Most business leaders are aware they need data to make informed decisions. So much so, a recent study of New Zealand C-suite, senior managers and IT managers found 33 per cent saw "data-driven insights to support decision-making" as the biggest tech opportunity in 2024. From sales data to customer data, brand metrics, marketing performance and everything in between, all of it can support business decision-making and help business leaders make smart, effective choices for their organisations.
However, there's a persistent misunderstanding about what that "data" should look like, the gaps businesses believe they have, and the options available to fill those gaps.
Perceptive team members, Ange Dunn, Senior Business Director, and Nikky Lee, Senior Communications Officer, talk shop on customer data and how businesses can often do more with what they have than they think.
Nikky: Hi Ange, thanks for stopping in on Perceptive LinkedIn! With the current economic conditions, a lot of businesses are looking for ways to be smarter and more informed in their decision-making as well as to minimise superfluous and ineffective spending. As someone who has worked across a range of businesses and industries, what’s the one thing you wish all business decision makers knew about data?
Ange: That they probably have more data than they think! Don't assume you have nothing; you could be overlooking a lot of valuable insights and information. Just because you haven’t invested in quantitative or qualitative research, doesn't mean you don't have data. Data comes in many different shapes and all of it has a role to play.
For example, when we audit an organisation’s data at Perceptive, we look at metrics from across an organisation’s market, brand, consumers, shoppers, media, digital platforms and employees. There’s a wealth of insight to be found here. Depending on your business, some metrics will be more important and impactful than others, and some will be more readily available and accurate than others. This is one of the benefits of running a data audit—you learn what you have, how valuable and reliable it is, and who uses it (or could use it).
Nikky: Most businesses have access to their own data and many are using it to uncover insights and make informed decisions. Are there any disadvantages or pitfalls to using this data?
Ange: Yes and no. A business’s own data (i.e. sales data) provides a good view of your performance, which is a great start, and is also very useful if this is all you have access to. However, a lack of customer and market data means that it is only your performance you’re seeing; your view of the world you’re operating in is very narrow.
This lack of holistic understanding of how a business sits within its market and competitor context can be a real challenge in New Zealand, particularly where industries are often too small to have external market data available, even at a cost. In these instances, the options to resolve this are also limited. You can use something that is ‘best fit’ but not perfect or turn to an external, third-party provider to supply you with this missing information. The latter option might involve commissioning a research study or purchasing loyalty data from providers such as Fly Buys.
For New Zealand companies in the FMCG space, this is not an issue given the amount of data available to them from the major supermarkets. This usually includes supermarket sales data, which captures the market at large, and shopper data from loyalty programmes. In the case of FMCGs, the challenge is ensuring a culture where the organisations are using the available data to make insight-led decisions.
Nikky: So, what you’re saying is there’s nothing wrong with businesses using their sales data, but they do need to look up from their navel-gazing to consider the bigger picture?
Ange: Yes, exactly.
Nikky: That makes sense. You need to have a clear view of the internal and external factors affecting your business to make well-informed decisions. However, as is often the way, there can be many barriers between ‘best practice’ and what’s practical. What are some of the common barriers you’ve seen?
Ange: The first barrier is siloed data—where different pockets of the business can only access and make use of certain data. For example, the marketing team may have a wealth of brand and media insights, but they do not realise this could be useful for a sales pitch and the sales team. In this instance where a team or department do not know this data exists or how to access it, they may try to replicate it (leading to a data double-up) or go without.
A second barrier is data quality—i.e. incomplete or imperfect data—whereby what exists does not entirely answer the business need. This could be because it is not granular enough, is not for your market, is not collected often enough or isn't collected at the relevant time. These instances often lead to a ‘squeeze the square peg into a round hole’ approach as businesses try to do what they can with what they have.
The third key barrier is when businesses have too much data. This can link back to organisations not always understanding what constitutes as data, specifically around what data is actually relevant and useful to them. In these cases, the data quality is often dubious and not centralised, meaning this mass of messy data is fragmented across the business with no clear overview of what exists where, and why. At its core, this barrier is the first two barriers we talked about combined and multiplied.
