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Incorta Named for First Time in Magic Quadrant™

CEO Scott Jones shares insights on Incorta being named in the 2022 Gartner® Magic Quadrant™ for Analytics & BI Platforms. "The data warehousing paradigm can no longer keep up with the demands of today’s world."

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Incorta Named for First Time in Magic Quadrant™

How Supply Chain Professionals Can Predict the Future with Analytics

by: Team Incorta

Supply chain professionals today sure could use a crystal ball.

Like it or not, it’s no longer possible to optimize a supply chain based on historical data and let it run. Geopolitical upheaval has all but destroyed supply chain stability and predictability.

In a recent webinar, Projecting Supply Chain Performance with Incorta Analytics, Mazen Manasseh, Business Analytics Director at Perficient, an Incorta solutions partner, discussed the challenges they see in their supply chain practice, today’s new requirements for analytics solutions, and key metrics that forward-thinking organizations should be tracking.

Supply webinar screenshot


Matthew Halliday, co-founder and EVP of Product at Incorta, was on hand to discuss how Incorta solves the unique challenges that surround supply chain data. Manasseh then presented a demo showing how they build solutions with Incorta to help their clients meet their supply chain analytics objectives.


Complexity on Top of More Complexity

Traditional BI reports tell you what happened in the past. But when you’re dealing with major supply chain disruptions, historical information often comes in too late and is sometimes not applicable to the current situation. What’s more, when your focus is rearview-facing, your teams are often unaware of forward-looking metrics such as the projected demand fill rate, or expected days of supply.

Even if an organization does have a solution for helping their supply chain team be more proactive, chances are that it’s not a scalable one. In many cases, the complexity of the analytics solution often rivals the complexity of the supply chain itself!

Long story short: There are many disparate datasets that have to be combined in order to get a complete picture — forecasting data from sales; inventory data from your warehouse; purchase order data; and if you're in manufacturing, data from scheduled work orders. Pulling all of this data together — which can come from hundreds of suppliers, tens of thousands of SKUs, and multiple warehouses, distribution centers, and manufacturing plants — typically requires a lot of manual work. 

Teams may be maintaining multiple interconnected reports with vlookups to correlate data. Reports may have redundant logic, requiring them to maintain different reports for the same calculations. It takes a lot of time and effort to pull together reports, and there are a lot of ways errors can creep in.


Inability to Evolve

With a setup like this, it's very difficult to evolve to address new requirements, especially when it requires pulling in more data. You can’t update the data pipeline without impacting the performance of the existing dashboards. There's a lot of information, but it’s tough to get all the details that you would need to take the right corrective action — and do so with confidence.

The ideal solution for supply chain analytics not only makes the process scalable, but also allows it to evolve at every point in the process, from data integration and storage, to data retrieval and usage. 

  • Data integration is done through direct mapping to the source data, allowing for near-real-time refresh of the data. It also allows you to bring in more columns and more datasets over time, without having to build new star schemas and data models.

  • Data is stored in the third normal form. This maintains all the detail of the source systems, as well as all the relationships between tables, so that you can navigate down to the underlying details. You can also do detailed exception reporting, for example, setting up an alert for customers at risk of being impacted by inventory shortages. 

  • Data can be queried and retrieved with very fast response times — even with millions or billions of rows of data. Speedy query performance, and the fact that you never have to build new data models, allows you to perform far more queries. This is what delivers faster time to insight, and lets you start to do predictive analytics.

During the webinar, Halliday explained how Incorta’s unique way of handling data integration, storage, and retrieval lends itself to building such a solution. People think of all data as being the same, he said, but the data that comes out of an ERP or other transactional system is very different from clickstream data, or data that comes out of sensors. Systems data is far more complex and interrelated. 

You can’t do analytics directly in the source system because it’s running transactions. The way the business intelligence industry has historically gotten around that is to extract the data from those systems to a data warehouse. From there it goes through a multistep transformation for use in analytics systems. 

Now you are dealing with a derivative of the data, so you can’t drill down into the details. But the bigger problem for supply chain professionals is that if your questions change, you're probably out of luck. And in today’s world, the questions change every day. Who could have guessed what the pandemic would do to shipping and logistics? Who would have known to study in January how Russia sanctions would impact their supply chain? 

If you’re working within the data warehouse paradigm, rebuilding your data models to answer those questions could take weeks. No supply chain professional has that kind of time today.

With Incorta, you don’t need it. Incorta leverages all the computing advances of the past several years — from cloud storage, in-memory computing, and columnar storage to invent Direct Data Mapping™. Direct Data Mapping allows you to bypass the transformation and work directly with your source system data in Incorta.

Because Incorta is connected directly to the source data, you can build reports that refresh as new data comes in. You can run queries in seconds. You can drill all the way down to transaction-level detail. Because Incorta is built around open standards, you can use any visualization tool you want — whether that’s Incorta’s native tool or Tableau or Power BI.

With this kind of performance on full fidelity data, you can begin to do predictive analytics and data science. 

In his demo, Manasseh showed an Oracle supply chain solution built with Incorta and demonstrated how teams can easily track forward-looking metrics so they can make timely decisions to affect those outcomes. 

Watch the webinar for the full solution demo, or spin up a free trial and try it out for yourself today. 




Join us on May 26 for a full-day, LIVE, and fully virtual event bringing data architects, data engineers, data analysts, and data scientists together to go in-depth on the latest trends, technologies, and best practices for building and managing modern data pipelines in the cloud.
At Zero Gravity, the technical visionaries who built and manage some of the world’s most robust and efficient cloud data pipelines will share their real-world use cases, what they’ve learned along the way, and what they see coming next. You’ll see thought-provoking presentations by Google Cloud CEO Thomas Kurian, Microsoft Azure Data Corporate VP Rohan Kumar, and top data management, data analytics, and data science experts from companies like Meta, Netflix, and Comcast.