Successful digital companies are organizations that use technology as a competitive advantage in its internal and external operations.

Many definitions abound but, in a nutshell, this is what makes them successful. Like any major achievement, success doesn’t occur unless the transformation changes the company culture as much as is does the company.

In this article, we study common characteristics. Here are 10 terrific traits of successful digital companies with examples.

1. ARE UNREASONABLY ASPIRATIONS: Being “unreasonable” is a way to jar an organization into seeing digital as a business that creates value, not as a channel that drives activities. However the vision is described, if you aren’t making the majority of your company feel nervous, you probably aren’t aiming high enough. Burberry, in 2006, was a stalled business whose brand had become tarnished. A series of groundbreaking initiatives, including a website (ArtoftheTrench.Burberry.com) that featured customers as models, more robust e-commerce catalog that matched the company’s in-store inventory, and the digitization of retail stores through features such as radio-frequency identification tags leaped frog Burberry over competitors and tripled revenues.
2. ARE CUSTOMER OBSESSED: Knowing what the customer wants has always been the key to successful digital companies. Advances in technology and data science make it possible to analyze the complete history of customer transactions and identify individual shopping habits, patterns and motives that drive behavior. 86% of consumers say they were willing to pay more for a better customer experience. And 89% begin doing business with a competitor following a poor customer experience. This mind-set is what enables companies to go beyond what’s normal and into the extraordinary. If online retailer Zappos is out of stock on a product, it will help you find the item from a competitor. While it might seem heretical to buy from competitors, it’s worth it for Zappos because 75% of its orders come from repeat customers.
3. KNOW CUSTOMER LIFETIME VALUE (CLV): It is 6-7 times more expensive to acquire a new customer than it is to keep a current one. Successful digital companies recognize that, while customer acquisition is always important, there is often more to be gained be exploring ways to increase the Customer Lifetime Value (CLV) of current customers. According to a 2013 study by the Consumer Intelligence Research Partners, Amazon Prime members spend $1,340 annually. And that was 3 year ago. More important, Consumer Intelligence Research Partners estimates that Amazon Kindle owners spend approximately $1,233 per year buying stuff from Amazon, compared to $790 per year for other customers.
4. ARE SOCIAL MEDIA ADEPT: If your company is customer obsessed, then you’re constantly listening to what your customers have to say about your brand, your competitor and your industry. Successful digital companies not only listen, they are adept at doing something with this data. McDonald’s decision to serve All Day Breakfast may have been a surprise to some customers, but shows how the massive company is trying to move nimbly and take some risks with its messaging. “Customers were saying to us ‘Hey, McDonald’s, this is the next big thing. This is what we want from you,'” said McDonald’s USA Chief Marketing Officer Deborah Wahl. With help from Twitter and Sprinklr, McDonald’s found 334,000 tweets mentioning All Day Breakfast at McDonald’s and found the most “engageable” accounts to send customized messages.
5. ARE QUICK AND DATA-DRIVEN: Rapid decision making that is data-driven is critical in a dynamic digital environment. A cycle of continuous delivery, experimentation and improvement, adopting methods such as agile development and “live beta,” supported by big data analytics, are characteristics of successful digital companies. U.S. Xpress, a US transportation company, collects data in real time from tens of thousands of sources, including in-vehicle sensors and geospatial systems. Using Apache Hadoop, an open-source tool set for data analysis, and real-time business-intelligence tools, U.S. Xpress has been able to extract game-changing insights about its fleet operations. Data on the fuel consumption of idling vehicles led to changes that saved $20 million in fuel consumption in a year.
6. PREPARE THEIR INFRASTRUCTURE: To take advantage of all that a digital world has to offer, successful digital companies have to be willing to invest in digital-ready infrastructures that will accelerate their digital strategies and the digital experiences of their customers, employees or citizens. As part of its three-year IT overhaul following a brush with bankruptcy, General Motors closed 23 of its data centers worldwide and moved most of that capacity into two new data center in Michigan. Reducing the distance for data to travel was a huge financial motivator for the company to not only saves on networking costs but improved responsiveness. New data centers are ‘private-cloud-meets-mainframe’ operations that run cloud-ready apps.
7. KNOW THEIR METRICS: Metrics are a proxy for what matters most to senior management. But the measurements of success varies widely between marketing, tech management, and business unit leaders. This can creates conflict and confusion unless your company is clear about outcomes being measured. P&G created a single analytics portal, called the Decision Cockpit, which provides up-to-date sales data across brands, products, and regions to more than 50,000 employees globally. The portal, which emphasizes projections over historical data, lets teams quickly identify issues, such as declining market share, and take steps to address the problems in measurement the company considers critical to success.
8. OPERATE IN A DIGITAL ECOSYSTEM: A digital ecosystem is the detailed visual of how all digital and social assets of a brand interconnect and interact. When managing multiple platforms, it’s important to understand how they will all work together to achieve the brand’s goal. Starbucks offers the largest and most robust mobile ecosystem of any retailer in the world, with more than 12 million Starbucks Rewards™ members (up 18% year on year), 8,000,000 mobile paying customers with one out of three now using Mobile Order & Pay, and more than $6 billion loaded onto prepaid Starbucks Cards in North America during the past year alone. Starbucks digital flywheel has also continued to gain momentum with the launch of true one-to-one personalization.
9. PUT THE RIGHT LEADERS IN PLACE: The fast-moving digital world is exposing gaps in digital leadership, especially with regard to front office disciplines (those related to the customer experience) and head-office disciplines (those related to enterprise strategy). According to Gartner, three types of digital business leader have emerged to fill these leadership gaps: 1) Digital strategist, 2) digital marketing leader and 3) digital business unit leader. “A lot of our digital talent is home grown – mavericks who have their own businesses and have adapted their business in entrepreneurial ways. It is important to find these people and leverage their skills,” says David Crowley, Chief Commercial Officer, British Airways.
10. SHOW ME THE MONEY: Many organizations focus their digital investments on customer-facing solutions. But they can extract just as much value, if not more, from investing in back-office functions that drive operational efficiencies. Successful digital companies know the value of reducing the costs of doing business. One-third of the digital innovation projects at Starbuck are devoted to improving efficiency and productivity away from the retail stores, and one-third focused on improving resilience and security. In manufacturing, P&G collaborated with the Los Alamos National Laboratory to create statistical methods to streamline processes and increase uptime at its factories, saving more than $1 billion a year.
    

Are these traits you associate with successful digital companies? Any there others you would add? Is your company interested in developing the capabilities of successful digital companies?

Data mining is: 1) The practice of examining large databases to generate new information and 2) the process of analyzing data from different perspectives to make it insightful and useful.

Data mining is used by companies to increase revenue, decrease costs, identify customers, provide better customer service, listen to what others are saying and do competitive intelligence. And that’s just some of the ways.

Here’s are 20 companies that do data mining and prove it makes their business better.

1. AMAZON: With $5 off, for those who use the Amazon Price Check Mobile App – to scan the products in store, take a picture of the product or perform a text search to find the lowest prices, the app also prompts the customers to submit the in-store price. Amazon is collecting intelligence and valuable pricing information from its competitors.
2. ARBY’S: The fast food company uses data mining to help them determine the best targets for their advertisements. They can see which advertisements are most effective, while seeing the channels that are most receptive to each ad pitch. This allows them to ensure every advertisement utilizes the appropriate channel to increase the number of leads from their marketing.
3. CAPITAL ONE: Data mining and big data management to help them ensure the success of all customer offerings. By analyzing the demographic data and spending habits of customers, they’re able to determine the most optimal times to present various offers to clients, thus increasing the conversion rates of their offers and gaining more leads from their marketing budget.
4. DELTA: Large airlines like Delta, monitors tweets to find out how their customers feel about delays, upgrades and in-flight entertainment. When a customer tweets negatively about his lost baggage, the airline forwards to their support team. The support team sends a representative to the passengers destination presenting him a free first class upgrade ticket on his return along with the information about the tracked baggage promising to deliver it as soon as he or she steps out of the plane.
5. DUETTO: Known online for their “hotel optimization,” Duetto makes it easier for companies to personalize data to individuals searching online for hotels. Duetto makes it easy for hotels to personalize their prices by taking data such as how much you typically spend at the bar or casino to incentivize you with a lower price for your room. Therefore the hotel can give you a better price, knowing you’ll spend money on other services. The hotel can give you a better price, knowing you’ll spend money on other services.
6. EXPRESS SCRIPTS: Which processes pharmaceutical claims, realized that those who most need to take their medications were 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.
7. FREE PEOPLE: The more bohemian segment of Urban Outfitters, uses millions of customer records (reviewed by an in house analytics team) to shape the next season’s offerings. Information like what sold, what didn’t, what was returned and more fuels the brand’s product recommendations, the look of its website and what kinds of promotions customers see to improve Free People’s bottom line.
8. GOOGLE AND CENTER FOR DISEASE CONTROL (CDC): Google proposed a different approach. Using historical data from the CDC, Google compared search term queries against geographical areas that were known to have had flu outbreaks. Google found spikes in certain search terms where flu outbreaks occurred and identified forty-five terms that were strongly correlated with the outbreak of flu. Google then started tracking the use of those terms and is now able to accurately predict when a flu outbreak is occurring in real time. Using this data, the CDC can act immediately.
9. KOHL’S: Customers are more likely to respond to an offer when it’s at the moment of purchase. That’s why Kohl’s does real-time, personalized offers. Shoppers can opt in for offers via their smartphones.  So if a shopper lingers in the shoe department, for example, they can receive a coupon on the shoes they looked at online but never bought,
10. KREDITECH:  European company, uses more than 8,000 sources including social media, to create a unique credit score for consumers, which is then sells to banks and other lenders. And they have discovered some surprising correlations between social media behaviors and financial stability. For example, if your Facebook friends use all capital letters, your score is docked.
11. MACY’S: Through sentiment analysis of big data, Macy’s finds out that people who are sharing tweets about “Jackets” are also making use of the terms “Michael Kors” and “Louis Vuitton” frequently. This information helps the retailer to identify what brands of jackets should be offered discounts in their future advertising campaigns to attract customers.
12. MCDONALD’S: With more than 34K local restaurants serving 69 million customers across 118 countries , 62 million daily customer traffic, selling 75 burgers every second, $27 billion annual revenue- McDonald’s is using big data analytics to gain lot more insight to improve operations at its various stores and enhance customer experience. McDonald’s analytics system analyse data about various factors such as wait times, information on the menu, the size of the orders, ordering patterns of the customers.
13. NETFLIX: To create data models and find what makes show or movie popular among consumers, according to the insights they gained from their data, House of Cards was the ultimate entertainment experience. They went all out, winning a bidding war with other companies over the rights and immediately scheduled two seasons of content before showing a thing. It was a huge success, and the best part is they almost knew it would be.
14. NORDSTROM: With 225 retail outlets, Nordstrom generates petabytes of data from its 4.5 million Pinterest followers, 300,000 Twitter followers and 2 million likes on Facebook. Their analytics system monitors customer behaviour by tracking – How many people enter the store, which section they walk in, how long they stay at the store and for how long they shop in a particular section. This helps Nordstrom decide what products should be promoted to which customers when and through what advertising channel.
15. PANDORA: With 72 million users and the data for approximately 200 million users’ listening habits, Pandora is a name to reckon with in the music industry for providing music recommendations that people really love. Apart from the data like gender, age, zip code that users provide at sign up, Pandora tracks all the songs that a particular user likes and dislikes, from which location they listen, from which devices they listen and more – to provide customers with curated music catalogue based on interests and demographics.
16. PREDPOL:  The Los Angeles and Santa Cruz police departments, a team of educators and a company called PredPol have taken an algorithm used to predict earthquakes, tweaked it and started feeding it crime data. The software can predict where crimes are likely to occur down to 500 square feet. In LA, there’s been a 33% reduction in burglaries and 21% reduction in violent crimes in areas where the software is being used.
17. STARBUCKS: As the leading coffeehouse company in the world, Starbucks manages to open new stores in very close proximity with their other stores, while still guaranteeing a high success rate. Normally, when expanding a company, it’s needlessly risky to open a new location just a block from another location.
18. T-MOBILE: Data mining helps reduce customer turnover rate. By analyzing big data, T-Mobile can determine the core causes for turnover, allowing them to implement effective solutions that will keep more clients on board. As a telecom company, they accrue boundless quantities of data every year, and without big data management, the ability to analyze the data would be greatly inhibited.
19. THOMSON REUTERS: Financial experts can gain competitive advantage by analysing the twitter sentiment data by tracking specific tweets from various companies and people. This helps financial professionals get an overview on the number of positive and negative sentiments related to any given company. Sentiment analysis along with other advanced big data analytics solution helps the financial professionals spot the financial market and any events impacting the company as they happen.
20. WALMART: The mega-retailer’s latest search engine for Walmart.com 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 has improved online shoppers completing a purchase by 10% to 15%. “In Wal-Mart terms, that is billions of dollars,” Laney said.

