14 companies seizing data analytics as a business asset 1

Posted on April 18, 2016 by Rob Petersen


Data Analytics

Data analytics as a business asset is changing dramatically. That’s because companies are applying data analytics to new problems and making data a driver of innovation.

Data analytics is the science of examining raw data to drawing conclusions that help companies make better business decisions. How?

Here are 14 companies seizing data analytics as a business asset.

  1. AMERICAN EXPRESS: Started looking for indicators that could really predict loyalty. They developed sophisticated predictive models to analyze historical transactions and 115 variables to forecast potential churn. The company believes it can now identify 24% of accounts that will close within the next four months.
  2. BOEING: Used data and devices to transform the company from an aircraft manufacturer to an aero-health service provider. To reduce its airline customer’s total cost of ownership, Boeing offered customers a data-based Airplane Health Management service. Performance data can be wirelessly transmitted from each Boeing aircraft directly to the fleet operator for real-time fault management, performance monitoring and customized alerts. The data service allowed Boeing customers to make fix-or-fly decisions quickly, which in turn, helped the airline improve maintenance efficiency and reduce servicing costs.
  3. CITY OF BOSTON: Introduced Street Bump, an app which enables people to use their smartphone’s accelerometer—a motion detector in the device—to record road conditions and send data to public works employees. With the Street Bump app, citizens simply turns on the app and, as the drive, data is automatically collected and sent to the city. Street Bump was expected to identify the location of potholes—a top concern of Boston residents. Thanks to analytics, the early data has provided some unexpected insights: trouble spots are eight times more likely to be “castings,” those manhole covers, grates and other cast metal lids that are supposed to be flush with the roadway surface but instead heave up due to the extreme cold of a New England winter. Hundreds of these castings have been repaired as a result.
  4. CONAGRA: Faced the challenges of figuring out the optimal pricing for its products in an environment where consumers are hyper-sensitive, while coping with the ever fluctuating costs for 4,000 raw materials used in some 20,000 products. The company turned to in-memory computing, which loads extremely large volumes of data from multiple sources into one database and enables users to answer questions almost instantly. ConAgra decision-makers had real-time insight into the company’s costs and consumers’ demands. ConAgra also shared data-driven insights with retailers.
  5. EXPRESS SCRIPTS: Processed pharmaceutical claims and realized that those who most need to take their medications are also those most likely to forget to take their medications. So they created a new product: Beeping medicine caps and automated phone calls reminding patients it’s time to take the next dose.
  6. INFINITY PROPERTY & CASUALTY CORP.: Realized it had years of adjusters’ reports that could be analyzed and correlated to instances of fraud. It built an algorithm out of that project and used the data to reap $12 million in subrogation recoveries.
  7. JOHNSONVILLE SAUSAGE: On average, consumer products (CP) companies spend 13.7% of their gross sales on trade promotions, according to a recent study by AMG Strategic Advisors. Immediate insights from analytics enabled Johnsonville to track special offers, such as coupons. With real-time data, Johnsonville correlated the offers with sales performance to adjust the programs based on those insights.
  8. KAISER PERMANENTE Made a multi-billion dollar investment to build its HealthConnect® health information system. The system securely connects 8.6 million people to their healthcare teams, stores their personal information and provides the latest medical knowledge. In an industry known for chronic high costs and quality issues, the system allowed Kaiser to not just identify and rollout best practices but it also gives the healthcare company a data-driven edge in providing lower-cost and higher-quality care.
  9. MERCEDES: In the automotive industry, manufacturers must manage a greater number of both models and customization options as they adjust to fragmented consumer demand, while also managing shorter product life cycles. They used real time sensor data from the engines being tested to enable engineers to identify possible problems more quickly. That change, in turn, creates more engine-testing capacity each week and a focus for engineers of refining engine quality. The strategy worked. Mercedes AMG sold more than 32,000 automobiles in a year, their most successful year ever.
  10. NORWEGIAN CRUISE LINES: Knew it needed to better understand who its customers are, what they value and what they most want to do when onboard the ship. Norwegian implemented a business intelligence (BI) system aimed at delivering insights to business users. From the data, they created onboard food and entertainment packages based on common spending patterns. The data also made it possible for Norwegian to introduce all-inclusive cruising, the first of the major cruise lines to do so.
  11. TESCO: The supermarket chain collected 70 million refrigerator-related data points coming off its units and fed them into a dedicated data warehouse. Those data points were analyzed to keep better tabs on performance, gauge when the machines might need to be serviced and do more proactive maintenance to cut down on energy costs.
  12. T-MOBILE: 77% of the most successful U.S. companies use Twitter to communicate with customers and 70% use Facebook. Wireless campaign typically get 1% to 3% negative reactions but sometimes response can go to 12%.  T-Mobile used social media for better listening to: 1) Identify trending issues and monitor customer sentiment, 2) Allow the tough conversations to take place, 3) Offer quick responses in real time 3) Make positive connections that deliver on the brand promise. The end result is better customer service. Customer complaints went down by 50% in 3 months because they have been able to come up questions and issues relating to how customers might respond.
  13. WALMART: The mega-retailer’s latest search engine for includes semantic data. Polaris, a platform that was designed in-house, relies on text analysis, machine learning and even synonym mining to produce relevant search results. Wal-Mart says adding semantic search improved online shoppers completing a purchase by 10% to 15%.
  14. ZILLOW: provided publicly available mapping of individual home values across the US by combining existing data sources such as transactional history from public records and listings from real estate brokerages with new data from Microsoft-based maps. Its media-based business model continued to expand with nearly 12 million visitors per month and an increasing array of services as Zillow mines the data on visitor actions and continuously builds its data assets.

