Organizations invest in analytics to make their business perform better. In our current economic climate, everyone is talking about “going back to the basics”, “doing more with less”, and the need to be impactful on the business.

In analytics, these “basics” have meant providing the tools for extracting insights, but this won’t cut it. We need to drill deeper into how business value is created. This may seem counterintuitive, but now is the time to go beyond the insight and find where that insight is converted into real impact. It’s time to switch our mindset from supporting to driving.

We still need to help stakeholders extract insights to make better decisions, but we must extend our core responsibilities to push these decisions into results. Insights don’t improve performance without the right “action”, so analytics needs to directly integrate into driving that action.

Building metrics, models, and visualizations are good - but how can we create a system that closes the loop? We must find a way to connect analytics assets with the business processes that trigger the downstream business activity. We must find a way to create an impactful experience for our stakeholders so they get to their desired, and superior, business outcomes. This is how we directly drive results.

Achieving this will not only help us to finally deliver on the promise of analytics but also establish us as indispensable to other business leaders.

Staying Relevant in Chaotic Times

In the past year, we all witnessed or heard of analytics teams’ cost cuttings and layoffs. There’s talk around the “data-verse” that the year will continue to be turbulent for us. The problem, which some are even calling a “crisis”, is that our data teams are not “in touch” with the businesses we work in. For data leaders, this means “[being] forced to articulate business value from every angle”, “a call to be multipliers of business impact” and needing to get “serious about maximizing the value of the products they have

The end of 2022 and beginning of 2023 has been nothing short of chaos: fires everywhere, cutbacks, and countless layoffs. The prevailing sentiment is to cut the fat, to remove anything that is not core to and not impactful on the business. Unfortunately, this might only be the beginning. Even if the macroeconomic environment improves, history has shown that it will take time - if ever - for businesses to return to the world we knew just two years ago. Executives will continue to prioritize efficiency above all else.

And unfortunately, analytics teams are part of this. Our sponsors are taking a hard look at their data investments - technology and headcount. The challenge will come when we are faced with the question: “So what does your team do for the business? How does it actually make a difference to our bottom line?” If we can’t show our efficacy: the impact of the assets and products we build – models, dashboards, etc. – we run the risk of becoming marginalized.

We need to get off the sidelines and onto the field, propelling our analytics directly into action.

Obsessing Over Value Creation

We all know that we aren’t disconnected from the business. We have deep business context and build business critical infrastructure that is used for creating business outcomes. We do a lot of “backend” work - high-performing analytics pipelines handling complex data integrations. We create automations that save time. We invest in comprehensive documentation. We create rich semantic layers and data models that incorporate important business logic, and so on…

But this approach leaves too many layers between our work and where the value is created. We take pride in keeping these backend analytics systems running smoothly, but this trades off opportunities to cut through these layers. The closer our work gets to the point of value creation, the faster the business performance improves.

We are advised to act like product managers for all the right reasons. PM’s are obsessed with helping their target customers achieve their goals - by solving their pain points with the easiest, most seamless experiences. They are obsessed with making an impact.

We need to adopt a similar mindset, obsessing over the outcome - considering each step along the path to delivering value.

Finding our Focus

Over the past decade, our field of analytics has been doing some soul searching with different branches splintering into focus areas like “business intelligence”, “predictive analytics”, and everywhere in between. As analytics became more mainstream, these role distinctions became more arbitrary. College students now get this training in school; new tools have made it easier to do more advanced methods, and managers started to think about “past-” and “future-” analytics as a package deal. Today’s job descriptions expect a full-stack skill set.

While this group has many similar characteristics, some analysts prefer to brand themselves as “analytics”, rather than associating themselves with “business intelligence”. Why?

We have always felt that we are not as technical as our developer counterparts. We also have this belief that we only provide supporting services without really being a “business function”. This leads to some of us using the “analytics” designation as a way to describe ourselves as “more technical”.

This theme has been reinforced by the growing focus on data-tech and the modern data stack. The message was clear: focus upstream. Work efficiently, like developers. Build pipelines, transform the data, codify it, test it, document it, etc. But analytics professionals need two strong feet: A technical one and a business one. Analysts need to simultaneously build reliable data products and push the business forward.

It’s time that we analysts cast off this self-doubt and believe in the value we deliver to the business. We must recognize that we are an integral part of delivering results and focus on converging these analytical capabilities directly into business value.

Unlocking our ROI

Let's reorient ourselves on what "moves the needle" for our business units. With the right focus, we will not only empower our stakeholders to make smarter decisions, but can use analytics to direct stakeholder actions that maximize results. We must identify the distinct actions that move analytical insights to stakeholder activity to the improvement of key metrics. This means following the analytics throughout the business process lifecycle so that we deeply understand how insights are turned into reality. Ultimately, we should trace what happens after a decision is made, how that decision translates into action, how that action is operationalized, and how that change is reflected in KPI improvements.

As analytics boost KPI's more directly, our executive sponsors will better understand how analytics converts insight into results. This will pull our teams’ seats to the table, bringing more enthusiasm and meaning to our work. We need to break through our traditional responsibilities and clarify how our work drives business action forward. As we concentrate on translating insights into business results, company leadership will see that investing in analytics growth is investing in business growth.

A Framework for Impactful Insights

Gary Klein, a renowned psychologist and leading expert on the subject of insight, spent years developing our understanding of how insight works. Klein was fascinated by the “magical” way insights make us capable of achieving beyond what we thought we could.

