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Locus Robotics raises $40 million to take its warehouse robots global

June 2, 2020 Big Data

Warehouse robotics startup Locus Robotics today announced it has raised $ 40 million, the bulk of which will be put toward accelerating R&D and the company’s expansion into new markets, including in the EU, where it opened a new headquarters. CEO Rich Faulk says Locus also intends to launch strategic reseller partnerships throughout 2020, following a year in which its number of customer deployments passed 50.

Worker shortages attributable to the pandemic have accelerated the adoption of automation. According to ABI Research, more than 4 million commercial robots will be installed in over 50,000 warehouses around the world by 2025, up from under 4,000 warehouses as of 2018. In China, Oxford Economics anticipates 12.5 million manufacturing jobs will become automated, while in the U.S., McKinsey projects machines will take upwards of 30% of such jobs.

Locus’ autonomous robots — LocusBots — can be reconfigured with virtually any tote, box, bin, or container or with peripherals like barcode scanners, label printers, and environmental sensors designed to expedite order processing. They work collaboratively with human associates, minimizing walking with a UI that recognizes workers’ Bluetooth badges and switches to their preferred language. On the backend, Locus’ LocusServer orchestrates multiple robots such that they learn efficient travel routes, sharing the information with other robots and clustering orders to where workers are. As orders come into warehouse management systems, Locus organizes them before transmitting back confirmations — providing managers real-time performance data, including productivity, robot status, and more.

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When new LocusBots are added to the fleet, they share warehouse inventory status and item locations. Through LocusServer, they detect blockages and other traffic issues to improve item pick rates and order throughput. Locus’ directed picking technology actively directs workers to their next pick location, letting them select their own pace while optionally accepting challenges through a gamification feature that supports individual, team, and shift goals plus events and a mechanism managers can use to provide feedback. In addition, Locus’ backend collates various long-tail metrics, including hourly pick data, daily and monthly pick volume, current robot locations, and robot charging levels.

Locus offers a “robot-as-a-service” program through which customers can scale up by adding robots on a limited-time basis. For a monthly subscription fee, the company sends or receives robots to warehouses upon request, and it provides those robots software and hardware updates, in addition to maintenance.

Locus claims that its system — which takes about four weeks to deploy — has delivered a 2 to 3 times increase in productivity and throughput and 15% less overtime spend for brands that include Boots UK, Verst Logistics, Ceva, DHL, Material Bank, Radial, Port Logistics Group, Marleylilly, and Geodis. The company’s robots passed 100 million units picked in February, and in April, UPS announced that it would be piloting Locus machines in its own facilities.

“COVID-19 has dramatically accelerated trends that have been taking shape over several years in the logistics market, including the movement to collaborative robotics to deal with the labor crisis,” Faulk told VentureBeat via email, adding that the company’s annual recurring revenue increased 300% in 2020 year-over-year. “Our pipeline is expanding weekly with major global brands needing to automate prior to peak season to address the labor gap they will face this year.”

Zebra Technologies’ Zebra Ventures led this series D investment in Wilmington, Massachusetts-based Locus, with participation from existing backers, including Scale Venture Partners. This round brings the Quiet Logistics spinout’s total raised to over $ 105 million as it looks to expand its workforce from more than 120 people to 200 by 2021.

Locus competes in the $ 3.1 billion intelligent machines market with Los Angeles-based robotics startup InVia, which leases automated robotics technologies to fulfillment centers. Gideon Brothers, a Croatia-based industrial startup backed by TransferWise cofounder Taavet Hinrikus, is another contender. And then there’s robotics systems company GreyOrange; Otto Motors; and Berkshire Grey, which combines AI and robotics to automate multichannel fulfillment for retailers, ecommerce, and logistics enterprises. Fulfillment alone is a $ 9 billion industry — roughly 60,000 employees handle orders in the U.S., and companies like Apple manufacturing partner Foxconn have deployed tens of thousands of assistive robots in assembly plants overseas.

