How well do you think your agency uses data? If your answer is anywhere on a line from a lukewarm “fairly well” to an assured “we’re smashing it” – you’re actually doing better than most. 

It seems counterintuitive that data analytics adoption should be such a struggle in the modern workplace. Our culture is increasingly infused with conversations about Big Data, algorithms, and AI. The fact is that most people on the street are familiar with these terms, or at the very least have heard them.  

Yet research shows that this cultural acceptance of the importance of these technologies does not translate across into people’s day-to-working lives. In fact, the Harvard Business Review found that fewer and fewer businesses every year describe themselves as “data-driven”. 

This shows us that it’s not enough simply to rely on your people to be naturally inclined towards adopting data and analytics. So, if you do want to unite your agency around a strong culture of data-driven decision making, you need to be deliberate about creating that culture.

Why Do You Need a Data-First Culture?

When everyone on the team has their head down working on their deliverables, it’s easy to get tunnel vision. With so much coming down the pipeline, you get stuck focusing on the next thing, and the next thing – without taking the time to look back and consider whether the way you do things is actually the most effective way.

But in an organization with a data-first culture, stopping to reflect is second nature. Data-driven agencies use analytics to find evidence of what works, and what doesn’t. They then use this information to make strategic decisions – replicating good practice and throwing themselves at whatever isn’t working.

Simple, in principle. And it’s clearly an effective approach. Recent research by Gallup showed that companies who apply their customer behavioral data outperform their peers by 85% in sales growth, and by 25% in gross margin. 

But, despite the massive potential benefits that lie in store, many organizations struggle to get people on board with data and analytics.

Why Companies Struggle to Make Analytics Stick?

If you’ve ever switched between phones that have a different OS, this dilemma will be familiar to you. It feels clunky; everything is in a different place. You want to be able to just get on with using it as a tool to get day-to-day things done, but until you get used to the new OS you might feel like you’re fighting with it.

The analytics adoption dilemma ultimately comes down to a similar issue. Challenging, unintuitive UIs put people off. Having to learn an entirely new platform puts people off. Programs that interact awkwardly with existing tools put people off.

Despite the best efforts of analytics software developers to make BI more accessible, something just isn’t working. Despite huge advancements supposed to make data more accessible for everyone, attitudes are still resistant. Human psychology has so far won out against the potential that a new era of data analytics could have delivered.

That’s why some developers have tried to deliver analytics in a new way that feels simple, intuitive, and fits into the user’s existing workflow with ease: embedded analytics.

What is Embedded Analytics?

Whenever you see that a platform has the built-in, integrated capacity to analyze data and generate reports and visualizations, you are looking at embedded analytics. 

Designed to fit within a user’s natural workflow, it makes data abundantly available in a context that suits the needs of the user. When done right, not only does it give users all the information they need to respond adeptly to changing circumstances, it also gives insights that can be used for preemptive action. And because it lives within the platforms and systems that users are already engaging within their day-to-day, users are more likely to buy into using it. 

Though this last point sounds like a fairly basic consideration to encourage analytics use, don’t underestimate how ease of use can impact user adoption. For the best results, embedded analytics need to be well-designed and fit effortlessly into people’s workflows. As Sisense’s Ashley Kramer wrote for the Forbes’ Tech Council, “Many of us unknowingly glean insights from data on a daily basis and use them in ways that benefit our lives. Our smartwatches, for example, leverage data to tell us when it’s time to stand up and walk around to meet our personal step goals for the day…Our favorite apps and products have made the process of extracting value from data completely seamless and, in a lot of cases, invisible. The data is so easy to consume because it’s right there when we need it and in the right context.” 

The lesson is that users will adopt analytics when it works for them: when access is frictionless and easy, and when the data they are being shown is clear and contextually relevant to whatever they are working on.

Without the right analytics interface, creating a data-first culture will be an uphill struggle. But with well-executed embedded analytics, individual users will be able to access insights in a seamless way that becomes second nature. 

Everyone has an analytical streak in them somewhere – who doesn’t love to see that tangible evidence that their hard work is paying off? – you just need to find the right tool that your team loves using.

Analytics That Works for Agencies

So, we understand that people are more likely to use analytics when it feels like an intuitive part of their workflow, especially when time is at a premium and they need answers fast. With this in mind, you need to look closely at your team’s workflow and consider whether it supports or sets back your analytics goal.

If your tech stack consists of several, single-purpose tools, the potential for decision fatigue is much higher. This increases further when each of these tools has its own embedded analytics. Having to jump from one platform to seeing resourcing information, to another to look at project financials, to yet another to review task time estimates – each jump creates more ‘friction’ for the end-user. Additionally (and this is important) none of this data will be able to interact. In order to bring it together, you may have to go into yet another tool.

So, the first big step you can make towards building that data-first culture is to unite these different streams of work in one platform that does it all. This will simplify your workflow and mean that you have one, unified source of truth for your data. You’ll never need to look at another spreadsheet again in your life (unless, of course, you really want to).

Once you’ve established this, bringing analytics into the picture is as simple as embedding them in this one platform. An example of a platform that does this with ease is Forecast. AvA is Forecast’s advanced analytics add-on, and it puts robust, totally custom reporting capabilities right at the heart of your workspace. As Forecast gives you all the power you need to plan your projects, track associated financials, integrate with your existing CRM system, and manage your resources, all the data is right there and ready to go.

AvA from Forecast puts insights right at your fingertips. Learn more.

At the end of the day, data has no real-world value on its own. It only becomes valuable when you analyze it and use the insights to make smart decisions. If your team isn’t engaging with analytics, you won’t see value from it. It’s well worth making the investment to create a data-first culture and to bring analytics to people in a contextualized, accessible way. Once it’s there, you’ll notice the difference in the way your agency operates – and the difference in your bottom line.