In this video, Joshua Rischin shares how to work with clients to understand sources of data and information. Learn how to outline your data requirements to provide insights through business intelligence.
- So you've discovered your client's core business problem. Great! Now you've just got to look for the data that will help you solve their problem. Good luck. Sourcing data can be a mine field. In my experience, and depending on the type of organization, business intelligence can come from a number of places. A client of mine once asked me to prove that the cost of their accounts payable and receivable function was comparable with their industry peers.
In this case, I knew that the relevant data sources were the financial management system as this housed all the relevant transaction data that I needed. The human resource system which identified all the staff performing accounts payable and receivable work and lastly, third party data for benchmarking purposes. But identifying the data sources is just the first step. Time and time again, I see data prepared that is either irrelevant, incomplete, or even unstructured.
So before accessing data, it pays to take the time to truly define your data requirements. And you can do this in a number of ways. But I like to prepare a basic list of data fields from each data source. And also include an example of how the data should be formatted and structured. In the case of my E-commerce client, who tasked me with assessing the cost of their accounts payable and receivable functions, I decided to prepare a brief for the financial management system data owner.
The brief included the following requirements, how the report should be run, in this case I needed the most recent full financial year and all the accounts payable and receivables processed. The data fields, the month processed, the transaction ID, the transaction type, that is whether it's an account payable or receivable function, and the transaction amount. I also made it clear that the data must be provided in a structured format.
And what we mean by this is data that is consistent and organized in rows and columns. Once the report had been prepared and provided back to me, it looked something like this. Now, as you can see, this report is well-structured. That is, there are no gaps in the dataset. It also has all the fields that I had requested so I felt pretty comfortable to continue with the analysis. In particular, I was confident that this dataset would allow me to work out whether or not the cost of their accounts payable and receivable functions were comparable with their industry peers.
Sourcing data can be a big job so consider teaming up with a business intelligence analyst or data scientist. Whether you do it yourself or seek external expertise, it's critical that data is high quality and well-structured.
- Determine the essentials of business needs.
- Recognize the fundamentals in reviewing source data.
- Define date granularity.
- Identify the importance of data relevance.
- Break down the meaning behind data-driven insights.