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4 Ways a Warehouse Management Solution (WMS) Provides Competitive Advantage

February 27, 2020 NetSuite

Posted by Abby Jenkins, Product Marketing Manager for Inventory & Order Management, Supply Chain & WMS

As e-commerce sales continue to increase, drop shipping becomes the norm and overall expectations around shipping make customer satisfaction more difficult to achieve, competitive advantage for companies that sell or distribute products ultimately comes down to a warehouse’s ability to deliver orders faster and more accurately than ever. Using spreadsheets and paper to track inventory as it moves through the warehouse will no longer suffice.

Deploying a Warehouse Management System (WMS) as part of a larger fulfillment and warehousing strategy helps growing businesses optimize warehouse operations in four key areas:

Inbound Logistics
Inventory Visibility
Outbound Logistics
Mobile Scanning

Inbound Logistics

As inventory arrives at the warehouse, a WMS helps expedite the receiving process. Using a set of automated processes and pre-defined rules, users are guided through the receiving and putaway process, including recommending bin locations and putaway rules, ensuring inventory is quickly and accurately processed so that it can be used to fulfill orders.

Using a mobile device to receive items automatically assigns the items lot number, serial number, bin location and inventory status as they are received. With NetSuite WMS, inventory is automatically allocated to outstanding open orders and can be taken directly to the packing locations, decreasing fulfillment time and handling costs of putting items away and then picking them to fill an order.

2. Inventory Visibility

With a WMS, inventory is tracked as it moves through the warehouse using barcodes, and lot and serial tracking, ensuring accurate visibility of inventory levels (including allocated stock), orders and fulfillment status at all times. It also gives traceability of perishable goods and can automate expiration dates based on receipt date.

You can also schedule regular cycle counting within the WMS as a means of checks and balances. Cycle counting can be scheduled based on category so that high-moving, or high-value inventory is counted with more frequency than lower value items. By assigning cycle counts to staff based on their function or warehouse location, you can ensure regular counts are completed without disrupting daily operations. Automated processes help by automatically sending reminders prompting staff to complete the required counts, including which products to count and how frequently.

With NetSuite WMS, you get a holistic view of inventory across all physical locations. By setting up physical locations hierarchically and using sub-locations, you can view inventory levels per physical location as well as enterprise wide, allowing for more efficient order fulfillment and replenishment.

3. Outbound Logistics

Using pick and pack logic and strategies available with a WMS, users are guided through the order fulfillment process, ensuring inventory is used when and how you want it to be. Defining a wave release strategy, selecting single or multiple picking type, and setting your wave status further customizes and controls the way orders are processed.

If you’re managing inventory across multiple warehouse locations, NetSuite WMS gives you visibility of inventory by location and allows you to define fulfillment and shipping rules. Ensuring whole orders are being shipped from a single location and orders are assigned to the warehouse closest to the destination minimizes shipping costs and simplifies order orchestration, something that would be impossible to do manually.

4. Mobile Scanning

One of the best ways to increase efficiency in warehouse operations is to integrate a mobile app. The combination of a wireless mobile device and barcode scanning helps to automate processes, such as shipping and receiving, putaway, and picking and packing, and it increases overall operator efficiency. Because information is being recorded in real-time with the mobile app, it’s easy to provide a real-time picture of inventory and ensures accuracy throughout the supply chain.

The NetSuite WMS mobile app was designed with the warehouse manager in mind. It has a clean, clear and easy-to-navigate interface that helps reduce the time operators spend completing everyday tasks, such as inbound, inventory and outbound processing tasks. Through the task manager, you have the ability to direct users to perform specific tasks, such as putaways and picking, based on pre-configured strategies defined during the setup of the WMS system, ensuring inventory is allocated according to plan and not haphazardly. With the use of GS1 barcode scanning you can easily enter items for inbound, inventory or outbound processing.

To further customize the user experience in your warehouse, you can create custom processes within the app. Customizing things like defining default values, hiding/displaying fields and adding fields for data capture can be done from the floor, without any technical expertise. You can export and import custom processes to other devices, ensuring the right users have access.

