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Bosch Research was exploring the Economy of Things, where IoT devices will need to buy and sell to one another. The first challenge was creating a secure platform to support automated micropayments between 'things'. This suggested distributed ledger technologies (DLTs) such as blockchain. However, blockchains don't scale well. Reaching consensus on a transaction takes time. And tiny IoT devices have limited power, memory, processing, and connectivity. They certainly can't run heavy code. The question was how any blockchain could possibly work around all these constraints and how Bosch could help pioneer the standards needed for IoT devices to buy and sell among themselves.

Change Healthcare, one of the largest independent healthcare technology companies in the United States, operates one of the largest healthcare networks in the country, linking 900,000 doctors and 5,500 hospitals to 2,200 payers. In 2017/18, the network handled nearly 14 billion healthcare transactions and claims worth $1 trillion. However, the American healthcare system, which Change Healthcare is a part of, is plagued by inefficiency, fraud, and waste. Patients, providers, and payers seldom see complete, up-to-date healthcare records. Incompatible file formats, data silos, and privacy concerns all hamper the free flow of information. Consulting firm McKinsey estimates that U.S. healthcare could save up to $450 billion a year by using updated processes and technology. Change Healthcare wanted to play a leading role in this transformation, by extending its Intelligent Healthcare Network with blockchain technology to provide a faster, better experience for all.

The Philippines is a rapidly emerging economy with a population of over 100 million. However, one third of its population lives on less than $2 a day and remains unbanked. This means that 35 million Filipinos have severely limited access to both the domestic and global financial ecosystems. This is a significant problem when up to 10% of the Philippines’ GDP is made up of international remittances sent from overseas workers to family members across the country. Unionbank of the Philippines, one of the largest banking institutions in the country, sought to tackle this challenge by partnering with ConsenSys Solutions, Microsoft Azure, Kaleido, Amazon AWS, ConsenSys Diligence and seven rural Philippine banks to create an inter-rural bank payment platform using Enterprise Ethereum. The challenge of facilitating financial inclusion was to encourage the financially excluded, traditionally located in rural areas, to begin and to continue to engage with financial systems. The key to this engagement was determined to be a partnership with the existing 492 rural banks located amid small rural communities across the country.

Greenfence Consumer, a company that creates blockchain-based mobile platforms for brands and retailers, was struggling to build a scalable, easy-to-use blockchain platform. The company wanted to change the way brands engage with consumers by using blockchain technology. However, their initial attempts to deploy a blockchain solution proved challenging. They had limited options: they could deploy on the public chain, which presented several problems when looking at transaction costs and speed, or they could deploy a private version of the Ethereum blockchain. They chose to deploy on a public Ethereum test network for their first test, but they were looking for a solution that could provide a simple, secure, stable, scalable, and cost-effective platform on which to deploy their solution.

Managed care organizations, health systems, physicians, diagnostic information service providers and other healthcare stakeholders typically collect provider demographic data in separate IT systems maintained by each organization independently. This promotes vast inefficiencies and duplication of efforts, while also potentially reducing data quality. Industry estimates indicate that $2.1 billion is spent annually across the industry chasing and maintaining provider data.

Experian Marketing Services was facing challenges in providing its clients with high-quality, highly customized reports in short time frames. The company was dealing with large, inconsistent client data files that spanned terabytes of data and contained a variety of data formats. The legacy system used for processing the data often required intervention from engineering and delivery resources to meet customer requirements. This process was lengthy and involved custom coding, multiple and complex analytical tools, and expensive data transformation resources. Experian wanted to lower processing and analysis costs, produce final products for clients more quickly, and improve overall customer satisfaction.

Great Clips, the world’s largest and fastest-growing salon brand, was facing challenges in its growth strategy. The company's success is based on rapidly opening new stores in the right locations and markets. However, the process of identifying these locations based on potential customer base, demographic trends, and sales impact on existing franchises was taking too long and was expensive. The process required analyst resources to manually access, integrate, and analyze multiple sources of data to produce a report for a single location. This delay was risking the loss of prime sites to competitors and alternative real estate interests. Additionally, costly external contractors were being used to deal with the backlog.

Southern States Cooperative, a large farmer-owned cooperative in the United States, wanted to optimize its marketing efforts, particularly its high-value direct marketing activities. However, the company faced significant challenges. It lacked a way to consolidate customer and marketing data from multiple sources across the company for analysis. It also lacked sophisticated tools to drive marketing analytics, relying instead on generic productivity tools like Microsoft Excel. This lack of access to relevant data meant that direct mailings were not targeted effectively, leading to unnecessary expenditure.

