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<\/p>\nYou have all the data you need. And the data to support that data. And the data to prove the accuracy of all your data. Still, all you have is raw material to make the right business decisions.<\/p>\n
You need to give it meaning by turning data into actionable insights and, with the enormous amounts of data constantly pouring in, that\u2019s no simple task.<\/p>\n
Read on to learn the fundamental steps and principles of turning data into insights.<\/p>\n
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For analysts, data and insights may almost be the same. But for the rest of us mere mortals who don\u2019t view the world in numbers, how do you turn data into information and from there, into actionable insights? Let\u2019s first define what actionable insights are.<\/p>\n
Data are tiny bits of measurement, while insights interpret what these measures tell us. Actionable insights provide information that helps stakeholders reach business decisions.<\/p>\n
Here\u2019s a real-life example. If you\u2019ve got one, you regularly measure your toddler\u2019s growth \u2013 that\u2019s your data. You then compare and calculate the difference between the previous and the current measure \u2013 that\u2019s the information you draw from the data.<\/p>\n
With every inch your child grows, they outgrow their clothes and shoes, and you need to start buying bigger sizes. So. if they grow x inches in 6 months, you need to renew their wardrobe every half year \u2013 now you have actionable insights.<\/p>\n
The same basic process happens in business on a large scale.<\/p>\n
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Traditionally, every business unit has a definitive scope of responsibilities. BI (Business Intelligence) and Analytics teams are in charge of collecting data and presenting it to marketers and other stakeholders whose job it is to reach decisions and strategize.<\/p>\n
More often than not, decision-makers get lost among the figures and statistics because they can\u2019t find the connection to their business reality. The data is there, but the insights are still missing, causing a gap in the decision-making flow.<\/p>\n
With the amount and complexity of data escalating, the gap is only getting wider.<\/p>\n
This divide no longer exists in digitally mature companies<\/a>, where teams comprise various department members. The first principle to remember when turning data into insights? Collaboration.<\/p>\n Collaboration.<\/strong> Teams need to combine efforts and assume mutual responsibility if they want to get actionable insights from their data. Communication and mutual support yield more valuable insights than confrontation and demand. Ultimately the teams work toward the same goal, and mutual understanding is a cornerstone in that cooperation.<\/p>\n Transparency.<\/strong> The analyst knows the data sources and the processes and types of data and metrics. Management knows what their goals are and which questions they need to answer. Communication between both parties needs to be open and transparent so each can understand what the other needs to fulfill their part of the task.<\/p>\n Specificity.<\/strong> Business units need to understand the key drivers of revenue, expenses, and risks in the relevant business area. For identification of the representative data sets, it\u2019s vital that all involved parties precisely define their requirements, intents, and goals. Specificity is critical to enable data analysts to identify the correct metrics to monitor.<\/p>\n Being vague can lead to chaos. Think of this example: If someone asks, \u201chow do I get to the airport?\u201d, you need more information before you can provide a valid answer. Which airport? What is their current location? Are they flying or picking someone up?<\/p>\n Understanding the context of the analysis, restrictions, motivations, and desired outcome enables you to decide which metrics to monitor and how. The goal? Create a connection between the metrics and what the data represents.<\/p>\n Define what kind of insights can be gained from the data you\u2019ll provide. For example, do you need to present a total number, an average number, or a change rate?<\/p>\n Make sure there are measurable metrics attached to the questions. You can use SMART<\/a> structure to verify (Specific, Measurable, Attainable, Relevant, Time-based).<\/p>\n Defining a hypothesis can help achieve all the above points. A hypothesis could look like this: if A is the outcome, it means xyz for our business. If B is the outcome, it means zyx for our business.<\/p>\n Choose the metrics capable of showing the desired information. You may need to correlate between several measures and create a plan for how to arrive at the results that lead to the answers needed.