Preparing NetSuite for Business Intelligence Webinar

Webina - Preparing NetSuite for BI Webinar

Get tips and best practices on preparing your NetSuite instance for analytics and business intelligence


Often NetSuite administrators set up data structures for running day-to-day operations, without longer-term considerations. While this is fine for daily running of the business, it sometimes causes issues when trying to perform analytics on that valuable information.


Stephen Peake, Customer Success Engineer at iCharts, offers guidance on how to best prepare NetSuite for analytics and business intelligence based on hundreds of interactions with iCharts customers in the NetSuite ecosystem.


In this webinar recording, Stephen covers:


  • How to approach NetSuite data structures for visualization and anticipate BI needs
  • Avoiding common problems and pitfalls
  • Tips and tricks to preparing, modifying, and optimizing NetSuite data
Transcript
Ted:

Good morning, this is Ted Sapountzis from iCharts and I’m joined by my colleague Stephen Peake, one of our senior customer success engineers. Stephen will be walking us through a number of topics around how to prepare NetSuite for business intelligence.

 v
Stephen:

I work with our Customer Success team, primarily on supporting them, and content production. Today, what we’re going to talk about is some of the trends that we’ve noticed. We’re going to touch on the foundations of what we consider Business Intelligence, some best practices and common pitfalls in NetSuite, as well as using formulas, the good and the bad of that, and some next steps and Q & A. In case you don’t know, we are iCharts, a native visualization platform for NetSuite, we are Partner of the Year for 2016. We’ve noticed some trends and we want to share that information with anyone who uses NetSuite. If you’re beginning to delve into analytics, or have been doing it for awhile, this can be helpful for you. What we’ve found generally is that NetSuite does a very good job, and our customers do a very good job of setting up their for day to day usage, and day to day capture of their data. One thing that we’ve noticed is that when you do this for analytics specifically, those two don’t necessarily go hand in hand. An example is if you capture information related to your customer. Let’s say you’re selling clothing, and your clothing sales are generally most directed by the regions of which you’re selling in that can cross state lines. If you don’t include some intermediate categories like regions as opposed to just state and city information, that is a gap in your data that can prevent analytics, but won’t clog up your day to day data capture.

 

Let’s just hop right into this. One of the best metaphors that I’ve seen that can line up with our customers as they begin to consider analytics, and develop them for their company, it’s the basic idea that every human has needs. Unless your basic needs are taken care of, it doesn’t free up any bandwidth to consider other needs. Physiological is first. If you have food to eat, once that’s taken care of, you can begin to consider your safety. Once you’re safe, you can consider your love and belonging. This lines up with companies and the positions they’re in as they begin to develop their analytics. This is the iCharts hierarchy of analytics. They develop their basic KPIs, and then we build out a dashboard for those. What that does, is it may clear up having to do a couple of manual processes to retrieve those analytics, and frees up their bandwidth of their time to consider second level insights. After perspective sets in on the second level insights, we develop third level insights. What our customers have done is use the platform to really shape the direction of their company in the direction they want to go.

 

So, one thing that’s a great thing to work on, is when you’re preparing for BI, it’s great to have an analytical mindset. What this means, is that there’s a way you can think of of storing your data that’s not just capturing your data, but it’s actually setting it up for the best possible form, so that you can use it for analysis later. If you look at the top line here, we have listed information. Both of these, the top and the bottom are capturing the same information, but the difference is, the top one, I would have to do extra steps to parse the data, and derive any insights from it. The bottom is the same information, clearly defined in separate categories, that’s absolutely ready for analysis. Having an analytical mindset, even if you’re very early on in NetSuite, can prepare you and save you loads of time later.

 

This is preparing your NetSuite for BI. What are the goals of Business Intelligence? The goal is to put the data that you need on a daily basis at your fingertips. When you have the data that is concerning the growth of your company, the things that are important, it allows your business to be faster, more precise, more profitable. It provides the clarity that you need because you don’t need search for the data that you need, to get decisions. You don’t have to do a process to get what you need to form your decisions. You can make better decisions, and ultimately it frees up your time to focus on other aspects of the business.