TargetSmart Communications, a political data firm, was tasked with building voter propensity models and scoring national files for political fundraising campaigns for the Obama for America Campaign. The company needed to process millions of records of data from dozens of data sources, including national, state, county, and city records, to identify voters, track their tendencies, and accurately count and call upon unregistered voters prior to an election. The company was using open source software solutions to merge multiple databases, two of which consisted of 200 million voters each. However, the lack of a universal format for government data for various voter categories and the need for continuous data aggregation, updating, cleansing, and enhancement with demographic profiling data presented significant challenges. The company's choice of data processing tools was leading to a troubling lack of efficiency, accuracy, and reliability.

AAA National wanted to better support its member clubs in selling additional products to existing members, finding new members in each of their territories, and ensuring that brick-and-mortar office locations were optimally located based on club member demographics and drive-times. The process of gathering and analyzing the necessary data was time-consuming and complex, often taking up to three days to generate a complete and accurate data set for analysis. The data was sourced from different systems, making it difficult to match and blend with third-party demographic and census data. The use of multiple tools to work with the data further complicated the process and increased the potential for errors.

Rent-A-Center, a leading retail company in North America, was facing challenges in creating maps for their 3,000 stores. The process was manual, involving a lot of clicking, joining, queries, and sub-queries. This was time-consuming and inefficient, taking up to 12-1/2 weeks to complete. The company needed a solution that could streamline this process and make it more efficient.

Schneider Electric, a global specialist in energy management, was facing a challenge in identifying its high-potential customers and deploying sales resources efficiently. The company's manual approach to determining how and where to deploy its sales resources was time-consuming and inefficient. Sales operations and more than 20 sales managers would collaborate with the analysis team to collect data and evaluate numerous factors related to each customer account, including account size, vertical market, and growth and purchase history. This process was not only slow but also resulted in the sales team waiting for the information it needed to start the new year, often not receiving it until February.

Novus, a leading advertising agency, has been experiencing significant growth, expanding both its client base and the range of services it offers. As a result, the complexity of data usage and reporting has increased, both internally and externally. The company was seeking a fast and efficient way to process the hundreds of variables involved in planning and measuring effective media campaigns. The continuous evaluation of success and application of optimization techniques across a universe of potentially more than 3,000 publishers for each client became cumbersome. It was increasingly important to be able to track progress and outcomes in an automated way.

The rise of e-commerce has led to the closure of many brick-and-mortar stores, with Americans spending over $400 billion a year online. However, the online shopping experience is largely transactional, lacking the human interaction and emotion that comes with physical shopping. Hush, a top social commerce app in the United States, aims to bring the social aspect of shopping to the digital world. The company believes that shopping is a social experience and that many purchases are experiential and emotional. This is particularly true for beauty products, Hush's focus, where people want to interact and talk with like-minded individuals about what they're buying.

Amaysim, Australia's largest MVNO with over 600k customers, was dealing with a massive amount of data. They had over 10 billion call data records to be analyzed, with 20-30 million call data records added daily. The velocity and complexity of data were high, with multiple data sources including Livechat, Zendesk, call data records, Google analytics/Website data, Point of sale data, and Exact target. The company had a small analytics team of three people covering a wide span of functions. They needed a solution that would enable line of business users to quickly build on a baseline of analytics, solve their own specific business problems quickly, and not have to wait on Business intelligence teams.

Scrum, Inc., a company that consults and coaches leading companies across the globe, was facing several challenges related to data management. The data was siloed in numerous systems, spreadsheets, and applications, which limited access to critical data. The existing Business Intelligence (BI) solutions were not able to provide the necessary insights that the company needed for its operations.

WhippleHill faced several challenges in their operations. They were unable to view data from multiple systems in one place, which made it difficult to turn raw data into actionable information. Additionally, the process of reporting was cumbersome and time-consuming. This lack of a unified data view and the difficulty in data interpretation hindered their decision-making process and overall efficiency.

Platinum IDS, a pioneer in outsourced litigation support services, was facing several challenges. The company was struggling to manage a variety of data sources, which was affecting its ability to track metrics in real time. This was a significant issue as real-time data is crucial for making informed decisions and driving business growth. Additionally, Platinum IDS was having difficulty disseminating information across the organization. This lack of effective communication was hindering the company's ability to operate efficiently and effectively.

