How To Use This Playbook

Each Quad Cities Manufacturing Innovation Hub playbook is created with the business growth needs of our area’s small and medium manufacturers in mind. By utilizing the information in the Data Analytics Playbook, you are taking the first steps to creating a competitive advantage for your company by innovating in the face of disruptive technologies. 

This playbook follows a logical flow to guide you as you learn more about Data Analytics (see Fig. 1). Review the sections as they apply to your individual opportunities and resources, either in the order they’re presented or jump around to fit your immediate needs.

Figure 1: Data Analytics Playbook Information Flow

Playbook Info Flow Fig 1

This is your toolkit for plugging into the data analytics in manufacturing network in the Quad Cities.

Together all eight of our playbooks uplift our regional manufacturers and Department of Defense suppliers through increasing digital readiness, working in concert to accelerate the understanding and investment in emerging technologies and to foster a culture of innovation in the manufacturing industry. We encourage you to review the other playbooks (see appendix for more information) as well.

Whom can I contact at the Quad Cities Manufacturing and Innovation Hub with questions?

Email and a member of the Hub team will respond to your question.

About the Quad Cities Manufacturing Innovation Hub and Our Partners

The Quad Cities Manufacturing Innovation Hub assists businesses by offering services such as operational assessments, registry in a regional catalog of manufacturers and suppliers, trade and business-to-business events, access to subject matter experts through the Chamber’s Critical Talent Network, connections to the Quad City Manufacturing Lab and national research, and training seminars targeted at key technologies. More information on the Hub can be found here

This content was prepared as part of the Illinois Defense Industry Adjustment Program, a partnership between the University of Illinois System, the Quad Cities Chamber of Commerce, and the Voorhees Center at the University of Illinois Chicago (UIC), with financial support from the U.S. Department of Defense, Office of Economic Adjustment (OEA). It reflects the views of the Quad Cities Chamber of Commerce and does not necessarily reflect the views of the OEA. For more information, please visit

Copyright © 2018 by Quad Cities Chamber of Commerce, Inc.

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying or other electronic or mechanical methods, without the prior written permission of the publisher, except as permitted by copyright law. For permission requests, write to the publisher at the address below:

Quad Cities Manufacturing Innovation Hub c/o Quad Cities Chamber 1601 River Dr., Ste. 310, Moline, IL  Visit the publisher’s website at

Data Analytics in the Quad Cities: At a Glance

What does “data analytics” for manufacturing encompass?

Data analytics in manufacturing is the strategic use of data collected from a wide range of business areas (eg. supply chain, finance, sales, marketing, machine sensors, distributors, customers, vendors, and more) to inform product and operational decisions. Data can be generated from external or internal sources, or even machine-to-machine interactions via the “Industrial Internet of Things (IIoT).” When in aggregate, this data is often referred to as “Big Data,” due to the large amount of information collected for analysis. 

Why do data analytics matter to the Quad Cities manufacturing community?

Manufacturers in our community are increasingly pressured to make decisions quickly, and even predictively, to retain product quality, achieve onsite safety standards, and remain competitive on national and global levels. Decision-making must be based on data. In order to make sense of all of a manufacturer’s data sources and analyze them for action, companies must have a data analytics strategy. Data analytics is a critical component of every innovative manufacturer’s roadmap in the Quad Cities, informing decisions related to product development, supply chain optimization, sales and marketing spend, and other onsite efficiencies. 

What are the biggest opportunity areas locally?

The Quad Cities Manufacturing Innovation Hub has identified three key opportunity areas in data analytics for area manufacturers (more information on each in the following sections): 

  • Opportunity #1: Product quality control. Collecting data from machine sensors allows manufacturers to see and correct problems quickly, often in near real-time (or even predictively!).
  • Opportunity #2: Cost and operational efficiencies. Analyzing data throughout the supply chain, as well as other employee and operational data, contributes to lower costs, faster outputs, and easier long-term decision-making. 
  • Opportunity #3: Predictive demand forecasting. Better data yields more accurate demand forecasting as manufacturers are able to use smaller amounts of current data to predict responsive customer behaviors in the future. 

What are the business benefits of a data analytics program?

