Discover the importance of performing a first pass quality review of the data you've been provided. In this video, Joshua Rischin shares the importance of making sure data is structured well enough to gather insights.
- Tell me something I don't know. I hope you never hear this statement from a client. The last thing we want is for our business intelligence work to simply confirm something that the client already knew. The insights from BI can transform a business, so we need to make sure that our business intelligence work doesn't just tell the client what the problem is, after all they probably already know this, but also why the problem exists and what they can do to turn their fortunes around. I like to call this data-driven insights.
Let's take the time to capture some of the likely data sources that will be available for an e-commerce client. Now, to make things simple, let's assume that our client has been in business for a number of years now, and that their main problem is that they're experiencing an ongoing profitability issue. Being an e-commerce business, they'll almost certainly have some kind of sales platform, with data containing customer records, sales orders, and inventory information. They'll also likely have several other data-rich applications such as, a financial management and human resources system.
But let's remember, as consultants, we've been engaged to provide expert advice. This means the client likely doesn't understand their own data, nor how to interpret it. They may not even know where the data resides. By this we mean whether it's in-house, in the cloud, or managed through an API or application programming interface. In the case of our e-commerce client experiencing a drop in profitability, here are just some of the data fields from a sales system, that when analyzed effectively will provide critical insights.
The transaction date and time. The customers location or ZIP code. The customers age. The inbound channel, that is, how did the customer land on the website? The time they spent on the website before making a purchasing decision. The product category. The product purchased. The quantity purchased. And lastly, the transactions revenue. Now, there are bound to be plenty of other fields available, but having access to just this information will provide a really good starting point for the analysis.
And to help with sourcing this information, you may actually wish to prepare a formal information requirements brief, as the client may need to seek assistance from a third party to extract this data. Now, once the data has been provided, I strongly suggest performing a first pass quality review, otherwise your effort on analyzing the data will be wasted. Check that the data is well structured, that is, organized into rows and columns, and also check for data consistency.
For example, make sure that for all sales transactions that there is no data missing for the quantity purchased. At this point you may be wondering how we can analyze such large volumes of data. Well, there are a number of tools around to assist with this. Some of these tools simply store the data and put the onus back on the user to analyze and chart the information, whereas others have more fancy features that will scan the data to look for trends and anomalies. Take the time to explore what's available in the marketplace, to ensure that you don't just tell your clients what they don't know, but you empower them with the insights they need to transform their business.
- 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.