Making the Move from Manual to Automated

By automating processes, improving operational efficiencies and reducing handling time, a WMS optimizes day-to-day warehouse operations, ensuring you can deliver on customer expectations quickly and accurately.

Read more about how NetSuite companies are using WMS to create their optimal warehouse, here. And learn more about the latest additions to the WMS features and functionality in the NetSuite 2020.1 Release

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4 Ways a Warehouse Management Solution (WMS) Provides Competitive Advantage

February 27, 2020 NetSuite

Posted by Abby Jenkins, Product Marketing Manager for Inventory & Order Management, Supply Chain & WMS

Deploying a Warehouse Management System (WMS) as part of a larger fulfillment and warehousing strategy helps growing businesses optimize warehouse operations in four key areas:

Inbound Logistics
Inventory Visibility
Outbound Logistics
Mobile Scanning

Inbound Logistics

2. Inventory Visibility

3. Outbound Logistics

4. Mobile Scanning

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High Customer Satisfaction Led to Teradata’s Leadership Distinction in Q4 Big Data Warehouse Landscape Report by The Information Difference

January 30, 2020 BI News and Info

Teradata (NYSE: TDC), the industry’s only Pervasive Data Intelligence company, today announced it has been recognized with the highest technology score in the Big Data Warehouse Landscape Q4 2019 report by The Information Difference, issued Jan. 21, 2020. This year’s report marks the 9th time that Teradata has been included.

Five major vendors were evaluated in the report, which represents the market in multiple dimensions based on customer set, customer satisfaction, maturity of the technology, data warehouse revenue, size of partner ecosystem, geographic coverage and more. Teradata ranked highest in the technology dimension, compared to IBM, Magnitude, Microsoft and Oracle.

“A significant part of the ‘technology’ dimension scoring is assigned to customer satisfaction, as determined by a survey of vendor customers,” said Andy Hayler, Analyst at The Information Difference. “In this annual research cycle the vendor with the happiest customers was Teradata. Our congratulations to them. This certainly confirms what we regularly hear from Teradata customers about the Vantage platform, which is clearly leading the data warehouse market.”

“Customer satisfaction is the ultimate measure of a company’s success and we are delighted that our customers have once again put Teradata at the top of this ranking,” said Chris Twogood, SVP of Global Marketing at Teradata. “We built Vantage to help our customers move from analytics to answers with a cloud-forward platform that unifies analytics, data lakes and data warehouses. This report confirms that we are delivering on that promise.”

Teradata Vantage, the company’s flagship product, delivers a modern cloud architecture that enables companies to start small and elastically scale compute or storage as business needs increase. With support for low-cost object stores and seamless integration of analytic workloads, customers can deploy Vantage across public, multi-cloud or hybrid cloud environments, paying only for what they use while leveraging 100% of their available data for analytic answers.

To learn more about this report visit: www.informationdifference.com

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Top 5 Metrics for Measuring Warehouse Productivity

December 19, 2019 NetSuite

Posted by Abby Jenkins, Product Marketing Manager for Inventory & Order Management, Supply Chain & WMS

Companies that make, sell or distribute products typically require receipt, storage and fulfillment of goods into, within, and out of a warehouse. Without a system specifically design for warehouse management in place, this can quickly become a manual, costly and inefficient process that won’t support a fast-growing business.

Over 90% of warehouses have adopted some sort of warehouse management system.

Optimizing day-to-day warehouse operations is critical to increasing warehouse productivity.

According to a 2019 Warehouse Education and Research Council 2019 Operational Benchmarking Report, the top 5 KPIs for benchmarking your warehouse productivity are:

Order Picking Accuracy (percent by order) – Incorrect order picking results in increased labor costs, inaccurate inventory counts, delayed shipments and decreased customer satisfaction if the error is not caught before the order ships.

At best-in-class operations, 99.89% of orders are picked correctly.

Average Warehouse Capacity Used – The average capacity used over a certain period of time – this is a key space utilization KPI.