Though dependent on the data analytics opportunity area(s) you pursue, manufacturers witness many benefits from implementing these technologies, including increased operational efficiencies, lower manufacturing costs, streamlined value and supply chains, continuous process and product improvements, less downtime, and greater nimbleness leading to competitive advantage. For a full list of metrics, refer to the Metrics for Success section near the end of this playbook.  

Where can I find help to get started?

There are local resources that can assist you with the development and implementation of data analytics software and strategies. There are also many free online resources as well as educational courses offered by Quad-City universities and colleges. Jump to the Find Help With Expert Partners section for a full list of area resources to jump start your use of data analytics to grow your business. 

Understand the Technologies

In the first section, we take a closer look at the technologies that enable data analytics. You’ll gain a better understanding of how data analytics can contribute to your company’s digital technology and innovation strategy through diagrams, frameworks, and definitions of key terms used in the space. This section also details additional online resources for greater understanding.

Data analytics in manufacturing is the strategic use of data collected from a wide range of business areas (eg. supply chain, finance, sales, marketing, machine sensors, distributors, customers, vendors, and more) to inform product and operational decisions. Data can be generated from external or internal sources, or even machine-to-machine interactions via the “Industrial Internet of Things (IIoT).” When in aggregate, this data is often referred to as “big data,” due to the large amount of information collected for analysis.   

According to Forbes, 68% of manufacturers are investing in data analytics strategies.[1] These data analytics strategies are often combined with the use of predictive analytics to reveal potential supply chain, operational, or other weaknesses before they become an issue. Data analytics is also used to streamline processes, create leaner operations, and predict demand.

Many data sources contribute to a robust data analytics strategy within the manufacturing environment. These may include:

  • ERP system (learn more about ERP in the ERP Playbook).
  • Machine-to-machine sensor data (includes location, weight, temperature, vibration, flow rate, humidity, balance, and more[2])
  • Operational data from distributors, suppliers, vendors, employees, customers, and other areas of the supply chain.

Glossary: Data Analytics Terms

Please refer to the glossary in the Appendix at the end of this playbook for definitions of key data analytics terminology utilized in this playbook.



Figure 1. How Software and Big Data Are Changing Manufacturing in the United States (abridged), via Ohio University[3]

Big Data And Mfg Ohio State Univ Fig1


Figure 2. Data Sources Within the Manufacturing Ecosystem, via iCrunchData[4]

Manufacturing Ecosystem V2 1024x503 Fig2


Figure 3. Types of Analytics, via Competing on Analytics. Presented in a Quad Cities Chamber of Commerce Data Analytics user group meeting.[5]

Types Of Analytics Fig3

Graphic presented in John Deere presentation, adapted from “Competing on Analytics,” Davenport and Harris 2007.

Additional Online Resources

There are many online resources for review to deepen your understanding of data analytics strategies, programs, software, applications, technologies, use cases, opportunities, challenges, and more. We’ve outlined a few below:

Identify Opportunities

Data analytics offers many opportunities to small and medium manufacturers in the Quad Cities. The Hub has identified three key areas that can bring greatest benefit to our area’s small and medium manufacturers: product quality control, cost and operational efficiencies, and predictive demand forecasting.    

Opportunity #1: Product Quality Control 

Utilizing machine and line sensors throughout production, manufacturers can monitor product quality data and make changes in real-time. With a strong data analytics program and software solution, product defects are spotted more quickly and sorted, leading to better margins and less discard. Resources are managed more efficiently, and product failures can even be predicted in some instances. Assembly lines become more efficient, batch quality is increased, regulatory compliance in design and practice is upheld, and waste reduced. Manufacturers witness similar benefits from monitoring machine-level data for potential issues and course-correcting before disruption. 

Opportunity #2: Cost and Operational Efficiencies

Implementing a data analytics programs allows manufacturers to reduce costs throughout their supply chain and increase operational efficiencies. By monitoring their machinery, production lines, assembly, warehousing, packaging, inventory, and transportation, they’re able to make decisions that reduce costs, waste, and downtime. Combined with the “Industrial Internet of Things (IIoT),” data analytics can also positively impact maintenance and repair schedules and employee workloads. Production floor error-rates and employee performance data is easily monitored, giving manufacturers the quick feedback mechanisms needed to implement changes quickly and effectively. 

Opportunity #3: Predictive Demand Forecasting 

Traditionally, manufacturers annually predict demand based on historical data (often YoY). But, with a robust data analytics program, they can identify trends or anomalies based on recent data capture. This leads to more accurate demand forecasting and the ability to alter operational components to course-correct before resources are wasted on product that will ultimately be warehoused. Operational efficiencies can also be achieved when demand is forecasted off of hypothetical “what-if” production scenarios, tested fluidly based on continuously collected data. 

Benefits and Use Cases of Data Analytics Opportunities

In this section, we’ll examine the key benefits of utilizing data analytics in each of the three opportunity areas previously identified. Below, you’ll also find a case example for each opportunity area that shows how a manufacturer was able to utilize data analytics to produce results.  

Opportunity #1: Product Quality Control

Improved product safety: Rather than simply discarding low-quality products after production, many manufacturers utilize data sensors and scans throughout the production process to sift through products and determine which are not fit for shipment and why. The data leads to continuous product improvements for quality and safety as well as lower waste over time.1

Lower product and machine failure rate: Data analytics combined with the IIoT helps manufacturers analyze product failures before customers experience them and machine failures before they disrupt operations or cause potential workplace inefficiencies. Machines that continuously capture data for analysis and reporting contribute a culture of preventative maintenance, allowing manufacturers to respond quickly to signals of breakage, torn belts, reduced product demand and/or load, and more.2

Reduced product development errors: When engineering modeling is combined with analytics-based computer simulations, errors are predicted, reduced, and corrections can be made before a product or tool even goes into production. Test data can also improve productivity in helping streamline processes when utilized in a rapid test-and-learn environment.

Ensure regulatory compliance: According to MSR Cosmos, data analytics can help detect, “digressions or patterns, or even the slightest hints of non-conformity to standard procedures or protocols.” This real-time monitoring ensures regulatory standards are met without material waste or the need for reconfiguration.

Case Example: Intel Improves Product Quality with Big Data and Automation Strategy in its “Smart Factories”4

Intel’s vision for smart manufacturing is based on the importance of big data and automated process control in ongoing analysis and decision-making. Utilizing the data captured from its factory automation processes, Intel’s engineers are able to identify opportunities for improved efficiency, velocity, and quality. For example, in producing silicon wafers, Intel must adhere to strict quality standards, including traceability, that require consistent data capture and measurement monitoring during production. Throughout its factories, historic and current data are used to plan and implement new or improved automation for better quality, cycle time, and yield. Engineering analysis targets product quality improvements and increases equipment performance over time. When processes exceed statistical thresholds, production tools are taken offline and materials are rerouted or put on hold for quality validation. Intel’s full case study details more benefits of automation and big data. 





Opportunity #2: Cost and Operational Efficiencies

  • Decreased warehousing and inventory costs: Acting on real-time insights into inventory throughout the supply chain, including delivery route optimization, can help dramatically reduce warehousing costs and increase profit margins.
  • Improved employee efficiency: Utilize big data analytics to study error rates on the production floor and assess specific areas where employees are excelling and under-performing. This leads to not only improved employee efficiency, but better-informed management, engineers, and operators.10
  • Quickly implement changes: When data is flowing throughout your organization and accessible to those who can act on it, your workforce is empowered and enabled to implement changes quickly and effectively. This increases workplace efficiency and collaboration as well as catches operational issues before they become downtime-inducing problems.
  • Identify bottlenecks: Data analytics can be used to pinpoint specific tasks throughout the manufacturing process or supply chain to assess components, processes, or employees that may be contributing to bottlenecks. In turn, managers can then create contingency plans to minimize the effect of potential inefficiencies.11

Case Example: Versatech Improves Manufacturing Quality, Efficiency, and Performance with Data Analytics Strategy and Inter-departmental Collaboration12

When Versatech realized their paper tracking system was no longer effective, the company sought a way to cut costs and improve manufacturing performance with a digital data analytics strategy. Partnering with software provider SensrTrx, Versatech began collecting and distributing data to the quality, engineering, maintenance, and production staff who could use it to enhance the efficiency of their operations, reduce overtime work, decrease downtime, and improve machine utilization. Versatech saw these measureable results in less than three months of implementing its data analytics program. This resulted from not only collaboration, but also less data entry and real-time access to the information needed to make critical decisions. Versatech was able to see downtime and scrap rates as they were occurring, correlate it with known causes, and improve “uptime” in the process. Read the full Versatech and SensrTrx case study here.




Opportunity #3: Predictive Demand Forecasting

  • Reduce stock levels: Utilizing predictive analytics as part of a data analytics strategy, manufacturers can reduce both raw materials and finished goods stock levels. This ultimately improves quality of service due to better product availability and reduced delays. Manufacturers can more quickly recognize anomalies in the supply chain and erratic demand patterns as well.13
  • Rapid test-and-learn: Operational efficiencies are achieved when manufacturers can quickly act on the data they gather in running test scenarios. If A is changed, what happens to B in real-time? This provides insight into everything from labor costs to inventory prices that may shift as a result of production changes.14
  • Better understand customer purchase behaviors: With consistently collected data at your fingertips, it’s easier to analyze buying patterns over time and adjust supply as necessary. Data analytics allows manufacturers to make decisions based not only on what they’ll likely ask for in the future, but also what they’re asking for now.

Case Example: Trenton Corporation Meets Product Demand and Predicts Production Capacity with Data-driven Forecasting Model15

Trenton Corporation manufactures anti-corrosion products for the pipeline industry and is challenged by a limited production capacity to make all of its offerings – as well as fluctuation in demand for those offerings. They sought a solution to quickly and systematically allocate production capacity each month to meet demand while also avoiding excessive labor or inventory costs. With input from sales data, Trenton Corporation worked with Simafore software solutions to build a high-quality forecasting model that provides monthly and quarterly demand projections for a range of products as well as historical trends and break-downs of input data into key segments as chosen by the customer. The results: 90% reduction in planning time, reduced reliance on experience and knowledge of a single person, and confidence in proactive planning by eliminating human error and bias. Read the full case from Simafore study here.




Build the Business Case and Begin Implementation

In this section, we’ll outline the steps to take in implementing data analytics technologies within your company, beginning with awareness and change management, through establishing partnerships and building use cases that will save you time and money. We understand that the idea of implementing a data analytics program is very different from traditional measurement and innovation processes that you may be accustomed to. We also understand that the prospect of this degree of change is daunting! It is our hope that through the following content and previous look at the benefits of data analytics, you’ll feel more comfortable exploring how you can utilize these technologies to achieve efficiencies throughout your company.  

Change Management: Building the Case Requires Data and a "Test-and-Learn" Approach

For most small and medium manufacturers, the prospect of launching a data analytics program seems risky, as it bucks the status quo and requires learning new technologies and procedures to remain relevant in a digital age. Only through experimentation, learning, and failing fast, can you quickly gain new expertise and experience that will benefit your company in years to come.

It is new technologies, like data analytics, that are shifting the manufacturing industry – beyond the Quad Cities. New strategies and tactics are emerging, and the only way to survive is to be proactive in your adoption of data analytics in ways that fit into your current culture and align with your business growth goals.

There are many ways for you to get started along the path to utilizing data analytics. Use the change management tips below to make the case for change and immediately begin proving results:

  • Understand the business value of data analytics separately, and set goals accordingly. Use our metrics outlined later in the playbook as well as your own research to set realistic expectations of how you will measure the impact and success of integrating data analytics into your existing manufacturing technologies, equipment, and processes. This will help in resource planning if you’re measuring the right benchmarks out of the gate. Focus on one or two main use cases first before building complexity.
  • Focus on getting every employee on board with the benefits of data analytics through peer education. Get all stakeholders involved from the beginning via one-on-one conversations with leaders and all-company meetings to drive the vision. Make them as knowledgeable as you possibly can, taking ownership of digital platform initiatives. Innovative companies like GE promote “reverse mentoring” to foster understanding, create mutual empathy, and promote collaboration between disparate generations and team members. In reverse-mentoring scenarios, a younger colleague mentors a more tenured employee as a way of getting everyone up-to-speed quickly with digital technologies and benefits. Turn to the Expert Partners section for education resources and tips.
  • Keep communication lines open during the trial-and-error portion of experimentation. Employees should understand that it’s okay to fail, and fail fast, if it’s part of a learning process that eventually leads to successfully implementing new data analytics strategies. This mindset must be led from the top-down within your company in order for employees to feel like they can experiment and innovate in order to achieve efficiencies. Breed risk-taking early.

Part of change management also lies in understanding and planning for the challenges you will encounter in integrating data analytics into your existing operations. Below are four challenges we’ve identified through our research and conversations with area manufacturers. Become familiar with the potential roadblocks so you can steer clear of their hindrances early on.

  • Challenge 1: Understanding and managing the data at your disposal. Every manufacturer collects troves of process data that is most often utilized for tracking purposes, not improving operations. The challenge becomes investing in systems and employees with expertise to optimize their existing data, centralizing it, and analyzing it efficiently in order to glean actionable insights.16  More on hiring later.
  • Challenge 2: Taking action based on events not time-based milestones. Most manufacturing runs are based on demand signals that feed into an ERP system, with no randomness, with each moving part in the production line based on a time trigger. This is a predictable process that thrives on stability, not data-based events that can be random. Manufacturers that base actions off of data signals respond to customer demand, machine performance, and other events. This dramatically shifts how data is collected and how systems are engineered to maintain stability.17
  • Challenge 3: Integrating data analytics with legacy systems. There are multiple systems within every factory, collecting its own data. Information is passed from these systems to an ERP solution, or even among systems in some mature cases. Challenges arise when legacy systems don’t have, “well-defined interfaces, documentation is scarce, or software engineers are not available anymore,” according to Harvard Business Review.
  • Challenge 4: Opening your factory to security risks. The promise of receiving, marrying, and analyzing data for rapid action within your facility is exciting, but it is not without its security risks in data collection, storage, and transmission practices. Systems can be exposed to attackers, especially if security precautions are not taken when data is transmitted from sensors and machines via the IIoT.18



18 Ibid.

Processes and Frameworks for Implementing Data Analytics

Integrating data analytics into your existing manufacturing processes requires a strategic approach. Utilize the workflows and frameworks on the following pages to aid in your high-level strategic prioritization of data analytics. We recommend you search out specific frameworks for each technology and use case chosen to guide your implementation.

Framework 1: Maturity Pyramid for Adopting Big Data in Manufacturing, via Inside Big Data.19

Dell Maturity Pyramid


Framework 2: The Four Components of Big Data Management in Manufacturing, via Inside Big Data.20

Dell Big Data Solutions 4 Components Marketecture Jpg


Resources Needed: Technology and Staffing

Resources required to implement data analytics technologies will vary by the use cases you’ve established. For example, concentrating on better product quality will yield a different cost structure than using data for predictive inventory forecasting. As previously outlined, you must create a strategic plan for how data analytics will augment or replace your current processes in the recommended opportunity areas before jumping the gun and investing in technology solutions.

Use these recommendations to assist in the process of planning for your hard and soft costs:

Hardware and Software: In implementing a data analytics strategy, especially where big data is concerned, hardware and software technologies go hand-in-hand. Acquire one without the other, and you’re either unable to securely store the data you’re gathering or unable to extract actionable insights or value from the complex data you’ve collected. Data analytics systems require many components that contribute to their overall “workload,” including ERP, CRM, SQL, and other data sources as well as processes of data validation, cleaning, transforming, aggregating, and loading to extract and organize data prior to its transition to a data warehouse. From there, companies use a variety of software to analyze, mind, visualize, and report on the data.

Other hardware considerations include:

  • Data warehousing – Where will you store the data you’re collecting? This is one of the first and most important decisions. Assess how much data you’re gathering, from what sources, and where computing will take place. Costs of data warehousing and datamarts vary by amount stored.
  • Sensors – automate the collection of data on machines, lines, shipping, etc. Often connected to other sensors and technologies through the IIoT.
  • Mobile devices – for employee use in on-the-job monitoring and reporting to give easy and quick access to data for decision-making.

Software selection requires an in-depth look at your data analytics use cases, the actions you hope to take based on the data, how much data you’re collecting, what components you’ll be analyzing, the velocity at which you’ll be extracting insights, and how the data is being combined with other sources.

