How to get started using data to drive your business decisions.
You know as a business leader that using data can drive innovation. Whether your business is struggling because of a change in the market, or growing beyond what it can handle, gaining insights from analyzing the right data will lead to improved decisions. However, getting started or knowing the right process for collecting, processing, and analyzing data is a tricky endeavor. Below are some steps that breaks down the activities needed to process and analyze data to gain insights for your organization.
1. Identify the business problem you are trying to solve.
You cannot begin to know what data to collect or if you have the right data unless you know what problem you are trying to solve. For instance, common business problems might be – Why does it take my business so long to attract new customers? How can I improve efficiencies within my business? Why am I losing current customers? I cannot keep up with demand, do I hire more employees or automate my technology systems? How do I use data to make smarter planning decisions with my business?
Once you have defined the problem you need to find the right data to best give you insights into solving the problem. For instance, you might see that the number of closed deals sold by your sales department is right on track, yet the overall customer lifetime value is much lower than anticipated. One could hypothesize that the operations team is not keeping clients happy with their services, thus customers are leaving earlier than anticipated. To test this hypothesis, you might what to collect information about the customer overall experience.
2. Data Collection
Once you have identified your business problem and a hypothesis of what you feel could be the issue of the problem, you will need to collect data to prove your hypothesis. Now you need to figure out what type of data to collect. The data could be quantitative such as financials, sales numbers, and inventory numbers; or the data could be qualitative such as a customer survey or an observation. You will also need to determine if you already have this data or need to collect the data from other sources. Below are the various sources to consider:
First-Party Data – Data your organization collects.
Second-Party Data –Data that is another organization’s first-party data
Third-Party Data – Data collected and aggregated from multiple sources by a third- party organization.
It is important to also know the data type, which could be structured (within rows and columns) or unstructured (pictures, videos).
3. ETL (Extract Transform Load) processing and/or Data Wrangling
Once you have acquired the data you need for analysis, you will need to aggregate, cleanse and prepare the data. Depending on the type of data and from what source, the data analyst/scientist will use an ETL or data wrangling approach to prepare the data for analysis. Data Analyst will typically use an ETL process to gather data from a variety of databases or operational systems. While a Data Scientist will use a data wrangling process to explore, structure, cleanse, add additional data, validate and then prepare the data for analysis.
4. Data Analytics
Now your data is ready for analysis. You will need to consider the type of data analytics you will conduct based on the business problem you are trying to solve or the insights you are trying to gain. There are many techniques data scientists use to analyze data such as statistical modeling, classification, regression, clustering, decision trees, neural networks, anomaly detection, association rules, natural language processing, DataOps, MLOps. The type of data analytics performed will fall into four categories listed below:
Remember analytics is an iterative process, meaning as you gain more insights, ideas and data, you continue to revise your analysis to gain better results.
Deploy insights into action.
Now that your analysis is complete it is time to deploy your insights into action. Deploying what you have learned is more than just sharing the raw results of the analysis; it is interpreting the analytic outcomes and deploying them in a meaningful way within the organization. Does the outcome change a process to gain more operational efficiency? Does the outcome give you insights into why you are losing clients? Does the outcome open your eyes to a new business segment or audience you did not know you had? Whatever the outcome, it is important that you deploy that insight in a meaningful way within the organization, otherwise you just spent a lot of money on a science experiment.
Conclusion:
Data analysis can seem a bit daunting and unobtainable, but the insights gained are worth the effort. Deploying the right data analytic process will help you make better decisions for your business. All you need to do now is get started. We are here to help. Scalesology can help you develop a comprehensive data analytics strategy so you can reach the next level. Contact us today, and we can discuss how to transform your data into meaningful insights.