the English language!

Natural language understanding has come a long way in the last 12 to 18 months, meaning it’s now possible to use natural language commands - or, to be a bit less technical, words - to create your own tools.

What’s that you ask; prove it? Alright, let’s take GitHub as an example. GitHub has had a tool called Copilot for some time that enables users to input prompts and commands using natural language, and receive code outputs and suggestions based on the project's context and style in return. Yes, yes, we know: this technically isn’t no-code, but it’s a good indication of where the landscape is heading.

And this direction is where it starts getting really exciting. If you have a premium version of ChatGPT (and if you don’t, we hate to break it to you, but you’re definitely missing out!), you’ve been able to add your own custom instructions in English for some time. However, you’re now able to create your own refined GPT using, drumroll please….you guessed it: natural language! That’s right: using the power of English, you can now create your own custom GPT to support whatever the aim of your model might be - and we know we’re not the only ones who think this is very cool.

What does this mean for your business?
We’re sure you agree that this all sounds awfully exciting, but how does it actually relate to business operations? Well, the next iteration of no-code looks set to impact businesses like yours in a couple of ways, and knowing the right steps to take today will help you to unlock the internal benefits of these tools well into the future.

But you need to spruce up your housekeeping.

Traditionally, the majority of businesses have been somewhat reliant on spreadsheets to manage their business, and while the way in which data is stored in these spreadsheets means that it is ‘readable’ most of the time, it often isn’t well structured or modelled to be effectively queried.

This isn’t always a problem; there are tools out there which will attempt to read and interpret your data regardless of the way it has been presented. But with a lot of no-code tools now having a layer of AI built into them to support features, functionality, and value, now is the time to carry out a review of your business in order to place yourself in the best position to take advantage of everything AI and natural language understanding can offer.

To do this most effectively, analyse how your data is structured and consider what no-code tools can support you from the very start when getting your house in order. And to be perfectly clear, this data can be a wide variety of things from a wide variety of sources, encompassing anything stored in your business which provides information - it doesn’t even have to be numerical, content counts as well! ‍

Let’s take a look at a couple of hypotheticals below to provide a broad overview and map this out a bit further. ‍

Example 1: A business uses Spreadsheets to manage production and manufacture. This spreadsheet is business critical but prone to errors and is not easily integrated without hours of work - it uses lots of sheets, some formulas and some magic Barry from IT did a long time ago, and no one else really knows how it works.

In this instance, the business could look at a tool such as DronaHQ. This is a no/low-code platform that can streamline business processes and is specifically designed to optimise ‘back of house’ operations.

Apart from all of the standard advantages of building out operations on a tool such as this compared to spreadsheets (we’re talking benefits like better visualisation, collaboration, and more), this business could also build out applications which would store data in a database of their choice. This then opens up the possibility of adding a layer of AI onto the application - like querying using natural language to source data on demand or predict inventory levels based on historical data - to optimise data management with far greater accessibility and effectiveness. No more pestering Barry from IT is required!

Example 2: A business is growing but a lot of their information is spread out all over the place. Some of it is out of date, other times it is simply inaccurate, or in some cases multiple copies exist. This has led to a lot of mistakes being made and some poor decision making costing the business money.

So in this example, how could the business use no-code and AI to address this issue?

Notion is a powerful combination of spreadsheets, documents and databases, with a lot of other cool stuff all baked in. This means you can use this tool as a basis for all of your data storage - think calendars, client information, internal SOP and guidelines to compliant policies.

Platforms like Notion are a great solution for businesses facing similar challenges to those in our example, as they make you think about and review your current content architecture. And better yet, when you have migrated all of your data to Notion, you can benefit from Notion AI and the ability to interrogate all of your data using natural language, from client meeting minutes to holiday entitlement (or the disciplinary procedure).

In short, it is one central source of truth that can be implemented into your business, and being able to take advantage of AI with these tools empowers your team and improves both productivity and output.

And it’s for this reason that the uptake of no-code solutions is accelerating so rapidly. In fact, Gartner predicts that a mammoth 70% of new applications will have been created with the use of no-code and low-code tools by 2025, up from fewer than 25% in 2020.

These types of statistics are a testament to the power of the no-code revolution: but remember to heed our warnings and not just dive right in head first, however tempting it may be. In order to properly take advantage of these tools, some housekeeping needs to be carried out to ensure you have the appropriate data strategy and plan in place to maximise not only what no-code has to offer, but all the amazing potential that AI offers along with it.

