We live in an age of increasing dependency on data. As a result, companies that quickly make effective, data-based decisions are best positioned to survive in this brave new world. Business Intelligence (BI) is one of the primary ways organizations translate raw data into business insights. Companies that implement a robust BI strategy have a major advantage over those that do not. It’s time to prepare your business for BI readiness.
iCharts is a real-time Business Intelligence solution for NetSuite. In the course of helping hundreds of NetSuite customers from many industries visualize their metrics and Key Performance Indicators (KPIs), we have observed several trends and best practices that make it much easier for organizations to implement third-party BI solutions down the road. While some of the practices are simple and intuitive, they are often overlooked early in the NetSuite journey, causing complexities and gaps in the data that lead to a lot of easily avoidable headaches and wasted time.
“We have the data for day to day operations. What else could we need?”
“We need to be more established before thinking about analyzing our data.”
“If only I had a field for that…”
When companies begin to map out their crucial use cases and metrics and relate them down to the specific fields needed, gaps in the data become obvious. They may need a detail or timestamp to achieve a metric that is not overtly crucial in day-to-day use and is therefore easy to miss. They might also record the data in a way that requires extra parsing and manipulation to analyze.
These are surprisingly common occurrences. Even companies that have been using their current CRM or ERP for years may find areas that need more granularity or different fields to extract data-driven insights more quickly. This is especially painful for companies that have a wealth of historical information but have not included these needed fields. Approaching data structures from a metric-centric mindset can expose potential gaps sooner, allowing your company to have everything it needs from its business data.
Here are some real-world examples we have run across recently.
Sales Opportunity Stages or Order Statuses
All companies typically have groups of transactions or customer accounts classified into stages, statuses or levels within their CRM or ERP systems. For sales, opportunities may be classified as “In Discussion” or “Closed Won”; customer service may assign a “green” to “red” health status to existing accounts. These statuses are easy to set up and frequently used in NetSuite.
But what happens when companies want to analyze internal processes and efficiency by understanding how long each customer remained in a certain status? Or by seeing how those stages compared with a previous year? We recently ran into a customer that, in hindsight, would have benefited from fields that recorded timestamps when customers changed status inside NetSuite.
Good and Bad Ways to Record Data
In the initial stages of a new business or process, the granularity of data fields is an easily overlooked topic. In the example below, the customer set up its data structure in a way that was fine when running day-to-day operations. However, the limitations become apparent when trying to analyze the best-selling materials in each product line.
With Table A, that analysis requires either a lengthy exercise of extracting the information from the description column of the table and using complex formulas that identify the materials used, or a data export to Excel.
Example of a portion of a BAD data table.
|Headwear||Scrunch Hat||Black, Medium, blue horizontal stripes, cotton stretch, tiger graphic|
|Apparel||Air Shirt||White, perforated neoprene mesh blend, red collar, diagonal stylized|
|Shoes||Jordan Jr’s||Red/blue, Leather with stretch mesh, pump tongue, air bubble sole, jordan thumb graphic|
|Shorts||Air Shorts||Black, perforated neoprene mesh blend, red vertical stripe, diagonal stylized|
In Table B, the analysis simply requires filtering by the well-prepared fields for “Line” and “Material.”
Example of a portion of a GREAT data table.
|Category||Item Name||Size||Color||Accent Color||Line||Graphic||Material|
|Headwear||Scrunch Hat||Medium||Black||Blue||Champ||Tiger||Cotton Stretch|
|Apparel||Air Shirt||Large||White||Red||Ninja||Diagonal Stylized||Perforated Neoprene|
|Shoes||Jordan Jr’s||9.5||Red||Blue||Jordans||Jordan Thumb||Leather|
|Shorts||Air Shorts||Large||Black||Red||Ninja||Diagonal Stylized||Perforated Neoprene|
Notice that we are capturing the exact same data in both examples, but Table B sets up the data structure in a way that enables easier analysis in the long term.
Given the number of different industries and business models out there, it’s tricky to offer general advice, but keeping future analysis in mind will relieve a lot of headaches. To set up data structures inside of NetSuite for easy analysis, start by asking yourself some general questions that work outward from your end result:
“What type of analysis might we want to do in the future?”
“How granularly will we want to be able to filter our products, services, activities, customers, locations, etc.?”
To learn more about best practices for visualization in NetSuite reporting, sign up for our monthly webinar, or try our Interactive Demo below: