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?

20 Best Big Data Visualization Tools Reviewed 2

Posted on March 27, 2016 by Rob Petersen


Big Data Visualization Tools

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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization - 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 ForceGoji and The New York UniversityPlotly 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

Big Data Visualization Tools - 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.



Big Data Viisualization Tools - 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. send you email notifications for all the milestones you are hitting, and will also let you give constant feedback to your creative team. 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

Big Data Visualization Tools - d3js

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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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 JavaScriptFusionCharts integrates easily with libraries like jQuery, frameworks like AngularJS and React, and languages like ASP.NET and PHPFusionCharts 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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

Big Data Visualization Tools -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

Big Data Visualization Tools - 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.


18. Sigma JS

Big Data Visualization Tools - Sigmajs

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 massiveSigma 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

Big Data Visualization Tools - 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

Big Data Visualization Tools - 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.

Big Data Visualization Tools - Edoardo L'AstorinoEdoardo 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.

How 4 types of Big Data analytics deliver data-driven results 0

Posted on January 25, 2016 by Rob Petersen


big data analytics

Big data analytics begins as a process. One that involves examining large data sets, containing a variety of data types, to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.

Why do companies pursue Big Data analytics?

  • 91% of marketing leaders believe successful brands use customer data to drive business decisions (source: BRITE/NYAMA)
  • By increasing the usability of data by just 10%, the average Fortune 100 company could expect an increase of $2 billion dollars (source: InsightSquared)
  • $300 billion could be saved if Big Data was used effectively in the US healthcare sector; thereby reducing expenditure by 8% (source: McKinsey)

But there are different types of Big Data analytics. And picking the right one for your organization is critical to delivering the right results. Because what good is the data if you don’t know what to do with it?

Here’s how 4 types of Big Data analytics deliver data-driven results.

  • PRESCRIPTIVE ANALYTICS – Seek to determine the best solution or outcome among various choices, given known parameters. For example, in the health care industry, you can better manage the patient population by using prescriptive analytics to measure the number of patients who are clinically obese, then add filters for factors like diabetes and LDL cholesterol levels to determine where to focus treatment. The same prescriptive model can be applied to almost any industry target group or problem. Prescriptive Analytics gives laser focus to answer specific questions. It usually results in recommendations based on rules. This is one of the most valuable types of analytics. Unfortunately, it is largely not used. According to Gartner, only 3% of organizations are using Prescriptive Analytics.
  • PREDICTIVE ANALYTICS – Show likely scenarios of what might happen with the goal of delivering multiple options. The deliverables are usually forecasts. For example, some companies use Predictive Analytics for sales lead scoring. They examine the data for the entire sales process, analyzing lead source, number of communications, types of communications, social media, documents, CRM data and, of course, conversions and closed sales. Then, they apply Predictive Analytics to different “what if” scenarios based on different marketing plans that deliver different forecasts. Like Prescriptive Analytics, Predictive Analytics is focused on delivering a data-driven solution. But not just one solution; rather multiple futures options on the decision-maker’s actions.
  • DIAGNOSTIC ANALYTICS – Examine past performance to determine what happened and why. The result of the analysis is often an analytic dashboard. Diagnostic Analytics are used for discovery or to determine why something happened. For example, for a social media marketing campaign, you can use Descriptive Analytics to assess the number of posts, mentions, followers, fans, page views, reviews, pins, etc. There can be thousands of online mentions that can be distilled into a single view to see what worked in your past campaigns and what didn’t.
  • DESCRIPTIVE ANALYTICS – Highlight what is happening now based on incoming data. To mine the analytics, you typically use a real-time dashboard. An example of Descriptive Analytics would be assessing credit risk; using past financial performance to predict a customer’s likely financial performance. Descriptive Analytics can be useful in the sales cycle. For example, to categorize customers by their likely product preferences and sales cycle. Descriptive Analytics or data mining are at the bottom of the Big Data value chain, but they can be valuable for uncovering patterns that offer insight.

Analytics is all about making the best decisions form the data that we have. Do these types of Big Data analytics help you see how your organizations can make better decisions from data. Is not the time for your company to learn how to use Big Data analytics for data-driven results?

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