‍The ability to create tailored programmes to suit your business using natural language is here. And even more excitingly, it will only get more advanced over the foreseeable future.

Data storytelling: creating insightful narratives from raw data

Land Digital — Wed, 17 Jul 2024 11:08:51 +0000

We’ve all heard the saying ‘a picture is worth a 1000 words’; it’s a powerful statement that, when Googling the phrase, is often incorrectly attributed to Albert Einstein. Wasn’t E = mc2 enough for him?

The saying is regularly used in advertising, but when used in this medium, it’s not so much the 1000 words that matter - it’s the feelings that they induce, and the connection it builds with that particular brand.

And when it comes to data, it’s no different. Providing context to data can change the way we understand it, and the narrative we intertwine plays a huge role in the way we interpret and connect with it.

To put it another way, the picture you paint is the key influence in the reactions and results we draw from any set of data.

Of course, this is great news when this data story is told correctly. After all, data has become one of the most important assets at your business’s disposal, and is a critical component in your decision making, growth, and strategy implementation. As such, any way of making this data more accessible and easier to interpret is invaluable…right?

In theory, absolutely! But beware: data stories can also be misleading, and this misguidance can have significant implications.

So, how can data be misleading but tell a good story? Let’s look at an example.

Using percentages is an age-old way of presenting a data story in a way that will only ever support the narrative you want to tell, as opposed to painting the true picture. For instance, your marketing team might provide an update that downloads have increased by 500% - and of course, that’s great news at face value! That’s until you discover that you only had one download the month before, meaning you now have a grand total of…5. Not so great news.

In this case, month-on-month percentage growth, while telling a clear and accessible story, isn’t the right metric for creating a supportive narrative that will be meaningful to the decisions you make. In fact, by not painting the full picture, it’s the complete opposite.

Instead, a fixed numerical value that measures progress against your objectives would be more effective here. Depending on your KPIs and the North Star metric you’re working towards, you could compare this fixed numerical value against a growth target (perhaps around 10%-20%) and use data visualisations to track and measure this progress more effectively. This enables you to tell a story that manages expectations and is less likely to be misconstrued.

Using data visualisation in these instances is a super effective way of breaking down data and presenting it in a way that’s more visually engaging (yep, hence the name). Think graphs, charts, maps, and the like. This makes the data much easier to comprehend, and when it’s combined with an engaging data story, it humanises the data by giving it real-world meaning.

And it’s this real-world meaning that’s the real difference between a good data story and a bad one. Narrative is universal in that it helps us process and remember information in a way that keeps us engaged, and helps us communicate ideas in a way that’s both digestible and impactful. By providing a new layer of context through real-world meaning, you’re able to enhance the narratives in your data stories by reinforcing understanding.

To put it in another way: isolated numbers numb us, while stories stir us. Through data storytelling, we can become less concerned with proving, and more focused on moving. We know, we know, we’re expecting to be named Poet Laureate any day now.

To prove our point, take this statistic as an example: in America, Just Eat (or as they call it in the US, GrubHub - yuck) receives 8,683 orders. On its own, this data is pretty meaningless - the number is simply too large for us to interpret or contextualise with any real value. However, if we humanise this data through a narrative, it becomes easier to assign real world meaning to it. For instance, every ten minutes, GrubHub receives enough orders to feed a capacity crowd at Wembley Stadium. Suddenly, this data is a lot easier to comprehend - it’s a whole lot of chicken tikka masalas.

An excellent example of effective data storytelling using this method is Spotify’s annual Spotify Unwrapped. In this campaign, Spotify takes its users’ listening data and tells the story of their year in music. For example, let’s say we’ve listened to 10,000 hours of Rick Astley in 2023 (what’re you laughing at, Rick is cool again in 2024!). This data doesn’t really tell us anything when isolated (other than we’ve probably listened to too much Rick Astley), so Spotify uses narrative and data visualisation methods to enhance its meaning. For example, they might tell us that we were in the top 0.5% of Rick Astley listeners last year, provide a graph comparing how much we listened in one month compared to the next, and even map out how our listening habits compared with others around the globe.