 

When we begin working with another customer, this is how we approach things. This is a bit about our chart building process as we work with a new customer. The first thing we do is try as a success team is understand the data that is present, and the metrics that you want to get out of the data. Understand NetSuite’s capabilities. Sometimes there are cases where you want to do something, and because of the way it’s set up in the system, you have to do it a different way. Evaluate what you already have. Are you retrieving your metrics outside of NetSuite? Are you using a manual process for anything? Once we have all of that in place, we start building. This is touching on some of the foundational elements. I want to get into that in detail.

 

These four points are what we consider to be the foundations of BI.

 
  • Know your Data.
  • Know your Metrics and KPIs
  • Know your System
  • Know your Audience
 

Why they’re foundations is because as we’ve worked with many customers, these are each of the development areas within every company that, depending on the status of them, will prepare you to be immediately ready to analyze your data. If one is not present, it will—when you begin to analyze your data, get metrics, and drive your company in the direction you want to go—if one of these is not developed it will immediately be exposed in the process of trying to do that. I’m going to go into these in a little bit of detail to make it practical to understand why that’s the case, and give you an example of some of the customers that we’ve worked with.

 

Know your Data: Knowing the data is super important. How can you know what to inventory if you don’t know what your inventory is? Each of these are things that you can grow as a company, or as individuals within the company. There’s no substitute for this. When you try to validate your data and ask questions like “where is that number coming from, what is the appropriate date and time field to key that off of?” Being able to answer those questions easily is huge. It’s a process that’s not always present, but it’s absolutely necessary to begin to delve into analytics. It’s a keen understanding of the data. You need to go deeper than standard reports—not because the standard reports are bad by any means. They are truly excellent and NetSuite provides a huge variety of them, and they’re great for reference, but there is a level of depth that needs to be beyond using just the standard NetSuite reports.

 

Know your Metrics and KPIs: What do you want to get out of your data? What are the ways that you can make it work for you, that will truly be helpful for your company? Two different customers that we’ve worked with: one is an example of one who knew their data well. We began working with them, and after two weeks with a little bit of direction of how to use our platform, they were able to build out a dashboard with over 40 charts, complete, because they knew and had outlined and has developed as a company exactly what was important to them, and exactly what kind of visualizations and BI would be helpful to them, and help them grow. An alternate scenario is starting from the same spot, we began working with a company that generally knew their data, but they weren’t quite sure what was valuable to them. We spent the next three to five weeks having probing sessions, and were asking about every aspect about their company, and helping define what drove their business, and outlined it for them, to even begin building their dashboard. They had a knowledge of what their data was, but not the things that would help push them to the next level. If you’re doing manual processes, a lot of the times, manual processes are in place because there’s something that you need to know that you can’t get out of NetSuite natively. Excel will always be a great tool, and be great for data validation, but if you get a KPI or metric on Excel, ask yourself what is it, why are we doing it that was, and do we still need to do it that way? That’s a great place to start.

 

Know your System: Being able to know your way around NetSuite is critical. You know your data, and you know what you need from your data, but if you don’t know your system, if you aren’t familiar with NetSuite, you won’t be able to retrieve it and put it in front of you in an efficient and quick manner. In a nutshell, you’ve got standard reports and saved searches are the main ways to digest and retrieve data our of your system. The reports are set up standard. Saved searches are also key because they give you more flexibility to filter, to adjust, and to validate. We’ve noticed a trend that where if you’re just using the standard reports, one thing that can happen is that the element of data validating and the element of knowing your data on a deep level can be absent, because the reports are set up standard. Searches make you identify the elements explicitly and really know what’s driving them, what’s driving your numbers, and where it’s located in the system, like What field should be the right one, what number should be the right one, and so that’s why we generally are able to do what we need to do much quicker.

 

Know your Audience: The audience would be the person you’re making these dashboards for. It’s not necessarily your whole company all the time. This is really key because to really step into their shoes will mean the difference between making something that’s truly actionable, as opposed to something that’s irrelevant, if it’s not truly geared for them. Knowing what their crucial data is, what’s relevant to them. One customer that we worked with, without a true knowledge, and an open communication with their audience, every litter iteration that we did, they had to check in someone that wasn’t out main point of contact, to validate the data and understand that it was showing what they wanted to see in an easy manner. What a dashboard looks like for an audience that it hasn’t been geared for, the first thing they do is change it in some way. You need to do extra steps to get the information that you want. That’s what we consider the foundations of BI, knowing your data, knowing your metrics, knowing your system, and knowing your audience.