This issue is often compounded with few people in the business (if any) knowing how to clean, use, or leverage the data. In these instances, the job of sorting this mountain of data often gets put in the too-hard basket.
Nikky: That's a danger, I agree. Businesses can't afford to put their data-transformation in the too-hard basket for too long. Again and again, we're seeing organisations realising the benefits of harnessing their data. From improving customer experience, business processes, operations and service design to new product development, there's a huge scope of potential uses data can have to an organisation. According to PwC[1] highly data-driven organisations are three times more likely to report significant improvement in decision-making. Closer to home, a Stats NZ report estimated that data-driven innovation could contribute an additional 3.2-8.7 per cent to New Zealand's GDP by 2030 (between $13 and $36 billion).[2]
So, for businesses that don't know what data they have, have data quality issues or have too much data to make sense of, what should their next steps look like?
Ange: Okay, there’s some good news on this front. The next steps are actually quite simple, at least on paper. They will, however, take some time, resources, and ownership to see through. You can do this exercise yourself or could look to an external agency such as Perceptive to conduct it for you.
Step 1: Engage your stakeholders
Talk to stakeholders to understand exactly what data they have, what data they need and the frequency at which they need it. For example, market sizing might only be needed once a year for brand planning, whereas brand health might be needed monthly to track marketing impacts.
Once you understand what your stakeholders' needs are, work with them on step two: auditing the data you have.
Step 2: Audit the data you have
All information can be good data, it’s just a matter of understanding where it fits into your organisation and how it can be used. Conducting a data audit not only gives you full oversight of the data you have across the business but also identifies the level of importance and value each data set has on your business. In some circumstances, certain data may have little to no business impact. Similarly, you may find instances where the data is impactful and/or important. Being able to delineate between these situations is critical, and in case of impactful/important data, the role of the audit is to also ensure you’re using the correct data to fill the need.
The critical part of this step is making the audit organisation-wide—you need to talk to stakeholders from all functions within your business to understand what data they have, what they’re using, and how they’re using it. This will help you break down silos of where and what data you have; it’s not uncommon to discover you have more data than you thought you did!
As you go, also make note of the quality and availability of the data. Who owns it? Is it complete? Accurate? Reliable? Timely? This will help you prepare for step three: identifying the gaps.
“All information can be good data, it’s just a matter of understanding where it fits into your organisation and how it can be used.” —Ange Dunn
Step 3: Identify the gaps
With your data audit completed, it should be clearer where your gaps are and what your requirements are. What’s left is to figure out how best to fill them. Start with focusing on the missing metrics you (or your stakeholders) have deemed both important and impactful and work backwards from there.
For each gap, consider whether it can be filled with small research (the Perceptive Omnibus is a good example of this) or purchasing data from a third party, such as transactional data. Or does the gap require full-scale research, such as a brand health study. Again, work with your stakeholders to understand what your options are to create a cohesive plan to bring this new data into the fold.
By the end of this process, you should have a solid grasp of what data you have, what data needs are met and unmet across your business, and where your critical gaps are. Knowing this allows you to effectively choose how to best fill those gaps—or whether you need to fill them at all.
Nikky: The approach you’ve outlined here sounds like a great way to get out of that ‘too-hard basket’ headspace. Sometimes that’s all that’s needed: a clear process and path to move forward with.
Ange: Yes, it’s an excellent exercise to do, even for businesses that are more advanced in their data use. Knowing what data you have and don’t have is an essential step in becoming an insight-led organisation. By establishing where your gaps are (rather than guessing or assuming), you’re already using data to make smarter decisions. From there, you're in a better position to formulate an effective (and efficient) insights strategy going forward.
Need help auditing your data? Book in a free consultation today.
Footnotes
1. PwC's Global Data and Analytics Survey, "Big Decisions™," Global, base: 1,135 senior executives, May 2016.
2. Stats NZ Tatauranga Aotearoa. 2022. Long-term Insights Briefing.