Using similar methods in your own data storytelling adds significant value, no matter if you’re sharing internal insights or telling data stories to your customers, and ensures your audience remains informed, invested and engaged. In other words, tell a good data story, and your audience is never gonna give you up…we’ll see ourselves out.

The do’s and don’ts of data-driven storytelling
Here are the techniques to embrace and mistakes to avoid in order to tell a powerful data-driven story that provides meaningful insight.

Verify your sources: before you can begin telling enticing stories with your numbers, you need to establish what data you need to collect. Remember that it’s unlikely all your data will be coming from one place, so whether it’s user data, customer insights, or internal metrics, ensure you’re always collecting from reliable and accurate data sources
Remain objective: as a good rule of thumb, remain objective by letting the data guide your narrative. It’s generally considered more ethical to tell the story around the data rather than shaping the data around a preconceived story, and although the latter does happen in some instances, it is not advisable in order to maintain trust with your audience and ensure your decision making remains well informed
Use visuals wisely: employ charts, graphs and infographics to enhance understanding, not to mislead. When adopted correctly, data visualisation methods can transform numerical and non-numerical data into an engaging visual summary that’s far more effective than looking at rows and columns stuffed full of numbers, all while reinforcing that ever-important context in your narrative
Keep it accessible: remember that the whole point of your narrative is to enhance the accessibility of your data. With this in mind, make sure your story is understandable to your audience by providing a relatable context, personalising where relevant, and avoiding jargon and overly complex analysis
Make an impact: whether it’s to educate, inspire or evoke action, all good stories have a purpose. Ensure there’s meaning behind your narrative by establishing the reason for telling your story; what narrative are you trying to tell and how can you paint that picture for everyone to understand?

Don't

Cherry pick data: avoid only selecting the data that supports your narrative, and never intentionally alter or misinterpret data to make it fit your story. Always be weary of the quality of your data too: never prioritise incomplete, inconsistent, or outdated data simply because it tells a better story. This is a sure-fire to compromise trust and misguide your decision making
Overcomplicate: don’t overwhelm your audience with too much data or overly complex visualisations. For example, avoid cluttering data dashboards with too much information, as this can quickly become overstimulating and undermine the entire point of your approach. Similarly, focus on one main area in your narrative to avoid diluting your takeaways by trying to communicate too much in one message. We recommend focusing on the areas you know best - deep insight on one takeaway is a lot more valuable than top-level insights on a number of different takeaways
Ignore Context: always provide the necessary context for your data to avoid misleading your audience. We all make assumptions and miscalculations based on perceived biases, especially when it comes to data and seeing the progress we want to see. Providing that all-important context to your narrative ensures understanding and impartiality, meaning insights remains consistent and well informed
Breaching data ethics: when collecting and analysing the data to drive your story, it’s super important that you respect the rights and privacy of data subjects and sources in order to ensure integrity and honesty in your analysis and presentation (oh, and the small matter of also ensuring you’re abiding by the law!)

*How to tell your data story *
It’s important to establish that data storytelling isn’t just another passing fad like the latest TikTok trend (not that camping overnight for the viral Stanley tumbler isn’t completely normal behaviour or anything…).

In fact, as businesses continue to harness more and more data every day, nailing your data storytelling approach only becomes more integral to your ability to leverage these insights effectively, and gain a competitive advantage as a result.

So, what steps must you take to create a clear and cohesive data-driven narrative that achieves its purpose?

Start with a clear question or statement It’s important to set the tone early.

Your story should be driven by a clear, concise and compelling question or objective that is relevant to your audience. This focal point guides your data exploration and analysis, and ensures that your narrative has a defined purpose and direction.

By considering what your key message is and what emotions you want to drive in your audience, you can better understand the language you must lead with in order to support that goal and maximise the chances of your desired outcome.

For example, if you’re analysing sales data, a question like, ‘what factors contributed to the highest sales quarter in the past two years?’, sets a clear path for investigation. In turn, this should then help you map out the direction you take, exploring what has actually led to the highest sales rather than assumptions about what you think contributed.

Use visuals to enhance understanding Use visualisations such as charts, graphs, and maps to make complex data more accessible and engaging.