 

Here’s a typical problem: Being able to retrieve the number of days between two dates. For example, if you had a closing date, and a transaction date, you might want to know the amount of days between those. To this is actually really simple, if you did a formula on a saved search, it would be as simple as subtracting one field from another. This is something that is universally common, all of our customers have done something like this, and I’m bringing it up because this is a very simple thing. Instead of having to reference a formula for it, you could make it easily accessible within your system. If you connect this to a field that lives on your shipping record, you can just report the number of days between your delivery date and your ship date. It’s just being aware that there are certain things you can use formulas for that you can make common, and available for everyone. It will save development time, and allow you to more easily get into analytics, and grow as a company.

 

The second one is similar but on a slightly different bend. Let’s say you want to do a similar thing but with different time stamps. I want to know how long something was in a certain stage. I want to know how long my prospects are in the prospect stage. If you look on the chart on the right, this is my average time in stages, and these are my different stages from one year compared to another. This is something that people assume is readily and easily available because NetSuite keeps very thorough system notes. The common use case is as follows: System notes is a great resource, but generally it requires development time. It’s a surprise to our clients because they think that we can just dip in and get it. When you start delving into system notes, it really requires a keen understanding of the information, and the NetSuite system. Generally an extensive filtering process as well. The system notes fields are so interrelated. You have to very extensively filter to get what you need, without bringing through additional records that would throw off the numbers. My point in this, is know what’s important to you. If you would like a time stamp every time a stage changes, identify that and do the development work up front to make it generally available through a custom formula field. Make it available to yourself early on so that it doesn’t surprise you, and you have to do development time later.

 

The third case is regarding descriptions. Let’s say you have a general description line. This is capturing a lot of good information. In order to use this information to even filter my products, I would have to parse it with a text formula, to be able to isolate what I need. You can create custom fields and put it on the records for each of these items, and have it populate and store the information. If you wanted to get really thorough, you could even create the fields and then do a mass update that would back populate the information. Knowing that this kind of detail is super useful for analytics, and that it could even inhibit you from going forward, is good to know as early as possible.

 

For any company, you have a large pool of data. You have a large amount of customers, a huge amount of products, a ton of employees. So much we’ve run into where you don’t have the levels of detail that you need. Think about it this way, say you want to make a set of information about your customers as easily digestible as possible. If you have 24,000 customers, that’s not something you’ll be able to look at visually in 3 seconds, and know what’s going on. If the only options you’ll be able to filter those customers by is state and city, that’s not a good amount of options to be able to filter that information down to a digestible set. Another example is the product. The basic things people have for product is the individual item, and product line. What I’m saying is: identify as many intuitive intermediary groupings as you can for whether it’s your customers, your products, your employees, any large dataset. If possible, record that on forms. If I take my clothing sales, looking at this map it would make more sense with different areas, different climates. Obviously my clothing sales are going to be much more dictated by the regions of the country, because of climate, terrain, and geography, rather than state lines. It would make sense that I’d have regions as something that is defined on my customer records. If you don’t do that development, it’s not going to be there. Making those groupings, and making them generally available, is huge. It’s a universal thing that’s sometimes been overlooked.

 

Formulas are a powerful way to do advanced calculations, but formulas can be used to mask missing detail, fields, and hinder widespread development. They can be used as a crutch, as a way that works but overall can actually hinder more widespread development. Here’s an example of having fun with a formula. This is a CASE WHEN statement. This is something you can use in a search. What this is essentially saying, is when you’re in the situation when a metric is unique, helpful, or specialized, then make a formula. But, when the data is common, widely used, or often needed, then make a custom field. If else, you may waste time by making a formula.

 

This has been a webinar on preparing NetSuite for BI. We talked about structuring data for analytics is not equal to simply capturing your data. Before you start, understand what you have and what you need. We also discussed some best practices and common pitfalls in NetSuite.

 
Ted:

Thank you Stephen! Thank you for tuning in to our webinar, have a great rest of your day!

 
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