According to criteria set forth in Industry Week, the goal of an effective data analytics software platform is to provide features that allow engineers and analysts to quickly interact with data without acquiring IT expertise. Look for the following in your deployment:

  • Accessibility via a browser or app to provide a web-based interface
  • Usability by process experts and manufacturing engineers
  • Lightweight deployment that does not require data duplication
  • Designed for time series data analysis in process plant and other manufacturing applications
  • Features that apply machine learning and other advanced algorithms to simplify analysis
  • Interactive, visual representation of data and results
  • Ability to quickly iterate, and to combine one result with another
  • Ease of collaboration with colleagues within and across companies21


Data analytics software choices are plenty, with the most often-used resources included in Fig. 4 to begin forming your consideration set.

Figure 4. Top Data Tools for Extraction, Storage, Cleaning, Mining, Visualizing, Analyzing and Integrating, via More information on each outlined on their site, including pricing.

Top Data Tools For Extraction Fig4 Png


Employees and Hiring: Assess your current employees for skillsets and experience in data analytics or data science to determine if expertise and interest exists. If not, you may opt to hire new employees with data analytics expertise to speed up the implementation process, as well as inject new, passionate approaches to innovation within the company.

Work with the education and hiring partners listed in the next sections to find data analytics employees with experience, or those that are freshly graduated, or as a temporary intern (with, ideally, intent to hire). Look for analytical capabilities and training in spotting patterns and drawing actionable insight from large quantities of information, advises McKinsey and Co.23 As data analytics programs can be quite complex, also consider partnering with an expert consultancy to assist with strategy and implantation.


When assessing existing or potential employees, consider their key competencies and ensure they meet the seven criteria in Fig. 5, below:

Figure 5. Building a World-Class People Analytics Team: Seven Key Competencies Needed for Long-term Success. Presented in a Quad Cities Chamber of Commerce Data Analytics user group meeting. Via David Green, IBM.24

Building World Class Data Analytics Team Fig7 Ibm Presentation


“Quick Wins” to Get Started with Data Analytics

Take a page from the best practices of other manufacturers that are already up-and-running with data analytics programs, by following a few of tips to jumpstart your use of these technologies.

  • Tip 1: Understand the data you already have. It can be tempting to expand your data analytics strategy into sources that don’t even exist yet, like line sensors or machine-to-machine interfaces. However, you likely have multiple data streams you’re already collecting (especially in ERP), but may not be connecting or analyzing in real-time. Spend time conducting a data audit as a starting point to see if you have the minimum amount of data required to apply analytics – typically, at least 15 data sets per influencing variable.25
  • Tip 2: Don’t boil the ocean. Focus on one use case or business function first to produce more relevant results (quicker and cheaper!) than an enterprise-wide approach. You’ll generate value early and often when implementing data analytics in smaller initiatives – contributing to more momentum and support throughout. Roll out your strategy in phases that align with your company’s strategic priorities over the coming three years.26
  • Tip 3: Determine what person or department should lead the data analytics program. According to advice from experts at Bosch, start by deciding what function makes the most sense to take charge. Start by holding a data analytics orientation workshop for management to cover the basics of data analytics. That will help uncover if there is already an understanding of data analytics within the company (and a natural leader will emerge), or if hiring someone in IT or data science is necessary. Either way, you’ll need buy-in from management, which starts in knowledge-sharing.27
  • Tip 4: Invest in seasoned talent and necessary infrastructure. The foundation of any data analytics strategy lies in not only IT solutions but also experienced analysts who can extract value from troves of data. One begets the other. Invest in your team and your technology infrastructure, and consider other professional consultants to help with initial strategy and set-up.




Metrics for Success: How to Measure Impact

When setting objectives for your data analytics program, you’ll need to tie goals to business impact using metrics for success. Without measuring and benchmarking the machine, product, and operational performance against traditional data management strategies, it will be more difficult to consistently improve processes, assess weaknesses, and secure future resources.

