Wavicle Data Solutions

The organization responsible for global restaurant operations improvement for this client captures and analyzes data about everything from food safety to speed of service and customer satisfaction. This data from over 37,000 restaurants across 108 markets worldwide, was stored in an on-premises IT ecosystem. 

To reduce its data center costs and improve flexibility and scalability of its data management environment, this group decided to migrate it’s entire on-prem IT ecosystem to Amazon Web Services (AWS). The move to the cloud prompted the organization to migrate its data integration technology as well. Concerned about diminishing support for its DataStage tool, the group chose to migrate to Talend and enlisted Wavicle’s ETL migration services.

Wavicle’s ETL migration experts approached the work in three phases: 1) Analyzing, categorizing, and scoping the work; 2) Converting ETL jobs from DataStage to Talend; 3) Testing of output across both platforms to compare results. 

Our team leveraged ETL conversion accelerators and proprietary testing tools to automate and accelerate these time-consuming tasks and dramatically reduce the development time. To get started, they simply fed legacy ETL components to the Wavicle's Data Integration Code Converter for analysis.

In this case, the analysis of ETL jobs identified a large inventory of DataStage components. Working closely with the group’s business analysts, we identified backup or obsolete jobs that could be eliminated from the conversion project. This left nearly 300 jobs to be analyzed and ranked for their level of complexity based on parameters such as number of nodes, objects, occurrences, SQL statements, system types used, and transformation expressions.

Using Wavicle’s accelerators and frameworks to automate the analysis, conversion, and testing tasks, we successfully converted all ETL jobs to Talend, saving 95% of the analysis work and 81% of the development effort.

This quick service restaurant chain's global digital analyst team needed to look at patterns in its POS system and supporting data to determine where greater adoption of digital technology could lead to gains in productivity, items sold, and check size.

The current data warehouse stored all domestic POS transactions, but was too large and the data too raw to return fast, trusted results.   

The Wavicle team worked closely with the client's user community, including business analysts and data scientists, and discovered the need for insights such as: comparing sales across different times of the day and across individual locations and regions, and understanding how different POS locations in the restaurant (e.g. kiosk, drive-thru) impacted measures of service efficiency.

We built a data mart on the client's existing database platform, Amazon Redshift. It integrates siloed data including POS, SoS, offer, and loyalty data, and presents actionable information in a high-performance and intuitive format.

We leveraged Amazon Redshift design features, such as stored procedures, sort keys, and distribution keys, to build tables that met the needs of business analysts and data scientists. While summary tables were optimized for specific queries, a table built specifically for exploration allows data scientists to search for patterns using a more granular level of data.

This solution gave the client insights it needed regarding sales channels. In fact, the timing was perfect for measuring effects of the COVID-19 restaurant shut down on restaurant sales and operations. Specifically, they needed to understand the impact of sales shifting away from dine-in options to take-out and delivery.

The new summary data mart made it easy for analysts to pull data summarized by POS area at the restaurant and regional levels, informing the leadership team as they navigated an unprecedented crisis.

Read this full case study and more at https://wavicledata.com/case-studies

When a global manufacturing company realized it didn’t have the right data to optimize its supply chain logistics, Wavicle delivered a real-time analytics platform that integrates data from internal SAP systems and external shipping vendors onto an AWS-based cloud data environment.

Company executives wanted real-time insights that would allow them to optimize the cost and logistics of moving tens of thousands of products and materials within their own global network and among shipping vendors. They needed a high-performance data management solution to integrate data from multiple internal and external sources and make it available to analysts for exploration and reporting.

Wavicle developed a solution to replace this company's current Excel-based reporting and analytics. We had already built a cloud data architecture and infrastructure for the client’s procurement organization. We leveraged this Amazon Web Services (AWS) environment to develop a logistics data lake on S3, a data warehouse using Amazon RedShift, and data integration using Talend. 

This allows the company to integrate data from multiple internal SAP systems, shipping vendors, and third-party logistics companies (3PLs), as well as Infor Nexus, which tracks and reports on the location of shipping vessels. End users now have the data they need to do reporting and analytics using Microsoft’s Power BI. 

This solution gives the client’s logistics organization fast access to accurate, updated information to evaluate vendor cost, service, and speed; identify logistical inefficiencies; negotiate with vendors; and optimize their global network. The combination of a data lake and data warehouse allows the client to perform enterprise reporting on an aggregated data set and explore a complete set of raw data for more complex, open-ended inquiries. 

Read this full case study and others at https://wavicledata.com/case-studies

This client needed to measure and track the performance of its growing third-party food delivery function and its effect on the business to ensure: sufficient inventory for popular menu items, adequate staffing, on-time deliveries, and ongoing customer satisfaction. Without these assurances, the company was concerned about losing control of revenue and consumer brand loyalty. An intelligence solution was critical for financial success in a highly competitive market.

Wavicle implemented a data model and process to load source data from multiple third-party food delivery partners into a single data warehouse, transforming disparate data into valuable operational intelligence. On-demand customizable reports gave the client’s management insight into sales, delivery, customer feedback, and fees, with the ability to “slice and dice” these metrics by restaurant location, food delivery partner, date range, and time of day. With access to visualized metrics, they could continuously measure and improve food delivery operations.

This solution provides the client’s operators with key analytics at a macro-level, giving them the ability to dive into details, resolve specific issues, and optimize food delivery performance. Since implementation, they have increased total food delivery orders, which now exceed more than 10 percent of sales in locations that offer delivery. 

Additionally, the average dollar value per transaction is significantly higher for food delivery vs. non-food delivery. They now attribute food delivery as a key driver of global growth. This solution combines existing corporate data such as point of sale (POS) with third-party food delivery data to track, measure, and visualize key metrics: sales metrics by channel; food delivery metrics; customer metrics; and fee metrics.

Procurement data about this company's thousand of vendors and the many thousands of products they supply, including pricing and negotiation details, are stored primarily in disconnected SAP systems throughout the company. Without integrated procurement data and modern analytics tools, the company endured a long, arduous process of collecting and consolidating data onto complicated Excel spreadsheets to manage the procurement process.

It was difficult to identify products and prices supplied by a given vendor, calculate average cost of each product, know when to renegotiate pricing based on changing commodity prices, and measure performance of the procurement staff.   

They hired Wavicle to develop a state-of-the-art procurement analytics platform in the cloud: to consolidate data into a single location and develop a portal that drives workflows to support negotiations.

We built the portal on the concept of “guided discovery,” allowing users to select which workflow to engage and then use a universal filter to view data by various criteria, including time frame, product, or vendor. We created more than 20 visualizations, which users can incorporate into their own custom screens. We put a lot of effort into performance to ensure visualizations come back in less than three seconds, or less than three clicks.

The solution is built on an AWS platform using Talend to integrate data from multiple SAP systems as well as external data feeds, such as commodity pricing, into a data lake on AWS S3. From there, it’s loaded into an Amazon Redshift data warehouse and Tableau is used for visualizations.

The solution delivers fast access to accurate data to negotiate with vendors, make purchase decisions faster, and improve productivity of the procurement team, ultimately maximizing cost savings on the thousands of purchased products.

Read this full case study and more at https://wavicledata.com/case-studies

Cars.com is a leading digital marketplace that connects car shoppers with sellers. Cars.com enables dealerships with innovative technical solutions and data-driven intelligence to reach and influence ready-to-buy shoppers, increase inventory turn, and gain market share. On a daily basis, Cars.com hosts an average of 4 million cars from 19,000 dealers.

Cars.com desired to accelerate innovation, reduce time to market, and serve highly contextual and relevant content to its shoppers and sellers. A transition to the cloud was critical to meet modern business needs while reducing operational inefficiencies and overhead. 

Wavicle partnered with Cars.com to architect a strategic foundation for the company’s entire data ecosystem. First, Wavicle helped develop an AWS Data Platform to perform data science and analytics on a wide range of data including vehicle inventory, shopper clickstream, and third-party data sources, with hundreds of terabytes processed weekly.