Best-in-class companies utilize on average 92.54% of warehouse space.

Peak Warehouse Capacity Used – Peak capacity is another KPI for tracking how well a warehouse uses its space during its busiest times.

Best-in-class companies utilize 100% warehouse space during peak times.

On-time Shipments – This is a delicate balance to achieve without compromising order accuracy. Today’s consumer is largely brand agnostic and concerned with getting the product they want in the quickest and cheapest way possible.

Best-in-class companies ship 99.7% of orders on time.

Inventory Count Accuracy by Location – Understanding total inventory available is important but equally important is how that inventory is distributed across multiple locations so that orders are routed to the proper location for fulfillment and out of stocks are not incurred.

Best-in-class companies have a 99.9% inventory count by location.

Click here to see the chart to gauge warehouse performance.

Nearly half of all warehouses are still relying on a paper-based picking method to fulfill orders (meaning Excel or some spreadsheet method).

Warehouse operations can be improved with intelligent pick-and-pack capabilities and a wave-release process, using mobile RF barcode scanning, automating cycle counting and integrating with shipping systems, all of which elevate visibility, accuracy and efficiency.

NetSuite Warehouse Management System (WMS) offers industry leading warehouse management functionality. Specifically designed with the warehouse manager in mind, NetSuite WMS functionality is aimed at improving the user experience and warehouse processing operations. Because NetSuite WMS is built into NetSuite – utilizing core ERP locations, items, bins, inventory and transactions – product companies don’t have to worry about integration efforts or data synchronization.

Reduce operating expenses, improve inventory visibility, achieve better labor management and increase customer service by implementing a WMS system. Learn more about how companies are incorporating a WMS into their overall fulfillment strategy.

Posted on Wed, December 18, 2019
by NetSuite filed under

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AI startup Gather uses drones and computer vision for warehouse inventory

August 17, 2019 Big Data

Gather, a company that uses autonomous drones for warehouse inventory, launched out of stealth today. Founded in 2017, the company of about 10 employees is based in Pittsburgh.

Gather’s founding team is made up of Robotics Institute at Carnegie Mellon University graduates, including cofounder and chief robotics officer Sankalp Arora, whose work together with a team at the Office of Naval Research on autonomous helicopters won the 2018 Howard Hughes Award.

Earlier this year, Gather closed a $ 2.5 million funding round to bring its products to market and grow its computer vision and software offerings.

Gather supplies software for the autonomous operation of drones that can connect with existing warehouse management systems and IoT devices such as motion sensors. Computer vision is then used to scan and count inventory.

The solution is about 60% cheaper than traditional methods that rely on people alone, Gather said. Gather software is currently used in an undisclosed number of warehouses, but an air cargo company was able to reduce inventory time from 8 hours to 15 minutes, and take inventory 10x more quickly than two employees with a forklift in another warehouse.

“People never have to leave their desk to go out and do an inventory,” Arora told VentureBeat in a phone interview. “We’re not outfitting these things with any specific hardware. It’s a drone that you can go buy at Best Buy.”

Gather was founded in January 2017 by Arora, Daniel Maturana, Geetesh Dubey, and Robb Myer, an entrepreneur-in-residence at CMU.

Since Gather can connect with IoT devices, drones can deploy anytime a motion sensor is triggered. Arora argues a team of drones can multiply savings for warehouses by eliminating the need to install cameras for security or surveillance.

While drones using Gather’s computer vision can read barcodes and count boxes, an added bonus is providing hourly inventory image sets that warehouse managers can use to assess inventory reports or look back through records.

Cameras are also used to help the drones avoid running into objects.

“Our semantic mapping enables a single camera to map identified objects up to a range of 150 meters,” Arora said. “So what that means is we can follow rules of engagement in these facilities. For example, if a manager tells us ‘I don’t want this drone within 15 meters of a forklift,’ we can make a map these using semantic cues from known objects and map these objects by following rules of engagement.”

