Simple, robust and flexible it fits the bill. Let’s take a more detailed look:

R is open source

R is open source. You can download it here. Developers can access the language for free over the internet. This is very different from some other statistics packages we could mention!

R has no license restrictions as it is issued under the GNU (General Public License).

It also means that it works on all platforms, and isn’t tied to one particular operating system. It works with Windows (both 32bit and 64 bit), Mac, Linux and UNIX (and it’s derivatives like Solaris).

R is efficient

R is a very efficient scripting language that makes it ideally suited to resource intensive languages. This means that it handles large data sets and resource intensive simulations very efficiently.

For very big datasets R can also be used on computer clusters. In one example a commercial R package was used to process 30 million rows of data for 60 variables in just 10 minutes!

R is easy to learn

R is simple to learn. Statistics and statistical testing are tough to master so your challenge will be there if you are new to statistics not in learning R.

If you are looking to master R, there are plenty of ways you can learn it. Online blogs and articles explain it very nicely, as do some YouTube channels (for example). If you’re short on time or motivation then there are also classroom training providers that will get you up the learning curve very quickly (for example)

R is growing

There’s no point in learning a language that no-one will be using in 5 years time.

R is currently the leading open source statistics and analytics package available, with over 2 million users. It’s established lead and community mean that it is likely to stay that way for the foreseeable future and that it will grow as the data analytics market grows in the year to come.

KDNuggets a site dedicated to data science software has had R as the top software package used by its readers for the last 5 years.

Because R is so widely used and so efficient many advances in statistics are available on R before other packages.

R programmers get well-paid

Learning R is a good investment of time. The current average salary of R programmers is $126k according to the 2016 Dice Technology Salary Survey.

Demand for R programmers is likely to grow not shrink in years to come and demand is likely to grow faster than the population of R programmers making a good knowledge of R an increasingly rare and so valuable commodity.

R is widely supported

R is a very well supported open source language. There are lots of places to go if you get stuck from  StackOverflow’s R forum to a wide variety of other online R forums.

Also because R is so widely used there are over 8,300 reusable libraries (see here) available for free online. If you’re trying to solve a complex problem the odds are that someone else has solved the same or a very similar problem before. They cover everything from identifying turtles to analyzing solar radiation.

This can make using R a very efficient way to work. If you are lucky and someone has solved a similar problem previously you can access their solution for free and won’t need to start with a blank computer screen.

There is also a growing community of for-profit companies that support R. The biggest is RStudio and Revolution Analytics (recently purchased by Microsoft) which provide tools and services relating to R.

R is easy to relate to other languages

Increasingly software is being shipped with R integrated into it ( for example, MS SQL Server 2016) because if works with them so simply. R can import data from a wide variety of sources including Microsoft Excel and Access, SQL MS SQL Server, MySQL, SQLite, and Oracle. Because it imports data using an ODBC (Open Database Connectivity Protocol) connection it can be linked to almost any database package.

R is outstanding at producing graphical output

R is the best package out there for producing striking, clear graphical output. It can produce any graph or visualization that you can name and a fair few that you can’t probably. The graphs can be static or dynamic.

Because R is a fully programmable graphical language your output is limited by your coding abilities, not the software itself.

The post R – What’s in it for me? appeared first on Big Data Made Simple - One source. Many perspectives..

Check out our previous article to see if you’re operating in one of the ten business areas that should be using big data already.

Macy’s: The Traditional Department Store is Ahead of Its Time

This upmarket department store has a long history of providing excellent customer service and has become a household name. Despite the heritage established since the first store opened in 1858, the brand has taken to the digital age like a fish to water.

Macy’s uses big data to offer a smarter customer experience. The brand analyzes multiple data points, such as stock levels and price promotions, and combines these findings with stock keeping unit data from a product at a particular location – as well as customer data – to ascertain which products are on sale in each store. This ensures that its chosen products suit the buying habits of customers in each location.

On top of this, Macy’s collects customer data ranging from visit frequency to style preference. This data is used to personalize the customer experience, offering incentives at the point of sale with loyalty rewards and promotions. This data also enables it to send targeted direct mail to its customers to boost conversions.