Do these examples show you how companies are applying data analytics in new and innovative way. Does your organization need guidance using data analytics as a business asset?

15 best Big Data Companies and why they stand out 3

Posted on April 11, 2016 by Rob Petersen


Big Data Companies

  • Only 18% of companies believe they have the skills necessary to gather and use insights effectively.
  • Only 19% of companies are confident that their insights-gathering processes contribute directly to sales effectiveness. (source: McKinsey)

Simply collecting data does not unleash its business effectiveness. Big data is a term for large volumes of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.

Here are the 15 best Big Data Companies and why they stand out according to sources that rank them highly.

Best Big Data companies with the biggest revenue 


Big Data Companies - Palantir

Founded in 20014, Palantir builds software that connects data, technologies, humans and environments. Big Data revenues topped $418 million — with a 50-50 split between software and services. Key customers include the S.E.C., which hired Palantir to help the government analyze data to find terrorists, and to help it uncover illegal trading activity. What does Palantir’s software do? It lets non-technical users visualize reams of data from several databases in a user-friendly way. That way, they can look at specific bits of information and the links among them, so they can find answers to complex questions and find the proverbial needle in a haystack. (Source: Information Management)


Big Data Companies - PWC

PriceWaterhouseCoopers generated $312 million from Big Data revenues in 2013. All of that revenue involved consulting services. The opportunities should continue strong, considering 41% of PWC’s customers are concerned about Big Data overload. PwC is one of the leading companies in the world offering advisory services for financial entities with complex, computerized systems and massive amounts of data. (Source: Information Management)


Big Data Companies - IBM

IBM has momentum in the Big Data market — generating $1.37 billion in revenues. Those revenues were split across hardware (31%), software (27%) and services (42%). Eager to potentially accelerate those Big Data revenues, IBM expanded cloud-focused releases of Cognos while also working more closely with Watson-focused software. IBM has a big big data play through which it is aiming to be relevant to all market sectors. The company released 20 industry solutions as it targets getting its analytics embedded into all areas of the maturing Big Data market. (Source: Information Management)

Most well funded Big Data startups


Big Data Companies - Domo

Domo isn’t just another business management tool. Rather, we understand that Domo is an intelligent dashboard that displays and analyzes various key business metrics in real-time. Its ability to not only aggregate information that’s typically scattered across different sources (like spreadsheets, social media, or databases) into a single dashboard, but to then also continuously refresh the results, is a transformation for those involved in business operations. Domo also announced that it received $200 million in Series D funding this past April. Domo’s been around since 2010, but that’s still an absurdly large amount of funding for a company that’s just publically entered the market. (Source:


Big Data Companies - Roundforest

Roundforest was named one of 16 Israeli Startups Ready To Take On 2016. Roundforest is a data-driven e-commerce startup that optimizes every step of the consumer’s purchase path. They’ve developed a proprietary automated engine that removes the guesswork out of performance optimization. Its founders previously worked at Google and Intel and are determined to apply their background in machine learning and data analysis to help consumers make better shopping decisions, especially since Roundforest is already reaching more than 10 million users a month. (Source:


Big Data Companies - SQream

It’s safe to say that 2015 was SQream’s best year yet. This past year, SQream won five major awards (Red Herring Global 100, Red Herring Asia 100, Best in Biz, Stevie, and Tech Trailblazers Regional), launched its second product (GenomeStack), and raised $7.4 million in Series B funding. In 2010, SQream Technologies introduced its GPU-based technology that, through massive parallel computing, boosts analytics performance up to 100x faster than its competitors—meaning Teradata, IBM Netezza, Oracle Exadata, and Amazon Redshift on the Cloud really ought to watch out. SQream is boldly taking on the Goliaths. (source:

Most recommended Big Data Companies by employees to friends


Big Data Companies - Cloudera

Quaero’s data management platform (QDMP) and its AdVantage platform are built upon Cloudera’s Distribution of Hadoop (Cloudera Enterprise). The AdVantage platform is targeted for clients in the media industry to better understand their audience, enhance engagement, create richer experiences, and increase overall audience value. Quaero has deployed the platform across several clients in the media industry (ranging in ingestion volume from ~3MM to ~1.5 Billion  records per day). Cloudera Enterprise offers the right mix of components to build a robust data platform which supports both reporting and analytics which can deal with all sorts of data. (Source: DeZyre)