Computer vision by companies like Autodesk and Indus.ai are being applied in industrial environments to improve safety and compliance with regulations. Such systems may also play a role in the future to orchestrate the movement of people and machines as more robots enter warehouses. A number of challenges stand in the way of fully autonomous warehouses, Arora said.

“I think a good way to put it is you have to deal with unpredictable decision makers, as well as flexibility of the environment as you are making these things automated. And that is where I guess the final hill for robotics is,” he said. “You’ve got amazing robots making our cars, doing manufacturing on manufacturing lines, but as as soon as the environment becomes dynamic, it changes and you have to interact with humans or you have to be flexible given the environment, then the perception challenges associated with it. And the planning challenge associated with it becomes several orders of magnitude more difficult.”

Recent robotics ramp-ups include Brain Corp’s initiative to bring robots to warehouses and a $ 46 million funding round for Fetch Robotics in June, while DHL introduced a $ 300 million investment last year to quadruple robots in its facilities in North America.

Amazon, who rolled out its new Pegasus sorting robot in June, predicted earlier this year that fully autonomous Amazon warehouses are at least 10 years away.

The $ 2.5 million funding round was led by Expa and joined by Dundee VC, Bling Capital, Comeback Capital, XRC Labs, Summer League Ventures, and Plexo Capital.

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5 Advantages of Using a Redshift Data Warehouse

March 26, 2019 Sisense

Choosing the right solution to warehouse your data is just as important as how you collect data for business intelligence. To extract the maximum value from your data, it needs to be accessible, well-sorted, and easy to manipulate and store. Amazon’s Redshift data warehouse tools offer such a blend of features, but even so, it’s important to understand what it brings to the table before making a decision to integrate the system.

So, what are the benefits of switching data storage to Redshift data warehouses? In addition to its significant storage capacity, the data warehousing solution delivers several key benefits that make it an intriguing and possibly ideal choice for business intelligence. These are five of the biggest advantages of using Redshift for your business intelligence needs.

It Offers Significant Query Speed Upgrades

With larger datasets—especially when reaching petabytes of magnitude—querying experiences an understandable lag in speed. However, most database and warehouse solutions today offer the ability to process requests and other functions in parallel. Redshift data warehouse architecture has clocked in at some of the fastest general and query speeds.

Comparing Redshift vs Hadoop, for instance, shows that overall the former is nearly 10 times faster than the latter. In some query tests, Redshift database easily outstrips Hadoop in returning results. Amazon’s Massively Parallel Processing lets BI tools with the Redshift connector process several queries across multiple nodes simultaneously while reducing workloads.

It Focuses on Ease of Use and Accessibility

Even though it’s more than 30 years old, MySQL (and other SQL-based systems) remains one of the most popular and easily usable interfaces for database management. Its simple query-based system makes platform adoption and acclimation a breeze. Instead of building a completely new interface that requires significant resources and time to learn, Amazon chose to create a platform that works much like MySQL, to great effect.

While it does change some aspects, Redshift keeps much of what makes MySQL, including the back-end tools that work with PostgreSQL, JDBC, and ODBC drivers while making it easy to connect with most business intelligence tools. Moreover, it can easily connect with other existing tools and provides an easy learning curve for new administrators and even end-users.

It Provides Fast Scaling With Few Complications

Redshift is cloud-based and hosted directly on Amazon Web Services, the company’s existing cloud infrastructure. One of the biggest benefits this provides Redshift is a flexible architecture that can scale in seconds to meet changing storage demands. A major issue facing organizations with rapidly changing data requirements is that scaling can be both costly and complex.

Thanks to AWS, Redshift can be scaled up or down by quickly activating individual nodes of varying sizes. This scalability also means cost savings, as companies aren’t forced to spend money maintaining servers that are unused or to quickly purchase expensive server space when the need arises. This is especially useful for smaller companies which experience significant growth and must scale their existing solutions.

It Keeps Costs Relatively Low

Amazon Web Services bills itself as a cost-effective solution for companies of all sizes. In keeping with the company line, Redshift provides a similar pricing model that delivers greater flexibility while empowering companies to keep a tighter watch on their data warehousing costs. This pricing capability comes as a result of the company’s cloud infrastructure, and its ability to keep workloads to a minimum on most nodes.

