Data is the foundation of successful businesses today. From improving customer experiences to optimizing operations, it enables organizations to make well-informed, data-driven decisions. But raw data is useless until it’s transformed into insights. This post discusses the 5-step process of extracting insights from data.
According to Exasol, 68% of data teams are unable to extract the data insights needed to make strategic business decisions. “The inability to access insights is hindering business’ ability to become truly data-driven” which today is a prerequisite to stay ahead of the competition and avoid disruption.
But how do organizations extract insights from data? While there’s no one-size-fits-all approach for all types of businesses, here’s a typical process of what it looks like.
1. Formulate a question and a clear idea of a result
It starts with a strategic question. What’s the purpose of the activity? Knowing your end goal helps to establish a clear understanding of what sort of result is needed. Are you doing it to uncover a trend or effect? Is it for product development or sales enablement?
Once there’s a defined outcome, then you should build a roadmap not only for turning data into insights. But also, for presenting and using the final results for reaching specific business goals. At this point, you’ll be able to know which kind of data is essential for your analysis and insights extraction.
2. Gather, clean up, and store data
Data comes in all forms, formats, and sources. It can be structured, unstructured, or semi-structured. The next step is to compile all the relevant data that you have such as data from ad platforms, CRM, marketing automation, email, social media, and web analytics.
When you have all the data you need, you should clean it and ensure it’s free of irrelevances and incorrect information. In data science, the resulting insights can only be as useful and good as the underlying data. High-quality data has to be valid, accurate, complete, consistent, and uniform.
An essential part of this step is deciding the technology or digital solution for data gathering, processing, and storing. Data and insights must be accessible at any time, especially those critical for business.
Selecting where to store your data depends on a combination of factors including volume, speed, reliability, and maintenance. But at its core, your data warehouse and architecture should be managed and scalable to meet your everchanging business needs.
3. Conduct strategic data analysis and uncover patterns
Now that your data is clean, it’s now time to uncover patterns using statistical models and visualizations. There are various techniques you can use here from clustering to semantic analysis. What you’ll end up with are data categories, segments, and groups based on common properties and features.
You can stop at this step if the results are enough to address the purpose of your data analysis. By this time, for instance, you can already get insights and track your performance across online advertising, sales, and customer success. Some tools allow you to present data and customize dashboards in real-time.
However, if you need more in-depth predictions and insights about future trends, then feel free to proceed to the next.
4. Find the right model for predictive analytics and validate
Depending on the patterns and groups that you formed as well as the outcomes that you require, you have to pick a suitable machine learning model. Here, you can use different approaches to see which one works best for your data and end goals.
After subjecting your data through multiple models, you have to evaluate the performance and see which predictions match the observed data. This way, you’ll know which technique produces the most accurate results.
5. Make decisions and communicate results
The final step is knowing how to communicate results. Data science must be connected to business outcomes for it to be effective. Your task is to make decision-makers and relevant stakeholders understand your insights, so they’ll make sound business decisions.
There are many ways to present predictive insights, you can use decision trees, flowcharts, and visual forecasting models. In the end, you should not only measure the impact of your insights by looking at ROI, process optimization, and business enhancements. But also provide a room to review and evaluate for continuous improvement.
The Exasol report further revealed that “80% of data decision-makers say their current IT infrastructure makes it hard to democratize data, further limiting their ability to extract value from insights.”
If you’re finding it difficult to gather, analyze, optimize, and monitor all your data points to enable big picture analysis, then Dealtale might help. It’s a single platform that unifies all your data and delivers real-time visibility and insights into your business. It takes only 10 minutes to set up. Go to: Activate Your Free Plan today.