And don’t just take our word for it. To dig into the science behind this for a second (we can hear Sheldon Cooper cheering from here), studies have shown that, of all the information transmitted to the brain, 90% is visual. And this is why, as proven by Robert Horn at Stanford University using studies from other academic institutions, data visualisation can harness significant results like:

A 21% increase in a group’s ability to reach consensus
A productivity gain by shortening meetings by 24%
A 43% increase in persuading audiences to take a desired course of action ‍ However, it’s crucial to choose the right type of visualisation for the data you’re presenting. For instance, use line charts for trends over time, bar charts for comparisons among categories, and maps for geographical data.

Ensure these visuals are clear, labelled correctly, and free from misleading scales or distortions to paint the clearest picture possible. Get it right and you can unlock a series of benefits, like breaking down complex data sets more effectively, identifying patterns and trends quicker, and making it easier to measure progress and outcomes - all key elements in extracting the most meaning and enhancing understanding to help inform your narrative.

Tell a story with a beginning, middle, and end Good news: you’re now in a position to begin crafting a narrative around your data. Without coherence and without a story, your data simply remains a collection of uncoordinated facts - or to steal Anthony Tasgal’s very fancy coinage, a ‘spewed litany of inert factoids’. Although we can’t be the only ones that think ‘inert factoids’ sound like the next Doctor Who villain?

But what makes a good story when it comes to presenting data?

Narrative serves to establish patterns with meaning. Therefore, a strong data story doesn’t just provide an overview, but rather frames your insights in a way that’s relatable and meaningful. In simpler terms, it provides meaning through structure.

Structure your data narrative like a traditional story in order to capture your audience’s attention and create a framework that they’re already familiar with. To hark it back to your old English classes, adopt a simple narrative arc of beginning, middle and end.

Start with an introduction that sets the scene and outlines the question, mission statement or problem you defined earlier - this is your beginning. Simples. At this point, you can also opt to establish a conflict - author John le Carré (you’ve no doubt at least heard of Tinker Tailor Soldier Spy) once famously said that, “the cat sat on the mat is not a story, but the cat sat on the dog's mat is”, and embracing this idea in your narrative will help to establish both relevance and importance in the story that the data is telling. What’s the problem you need to overcome?

The middle of your narrative is where you’ll highlight your key findings and how they relate to your question, statement or conflict. It’s important to note that, in order to keep things engaging, this part doesn’t just include the quantitative data you’ve collected, but also qualitative data that adds context, texture, and nuance. The combination of the two is what ensures your data story resonates with your audience.

Finally, conclude with a summary of your insights and their implications. What has the data told you, what does this mean for your initial question or statement, and how does resolve your conflict? This is your ending, and the point at which you really drive home the purpose of your story - what are the key insights your audience needs to come away with and how does this enhance their understanding of the matter at hand? Where relevant, you can also include a call to action or suggestions for further inquiry at this stage; again think back to the purpose of your story, and consider what actions you wanted to inspire.

Adopting this simple but effective narrative structure helps maintain attention, enhance understanding and improve the memorability of your data story, and makes complex data more digestible by framing it in a way that’s familiar and engaging. There’s a reason it’s the go-to structure for storytelling!

TL;DR
Not a fan of long posts? Too busy to read the whole thing? Need to go out and queue for a Stanley quencher? No worries, we’ve got you covered! Here are the main takeaways you need to know:

Providing context to data can change the way we understand it, and the narrative we intertwine plays a huge role in the way we interpret and connect with it
Data stories can be misleading when data sets are picked to support a narrative, and this misguidance can have significant implications
When telling a data-driven story, you should always verify your sources, remain objective, use visualisation wisely, keep it accessible, and strive to make an impact
When telling a data-driven story, you should avoid cherry picking data, overcomplicating, ignoring context, and breaching data ethics
Your story should be driven by a clear, concise and compelling question or objective that is relevant to your audience
Use visualisations such as charts, graphs, and maps to make complex data more accessible and engaging
Structure your data narrative like a traditional story with a beginning, middle, and end in order to capture your audience’s attention and create a framework that they’re already familiar with

Remember, the goal is not just to present data, but to make it tell a story that is both informative and captivating. This involves not only showcasing the data in a way that’s engaging, but also connecting it to a larger context that resonates with your audience.

So the next time you’re faced with a data set, remember to ask yourself: what story does this tell and how do I communicate this most effectively? That’s the secret to a good data story.

What is a North Star KPI & why should you care?