Amazon’s purchase recommendation engine 

The ecommerce heavyweight Amazon has truly mastered its recommendation engine, but its functionality is actually quite simple. The algorithm is based on a user’s purchase history, the items they have in their cart already, items they have rated or liked in the past, and what other customers have viewed or purchased recently. In fact, it has been reported that over 35% of all Amazon sales are generated by the recommendation engine – a testament to the importance of product recommendations.

The primary reason for recommendation engine is to address the ‘long-tail problem’ – the fact that rare or obscure items are frequently not searched for, and therefore don’t drive revenue. By recommending long-tail items to shoppers, you can seriously drive the ROI potential of slower-moving ecommerce listings.

Kohl’s

Kohl’s is a brand with big data plans. This brand has recently suffered a decline in sales of 2.4%, along with decreased shopper traffic, and the brand’s CEO contemplated closing around 1,100 stores. However, in a change of heart, the brand has decided to implement new technologies to streamline its shopping experience and make stores smaller. To achieve this, it has invested over $2 billion in tech and big data initiatives. Product recommendations aside, the brand is on a mission to use big data firstly for the benefit of its customers, as well as to make the stores more profitable.

The entire online and physical shopping experience is personalized, from when a visitor lands on the homepage and is faced with deals and products on every page, to personalized offers that counter shopping cart abandonment. Kohl’s also uses its big data to create tailored marketing campaigns, which have been produced with customer data in mind. The brand now plans for data science to assist merchandising allocation, including external data like macro-economic conditions and social data, which will determine which products are stocked. This will ensure that products fly off the shelves faster.

Mall of America navigator chatbots

IBM has provided the Mall of America with a chatbot named E.L.F to assist shoppers navigating the vast complex. The Mall of America is in Bloomington, Minnesota, and it is the largest shopping complex in the northern states. It plays host to 520 retailers, 50 restaurants, 14 movie theaters, 2 hotels, an indoor theme park and a museum.

E.L.F. can create personalized shopping itineraries for each customer, finding the right experience for them (dependent on their needs). The chatbot is operated by a simple interface akin to a text messaging platform. E.L.F. is available via the Facebook Messenger app, the browser page, or kiosks in the Mall of America.

Nordstrom: fusing the online and offline shopping experience

This luxury retailer has mastered harnessing big data to fuse online and offline shopping experiences. Nordstom’s marketing team tracks Pinterest pins in order to identify which products are trending, and then employs this data to promote the right products in its physical stores.

Over 30% of Nordstrom’s budget is spent on technology, having established the ‘Nordstrom Innovation Lab’ based in Seattle for product development and testing. On top of this, Nordstrom hosts interactive touchscreens in changing rooms to allow customers to order products and view stock online.

TopShop

TopShop has been experimenting with new technologies to implement augmented reality into its shopping experience since 2010. Flagship stores have virtual fitting rooms where customers can select clothes to see how they would look wearing them on a screen. This saves the customer the time and effort of trying on clothes themselves.

In 2015, TopShop partnered with Twitter to analyze real time data on the social network, and identified trends as they happened during the five day London Fashion week event. These trends were grouped together on billboards using Twitter hashtags, so customers walking by would be encouraged to tweet a hashtag to their TopShop account indicating their favorite products. The fashion retailer then responded with a curated collection of the top picks.

This novel use of big data ensured that TopShop knew exactly what its customers were looking to buy following London Fashion Week.

IKEA

The Swedish interior giant IKEA featured image recognition and augmented reality for the first time when it showcased its 2013 catalog. Customers could scan through the catalog with their mobile devices to highlight products they were interested in, and from this, the brand offered personalized digital content and reviews to inform their purchase. The brand also used image-recognition technology, with which customers can scan catalog items and virtually place them in their own homes to see what they would look like. They can then select the colors and sizes that work best in the space, without having to actually go to store and purchase the product. This allowed the catalog readers to make informed purchases, resulting in higher customer satisfaction and fewer returned items.