Big Data Companies - InsightSquared

InsightSquared is a sales performance analytics company for fast-growing tech businesses. Unlike spreadsheets, InsightSquared’s visual, maintenance-free reports and dashboards provide a custom lens into real-time sales results. InsightSquared offers robust powerful data intelligence in a system that is accessible and affordable. InsightSquared is entirely web-based, so setup is quick, painless and doesn’t require dedicated IT personnel. Pricing is monthly, eliminating costly up-front fees. Features include activity tracking, employee scorecards, sales forecasting, ratios and KPIs, data quality monitoring and more. With InsightSquared, users have access to dashboards and interactive visualizations that take static data and create charts with dynamic drill-down capabilities, configured to track just about any kind of data critical to business operations. (Source: InsightSquared)


Big Data Companies - Trifacta

Trifacta enables organizations to use data to drive innovation by providing a more productive and accessible method of exploring and experimenting with data of all shapes and sizes. Data wrangling — formatting and cleaning — is a sore spot and stumbling block for many, but you often can’t do much visualization- or analysis-wise until the data is in order. My projects folder is filled with one-off Python scripts written for specific datasets (and steps within steps). Trifacta Wrangler aims to streamline the process with a click interface and automation. The desktop software is free to use and available for PC and Mac. (Source: DeZyre)

Coolest Big Data Start Ups


Big Data Companies - Arcadia Data

An increasing number of businesses are implementing Hadoop systems, using them to collect huge volumes of disparate data from multiple sources. But making use of that data isn’t so easy — most traditional business analytics tools can’t directly access Hadoop data, and IT departments have to step in to prepare the data or move it to another system to make it available for everyday business workers. Arcadia Data is developing visual analytics software that overcomes those hurdles by directly accessing data stored in Hadoop clusters. The technology uses Hadoop as an operating system, allowing it to run directly on Hadoop servers and access data stored in the Hadoop Distributed File System. (Source: CRN)


Big Data Companies - DataHero

San Francisco-based DataHero is focused on developing “self-service” business analytics software. The DataHero cloud-based service collects data from such disparate sources as Box, Dropbox, Google Drive, Excel, Office 365, Marketo, HubSpot and Eventbrite, and turns it into charts and dashboards. For the business analytics software industry, the challenge has been developing analytical applications that can be used by a broad range of everyday business users without a lot of assistance from the IT department. DataHero is among the few companies that’s close to achieving that. (Source: CRN)


Big Data Companies - Interana

Interana is another big data startup that’s developing technology to help businesses analyze streaming data in realtime. The company’s events-based analytical software works with clickstream data and other “events-based” information to help users answer questions about how customers behave and how products are used. The goal is to provide actionable business intelligence for nontechnical users. No longer do you have to have a department of people who are the high priests of data. Now, the person who needs the data, the person who wants the business answers from the data can have direct access to it. (Source:

Best Big Data companies to watch 


Big Data Companies - Cazena

Fast and inexpensive processing of big data in an encrypted cloud via what it calls enterprise Big Data-as-a-Service offerings broken down into Data Lake, Data Mart and Sandbox editions.  Cazena aims to greatly simplify big data processing for businesses. Ideally, it should only take three clicks to set up a data processing job with Cazena. The service strips away the complexities by trying to automatically figure out what technology to use to analyze a given set of data. It then automatically provisions, optimizes and manages that workflow for its customers, no matter whether it’s a Hadoop, Spark or MPP SQL (think Amazon Redshift) job. The company is led mainly by former movers and shakers at Netezza, a data warehouse company acquired by IBM in 2010 for $1.7 billion. (Source: Network World)


Big Data Companies - Experfy

Cloud-based consulting marketplace designed to match up big data and analytics experts with clients who need their services. Experfy provides advisory services, big data readiness assessments, road maps, predictive dashboards, algorithms, and a number of custom analytics solutions. Expert Panels consist of closely curated group of Big Data leaders within different areas of specialization—from Marketing Analytics, Personalization and Security Analytics to Financial Services, Healthcare and Retail. In addition, Experfy’s proprietary data platform, tools and processes support repeatable projects and use-cases in specific verticals. Experfy provides a self-service model for smaller companies and a high-touch concierge service with project management for larger enterprises. (Source: Forbes)


Big Data Companies - Tamr

Tamr uses machine learning and human input to enable customers to make use of data currently silo-ed in disparate databases, spreadsheets, logs and partner resources. Most companies have many dozens of Oracle instances, hundreds of databases, and many thousands of tables. There is [currently] no way to catalog and know what’s out there. Tamr can create a central catalogue of all these data sources (and spreadsheets and logs) spread out across the company and give greater visibility into what exactly a company has. Tamr’s tech got its start at MIT’s CSAIL. (Source: Network World)

Do these best Big Data companies help you see how to make data an asset at your company? Do you see why these Big Data companies stand out? Does your organization need guidance navigating Big Data?

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