Additionally, organizations can choose which type of pricing model they prefer: on-demand or reserved instances. The first is generally more appealing to smaller companies, or those with lighter data warehousing needs, while the latter offers a more stable ecosystem for data storage. More than simple dollars and cents, this pricing flexibility means you can always ensure that scalability is possible and straightforward.

It Gives You Robust Security Tools

Massive data sets often contain sensitive data, and even if they don’t, they still hold important information about their organizations. As such, the right data warehouse solution should have powerful protection tools to lock down data. Redshift presents a few different encryption and security tools that make protecting warehouses even easier.

This includes a VPC for network isolation as well as different access control tools that give you more granular management capabilities. Additionally, Redshift includes SSL encryption for data in transit, and AWS’ S3 servers offer both client- and server-side encryption, giving you greater control over when data is viewable and accessible.

Choosing the Right Warehouse

Building a successful BI ecosystem for your organization begins with data. By choosing a warehouse that meets your requirements and grants you the flexibility to grow and scale, you can give your business intelligence even greater value while concurrently deriving much better insights and analytics.

Tags: Data Preparation | Data Warehouse

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5 Ways a Custom-built Warehouse Management System Can Come Up Short

January 28, 2019 NetSuite

5 Ways a Custom-built Warehouse Management System Can Come Up Short

Posted by Hayley Null, Manufacturing Industry Marketing Lead

If your company is using (or considering) a homegrown or built-to-order WMS, it’s time to start asking the tough questions about the current and future viability of those systems for your growing organization.

If you’re deciding between a built-to-order, homegrown, or single-platform warehouse management system (WMS), there are a few key points to keep in mind. Homegrown systems can consume an inordinate amount of IT resources, for example, while made-to-order software options are often highly customized and expensive. Here are five areas where a custom-built or made-to-order solution can be problematic:

Integration requirements. In most cases, you need an enterprise resource planning (ERP) system in order to implement a built-to-order or homegrown WMS (which, in turn, has to be integrated in order to function). With a WMS built on an ERP platform itself, you can skip this step and just start using your new warehouse management system without any extra integration.
Effective use of IT resources. In a world where the pace of technological innovation continues to accelerate, the average internal IT group simply can’t keep up with the pace of change. Not only does that group have to develop the software, but it also has to upgrade and maintain it forever. With a modern, cloud-based system, a veteran software engineer that’s brimming with tribal knowledge won’t set the company back when he or she retires (whereas a homegrown system relies on that knowledge for updates and modifications). A software vendor with experienced software support team available 24/7 is vital.
Data synchronization. One of the biggest pain points with any new WMS implementation is the synchronization of tables, data, and other key information. A company that has separate tables for inventory, bins and single items, for example, may wind up with mismatches if the synchronization doesn’t go as planned. A unified system with both applications using the same records, tables and other data eliminates these synchronization issues.
Reusing broken process. If you’re not working with a vendor that has WMS implementation across many different industries, then there’s a good chance you’ll just recreate your own processes without taking into consideration leading practices across the industry. That’s because without that hands-on knowledge of how specific industries use the software, WMS vendors lack leading practice insights needed to ensure the best possible fit for every customer. Look for a vendor and implementation team that has done a lot of these implementations with experience across most industries—experience that ensures that every new installation gets as close to industry best practices as possible.
Scalability. A homegrown software system will only take your company so far. At some point, whether it’s because you’re expanding your warehouse or moving out into new geographies, you’re going to need a system that can scale with it. Find a vendor that takes the time to get to know its customers and what’s going in their organization, providing options that they may not have thought of.

As you explore your WMS options, evaluate the “hows” and “whys” of what you’re doing today with your built-to-order or homegrown system, and make sure those answers fit well with the needs of your company as it grows. If your current system isn’t scalable, and if it doesn’t offer the right array of add-ons, modules, and other advanced features, then it’s time to reconsider your approach and expand your horizons to include NetSuite WMS.