Next, the existing on-premise legacy environments were re-platformed onto Amazon Web Services – which meant faster data retrieval and lower resource costs by addressing data integration inefficiencies. Re-platforming involved moving from Talend on-premises to Talend Cloud, building a data lake, and moving reporting tools such as Tableau and SAP Business Objects to the cloud.   

The transition to AWS puts Cars.com in a ready position for agile innovation, time-to-market efficiencies, and service level guarantees through advanced orchestration. Matchmaking between shoppers and sellers will be enhanced. In real-time, dealers will know which vehicles are getting more attention and more likely to sell based on price and market dynamics. Additionally, new image-recognition tools built in-house will scale better on AWS in detecting vehicle attributes, such as trims and conditions, to increase the quality and accuracy of car listings. 

Read this full case study and others at https://wavicledata.com/case-studies

A global medical equipment manufacturer had grown through years of product innovation and mergers of several established brands, each with its own system for managing business information. 

With a catalog of more than 20,000 respiratory care products and 370,000 customers, the company was unable to get a single, accurate record of customers, vendors, and products across all areas of the business. 

The customer onboarding and ordering processes required time-consuming, manual consolidation of data from multiple ERP systems, which led to several challenges: Tracking and moving a customer through the sales process; reconciling definitions of product names, item numbers, and units of measure; managing customer credit limits across all areas of the business; and measuring the revenue generated by customers.

The manufacturer worked with Wavicle on a master data management solution to generate a single “golden record” of each customer, vendor, and product. We created a unified portal where all master data is created, managed, and communicated to ERP systems in all areas of the business. The portal aligns with and automates business processes, such as customer onboarding, credit approval, and contracting. Once data is entered into the portal, it has to be approved by a series of business owners before it moves onto the next step in the process.

For each category of data, the solution includes: centralization of master data; master data governance; data integration; data modeling; platform integration; and machine learning. 

This solution delivers operational and analytical master data, giving the client more control over creation and maintenance of data and more confidence in the quality of its data and analytics. From one location, the client can create a customer, update a customer, extend customers from one sales organization to another and from one ERP system to another. Likewise, the portal can be used to create and update material records, manage consistent product hierarchy across ERPs, manage end of life (EOL), and manage the reserve process.

One of the world’s largest quick service restaurants is leveraging its Customer 360 initiative and micro-segmentation strategy to get to know its customers and their preferences better than ever.

The company wants to provide highly relevant offers that match customer preferences for menu items, frequency of visits, purchase patterns, sales channels, and more. Rather than relying on mass offers to “macro” segments based simply on geography and demographics, the company will create thousands of micro segments to generate more personalized experiences at any touchpoint.

The company engaged Wavicle to build a 360-degree view of customers using data from all systems and channels and leveraging machine learning and advanced analytics capabilities to segment customers and create offers.

Wavicle developed a customer data platform that captures data from more than 50 sources, including point-of-sale transactions, digital analytics, clickstream, customer loyalty program, third-party delivery such as Uber Eats, and privacy preferences. The data is matched, merged, and enriched to create the complete customer profile, which is correlated to omnichannel interactions and transactions.

The platform relies on custom algorithms to identify segments and micro segments and build audiences. A custom portal allows the marketing team to choose segments, generate relevant offers, and manage campaigns. Customer responses are captured, analyzed, and used to continuously refine segmentation and offers.

Bringing together all customer data in one place has allowed this company to develop the personalization and loyalty programs that:

• Drive engagement

• Increase customer retention

• Grow revenue

Previously challenged by data quality and latency problems due to the amount of data and varied sources, marketing and analytics teams now have the data and the tools they need to get a trusted 360-degree view of customers and engage customers with more meaningful messages.

Vyaire Medica is a global manufacturer and marketer of products for respiratory diagnostics, ventilation, airway management, and operative care consumables. After a series of acquisitions, the organization found itself with nine enterprise resource planning (ERP) systems, including SAP, Microsoft, and several other applications, some of which had been in production for at least 20 years.

“It was really difficult for the company to consolidate data from its many legacy systems,” said Ranjith Ramachandran, Wavicle big data lead.