  • Better forecasting of products/production
  • Greater understanding of single and multi-plant performance
  • Faster service and support to customers
  • Real-time alerts and actionable data analysis
  • Predictive modeling on manufacturing data, including inventory forecasting and supply/demand
  • Improved interactions and relationships with suppliers
  • Understand requirements for new products based on existing product/machine data28
  • Reveal potential supply chain, operational, or other weaknesses before they become an issue, leading to better margins, increased batch quality, and less discard
  • Streamlined processes, including maintenance and repair schedules and assembly lines
  • Create leaner operations and manage employee productivity and error
  • Quicker test-and-learn process prototyping leading to more operational innovation
  • … And more, depending on your use case.


Find Help with Expert Partners

In delivering this Data Analytics Playbook, among the seven other playbooks provided by the Quad Cities Manufacturing and Innovation Hub, our goal is to connect you to resources you need to learn about and implement new technologies that will impact your business and our region in the future. In this section, you’ll find experts, consultants, and specialists to help you succeed. This is only a partial list of the experts that can help you. We recommend researching partners based on your exact use case to narrow down the pool. 

Consultants and Vendors

Advizor combines its data discovery software with business consulting and analytics expertise to deliver full solutions customized to help manufacturers answer key business questions. They access, configure, blend, synthesize, visualize, and present data in a format that makes for easy analysis and decision-making.

Bimotics’ cloud-based big data solutions address manufacturing data analytics programs. Its data scientists and consultants work powerful cloud platforms to deliver advanced inventory planning and optimization, business intelligence-based production performance analysis, dark data analysis for optimized preventative maintenance, error-log analysis for predicting recall and customer service issues, secure cloud solution infrastructure, and more.

Bosch’s data analytics consulting contributes to specific, implementable results that will help manufacturers take a systematic approach to their analytics projects. Their tool set provides added support. Workshop trainings offered for employees. 

Teradata works with large industrial manufacturers on data analytics strategies to streamline supply chain performance and provide end-to-end visibility; establish asset and process performance improvements using analytics, not capital; bolster bottom line using analytics to reduce overall spend; and develop a deeper understanding of customers to grow revenue via customer engagement. 

WCI Data Solutions
WCI works with manufacturing organizations to reduce ineffective systems and help structure data analytics plans to help your business with data organization, trend forecasting, improved after-sales services, improved supplier relations, consolidated information, and regulatory compliance.

Educational Institutions

Augustana College
Augustana will be part of a small group of liberal arts colleges at the vanguard of offering an undergraduate degree in data analytics. The first course required of the minor in data analytics is coming this spring 2018. They are also in the process of interviewing candidates for the Data Analytics Chair, funded by John Deere,  and hope to complete the search later this term. Starting in the 2019-2020 academic year, Augustana expects to begin offering courses toward the major of Data Analytics.

University of Iowa – Tippie College of Business
Earn a Master of Science degree full-time in Iowa City, or continue working while you learn in their part-time master's and certificate programs in Cedar Rapids, Des Moines, and the Quad Cities.

Western Illinois University – MS in Applied Statistics & Decision Analytics; Post Baccalaureate Certificate in Business Analytics
WIU is currently offering following graduate programs related to data analytics at the Macomb and Quad Cities campuses: 1. MS in Applied Statistics and Decision Analytics, and 2. Post Baccalaureate Certificate (PBC) in Business Analytics. Plans are underway to create a new undergraduate major in Bachelor of Business in Business Analytics at the Macomb campus, starting fall 2018.

Hiring Solutions

Robert Half Technology 
Robert Half Technology specializes in placing application development, systems integration, information security, infrastructure management, networking, database development, help desk and technical support professionals in project, contract-to-hire and full-time positions.

Chenhall Staffing Services 
In addition to staffing and HR, the Chenhall’s team provides solutions in a wide-ranging area of IT needs. Whether they are simply identifying and placing highly qualified technical experts to fit clients’ staffing needs or serving as a prime or sub-contractor on an operational program, their preferred operating model is to build long-term partnerships and trusted relationships with the common purpose of delivering, sustaining, and supporting quality IT services.


Glossary: Key Data Analytics Terms

Definitions from Liaison,29 Sight Machine,30 and Lean Methods31 for educational purposes. 

Aggregation: A process of searching, gathering and presenting data. 

Algorithm: A mathematical formula or statistical process used to perform analysis of data. 

Analysis: Data analysis tools enable manufacturers to identify patterns, measure the impact of those patterns, create actionable insights, and even predict outcomes. By breaking down equipment, production, and supply chain data, analysis tools help manufacturers drive outcomes through better decision-making.