Check Into Cash, a national leader in affordable short-term, small dollar credit solutions, was facing several challenges related to data management and reporting. The company was dealing with an overwhelming number of ad hoc reporting requests. The executives were not receiving reports in a format that was easy to understand and digest. Moreover, it was taking too long for the company to get updates on important metrics. These challenges were hindering the company's ability to make timely and informed decisions.

Litigation Services, a law firm serving several large insurance companies, was facing significant challenges in managing its data. The firm's reporting was static, often resulting in outdated information. Furthermore, the data was not easily accessible to decision-makers, hindering the firm's ability to make informed, timely decisions. The firm needed a solution that would enable it to become truly data-driven, providing real-time insights and facilitating a more proactive, consultative approach with its clients.

BodyLogicMD, a network of highly trained physicians specializing in natural bioidentical hormone replacement therapy, was facing a significant challenge in managing and interpreting their vast amounts of data. The company, which operates more than 40 practices around the United States, was struggling with slow and tedious reporting processes. The other Business Intelligence (BI) solutions they considered appeared outdated and complex, which would not solve their problem of digesting the data effectively. The company needed a solution that could provide immediate access to real-time data in a simple and user-friendly manner.

RelayRides, the world’s largest peer-to-peer car-sharing marketplace, was facing challenges in making sense of their vast amount of data. The manual reporting process was tedious and slow, which hindered the company's ability to make quick, data-driven decisions. The information wasn’t delivered in real time, which further complicated the decision-making process. The company needed a solution that could automate the reporting process and provide real-time insights.

One Click Ventures, a fast-growing network of online brands, faced several challenges in managing their data. Their data was scattered across multiple third-party tools, making it difficult for decision-makers to see the big picture. Their business analysts were spending more time compiling reports than providing valuable analysis. The lack of a centralized location for data made it difficult to diagnose issues quickly and efficiently.

Betable, a fast-paced startup disrupting the world of real-money gaming, was producing more data than it could confidently manage. The CTO was spending over 10 hours a week generating ad hoc reports. Furthermore, executives and other non-engineers were having difficulty making sense of the final reports they received. The company needed a solution that could handle the vast amount of data being produced and present it in a way that was easily understandable for all stakeholders.

CoverHound, an insurance comparison shopping platform, was facing challenges with its data management. The company's important metrics were not available in real time, leading to inefficiencies and delays in decision-making. Employees relied on different reports in different formats, leading to inconsistencies and potential misinterpretations of data. The lack of a unified, real-time data platform was hindering the company's ability to operate efficiently and make informed decisions.

Goodwill Industries of Central Indiana, one of the largest branches in the nation, was struggling with managing vast amounts of data ranging from employment to eCommerce. The data was scattered and difficult to access, which made it challenging for the organization to make informed decisions. Valuable resources were tied up in manually creating reports, which was a time-consuming and inefficient process. Furthermore, executives had limited visibility into key metrics such as job placements, which hindered strategic planning and decision-making.

OGIO International, Inc., a company that designs and manufactures bags, backpacks, and travel luggage, was struggling to leverage its sales analytics to capitalize on sales trends. The company was dealing with multiple sets of reporting tools, which made data management a complex task. Additionally, there was no system in place to share data in real-time, which hindered the company's ability to make timely decisions based on current data. Furthermore, the company lacked a user-friendly, drillable format for sharing years of data, making it difficult for different departments to access and utilize the data effectively.

Rhodes Bake-N-Serv, a leading manufacturer of frozen bread and rolls, was facing challenges in its data gathering and sharing process. The executives and plant managers had limited visibility into the operations, and the process of gathering data was slow and manual. The company was using Excel for reporting, which was time-consuming, error-prone, and difficult to maintain. The lack of real-time data also meant that any issues in the manufacturing process would take a long time to be identified and addressed.

Sage North America, a provider of business management software and services, was facing challenges in its data management. The company understood the importance of being data-driven but was limited by the lack of appropriate tools. The process of building and sharing reports across teams was time-consuming. Additionally, the visibility into multiple data platforms needed to be increased. The company was operating in a culture of guesswork and assumptions, which was not conducive to its growth and efficiency.

Telus, a Canadian national telecommunications company, was facing a significant challenge in making informed decisions due to a lack of accessible data. The data they needed was stored in disconnected systems, making it difficult to gather and analyze. The process of disseminating information was time-consuming, and building reports took longer than was efficient. The company was in need of a solution that could consolidate their data and provide real-time information to drive decision-making.