Businesses have never had more data available than now, and it’s never been easier to access. There’s even data about data and analytics!
With so much at our disposal, there’s no reason to be uninformed about your customers or business – unless you’re just not trying. But to be informed on a human level, connecting with humans in a way that really matters, takes more work.
How can you make human-focused, data-driven decisions for your business? You have all the raw materials, but it can’t do the work for you. You have to humanize the data, and here’s how.
Understanding Data, Analytics, and Insights
The information you collect is the data that informs the analytics, which can then be compiled into reports and visualizations to understand and contextualize it. Insights are the ideas you glean from what the data demonstrates about your business.
Let’s break it down further.
Data is raw information that has not been processed, categorized, or analyzed. It’s essentially a high volume of facts and numbers that can be put into context to find key insights. In short, data is just the information – the facts – with nothing to classify it or give it meaning.
Analytics takes the data and groups it into categories, classifications, or contexts. It’s analyzing the facts and organizing them into a broad overview, based on the rules or key indicators you’ve chosen.
Insights are the findings you gain from analyzing the data. It’s telling you the “why” behind the information you’ve gathered, giving you context to draw conclusions and make decisions about your business or customers.
Principles of Gaining Insights from Data
Data, reporting, and visualization aren’t capable of driving action on their own. Human decision-makers have to understand that information and what it means for the business. It connects the data and the action you should take.
For example, looking at one indicator in a vacuum, such as an abandoned cart rate, doesn’t tell you anything. But looking at what that could mean can reveal barriers to the purchasing process for customers, such as a website error or a suspicious element that scared your customers off.
Comprehending these facts and statistics is what makes data actionable. When there’s a wealth of data, which is usually so for businesses, bringing it all together is even more important.
Here’s how you can gather actionable insights from available data:
No matter how large or small your business is, the more cooks you have in the kitchen, the fewer people will collaborate and naturally communicate with each other. Information silos form, communication barriers go up, and everyone struggles to foster organic communication.
If you want to have a full understanding of your data in relation to your business goals and objectives, you have to have communication. Get everyone on the same team, working for those goals, and you will get more from your data.
Communication and Transparency
When you have stakeholders interested in the data your business gathers, they all have different reasons for interpreting the data and ways to use it. For example, financial stakeholders are interested in data as it relates to revenue and profits, while the marketing department may look for more granular data about customers.
All teams and departments have to communicate transparently and completely about the data. Analysts need raw data to start, then they can determine how it should be compiled and processed. Decision-makers can stay focused on their interests and specific goals. Transparency also welcomes fresh perspectives, which helps everyone get more from the available data.
Every stakeholder in your business’s data, from data collectors to aggregators to decision-makers, should have specific questions, requirements, parameters, project intent, goals, and expectations for the business data.
Having this level of specificity ensures that the right questions are asked and the reports are tailored to the individual team members involved – offering the best possible insights.
How to Apply Data Insight Concepts
There’s no one-size-fits-all approach to gaining insights for your business, but there are best practices to relieve the pressure to come up with revolutionary new ways.
Ask the Right Questions
If you want specific answers, you have to ask the right questions. Data isn’t a magic 8 ball – you get out of it what you put in. If you’re too broad, you’ll only end up with vague and unhelpful answers.
Your goals must be SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound), and so should your questions. Instead of asking something vague like, “How do I get more profits,” ask a targeted question like, “What marketing channels offer the best ROAS?”
Clarify Operational Context
If you’re struggling to define your metrics and indicators, you don’t have a clear enough understanding of the context of your data. What are the restrictions? What are the results you hope to achieve?
Put your metrics in context with your end goal. Everything should connect.
Set Clear Expectations
The data you collect could offer multiple insights, sometimes providing answers to more than one question. You must know the end goal you have in mind before you start asking questions. Work backward from the insights you need.
Develop Key Performance Indicators
We already went over SMART goals, and your key performance indicators (KPIs) need to be outlined the same way. Unrealistic, vague, or unmeasurable goals or KPIs won’t do you any favors.
We often think of data as hard numbers, but it’s not always. There’s “murky” data like customer sentiment – otherwise known as soft data – that can still inform your goals and process.
Following the scientific method, a hypothesis is an assumption you make before you do any research, which is what you test to see if it’s true or false. A theory is what you use to explain the things the data reveals.
For example, you may think you need to switch the creative aspects of your ads to drive better results or update your brand typography to connect with customers. This is a hypothesis since you have an assumption you’re trying to prove or disprove with data. If the data supports this hypothesis, now you have a working theory that you can use to improve other areas of your marketing strategy.
Before you ask questions about the data you’ve collected, make sure it’s relevant. Raw data can include a lot of worthless information that will only cloud and hinder your progress.
Consider if the data is relevant to the outcome you’re trying to achieve. It may take a few rounds of testing to get the answers you really want.
Segment Your Data
Data needs to be categorized and classified to get the best insights from it. There will inevitably be useless or incidental data, but that must be weeded out for a more granular view. Segment the information based on the audience, demographics, industry, and other relevant metrics that give you more accurate insights.
Correlate and Incorporate
When you collect data from a range of sources, you have a big-picture view. Without that, you could miss out on key details that would dramatically alter your decisions. Incorporate as much data as possible from all available sources.
You have your data, it’s starting to come together, and the bones of a story are emerging. Now it needs context.
Consider your data against relevant benchmarks, such as last year’s or last quarter’s data, your last campaign, or your competitors’ data. If you don’t have that information, work off of industry benchmarks to see how you’re performing.
This will help you discover anomalies, user behavior, emerging trends, and other key factors to see where you are in the competitive landscape.
Once you have enough data, you will start to see patterns. Where the human element comes in is interpreting the meaning and relevance of that pattern. You may notice anomalies, spikes or declines in some seasons or surrounding external events, or industry changes.
Make It a System
Data isn’t the sort of thing that you can set and forget. Despite being largely empirical and “hard” information, it comes from humans, so it can be fluid and organic. You have to systemize the process to ensure your long-term success.
It’s crucial to have a system to ensure that all team members are consistent in the manner they gather and analyze data. What works right now may not next year, next month, or even next week.
Bring Humans into Data Analytics
Numbers are elegant and reliable. Humans are complicated, dynamic, and unmeasurable. Though data can give us insights, it can’t fully quantify human intent, dreams, and desires. That’s why humans need to guide data analytics and use intuition to inform the process. Let the information lead your insights to your goals for the best results.
Author Bio: Kyle Johnston
Kyle Johnston is a Founding Partner and President of the content creation & brand strategy consulting firm, 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 the next phase of growth.