Do these companies prove how data mining could make your business better? Could your company benefit from data mining?

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 Walmart.com 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?

• 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 

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)

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)

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

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: Tech.co)

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: Tech.co)

SQREAM TECHNOLOGIES

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: Tech.co)

Most recommended Big Data Companies by employees to friends

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)

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)

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

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)

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)

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: Tech.co)

Best Big Data companies to watch 

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)

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)

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?

By Edoardo L’Astorina

Big Data is amazing. It describes our everyday behavior, keeps track of the places we go, stores what we like to do and how much time we spend doing our favorite activities.

Big Data is made of numbers, and I think we all agree when we say:

Numbers are difficult to look at.

Enter Big Data visualization.

Google, Facebook, Amazon, Apple, Twitter and Netflix all ask better questions of their data – and make better business decisions – by using data visualization.

It gets better:

Data visualization lets you interact with data. It goes beyond analysis. Visualization brings a presentation to life. It keeps your audience’s eyes on the screen. And gets people interested.

You might be wondering:

How do I get a clean and engaging visualization for all my data? Will it be time consuming? Am I going to spend months preparing everything?

Here’s the deal:

There are tools that help you visualize all your data in minutes. They are already out there. All you need to do is pick the right tool that suits your needs.

Whether you’re looking to wow your audience at your next presentation or you are a developer looking for a practical way to visualize large sets of data, there are amazing tools out there for both parties. So many that it’s hard to find the right tool for your project.

Want to know the best part?

We made everything easy for you and prepared a series of reviews that cover all the features of the best data visualization tools out there. And we divided our reviews in two sections: data visualization tools for presentations and data visualization tools for developers.

Here are reviews of our 20 best tools for Big Data visualization.

Data Visualization Tools for Presentations: Zero Coding Required

Your data is all in a spreadsheet. Ready to go. All you want is to visualize it beautifully and maybe add a few smart interactions. And make a killer presentation. If this is you read on and enjoy our list of amazing data visualization tools for presentations.

1. Tableau

Tableau is the big data visualization tool for corporate. Tableau lets you create charts, graphs, maps and many other graphics. A desktop app is available for visual analytics. Don’t want – or can’t – install software on your desktop? A server solution lets you visualize reports online and on mobile. A cloud hosted service is also an option for those who want the server solution… but don’t want to set it up manually. Customers of Tableauinclude Barclays, Pandora and Citrix.

2. Infogram

Infogram lets you link their visualizations and infographics to real time big data. And that’s a big plus. A simple 3-step process lets you choose among many templates, personalize them with additional visualizations like charts, map, images and even videos, and you are ready to share your visualization. Infogram supports team accounts for media publishers and for journalists, branded designs for companies and classroom accounts for educational projects.

3. ChartBlocks

ChartBlocks is an easy-to-use online tool that requires no coding, and builds visualizations from spreadsheets, databases… and live feeds. A chart building wizard does all the magic for you. Your chart will be created under the hood in HTML5 by using the powerful JavaScript library D3.js – read our developers review of D3.js below – and your visualizations will be responsive and compatible with any screen size and device. You will also be able to embed your charts in any web page and share it on Twitter and Facebook.