Land Digital — Mon, 15 Jul 2024 13:25:08 +0000

So you’ve nailed down a growth strategy and you’ve taken the steps to begin implementing it into your business - time to kick back, relax, and watch your empire expand, right?

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Not exactly.

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Ferris Bueller taught us that life moves pretty fast, and if we don’t stop to look around every once in a while we might miss it. And a similar rule applies to business: if you’re not regularly stopping to ‘look around’ and measure your performance against your goals, you might not notice when your plans have begun to derail.

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And that’s what makes data so integral to business growth. Metrics give you invaluable insight into how your business is performing in the here and now and what trajectory you’re on for the future. And the good news is, there’s a whole lot of data at your disposal.

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But to quote Mark Twain, “too much of anything is bad” - and while the second half of that quote, “but too much good whiskey is barely enough”, is certainly up for debate, the first half definitely rings true.

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You’re no doubt already tracking a range of key performance metrics (KPIs) across different areas of your business, but with so many different aspects of performance to measure, this can often result in a convoluted collection of data. For example, this guide offers a mammoth 170 examples of different KPIs you could choose from.

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While adopting processes like data visualisation can help bring some method to this madness, it’s still very challenging to extract true value from all this data. You need to be able to understand what all these KPIs mean for your overall business performance, and without this ability to track progress efficiently and effectively by deriving meaning from your data, you risk losing focus and direction and missing vital opportunities for business growth.

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And that’s not just at the top of your business; every team member should have the ability to understand how their efforts are contributing to moving your business forward, and what they need to do to improve this further.

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And that’s where a North Star KPI comes into play.

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Your North Star KPI is the one metric that should be placed at the very heart of your digital growth strategy. It’s the primary outcome that measures the success of your business against its main objective, enabling you to better judge overall performance based on the impact of your strategy.

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In short, it’s a more effective way of gaining a proper understanding of the bigger picture.

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It’s important to understand that this North Star isn’t your business goals themselves, but rather the metric that guides you towards these objectives. You should think of objectives like revenue, retention and the likes as the goals you’d like to accomplish, and your North Star as the metric spurring you towards achieving these.

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But you can’t focus exclusively on your North Star - this metric is simply too broad to influence directly. As such, you must consider how your individual KPIs all feed into your North Star, as these performance indicators should all be capable of driving a positive impact on the primary metric you’ve identified.

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To think of it another way, imagine you’re a football team. Your main objective is to win your next match, meaning your North Star is the scoreline - after all, this is the ultimate metric that determines if you win or lose. But you also need to consider how you’re going to win the game - what tactics will you adopt to positively impact the scoreline? These tactics are your other KPIs feeding into your North Star; the aspects you can directly influence to impact your North Star metric and drive you further towards your overall goal.

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And this is what makes a North Star so effective in driving vision, strategy, and decisions - it offers you a central metric that aligns your whole company around a common goal, helping you to stay on course, be more informed, and ultimately drive more growth.

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So why should you care?
Most business objectives are centred around revenue and cost - after all, these are the foundations vital to successful and sustainable growth.

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But these goals are ultimately driven by the value you provide to your customer, and as a unified metric that captures the effect all your team is having on business objectives, a North Star metric is the best indication of the value you’re providing.

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Your North Star metric is ultimately measuring how effectively your business is driving that ‘aha!-moment’ in customers. No, no, not the 80s band, but rather the moment in which your customers recognise the value of your product or service. By identifying what the ‘aha!-moment’ is among your users and setting your North Star metric around it, you can successfully focus your team around a metric that ensures you’re driving real, tangible value that skyrockets your growth higher than the chorus of ‘Take On Me’.

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Now here’s the catch: it’s important to note that the ‘aha!-moment’ itself isn’t the North Star. Perhaps the most well-known example of this is Facebook’s ‘seven friends in 10 days’ - a concept first shared by former Facebook executive Chamath Palihapitiya back in 2013. This proposed that, if a user was to make seven friends within 10 days on the platform, this would act as the tipping point in which they realised the value of the platform and became a recurrent user. But in this example, the North Star metric isn’t the number of friends a user adds, nor the amount of time in which they do it. It’s what they become upon reaching this point of realisation: an active user. Facebook’s North Star metric is therefore monthly active users, as it's the engagement of their users that ultimately drives the platform’s growth.