“Insights transform us….transform our abilities as well as our understanding; Insights change our notions of what we can do.” - Gary Klein

Klein presents a framework - the “Triple Path Model of Insight” - that lays out the mechanics of insight: a transformative change in the way we “act, see, feel, desire”.

Klein’s model frames how to foster and accelerate the production of quality insights. “Quality” insights achieve their ultimate purpose – “change our understanding, change what we notice, change what excites us, and set us on the path to making a discovery.” How can we fit this framework for analytics in our business’ operations?

To build a value creation framework, we’ve adapted Klein’s model to connect the understanding of insights to changing business outcomes.

Trigger: Notice a Gap

A common example is when a customer success manager at a SaaS company notices on one of their dashboards that the billing for a particular account was changed from annual to monthly billing. This behavior goes against, i.e. ‘contradicts’, the expected behavior for this account.

Activity: Identify the Changed Behaviors

Each trigger is then accompanied by its own distinct ‘activity’ - a process involving a series of activities that result in a change in our beliefs, called ‘anchors’. In our example, ‘activity’ would be the CSM’s investigation into what exactly, and why this happened: In a report that one of the analysts created for the customer success team, the CSM sees that an admin user changed their billing from Annual to Monthly after the account usage gradually dropped by 90% in the few months prior.

Understanding: Generate the Insight

All of these paths lead to the same type of outcome – a change in our ‘understanding’. The CSM now deduces and sees the real situation with this account: it is at a high risk of churning.

The insight was delivered and the CSM now has a much better perspective on the full story than before. With this framework, Klein shows us the blueprint, a theoretical design plan for what is the “insight supply chain”: from trigger to understanding.

Now we must close the gap between knowing and doing.

Effectuate: Operationalize the Decision

If we want to apply the model to our day-to-day operations, it's not enough to settle for a mere change in understanding. The goal should be a tangible improvement in business performance.

In our example, the CSM had an "aha moment" and realized that an account was on the verge of churning. However, this insight is useless if it doesn't translate into a change in the churn KPI trajectory and prevents the ARR drop. This is where the rubber meets the road.

Dissecting this further, we see that it's not the insights themselves that drive business performance; it's the actions of the business functions who understand and act on these insights. In the case of our CSM, having a new perspective on an account’s potential churn is only half the battle. They still need to ‘engage’ with that insight and take action to prevent the churn.

It’s the ‘effectuation’ of our understanding - the ‘prompt’ that lays out our action-options and stimulates the decision and action that enables capitalization on our insight.

One of the biggest challenges anyone faces is moving from "understand" (knowing) to "act" (doing). Figuring out what, when, how, and where to take the next action is never simple. This is especially challenging in the busy, noisy, and often overwhelming business environment.

Consider a day in the life of a CSM: between juggling the needs of dozens of customers and members of the sales team, dealing with unexpected and unresolved customer issues and providing critical feedback for the product team, CSMs, just like many other business functions, are swamped and clouded with dilemmas and noise.

What’s distinctly noticeable in the business setting, is that even though an ‘understanding’ has been achieved, we still need the additional cognizance that an action needs to be taken, what that action should be, and when it should be taken to make sure we don’t miss a business opportunity that was surfaced through the insight.

Returning to our CSM: following the understanding that the account is on the verge of jumping ship, the ‘effectuate’ step needs to kick in with a prompt to help the CSM decide on what to do next. This could involve exploring the account’s users’ behavior, consulting a team playbook with best practices, or having a quick chat with the team lead to ask for recommended next steps. Ultimately, the key is to have the right "nudge" – that prompt that stimulates the CSM to take the most impactful action.

Engage: Ready, Aim, Engage!

Armed with the motivation to execute, it's time for our stakeholders to do what they know best - ‘engage’ in the business action that's core to their job. In our case, the CSM can reach out to the customer to schedule a meeting or email them with a discount incentive to keep them from churning.

If successful, this delivers the desired outcome to the business function. The CSM now knows they have made the right action, at the right time, to optimize their performance based on the analytics. They have retained the customer and dodged the dreaded churn.

Outcome: A Blueprint for Impact

With our adaptation, we've got ourselves a blueprint for the "analytics-to-impact supply chain". An operational pattern that takes us from the very beginning (raw materials) all the way to the end (measurable outcomes), from 'trigger’ to data-driven business results.

  1. Trigger
  2. Activity
  3. Understand
  4. Effectuate
  5. Engage
  6. Outcome

It includes the crucial steps that happen once the stakeholder has a new ‘understanding’, all the way to the desired ‘outcome’ - a positive change in the business results.

Answering the Call for Impact

Now that we've established a framework for achieving impactful outcomes, it's time to explore how we can implement these ideas in our and our stakeholders’ day-to-day.

We all want the same things. To end each day with a sense of accomplishment, knowing that we made an impact.

For us in analytics, it means refocusing on one objective: create the most frictionless experience that feeds actionable insights to our stakeholders and helps them achieve superior, data driven business outcomes.

We have a unique advantage – the privilege of really knowing what’s happening in the business, through its data. We need to capitalize on it and bestow its gifts to our stakeholders.

Heeding the call will secure our rightful seat at the business table, as trusted partners.

Next, we’ll share the second part for this post in which we explore how we can apply the framework in our day to day and produce a machine that generates and drives actions at scale.

At Rupert, we are bringing this framework to life, and fully automating it so anyone could use it effortlessly, at scale. As you await part II of this post, if you want to learn more about how we do it, schedule a demo here!