4. Datawrapper

Datawrapper is aimed squarely at publishers and journalists and is adopted by The Washington Post, The Guardian, Vox, BuzzFeed, The Wall Street Journal and Twitter – among the many. Datawrapper is easy and requires zero coding. Upload your data and easily create and publish a chart or even a map. Custom layouts to integrate your visualizations perfectly on your site and access to local area maps are also available.

5. Plotly

Plotly will help you create a sharp and slick chart in just a few minutes, starting from a simple spreadsheet. Plotly is used by none other than the guys at Google and also by The U.S. Air Force, Goji and The New York University. Plotly is a very user-friendly web tool that gets you started in minutes. If you have a team of developers that wants to have a crack, an API is available for languages that include JavaScript and Python.

6. RAW

RAW boasts on its homepage to be “the missing link between spreadsheets and vector graphics”. Your Big Data can come from Microsoft Excel, Google Docs, Apple Numbers or a simple comma-separated list. The kicker here is that you can export your visualization easily and have a designer make it look sharp as RAW is compatiple with Adobe Illustrator, Sketch and Inkscape. Easy to use and quick to get results.

7. Visual.ly

Visual.ly is a visual content service. I decided to include it because they do have a dedicated big data visualization service and their portfolio is impressive: it includes work for VISA, Nike, Twitter, The Huffington Post, Ford and The National Geographic. If you want to entirely outsource your visualizations to a third-party you can do it through a streamlined online process where you describe your project and are connected with a creative team that will stay with you for the entire duration of the project. Visual.ly send you email notifications for all the milestones you are hitting, and will also let you give constant feedback to your creative team. Visual.ly also offer their distribution network for showcasing your project once it’s completed.

Data Visualization Tools for Developers: JavaScript libraries

And here is our developers list. This is for when all your data is in JSON or XML. You love APIs. You want to create gorgeous interactive data visualizations, put them on a web page and let the world see. JavaScript knowledge is highly recommended to make it through our developers list of extraordinary data visualization libraries.

8. D3.js

The best data visualization library there is, D3.js runs on JavaScript and uses HTML, CSS and SVG. D3.js is open-source and applies data-driven transformation to a webpage and – as you can see from their examples – allows for beautiful and fast visualizations. D3.js is also the absolute best to add data-driven real-time interactivity. Warning: this library is as powerful as it is cutting-edge, so it comes with no pre-built charts and only IE9+ is supported.

9. Ember Charts

Ember Charts is – as the name suggests – based on the Ember.js framework and uses D3.js under the hood.Ember Charts features time series, bar, pie and scatter charts. It’s very elegant and easy to extend. The team behind Ember Charts – the same that created Ember.js – put a lot of focus on best practices and interactivity. Error handling is graceful and your app will not crash when fed bad data.

10. NVD3

NVD3 runs on top of D3.js – surprise surprise – and aims to build re-usable charts and components. The goal of the project is to keep all your charts neat and customizable. NVD3 is a simpler interface on top of D3.js and keeps all its powerful features under the hood. NVD3 is developed by the front end engineers at Novus Partners and uses their insight in charting technology.

11. Google Charts

Google Charts runs on HTML5 and SVG and aims at Android, iOS and total cross-browser compatibility, including older Internet Explorer versions supported via VML. All the charts you will create are interactive and some are even zoomable. Google Charts is very user friendly and their site features a really nice and comprehensive gallery where you can see the kind of visualizations – and interactions – at your disposal.

12. FusionCharts

FusionCharts is – according to their site – the most comprehensive JavaScript charting library, and includes over 90 charts and 900 maps. If you aren’t particularly fond of JavaScript, FusionCharts integrates easily with libraries like jQuery, frameworks like AngularJS and React, and languages like ASP.NET and PHP. FusionCharts supports JSON and XML data, and is able to export your charts in a multitude of formats: PNG, JPEG, SVG and PDF. Among their products an out-of-the-box business dashboard is worth having a good look at.