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In the world of eCommerce, growth relies on retention, and therefore there’s a greater focus on repeat purchase behaviours - Amazon’s North Star metric, for example, is ‘monthly purchases per user’. While smaller eCommerce brands and SMEs may not be able to expect recurring monthly custom at the same rate as Amazon, focusing on a North Star KPI like ‘monthly returning customers’ spurs an emphasis on growth through customer loyalty, referrals, and users returning to your ecosystem.

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And in supply and demand industries like transportation, the North Star metric centres around solving a customer problem when the demand arises. If we take Uber as an example here, not only do they have to deliver value based on this supply-demand, but as a two-sided platform, they also need to deliver value to both drivers and riders: Uber’s North Star metric therefore is ‘rides booked’. Similarly, when shifting to the accommodation industry, ‘number of nights booked’ acts as an informed North Star KPI for Airbnb by successfully measuring value provided to both host and guest.

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It’s all about understanding customer behaviour and choosing a North Star metric that increments each time you deliver value to your customer. This is why it’s important to avoid monetary measurements like revenue, costs, and margins - these don’t reflect what matters to your customers and can’t be directly influenced by everyone in your team. Therefore, these metrics aren’t a reflection of how successfully or sustainably you’re delivering value to customers as a business.

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And establishing this way of thinking has a whole host of advantages. A North Star metric doesn’t just measure progress, but also promotes the internal culture that drives this progress by uniting teams and driving more insightful and dynamic thinking.

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As a result, identifying a North Star metric and placing it at the heart of your internal processes and decision making sees you benefit from:

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Better alignment and focus: your North Star should be something that every member of your team can influence in some form, which means each area of your business is united by a singular focus. This ensures all your efforts, from sales and marketing to customer support and production, are aligned and working towards a unified goal
Greater impact: emphasising the importance of customer experience guarantees your efforts have a greater impact, accelerating your growth by delivering more value, more effectively
Communication and collaboration: by uniting your team around a North Star metric, cross-departmental collaboration and communication is improved. Not only do teams gain a better understanding of what each other is working towards (and why), but they’re also able to make more informed decisions around prioritising the activities that have the greatest impact
Better reporting: reporting is hard work. With so much data available to you, it’s key you understand what the business goals are in order to report the relevant data, and just as importantly, understand how it translates. By identifying your North Star metric and the KPIs that feed into it, you can avoid feeling overwhelmed by masses of data by focusing on the metrics that really matter to your strategy, ensuring you remain on-track, in-the-know, and well informed of any opportunities for further growth you may uncover along the way
Accountability: a North Star metric improves transparency and accountability in your business from the top down. Not only is it clear at a glance how your business is faring against its primary objectives, but it’s also easier for teams to be held accountable for the outcomes they have influenced

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How to find your North Star KPI
With the advantages laid out, you’re no doubt wondering how you can find your own company-specific North Star KPI.

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While the metric you use as your North Star will ultimately be dictated by the type of your business you are, what your primary objective is, and how you go about delivering value to your customer, most North Star KPIs fit into one of the following categories:

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Spurring customer growth: number of orders, order per customer etc.
Accelerating usage growth: monthly active users, number of views etc.
Empowering engagement growth: number of likes or comments, number of posts etc.
Improving efficiency and productivity: customer acquisition, customer retention etc.
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That being said, it’s incredibly important you identify the North Star KPI that’s unique to you. Sure, borrowing somebody else’s playbook might save you some time in the short-term, but this will have significant implications on how well you can measure your value deliverance, ultimately hindering your growth strategy long-term. After all, how you deliver to your customers is rarely the same as how another business offers value to theirs.

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So where do you start?

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Step 1: Define what makes your business successful
For you to be able to measure your success, you must first define what success looks like for your business. To ensure this definition is well-informed and strategically-driven, avoid making assumptions by breaking this success down into three distinct areas:

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Your primary business objective: what is the main goal of your growth strategy? This is the objective that you will measure your North Star against to judge your progress and inspire direction
Your customer value proposition: why do customers use your product or service? Your North Star KPI should reflect the value you create for your customers, and your value proposition will help you to understand how successful you are in delivering this promise to your customers
Your customer’s ‘aha!-moment’: at what point do your customers understand the value of your product or service? This will help you define the point of success, and better uncover the steps you need to take in order to reach it
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Step 2: Choose which KPIs measure that ‘success’
Now that you’ve laid out what success looks like for your business, the next step is breaking down how you’ll measure your progress towards it. This involves identifying the KPIs across all areas of your business, and mapping out how these translate to real customer value.