Ready to make the transition away from your homegrown or built-to-order WMS and over to NetSuite?

Posted on Wed, January 23, 2019
by NetSuite filed under

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So, You’re Getting a Data Warehouse for Power BI…

October 15, 2018 Self-Service BI

Data Warehouses: What Do I Know?

Well, I’m no expert. I learned Power Pivot and Power BI through Excel. What I know is from helping to build a SQL data warehouse. That is, I’ve worked with a data warehouse pro. So, BI pros or others, please add your observations in the comments!

Data Warehouse: the Promise.

In 2009 on Twitter, Rob Collie noted that the data model in Power BI and Power Pivot: “very much is meant to work in conjunction with, and be fed by, well-designed data warehouses.” (Power Pivot’s Impact on BI Pros). For me, a good data warehouse enables business pros to create reports without needing to think too much about the data model. Another benefit I see is the ability to deliver operational reports from transactional databases (ERP, CRM, financial systems). By design, these systems are for logging events, not for creating reports. And yes, even Salesforce is for logging tasks. Reports are secondary.

Data Warehouse: the Hype.

I’ve heard some idealistic claims from data consultants and Microsoft TSPs. For instance, data warehouses will soon replace Power Query and data modeling. Really? When I hear these claims, I tend to smile and nod. Why? Because, business needs and data sources evolve and the data warehouse lags behind these needs. Another benefit is having a single source of truth. For me, the real promise here is empowering business pros to get reliable data.

Data Warehouse: a Couple of Things.

Star Schema. Data warehouses use a star schema. In a star schema, data falls into lookup tables and transaction tables. Or, for the data warehouse pro, dimensions and facts. Typically, transaction tables hold time and event-based data. Whereas lookup tables hold the descriptive details for contacts and accounts (attributes). For more info about star schema, see Data Model: Beast to Beauty. One lookup table can contain data that’s in multiple hops in the source database. For example, transaction type code will be one field and its description will be another field– instead of in a separate table.
Historical Analysis. The purpose of a data warehouse is historical analysis, and this shapes design decisions. Since longer text fields won’t aggregate in a simple way, the first urge is to drop them.
Platform Agnostic. The data warehouse doesn’t care how users connect. Users can use Power BI, Excel, Tableau, etc.
Other Abstractions. Data warehouses have other abstractions that may confuse end users. For example, how they handle blank values in data (nulls) or how they handle transactions that don’t connect to lookup tables. And, the data warehouse may use internal IDs not found in the source. While I’m less concerned with these abstractions, they are good to be aware of. A data warehouse will involve a learning curve.

5 Practical Suggestions for aligning a data warehouse with Power BI.

(your mileage may vary)

To begin with, views are critical to Power BI. Views are versions of tables. Typically, these views are for specific purposes. While a good data warehouse person won’t adapt tables to Power BI, they will adapt the views. But you have to know what to ask for.

First, depending on how your data warehouse handles nulls, your dates may not be continuous. If your data warehouse replaces nulls with 12/31/9999, the date table will have gaps. The view for the Date lookup should filter out this invalid value. While you’re at it, be sure that all of your date fields have sort columns. A Month Year column formatted Apr 2020 will need a Year Month column like 202004 that will sort.
Second, naming conventions. Some platforms (Tableau, for example) can’t recognize spaces in table names and/or column names. Because of this, the data warehouse may use CamelCase for everything. While Gil Raviv has a query for fixing camel case column names, it’s best to fix this in a view. Similarly, prefacing every table name with the word Fact or Dim can make a data source alien to a business pro. Rename in the view. Always prefer clear human readable names for Power BI.
Third, long text fields in transaction tables. Long text fields can snag performance in Power BI. But you may need them for operational reports. One solution is to truncate these fields to 256 characters or less. Another approach is to add an index to the transaction table and split the text off into a special lookup table. However, with this second solution, see below.