13, Highcharts

Highcharts is a JavaScript API that integrates easily with jQuery and boasts being used by 61 out of the world’s 100 largest companies. Charts are rendered in SVG and a VML fallback is available for older browsers. It offers two specialzed chart types on top: Highstock and Highmaps, and also comes bundled with a wide range of plugins. You can use it for free on your non-commercial projects or pay for a license if you’re interested in building a paid for application. Also, check out their Highcharts cloud service.

14. Chart.js

For a small chart project, Chart.js is your go-to place. Open source, tiny – ships at only 11kb – fast, easy to use, it supports six chart types: doughnut, pie, polar, line, bar and radar. What’s more, you can add and remove any of these 6 types to reduce your footprint. Chart.js uses HTML5 Canvas and ships with polyfills for IE6/7 support. The Chart.js GitHub page is skyrocketing in popularity and is definitely worth keeping an eye on if you decide that simple and fast charts are what your project needs.

15. Leaflet

Are you after a specialized Big Data map solution? No need for pie-charts and bar graphs? Leafleft leveragesOpenStreetMap data and adds HTML5/CSS3 visualizations and interactivity on top to ensure everything is responsive and mobile ready. You can use their extensive plugin repository to add heatmaps , masks and animated markers. Leaflet is open source and ships at only 33kb.

16. Chartist.js

Chartis.js is born out of a community effort to blow all other JavaScript charting libraries out of the water. It leverages Sass and styles are fully customizable, there is complete separation of concerns between CSS styles and JavaScript functions, and its SVG output is responsive – media query based – and DPI independent. And you can intergrate Chartist.js easily with AngularJS, React, Meteor, Ember and WordPress through a wide range of wrapper libraries – find out more on the Chartist.js homepage.

17. n3-charts

NVD3 runs on top of D3.js – surprise surprise – and aims to build re-usable charts and components. The goal of the project is to keep all your charts neat and customizable. NVD3 is a simpler interface on top of D3.js and keeps all its powerful features under the hood. NVD3 is developed by the front end engineers at Novus Partners and uses their insight in charting technology.

18. Sigma JS

Sigma JS is what you want for interactivity. It comes out-of-the-box with mouse and touch support, refreshing and rescaling, and renders on WebGL by default with an HTML5 Canvas fallback. The two data formats of choice areJSON and GEXF. Their plugin assortment for interactivity is massive. Sigma JS is a rendering engine specialized on drawing networks and graphs on web pages with a customizability that is unparalleled. If representing Big Data networks is your goal, use Sigma JS and don’t look back.

19. Polymaps

Polymaps visualizes…. you guessed it: maps. Polymaps is a JavaScript library that uses SVG to represent geographical data from country-wide level all the way down to your local street. You use CSS rules to style your visualization and your data can be easily interpreted by Polymaps via the GeoJSON standard. This is the best tool there is if you’re after creating heatmaps. All the maps you create can be interactive. And you can visualize cartography from OpenStreetMap, CloudMade, Bing and many other maps providers.

20. Processing.js

Processing.js is a JavaScript library that sits on top of the Processing visual programming language. As everyJavaSript library is, Processing.js is web oriented and lets you bring the Processing power to your web pages. This is the smartest visual interactive library there is. Processing.js requires an HTML5-compatible browser to do the magic. Do check out the exhibition page to see what this incredible JavaScript library is capable of.

Now it’s your turn

I just presented you with the best Big Data visualization tools out there.

Go and grab one.

Make your presentation smarter and impress everybody in the room with an interactive visualization.

If you’re a developer, make your app stand out with data that responds to a user’s actions.

All these tools are here at your disposal.

Edoardo L’Astorina

Edoardo L’Astorina has 8 years of experience in software development. He has had a major role in the new Transport for London site and has developed sites and apps for JPC, The Crocodile and Miura. Edoardo started Blu Frame to help companies develop sites that stand out, load fast and are easy for users to access. Edoardo is passionate about risotto, Terrence Malick movies, Oasis songs and rowing. Edoardo is the founder of Blu Frame.