A Problem of Plenty: Making Sense of Too Much Data

Today we have massive amounts of data living in silos: from Snowflake to Excel and everything in between. It’s overwhelming and underutilized. How can this historical data be leveraged effectively to inform our strategy going forward? Human decision-makers are not machines; you can’t just feed them data. They need to see, visualize, understand. This is the crucial bridge between data, and decision-making.

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Business leaders today strive for better data-driven decision making. And with more data being generated than ever before, this should be good news for founders, CEOs, and other executives. 

Yet harnessing this data has proven challenging. 

For one, much of the data is in silos: from Snowflake to Excel and everything in between. 

There’s also just so much of it. It’s overwhelming and underutilized. 

How can data be leveraged effectively to inform strategy going forward? And how might we leverage increasing volumes of data to make better decisions? 

The Data Deluge Challenges Data Driven Decision Making

While there are multiple challenges arising from the exponentially increasing volume of data being produced and processed – including those dealing with the extraction, transformation, and loading of data – we’ll focus on two issues which directly affect data driven decision making; namely, data silos, and the effects of “drowning in data.”

Data silos

Siloed data is one of the biggest challenges faced by organizations today. Each department or team often uses different tools and platforms to collect and store their data. 

For example, sales teams might use a CRM system like Salesforce, finance teams might rely on Excel spreadsheets, while product teams might use databases like Snowflake. Each might use its own data visualization tools. This fragmentation leads to data being isolated, making it difficult to get a comprehensive view of the organization's information.

As CIO magazine puts it, “For years, IT and business leaders have been talking about breaking down the data silos that exist within their organizations. Given the importance of sharing information among diverse disciplines in the era of digital transformation, this concept is arguably as important as ever.”

Drowning in Data

In the past, organizations often struggled with having insufficient data to inform their decisions, making data driven decision making difficult if not impossible. Decisions were frequently made based on limited information, gut instinct, or incomplete datasets. This scarcity of data posed significant challenges, as it left room for errors and uncertainty. However, today's challenge is quite the opposite: an overabundance of data.

A recent survey showed that 80% of companies feel that they're collecting too much data, resulting in information they feel they cannot use effectively. 

There are several key decision-making challenges that arise out of this problem of plenty. 

One of these is “analysis paralysis.” This occurs when the sheer volume of information overwhelms the ability to make a decision, leading to inaction or delays.

Another is “signal vs. noise”. With large volumes of data, distinguishing meaningful insights (signal) from irrelevant information (noise) becomes a critical task. Misinterpreting data or focusing on the wrong metrics can lead to suboptimal decisions.

Given these challenges, how can leaders engage in data driven decision making for themselves – and encourage this thinking throughout their organization?

Strategies to Overcome Silos and Data Overload

To navigate the challenge of too much data, founders, CEOs and other business leaders need to adopt strategies that streamline data analysis and enhance data driven decision-making processes.

Here are two important ways they can achieve this:

1. Focus on key metrics

Identifying and prioritizing the most relevant metrics for data driven decision-making helps reduce the noise and focus efforts on actionable insights. 

This is where a leader needs to marry numbers and narrative to choose, share, and contextualize the metrics most important to the organization. 

How can leaders ensure they’re choosing the right metrics? 

Bob Frisch and Cary Greene, writing for the Harvard Business Review, suggest asking what they call “the champagne question.”

“If we were drinking champagne on New Year’s Eve because your organization had just completed an outstanding year, what would have been accomplished? How would you measure that success?”

The authors highlight the fact that in too many organizations, the answer to this question is fuzzy, with no real consensus. 

It’s incumbent upon leaders – especially founders in fast-growing startups where distractions abound – to set the tone for the organization in choosing the key metrics, communicating these, tracking progress and course-correcting where necessary.

This way, all data can be channeled into effective data driven decisions.

2. Incorporate data visualization tools

Using data visualization tools can make complex data more accessible and easier to interpret, helping decision-makers and their reports quickly grasp essential information.

Leaders can leverage such tools to share context and unite their team around key metrics. 

For example, take a leader leveraging Decipad’s Revenue Forecasting template. 

The founder has set an ambitious revenue goal for 2025 – MRR of $570,000. 

In an all-hands meeting, the founder can discuss and change the inputs and assumptions used, demonstrating how each team, each individual, has a huge part to play in the company’s success. 

In this case, the founder starts by outlining the current number of customers, and the average revenue per customer. All of this is done in a visual, intuitive way. 

The founder then presents the contributive metrics that will achieve the goals set. This can be done for each team, in this case sales and marketing:

Notice that these are dynamic. For example, the sales team might ask, “We’re confident about selling the product. But R&D have to do their part to ensure it doesn’t have any more glitches. What if churn goes up? How will this affect the model?”

The founder can adjust this, in real-time, and everyone can observe the results:

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