Businesses and marketers have access to more data than ever before, as well as data to support the data, and data to prove the accuracy of the first data…and so on.

If you want to make data-driven decisions for your business, the raw material is there. Without the human element, however, data has no meaning.

Data vs. Insights: What’s the Difference?

It’s important to understand the role of data and insights in the research process and how they can make a difference in the end result.

Data is raw, unprocessed information that’s captured according to existing standards. It could be in the form of numbers, images, transcription, or other formats. Processed, aggregated, and organized data is put into data visualizations, reports, and dashboards as information, which is more easily interpreted by humans.

Insights are gained from analyzing the information to contextualize circumstances and draw conclusions. These conclusions can then be used for actionable decisions to support a business.

“Data-driven” is often thrown around in business, marketing, etc., but it’s truly about the insights that you can gain from data. Modern businesses have virtually endless streams of data, but without analysis and insights, it’s basically useless.

Principles of Gaining Insights from Data

A business’s decision-makers may not be data savvy – the clear context and connection to the business gets lost among the figures and statistics. When the insights are missing, there’s a gap in the flow of decision-making.

As data gets more abundant – and more complex – this gap can widen.

Here are the principles of gaining insights from data:

Collaboration

Established companies often have multiple departments or teams to handle each step of the data process. Still, this can create information silos that leave gaps, unless everyone is on the same page.

Collaboration from all departments is necessary for a comprehensive view of data, circumstances, and goals. Communication and support allow key decision-makers to glean valuable insights from the data and see the “big picture.” Everyone is working toward the same goal, and thus committed to helping each other out.

Transparency

With multiple departments handling the data analysis and decision-making processes, each of them is privy to different aspects. The analyst knows the data sources, processes, types, and metrics, while the decision-makers know the questions they’re trying to answer and the goals they’re trying to reach.

All departments need to communicate openly and transparently to understand each other’s needs and ensure their part of the task is completed to its fullest.

Specificity

Like transparency, all departments involved in the data process need to understand the end goal and the larger business objectives. Data, management, decision-makers, and any other involved parties need to define the requirements, intent, and goals.

This ensures the right questions are being asked and the right data sets are used, rather than clouding the issue further.

Applying the Principles to Data Insights

Define Questions

As mentioned, specificity matters. Vague, broad questions make it more difficult to glean actionable answers.

For example, asking how to raise more revenue could lead to a wealth of hypotheticals. Instead, ask what channels you should focus on to raise revenue without raising costs, leading to a wider profit margin. Or ask which marketing campaign yielded the best ROI in the last quarter, and what you can do to replicate its results.

And remember, if you don’t like the answer, ask a different question.

Clarify Context

Understanding the context of the analysis, motivations, restricted, and desired results allows you – or your data scientists – to determine the best metrics to monitor. If you want the data to be contextualized, every step needs to be connected to your overall business objectives.

Set Clear Expectations

Different data sets can be used to answer different questions. It’s important to understand what kind of insights can be gained from the specific data set you’re collecting.

For example, are you looking for an average? A rate of change? A total? These specifics matter and inform your process.

Define Measurable KPIs

Your metrics and KPIs should always be measurable and connected to SMART (Specific, Measurable, Attainable, Relevant, Time-Bound) goals.

Data isn’t always numbers, it can be words, measurements, observations, descriptions, or simple facts, but your KPIs need to be measurable to see if you’re on track to reach your goals.

Formulate a Hypothesis

Data science is a science, and a hypothesis is a key component of the scientific process. A hypothesis is the proposed explanation or supposition made on the basis of limited evidence as a starting point for investigation.

For example, a hypothesis could be that improving the clarity of your landing page will reduce confusion and improve your conversions. If the results are negative, you could look at the other barriers that may be impacting customer behavior. If the results are positive, you can work on refining the clarity of the page and testing to see what’s most effective.

Collect the Right Data

As mentioned, it’s important to look at the right data sets when asking questions. Choose the data and metrics that are likely to show you the desired information and influence the desired outcome.

You may need to examine several measures and formulate a plan to determine how to get the results that lead to the answers you’re looking for.

Leverage Segmentation

Segmenting your data gives you more specificity and a granular view of the information. Depending on the question, you may want to focus on a chosen subset of a larger data set, such as the industry, audience, or website segment, then go for a more granular view of the behavior.

Integrate and Correlate Data

Multiple data sources offer a more comprehensive view of your business information. integrate your data sources and choose the highest quality data to support the question you’re asking.

Data should also be correlated. Consider the metrics that may affect each other, such as looking at bounce rate to get the right perspective on traffic metrics.

Discover Context

Sometimes, specific data points need to be put in context to see how they fit into the larger view.

A benchmark is a standard by which all others are measured, such as a novel that is the first in its genre. Similarly, your data should be relative to other data, such as the industry standard, your goal, or your competitors.

Having benchmarks such as these help you identify patterns, behavior, and growth rates, as well as identify trends, anomalies, and inconsistencies. This will also show you where you are in the competitive landscape.

Identify Patterns

All metrics have patterns, which is how you can determine the relevance of a data point. It’s crucial to recognize patterns and how they illustrate user behavior, such as seasonal fluctuations in buying behavior or online searches. 

For instance, we noticed that our content creation studio page would experience a spike in online traffic right before the holiday season. Our team immediately began strategizing on how to capitalize on this type of increased traffic. 

When you can see the patterns, you can quickly identify unusual behavior and evaluate it more effectively.

Develop a Repeatable Process

Data analysis isn’t a “set-it-and-forget-it” situation. You will consistently gather and analyze data throughout the life of your business, and it’s best to have a standard, repeatable process to do so.

Turning data into insights is a scientific process, and that’s exactly how you should approach it. Set up a structured workflow for data analysis based on the steps you go through, turning data analysis into a repeatable, human-driven process.

Embrace Human-Driven Data Analysis

Data is incredibly valuable for a business, but it can’t stand on its own. The data can only take you so far – actionable insights come from human inquiry and interpretation. 

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Author Bio

Kyle Johnston is a Founding Partner and President of award winning brand, content creation & creative agency, Gigasavvy. After spending the last 20+ years in Southern California, Kyle recently moved his family to Boise, ID where he continues to lead the agency through their next phase of growth