<\/p>\n Segmenting your data helps you get more specific and gain a more granular view. You can focus on a selected subset of data, such as a website segment, industry, or audience, and then dive deeper into the data behavior.<\/p>\n Read More: What Is Market Segmentation? Tips, Types, and Benefits Explained<\/a><\/p>\n Integrate different data sources. Choose the tools that provide the highest quality data<\/a> to support the result you\u2019re looking for. Consider integrating different sources and secondary research data<\/a>.<\/p>\n Investigate related metrics that impact each other. For example, you always want to keep an eye on your bounce rate<\/a> to put the traffic metrics in the right light.<\/p>\n So far, we\u2019ve emphasized the importance of being specific. However, to understand the meaning and be able to interpret the impact or the outcome, you need to view this precise data point in context.<\/p>\n Is 100 a lot or a little? What about a 10% increase? Is that good or bad? It depends. You must always present data relative to something, such as the competition, the industry average, the desired outcome, etc.<\/p>\n Benchmark your company data against industry data. Also compare data patterns, behavior, and growth rates to identify trends<\/a> and anomalies.<\/p>\n Find out where you fit into the competitive landscape<\/a> and how you measure up in different business areas.<\/p>\n Metrics have patterns. To determine the relevance of a data figure, you need to identify the pattern and put it in context. Recognizing patterns provides an understanding of behavior. For example, there are daily and seasonal fluctuations of activity on every website. Recognizing them helps spot unusual data behavior and therefore, evaluate it more accurately.<\/p>\n The analysis is done to get to the information. Next, you need to present it in an understandable way to stakeholders. Here are a few tips on how to do that:<\/p>\n Reports that include only numbers are a C-suite\u2019s nightmare. Help them get clarity and avoid misunderstandings, confrontations, and unnecessary challenges.<\/p>\n Visualize the data in a way that highlights crucial information. You can use graphs, matrices, pies, and even infographics.<\/p>\n Don\u2019t just send the report by email. Explain what the numbers mean in words, directly to the relevant stakeholders. Communication is at the heart of digital transformation<\/a>.<\/p>\n Read More: Digital Transformation Strategy: How to Drive Change (and Stay Ahead)<\/a><\/p>\n Instead of showing only your company data, provide the context that helps understand the significance of the data you\u2019re delivering. Set the stage for your managers to understand the meaning and translate it into action.<\/p>\n Explain the competitive environment, or present some historical data as background leading to specific results.<\/p>\n Accurately represent what you\u2019re up against with competitive benchmarking<\/a>. Most businesses have one top rival they measure themselves against. Show examples of how that rival is doing. Add examples of other representative companies to help illustrate your point.<\/p>\n Make sure you can provide the sources of your data and explain the relevance. Business leaders need confirmation, and you may have to explain how you arrived at the results you got.<\/p>\n Set up a repeatable process for generating insights from data based on these principles and steps.<\/p>\n The steps we\u2019ve shown here follow the Six Sigma<\/a> concept to optimize the quality of business processes. Six Sigma is a data-driven<\/a> concept of process evaluation and consistent improvement.<\/p>\n The first three steps in the methodology are: Define. Measure. Analyze. For new processes, these are followed by Design and Verify (DMADV). For existing processes, Improve and Control follow the initial DMA (DMAIC).<\/p>\n Turning data into insights is a process, and you should treat it as such.<\/p>\n Set up a structured workflow for data analysis based on the steps you\u2019ve just gone through. This way, you turn data reporting into a repeatable, insights-generating process with high operational value.<\/p>\n Similarweb Web Intelligence Solution<\/a> provides the most accurate website data for analysis<\/a> and tools to monitor and segment your data and then benchmark against the industry<\/a> and your competition.<\/p>\n 3 basic principles in generating insights from data<\/h3>\n
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<\/p>\nHow do you apply the principles?<\/h3>\n
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How do you put data in the proper context?<\/h3>\n
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How do you make the data relatable?<\/h3>\n
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Create a workflow for turning data into insights<\/h2>\n