A growing theme in business technology today is the need to bring together data and processes across an organization to improve operational efficiency, customer engagement and growth potential. Most businesses are hampered by data trapped in disparate silos of operation, with processes split across separate applications and manual workarounds. Breaking data out of these silos can often yield striking results. At this week's SuiteWorld conference, NetSuite customers have been speaking about their use of the vendor's analytics warehouse product to surface data insights that are often unexpected. For example, Mark Chuberka, NetSuite administrator at home goods maker Birdrock Brands, reveals what happened when the company was able to combine data about its Amazon marketing spend with core NetSuite product data:
We had some products we thought were making money. They weren't making money. It's really made a huge difference, getting the data in a new way of looking at it than just tables and spreadsheets. It's really opened a lot of people's eyes to what they need to do.
There was a similar impact at Terlato Wine Group, a family-run international luxury beverage business, specializing in fine wines and spirits. The data often tells a different story than the instinctual feel that sales and product managers have for what's working or not working. Jeff Hampton, the company's Head of Reporting and Analytics, comments:
The one thing about analytics visualizations that's great, is that it lays everything out on the table. It's undeniable. It lays everything bare. Your weaknesses, your strengths, who's performing, who's underperforming, what brands are doing well, what brands aren't ... That was an aha moment.
Cobbled together spreadsheets
Before moving to NetSuite a year ago from an on-premise JD Edwards system, Terlato's data had been spread across many different sources, and people resorted to homegrown solutions or cobbled together spreadsheets to analyze the data, often coming to inconsistent or contradictory conclusions. The problems were compounded by the consequences of human error when data had been manually keyed across from one source to another. He goes on:
There were just a lot of inaccuracies in the data, things keyed in wrong, that had to be corrected. A lot of data cleansing had to happen as a result of the migration. And the analytics tool was very useful in identifying those outliers, because obviously they stick out visually, so they're easy to identify that way. That was very helpful for us in cleaning up our dataset.
Now that all the historical data has been brought across into a single analytics platform alongside current data from the new NetSuite system and other sources, all the information is in one consistent form. He explains:
Everyone's singing from the same hymn book right now. We don't have people drawing different conclusions based on different data sets because of erroneous data that's been collected from one source or another ...
What the tool has allowed us to do is get everybody on board, making the same decisions ... With all of the data sources that we pull in through NetSuite Analytics Warehouse, we're able to put side-by-side now — which we've never had the ability to do without an exhausting process of days of work cobbling together an Excel spreadsheet with actuals next to budget next to our positions — what we're selling at our stores.
Adding Amazon metrics
For Birdrock Brands, the issue was being able to marry up data about its Amazon marketing activity with NetSuite finance and product data. The company makes home goods, kitchenware and pet supplies for sale through warehouse clubs such as CostCo and Sam's Clubs, and online via Amazon, where it's a Seller Fulfilled Prime Partner, which means it fulfils Amazon Prime orders direct to consumers, according to strict timescales set by Amazon. It has been running on NetSuite for six years, initially for basic accounting and inventory management, and then gradually expanding to add EDI connections to customers, a SuiteCommerce web store, and finally NetSuite Analytics Warehouse. This imports daily Amazon data on traffic and ad spend from a Snowflake data warehouse — or if needed, Birdrock can also access live data via the Snowflake connector. Adding the data from Amazon was crucial to understanding how products are performing, as Chuberka explains:
If you're making money or not, does not rely on how much you sold the product for. Amazon advertising is critical to success ... Now we can actually, day by day, see how much we're spending on advertising, down to the product level. Category managers, for example kitchenware, can see how they're doing over time.
The discovery that the ad spend on some products added up to more than they were bringing in led to "some very intense sales meetings" at the time, he says. The result was more focused marketing in some cases, price changes in others, and some products will be discontinued. Work continues to collect even more accurate data, as he explains:
We also have been working very hard on building out internal systems within NetSuite, customizing it to give us more effective margin data, so we can get up to that moment where it ships and now we can take everything we learned with all that NetSuite data and just tweak the advertising spend ... The purchasing teams are coming on with it now for the factories and getting a better idea of how they're going to cost more efficiently.
So the ripples from that big rock in the pond of launching those dashboards, they're spreading wide.
Single source of truth
NetSuite Analytics Warehouse, often abbreviated to NSAW, is based on the Oracle product of the same name, tuned to the specific needs of the NetSuite customer base, such as built-in connectors to the likes of Google Analytics, Shopify, along with the ability to connect to legacy systems. It provides the ability to generate quick and easy visualisations and do analysis on all of this aggregated data. Having a ready-made platform made it an easy choice for Terlato, as Hampton explains:
It just made sense to go with this pre-connected [solution]. Subject areas are all built out for you. It was just a no-brainer as far as that goes ... We needed a single source of truth ...
Once you go through the process of connecting to the tables and creating data flows, to move the data over into NSAW, Oracle analytics, whatever, it puts it in the Oracle Autonomous Database platform. From there we created, just for speed's sake, a materialized view that joins the legacy data with the NetSuite data. So in that respect, it is all one view. it is all one data. It all pulls together, and it's all treated the same.
The result is a much clearer picture of profitability, right down to each individual transaction. He goes on:
Operations has really benefited on this, with the ability to pull in actuals and budget data, and our depletion data from an outside vendor and things like that. We're able to calculate a gross profit far more accurately now because you have access to your journal entries. In NSAW you can pull in your revenue, pull in your allowances and your cost of goods, and compute an actual gross profit for each sale that you do.
Having a single source of truth has helped the organization move beyond previous debates that arose because people had categorized certain metrics in different ways. He goes on:
We've come to an agreement as a company on the definition of those very simple things, and it's allowed us to move on past that and to focus on the bigger picture, which is, what's selling? What's not selling? Why isn't it hitting budget and what can we do about that?
Terlato is still expanding its use of NetSuite. When Hampton gets back from SuiteWorld, he'll be rolling out a new dashboard to the sales team, who have never previously had access to analytics, just a daily spreadsheet showing their sales numbers. With so much still to do to exploit the capabilities of the existing system, all the talk of AI at this week's show has seemed premature for Terlato. He comments:
AI is exciting, but it's putting the cart before the horse a little bit in our instance. We're just now at a place where the data is accurate, we can build solid visualizations and solid data sets based on it. We need to get together as a company and start talking about what what we want to see solution-wise and what AI can do for us.
An invaluable reminder of the gritty reality most businesses still have to grapple with before they can embrace the glittering futures promised by enterprise technology vendors.