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Remember that the aim of a North Star KPI is to unite your entire business by aligning teams around a core goal, so be sure to promote involvement from employees across all areas of your company at this stage of the process.

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It’s likely that different teams will have different customer knowledge, and it’s vital that you leverage all of these insights to paint a proper picture of how you can influence success. Explore the KPIs each team measure their own success by, and then evaluate the commonalities across this data.

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Step 3: Prioritise the most crucial KPIs
As we’ve already touched upon, there are a whole lot of KPIs to choose from, meaning you’ll likely still have a long list of data at this stage. Now’s the time to start separating the ‘nice to haves’ from the crucial metrics.

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Which of your KPIs contribute to the direct value you deliver? Identify these metrics as your priority, and segment them based on the successes they contribute to.

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Be sure to steer well clear of vanity metrics here, too. While this data might look good on paper and make you feel better about your business, these types of metrics are often easy to manipulate and inflate, meaning they offer little meaningful insight. In fact, they don’t reflect customer value or measure progress in any shape, way or form.

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And although this might not sound like too big of a deal, using vanity metrics to inform your North Star can actually have significant consequences. Vanity metrics can give you a false sense of success that misaligns your North Star with your actual progress, misguiding strategies and contributing to ill-informed decisions as a result. A good example of this is YouTube, which switched its North Star from view count to watch time after realising this was a better indication of user engagement and the value they were receiving from the platform. So with that in mind, sieve out the vanity metrics in favour of outcome KPIs that are directly tied to your product value and business goals.

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Step 4: Identify your North Star (and how other KPIs feed into it)
With your objectives established and your primary outcome-based KPIs collected, you’re now in a position to identify your North Star.

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Remember that this metric should speak to your business’s unique value proposition, and shouldn’t be something overly complex - every member of your team should be able to contribute towards it and influence the outcome in some way.

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Pick a metric that is simple, measurable, and actionable to be your North Star, and ensure it’s something that properly represents both the value you provide your customers and the objectives you wish to achieve.

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Once established, also look to set out your secondary metrics; these are those other KPIs that feed into your North Star and help you to successfully quantify it. This is a good opportunity to once again open it up to the floor and collaborate with your team, asking each department to identify the leading indicators that they can directly influence, and mapping out exactly what impacts these have on your North Star.

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These KPIs should be things that can be actively measured and improved, and help you to predict your North Star by suggesting something about your potential success. As a result, they enable you to iterate and learn quickly as a team, better understanding the direct actions you need to take to impact performance and accelerate growth right across your business.

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TL;DR
Too long to read all at once? Too busy going through those countless KPIs? We’ve got you covered - here are the most important takeaways from this post:

Your North Star KPI is the primary outcome that measures the success of your business against its main objective, enabling you to better judge overall performance based on the impact of your strategy
Consider how your individual KPIs all feed into your North Star, as these performance indicators should all be capable of driving a positive impact on the primary metric you’ve identified
Your North Star metric is ultimately measuring how effectively your business is driving that ‘aha!-moment’ in customers - the moment in which your customers recognise the value of your product or service
Avoid monetary measurements like revenue, costs, and margins - these don’t reflect what matters to your customers and can’t be directly influenced by everyone in your team
A North Star metrics offers your team better alignment and focus, better communication and collaboration, improved reporting, more accountability, and the potential to make greater impact
To find your North Star, first define what makes your business successful and identify the KPIs that measure it. Then prioritise these KPIs, filtering out vanity metrics in favour of outcome metrics, and identify the one that matters most
Pick a metric that is simple, measurable, and actionable to be your North Star, and ensure it’s something that properly represents both the value you provide your customers and the objectives you wish to achieve
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As a business or team leader, it’s your job to rally your team around your North Star and keep everyone working towards positively impacting this metric. It’s your job to make it real, so don’t be afraid to talk about it incessantly; take a few minutes in each morning meeting to ask each team member what they’re doing today to drive the North Star. Not only does this keep everyone engaged and motivated in your team, but it also ensures you spur the growth you want to see by actively delivering value to your customers day after day.