What begins as an advantage often becomes a requirement. Analytics is a good example. At one point it was a way to get ahead. Now it’s just a way to keep from being left behind. Analytics is more affordable and easier to use than ever, but it can still be intimidating to newcomers. One of the biggest hangups is how to prepare your data for analytics. Unfortunately, even lightweight analytics tools require some amount of preparation. Without it, you can seriously hamper the potential of your investment.

In our recent webinar, “Preparing NetSuite Processes and Data for Reporting & Analytics,” we got to hear from NetSuite experts Stephen Peake and Shaun Porcar from the iCharts Customer Success team.

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A big part of Sean and Stephen’s job is talking to customers and helping them prepare for analytics. As you can imagine, they’ve seen a bit of it all. Shaun and Stephen had a bunch of advice, but the common theme is that a little preparation can go a long way towards the success of your analytics project.

Structure your data properly, and divide it into subcategories

Essentially, the goal of preparing your data for analytics is to make it accurate, detailed, and searchable. Generally speaking, the more detail you add to your data, the easier and more valuable it will be to analyze. For example, let’s say you have 10,000 customers, and you want to know more about them. If all you had was everyone’s name, there wouldn’t be much to analyze. Furthermore, you’d have to analyze everyone at once because there’d be no way to narrow the results. However, if you were to add information about what state they live in, suddenly you can narrow results by region. With each added layer of data, you can make your data easier to search, not just for you, but for your analytics tools. That said, the way you structure data matters just as much as the data itself. Without properly formatted data, analytics tools won’t know how to make sense of things. A common example is a catchall “description” field that people use as a dumping ground for multiple data points. To format your data properly, you’ll need to parse out these items into their own fields.

Know what your goals are, and structure data accordingly

More detail makes your data easier to search and easier to analyze, but that detail can come at a price, so make sure you’re adding details that matter. The amount of detail you could add is theoretically endless, but by adding a field, you’re on the hook for keeping that field populated for every entry. So don’t just add detail for its own sake. Make sure that every field you add has a real purpose. For example, if you make baseball gloves, you might not need a field for color, but you would need a field for what type of glove it is (pitcher, catcher, etc.). Analytics offers endless possibilities, and it’s easy to go down a rabbit hole chasing an insight you never really needed. Figure out your goals for analytics as best you can, and work backwards from there.

Validate your data

Analytics is only as accurate as the data you give it, so take steps to make sure your data is as accurate as possible. There are a number of ways to verify your data, and you may want to use more than one.

  • Email alerts that let you know if a data point is abnormal
  • Third-party data cleansing services
  • An on-staff data analyst (this option may be out of reach for smaller companies)
  • Putting the burden on stakeholders to identify bad data and let the organization know about errors

Another approach is to wait until errors in your data present themselves, then find the source of the errors. It’s not an ideal approach, but it might make sense if you have a barebones operation and validating all of your data at once isn’t feasible.

Get to know NetSuite

If you’re new to NetSuite, you’ll want to wait until you have the hang of things before embarking on an analytics roll out. Now, you don’t need to be an expert by any means, but an intermediate-level understanding of NetSuite will go a long way. NetSuite is a powerful, versatile tool. If you don’t know what you’re doing, NetSuite will give you enough rope to hang yourself. A good example is customization. NetSuite makes it easy to customize your experience, but as Stephen puts it, “Over-customization can disrupt some of the great functionality that NetSuite offers out of the box.” Much of NetSuite’s built-in functionality assumes that your data follows a certain standard, and customizations can disrupt that.

Get to know Oracle and SQL

NetSuite is essentially a user interface on top of a series of Oracle SQL databases, so an understanding of Oracle and SQL can come in handy. When preparing for analytics, you may feel constrained by NetSuite, but sometimes a solution is as simple as an SQL formula. Furthermore, a basic knowledge of Oracle databases, SQL, and syntax can provide valuable context for any problem you run into in your analytics rollout. While learning the basics of SQL can sound intimidating, there’s a ton of resources to get you started. Oracle itself has an excellent library of SQL documentation.

Find what’s slowing down your searches

If your saved searches take a while, there’s probably something wrong with your data. Saved searches often serve as the basis for analysis, so optimizing your searches can help everything work faster. It’s all too easy for saved searches to take on a life of their own and get bogged down with ad hoc additions. Take stock of your saved search and look out for things that might slow it down.

Taking a look at your formulas is a good place to start. Users often use formulas in the wrong way, and end up slowing down their searches. For one, people often use formulas when they should be using a field instead. Formulas should be for special situations only, not for information you use on a regular basis. Also, if you do need to use a formula, make sure to set parameters so you don’t end up searching every last item in your database. The more data you search, the longer it will take, so filter your search with criteria like date ranges, and transaction types.

Only worry about legacy data if it really matters

Your first instinct is to bring as much legacy data as possible into your new analytics environment, but this can be a waste of time. Depending on the format of your old data, it may be a challenge to get it into a new system, so be sure it’s worth it. If it’s data you use on a regular basis, it’s a good idea to get it up to speed, but if no one has used it in a while, you should consider leaving it behind. One way to identify data that matters is by logging data requests. You may be surprised by the results. The customer success team has plenty of stories about companies bending over backwards to import certain data, only to find they never use it. Try focusing on the top 10% most important data before you worry about the rest.

To prepare your NetSuite data for analytics, the best thing to do is optimize it for NetSuite itself. By structuring data properly, sniffing out errors, and shedding snags on your searches, you’re most of the way there.

For more information on how to prepare your NetSuite data for analytics, check out the webinar that this article is based on.