Analytics: The computational analysis of data to discover patterns and information.

API: Application program interface (API) specifies how software components should interact and facilitates the integration of features, sharing of data, etc.

Batch processing: Batch data processing is an efficient way of processing high volumes of data where a group of transactions is collected over a period of time. Hadoop is focused on batch data processing. 

Big Data: The analysis of extremely large and diverse data sets to reveal patterns, trends, and associations.

Cleansing: As Big Data comes from numerous structured and unstructured sources, it is critical for manufacturers to ensure the quality and integrity of their data for analysis. Big Data analytics tools enable this by cleaning and transforming data into readable, unified data sets for multiple users. Cleansing also involves standardization and parsing data into consistent formats that are usable by different enterprise applications and systems.

Cloud: A broad term that refers to any internet-based application or service that is hosted remotely. 

Cloud computing: A distributed computing system hosted and running on remote servers and accessible from anywhere on the internet. 

Cluster: A group of servers and other computing resources to enable high availability.

Data collection: The process of identifying data sources and variables of interest and systematizing the gathering ring of those variables.

Data conditioning: The process of cleansing the data, transforming and blending it into useful data models, and optimizing the data for future analysis.

Data governance: A set of processes or rules that ensure data integrity and that data management best practices are met. 
Data integration: The process of combining data that’s been collected from disparate sources including merging multiple sources of the same type or combining data from multiple different types of sources into single records.

Data integrity: The measure of trust an organization has in the accuracy, completeness, timeliness and validity of the data.

Data lake: A large repository of enterprise-wide data in raw format. Supposedly data lakes make it easy to access enterprise-wide data. However, you really need to know what you are looking for and how to process it and make intelligent use of it.

Data modeling: Understanding business and technical requirements, and identifying data sources and variables to satisfy those requirements.

Data storage: Gathering data and having the capacity to store data are the first steps in utilizing Big Data analytics. Data storage allows manufacturers to keep equipment, production process, and supply chain data for analysis.

Data visualization: Presentation of data to improve the ability to understand and communicate meaning.

Discovery: Data discovery or data mining tools enable manufacturers to quickly identify and access the information they need to make production and supply chain decisions.

Industrial Internet of Things (IIoT): The application of data science to sensor data, M2M communications, automation technologies, and industrial systems.

Machine-generated data: Data automatically created by machines via sensors or algorithms or any other non-human source. 

Machine learning: A method by which computer programs examine prior data to discover trends and outliers, and predict potential future outcomes.

Mapping: Data mapping tools help manufacturers understand the flow of data within data environments, production processes, and supply chains. These tools enable manufacturers to identify dependencies and address potential problems at the cause. At the same time, they help identify potential data risks and leakages in the data environment.

MapReduce: A programming model for processing and generating large data sets. This model does two distinct things. First, the "Map" includes turning one dataset into another, more useful and broken down dataset made of parts called tuples. Tuples may typically be processed independently from each other across multiple processors. Second, "Reduce" takes all of the broken down, processed tuples and combines their output into a usable result. The result is a practical breakdown of processing.

Metadata: Data about data – can describe how the data is structured or provide summary descriptions of the data. 

Monitoring: Monitoring tools ensure that compliance with data quality standards are met on an ongoing basis. They also help ensure the good performance of equipment and the efficiency of the production process. Monitoring tools also enable manufacturers to automate quality assurance processes.

Predictive analytics: A branch of analytics in which historic data is used to make predictions about future events or states.

Predictive modeling: The process of developing a model that will most likely predict a trend or outcome. 

Profiling: Profiling tools provide greater visibility into a manufacturer’s production and supply chain. Profiling tools capture information up to the metadata level, enabling manufacturers to create a comprehensive inventory of their critical data so that they can make the most of the information they have.

Real-time data: Data that is created, processed, stored, analyzed and visualized within milliseconds.

Visualization: Visualization tools communicate the results of analytics to manufacturers and other professionals. It transforms data in spreadsheets and SQL databases into user-friendly graphs and charts, making it easier for manufacturers to generate insights and make data-driven decisions regarding their production processes and supply chain.




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