The Nimble Way: Navigating Change and Innovation in the World of Data
Nimble, a Journey Born Just Before the Pandemic
In a world characterized by constant uncertainty, the emergence of Nimble took place merely two weeks before the onset of the global Covid-19 pandemic. Amidst the challenges posed by the pandemic, economic downturns, inflation, and fluctuating interest rates, it became increasingly evident that we live in an era of perpetual change. What once was considered a phase or a new normal now seems like an ongoing state of transformation.
So, what do we need in this world of change? These are our six approaches to data initiatives that will enhance success, drive greater business value, and foster innovation. We call it the Nimble way.
Buckle up, we’re in for a long ride…
Embracing Change: A Necessity for Survival
Recent years have clearly demonstrated that organizations unwilling or unable to adapt to change face the risk of obsolescence. The ability to evolve has become synonymous with survival. This principle extends to the realm of data – an essential asset in today’s business landscape.
Changing and evolving might be easier said than done. After all, most big corporations have made everything into a robust and structured framework – where detours quickly fall in line with the ranks. But data teams have all the possibility to nudge companies in the right direction, it comes down to persuasion, competence and creativity.
The Data Dilemma: Quantity vs. Quality
In the data-driven landscape, the maxim “bigger is better” held sway for some time. The era of “big data” hailed the integration and management of massive volumes of information. However, this mindset has undergone a transformation; what was once called “big data” is now simply referred to as “data.”
This shift prompts a critical question: Is more data always better? The reality is that we don’t actually need vast amounts of data; we need the right data. Rather than chasing after immense datasets, we should focus on acquiring the data that truly matters and brings substantial value.
An excellent example of this was a hotel that sought to understand its guests’ health habits. Instead of conducting a complex demographic or psychographic analysis, they focused on just two factors: whether the guest used their gym facilities and whether they ordered a salad or a cake in their room. This represents straightforward data with a clear signal. Sometimes, it does not have to be more complex than that.
Analytical systems strive for continuous improvement towards perfection, yet at a certain point, we must recognize when to halt. Would Shakespeare’s sonnets be better if they were longer, or would Mona Lisa be more complete with an extra brushstroke? Conflicting strategies arise in human psychology—maximizing and satisfying. Maximizers seek the absolute best answer to the posed question – and this usually results in the best possible answer. However, even a maximization strategy fails to yield the perfect answer. Sometimes, it’s more effective to just opt for the satisfiers way of life and be “good enough”.
The Art and Science of Data Management
Just as in life, balance is crucial. Taking on a data diet, akin to cutting out excess and unnecessary elements, can lead to healthier systems, reduced costs, and heightened focus on the data that genuinely supports growth. However, sometimes more is indeed more, especially when new perspectives are crucial.
Unleashing the Potential of Data Warehousing
Data can be approached both scientifically and artistically. Science guides us toward well-informed answers, while art leads us to insightful questions. Like a skilled artist, adept at evoking emotions and reactions, we must learn to pose questions that challenge the status quo and provoke innovative thinking. Make sure to let your inner artist join the conversion every once in a while.
Art is a lens that prompts us to perceive the world in diverse and more insightful ways. Personally, I’ve encountered numerous technical artists who ranked among the finest developers, data scientists, and data architects I’ve known. When we regard analysis as an art, we are encouraged to take two actions. Firstly, we must intensify our focus on identifying the right question, not just the correct answer. The essence of art often lies in its ability to provoke and challenge.
The Power of Timing: Insights and Augmented Analytics
At the heart of effective decision-making lies a crucial factor—timing. Earlier this year, the IDC conducted a study on decision-making, revealing that a staggering 89% of executives agreed that any decision is better than no decision at all. This statistic speaks volumes, shedding light on a fundamental truth.
Executives are inherently wired to make decisions; it’s an integral part of their roles. Decision-makers are the architects of action, the driving force behind progress. However, more often than not, the synchronization between analysis and necessity remains elusive. Historically, traditional reporting tools served as a mechanism to alert decision-makers of challenges. Imagine sitting in a monthly meeting and realizing that last month’s lead count was lower than the previous month. This reactive approach has been the norm.
Yet, a paradigm shift is underway. Augmented analysis tools are poised to revolutionize how insights are delivered to decision-makers. Unlike their predecessors, these tools are equipped to proactively furnish analyses when they are most needed.
Augmented analysis has surreptitiously integrated itself into some of our most commonplace analytical tools, such as GA4. This transformative capability ensures that analyses are readily accessible and aptly timed. But the true potential lies in going beyond access and veering into action.
Imagine presenting decision-makers with a clear understanding of impending opportunities or challenges that warrant timely decisions. The capacity to intervene in a crisis or seize a fleeting opportunity holds immeasurable value. After all, the sooner we identify an impending problem, the more options we have at our disposal to confront it effectively.
Understanding the Cultural Context
When embarking on the journey of adopting new technologies and innovative ways of working, a common pitfall is to overlook the role of culture and the necessary change management to influence it. The notion of a “best practice” often implies a “one size fits all” approach.
Much like in professional sports, successful organizations should adopt strategies that align with the skill sets of their employees. Just as a sports team with a strong defense should focus on winning through defense rather than attempting to mimic another team’s offensive strategy, organizations equipped with a robust bench of financial data analysts should leverage these talents instead of striving to transform into data engineering powerhouses.
Rather than completely overhauling your culture, consider how to adapt and play within its framework. Strive to create a psychologically safe work environment where teams are unafraid of collaboration and comfortable with the notion of safe failure. This creates the space for experimentation and the exploration of new methods.
Attribution Models – what it can do and why it’s important
For data teams, bridging the gap between technology and business is crucial. The question arises: How do we achieve this? Here are some thoughts.
Get to know the business by getting to know the people. Have you ever bought a gift for someone you haven’t seen in years? Perhaps an old childhood friend or a distant cousin, solely based on a random fact you know about them? Like the fact that they adore penguins.
If your answer is yes, you’re certainly not alone. The data team can be likened to a distant friend who presents you with a seemingly thoughtful gift. They aim to deliver value to their stakeholders through rich insights that enhance their work and contribute to the organization. However, data empathy cannot be fabricated.
If you fail to genuinely grasp the needs of your consumers, your reports and analyses will hold approximately the same value as a stuffed penguin given to a distant friend. For data leaders aiming to create value, the first step involves engaging with consumers and business stakeholders.
Being a Data Concierge: Embracing the Nimble Way
The role of a data team should transcend that of a mere data plumber; instead, they should become a data concierge. This transformation involves understanding the consumers of data within an organization, akin to buying a thoughtful gift for someone based on personal preferences. Real value emerges when we grasp the nuanced needs and provide insights that enhance their work.
In conclusion, the Nimble way involves balancing innovation and adaptation. It calls for embracing the art and science of data management, understanding the power of timing, and fostering a culture that embraces change. By knowing when to ask the right questions and when to be satisfied with “good enough,” organizations can harness data’s true potential to navigate the ever-changing landscape successfully.