About Relationships…

20151105 RelationshipsCreated So, You’re Getting a Data Warehouse for Power BI…

Behold, the lovely star schema

Fourth, prevent one-to-one relationships. (Only you can prevent monogamy). With a great data warehouse, Power BI will set up the relationships between transactions and lookups automatically. What can block this automatic setup are tables where both sides have unique IDs. I prefer to add two invalid rows with blank data to the table that needs to be on the many side.
Fifth, decide what to do about tables that may have multiple lookups to the same table. Your sales table may have a Sales Date and a Ship Date. Because Sales Date is the usual link between the tables, I prefer to rename Sales Date to Date Id (or whatever you Date key is called). This enables Power BI to create the main relationship without the business pro having to think about it. Other cases are when there is a credit account and a debit account. The user can always create a second inactive relationship to the other field.

Note on Relationship Auto-detection in Power BI.

Power BI can match up lookups to data tables. Automatically. Sadly, Excel Power Pivot can’t. Power BI recognizes lookups because they have a column with unique values. It links lookups to transactions on identical column names. Power BI will create one-to-many relationships. It does not attempt to create many-to-many relationships. Thank goodness! It will, however, create one-to-one relationships. Ruh oh. This is because it has no way to determine which side is NOT the lookup side. This problem is what the Fourth suggestion above remedies. What’s so tricky about one-to-one relationships is that it throws off auto-detection of all of the other relationships. And there are other results in the data model from these dreaded one to one relationships.

No one knows the Power BI ecosystem better than the folks who started the whole thing – us.

Let us guide your organization through the change required to become a data-oriented culture while squeezing every last drop* of value out of your Microsoft platform investment.

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Snowflake Computing lands whopping $263 million for its data warehouse

January 25, 2018 Big Data

Snowflake Computing announced today that it has raised a massive $ 263 million round of funding to propel its cloud data warehouse business forward. The company is now valued at $ 1.5 billion.

Snowflake sells database software that runs in Amazon’s cloud and provides businesses with a high-performance, highly scalable environment to store massive amounts of information for processing by a range of applications, including machine learning algorithms.

The company was founded in 2012, and this latest round brings Snowflake’s total funding to $ 473 million, putting it among an elite group of startups that have taken on a massive infusion of cash over a short lifespan. Snowflake’s previous round of funding, worth $ 100 million, closed just last year.

Iconiq Capital, Altimiter Capital, and Sequoia Capital co-led the round. Returning investors Capital One Growth Ventures, Madrona Venture Group, Redpoint Ventures, Sutter Hill Ventures, and Wing Ventures also participated.

Snowflake CEO Bob Muglia said in an interview with VentureBeat that back in September investors recommended the company raise money to fuel its growth, given the success it’s having and the speed at which the market is moving.

“With this financing, we have the financial strength that’s now coupled with the technical strength and the product strength and the people strength that we have at Snowflake,” Muglia said. “And we can bring the whole package together and solve problems for customers.”

This round also provided an opportunity to bring Sequoia Capital on as an investor. Muglia said that he wanted to work with the firm because of its long and storied pedigree in venture capital, as well as its track record helping enterprise software startups.

With all this cash in the bank, Muglia said the company plans to expand its sales and marketing presence overseas, with a special focus on Europe and Asia. And he doesn’t expect to be going on an investor roadshow anytime soon, as this funding should last for a couple of years while the company continues to scale its business.

Even with this eye-popping raise, Snowflake had more money left on the table. Muglia said that the round was “tremendously oversubscribed” and that he was still getting requests from investors interested in joining the round just days prior to its official announcement.

Now Snowflake needs to live up to its valuation and make good on all the capital investors have poured into it. The company’s customer base has grown 300 percent over the past year, and it’s working in a hot space, which explains the excitement. But still, the bar is set high.

In the near future, the company is also working to expand its reach to other cloud providers. While Muglia didn’t say which companies would play home to new Snowflake regions, broadening the number of platforms that customers can use to host their data is part of the plan. That, in turn, will open the startup to new customers, since some companies will choose not to run workloads on Amazon Web Services.

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