“It would take two to three hours to create standard enterprise reports. Other reports could take five or six days of gathering data from the multiple systems and combining it manually using Excel spreadsheets. Clearly, this wasn’t sustainable. All areas of the business needed faster access to consolidated data for order management, sales analytics, inventory management, and accounts payable and receivable, to name a few.”

Wavicle and Vyaire built a data warehouse using Amazon Redshift on the Amazon Web Services (AWS) cloud platform. Along with data integration and orchestration using Talend, this solution gives Vyaire a massively scalable infrastructure that can quickly capture data from around the world, store it in a single location, and feed it to a single unified reporting platform. The solution synchronizes master data to ensure customers are defined and accounted for consistently across all data.

This three-month project has given Vyaire a single, consolidated view of its data from all ERP systems on a scalable environment that will easily and cost-effectively grow as the company and its data requirements grow.

Standard reports and queries that used to take anywhere from 3 hours to 6 days, can now be complete within minutes.

Read this full case study and others at https://wavicledata.com/case-studies

A national retailer is deploying cloud technology with machine learning and artificial intelligence capabilities to provide its network of more than 4,500 independent retail stores with a state-of-the-art ordering solution. 

Its previous ordering system was built on a mainframe environment, which was overwhelmed by a growing network of stores and an increasingly complex product catalog. It couldn’t keep up with growing sources and volumes of data, and was difficult to integrate with store point of sale (POS) systems, mobile devices, and other modern technologies. A number of challenges put this company at risk of losing customers and potential new stores to competitors with lower prices, better product selection, and more advanced technology.

The client engaged Wavicle to build a modern ordering solution that makes it faster and easier to evaluate and optimize prices, search and order products, and manage inventories. The solution includes a web-based order management portal powered by cloud technologies that enable faster integration and analysis of more data from more sources. The company now draws product data from the original mainframe database into a cloud data warehouse, where it is combined with competitive pricing data that is updated regularly. 

It uses machine learning algorithms to match the company’s products with other vendor products and pricing, which not only shows competitive pricing, but eventually will enable them to optimize pricing models based on the ever-growing data history. This high-performance system allows the use of more digital product images and scanning technologies, which improve and accelerate the users’ ordering experience. It also integrates with store point-of-sale systems, which improves their inventory management. 

The new order management system delivers a more accurate, personalized, and meaningful ordering experience for stores and gives customers the confidence that they will find the products they need when they need them.

The leading global foodservice retailer wanted to more quickly and accurately interpret customer experience data for improved decision-making. While unstructured text and natural language data can provide actionable insight, data from customer satisfaction surveys and social media comments were analyzed by different text analytics solutions, resulting in disparate outputs. 

Written comments were processed as a whole, leading to less accurate results than analyzing sub-comments that contain unique sentiments and tones. The QSR’s consumer Insights and social listening teams sought a more robust and flexible text analytics solution to effectively uncover sentiment and context at scale.

Wavicle implemented a text analytics solution that integrates raw data from multiple sources, such as real-time social listening and customer satisfaction surveys, in an Amazon Redshift database. We captured the full scope of written feedback by first breaking complex sentences into sub-comments, then assigning a positive, negative, or neutral sentiment to each referenced topic, ensuring consistency across categories and themes. 

To preserve information and minimize information loss, the data model captured and replaced all previous references and co-references, offering tremendous visibility and insight into millions of surveys and comments already collected. 

Approximately 95,000 user comments are processed daily by Talend cloud data integration software and Amazon Comprehend, a natural language processing service that uses machine learning to find insights and relationships in text specific to the QSR’s topics and sentiments. The output is transformed into dashboard intelligence and ad hoc reporting by Tableau data visualization.

Wavicle helped the global foodservice retailer to improve the speed, completeness, and accuracy of customer feedback analysis. The new text analytics solution processes unstructured text and natural language data 10x faster than before, with up to 70% greater accuracy. With the ability to gauge the sentiment and tone of complex comments across myriad specific items, themes, and categories, the QSR’s consumer insights and social listening teams can quickly discover trends on customer experience with little to no lag time, driving meaningful action.