These innovative uses of big data really enhance the customer experience, and have the potential to boost your sales. You don’t have to be a big player in retail to use big data. You could use it yourself to get ahead of your competitors, particularly if you use a Shopify storefront. This platform integrates with Blendo, a big data analytics plugin. Plugins and apps can be very useful ways for you to automatically collect and pull up data from multiple sources to inform your business decisions.

How will you integrate big data into your retail business? Have any of these examples inspired you? Let us know in the comments.

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Another technological growth area in recent years has been big data. Credit card companies track and collect all sorts of data on consumers, such as what they buy, how they buy it and when they do their shopping. Smartphones are another key player in big data because they can also track shopping data, as well as data on media consumption and even your location throughout the day.

With so much data available, what role will it have in the upcoming fourth industrial revolution? Here is a quick look at what that revolution entails, big data’s role and what it all means going forward.

What Is the Fourth Industrial Revolution?

When defining this fourth industrial revolution, it’s important to look at the three that preceded it.

The original industrial revolution took place in the 18th and 19th centuries and involved innovations such as steam engines and mechanical production. The second industrial revolution came along toward the end of the 19th century and just before World War I. It included advancements such as the telegraph and industrial sewing machines.

Fast forward to the late 1970s, and you have the third industrial revolution — a period that’s still ongoing and brought along things such as the internet and smartphones.

Since the buildup to the fourth industrial revolution is still happening, the idea of it is a bit abstract. However, it will involve a future in which artificial intelligence allows machines of all types to communicate with and learn from each other, an idea that could potentially have a huge impact on production.

The Lifeblood of the Fourth Industrial Revolution

When analyzing the fourth industrial revolution, also known as Industry 4.0, one natural question that arises is what role big data will play.

Put simply, big data will be vital to the fourth industrial revolution. In fact, some go as far as to say big data is Industry 4.0. In manufacturing, for example, improvements and efficiencies in the analysis of big data are expected to bring billions of dollars to the industry over the next five years.

Others look at it as an equation in which artificial intelligence plus big data equals the fourth industrial revolution. On one hand, you can see the possibility of job losses as autonomous machines take over tasks that humans have handled for years. On the other, there could be a slew of new jobs created when it comes to harnessing the power of data and using it in a meaningful way.

Beyond the Manufacturing Industry

Throughout history, industrial revolutions have often been judged by their impact on the production and manufacturing of goods and products. That’s no different with the looming Industry 4.0, but it will affect many other industries as well.

Take financial services, for example. In this field, experts view big data as “the new electricity” — the power source driving change in the way that steam, actual electricity and digital technology did before it.

In one example, a company in Chile uses big data and machine learning to predict the likelihood individual customers will be able to repay loans. If you look back 20 or 30 years, it took groups of human employees time and effort to determine your credit score. Now, using information such as automotive credit history, utility bills and census data, combined with predictive machine learning, that process can be almost instantaneous.

Big Data and the Internet of Things

Part of the fourth industrial revolution is the manner in which all types of machines and devices interact, communicate and learn from each other. At this early stage, it’s similar to the Internet of Things, or IoT — the concept in which everyday objects such as cars, refrigerators, TVs, ovens and home security systems are all connected to the internet.

Add on top of that a layer of artificial intelligence that saves time by making decisions for you, and you see how these products and ideas can make your life easier and even create new business opportunities.

As one IBM analyst puts it, the thing that AI and IoT have in common is the use and interpretation of big data. Companies that invest in all three areas — AI, IoT and big data — stand a good chance at becoming leaders and innovators in the fourth industrial revolution.

A Closer Look at Jobs

One way to look at the fourth industrial revolution and the rise of things such as IoT, robotics and big data, is that it will lead to the loss of jobs humans have held for years. That could in turn create a paradox in which societies can produce things much more efficiently, but there are fewer workers left who can afford those things.

That’s the doom-and-gloom forecast. Another sunnier vision of the future suggests big data, machine learning and AI could create a society in which everyone lives in luxury. And since humans won’t have to worry about doing the grunt work that machines have taken over, they can instead focus on other tasks that are exclusively human, such as invention, art, science and exploration.

In the end, it’s likely that this Industry 4.0 will cause many industries to lose jobs, but it’s up to humans to create new opportunities.

Big data already plays a huge role in your life, even if you don’t know it. While we’re not quite to the level of “Minority Report,” big data is already used to target advertising based on factors including your age, gender, shopping habits and location. Companies say they use such data on an aggregate and anonymous level, but the technology is there to target advertising specifically to you.

This is already happening, and the surface has only just been scratched. As we dive deeper into the reality of a fourth industrial revolution, big data will play an increasingly crucial role.

The post What is big data’s role in the fourth industrial revolution? appeared first on Big Data Made Simple - One source. Many perspectives..

1. Sisense

Sisense is one of the leaders in the BI market and a winner of the Best Business Intelligence Software Award for 2016 from FinancesOnline, one of the most popular business software review platforms. This solution capable to effectively simplify complex data analyses, and make big data insights accessible even for startups and small companies. The competitive edge of Sisense is primarily its capacity to collate data from multiple sources without pricey preparations (sources can be Salesforce, Google Analytics, AdWords, and many more). Users will also enjoy the tool’s very efficient use of in-chip technology in a database that processes data 10 times faster than traditional systems. Sisense also works with the innovative ElastiCube technology, which means it can import large sets of data and work with any CPU layout without compromising the quality of your results. If you are interested to learn more about its features you can actually try out the software yourself with a great free trial plan they offer. You can easily sign up for Sisense free trial here.

2. Actuate Business Intelligence and Reporting Tools (BIRT)

BIRT project is a flexible, open source, and 100% pure Java reporting tool for building and publishing reports against data sources ranging from typical business relational databases, to XML data sources, to in-memory Java objects. BIRT is developed as a top-level project within the Eclipse Foundation and leverages the rich capabilities of the Eclipse platform and a very active open source community of users. Using BIRT, developers of all levels can incorporate powerful reporting into their Java, J2EE and Eclipse-based applications.

3. Domo

Domo’s Business Optimization Software brings together the people, the data, and the insights business users need to deliver a detailed view of what’s happening in your organization. Connect all of your crucial business data, collaborate with fellow employees, and get powerful visual data—all within one customizable platform.

4. Board Management Intelligence Toolkit

BOARD toolkit combines various BI and CPM functionalities within a single graphical software environment. BOARD’s BI capabilities include multi-dimensional analysis, ad hoc querying, dashboarding and reporting, while its CPM capabilities include budgeting, planning and forecasting as well as “other finance-related activities”.Like Business Intelligence software in general, BOARD is used in an effort to improve productivity and decision making while lowering costs. It does not require any programming skills to build BI and CPM applications.

5. Clear Analytics

Clear Analytics is incredibly intuitive Excel-based solution with minimal training required. Employees with a basic knowledge of Excel can learn the system rapidly, so businesses can implement a fully-operational, self-service Business Intelligence system with little downtime and almost no learning curve. Clear Analytics offers a variety of BI-specific features to help generate, automate, analyze, and visualize a company’s key data and information. Clear Analytics also enables consolidation of data from multiple data sources and all within excel.

6. Ducen

Companies need to keep an eye on every revenue generating event and cost saving opportunity while improving customer satisfaction and retention. By combining historical data with real-time operational data for analysis, business users can make more informed, proactive decisions. However, to achieve these efficiencies, data must be available real-time.

7. Gooddata

GoodData powers the All Data Enterprise by offering an Open Analytics Platform that supports both IT’s need for Data Governance, security and oversight and business users desires for self-service Data Discovery.The platform consolidates data of any size, typically found both inside organizations and in the cloud, creating an analytic experience that is both fast and agile for users, yet protected, managed and secured for IT.

8. IBM Cognos Intelligence

Information silos, multiple platforms and excessive reliance on spreadsheets can hinder the process of analyzing your business data to understand performance and recommend improvements With business analysis software from IBM, you can explore information from different angles and perspectives and compare it with data in motion and trends for a more extensive view of your business. The facts you need for better results are right at your fingertips.

9. Insightsquared

Successful sales strategy is dependent on understanding the customer. But for small and medium businesses building up the kind of intelligence database needed can be time consuming and take staff away from the task of actually selling. It can be many months before the implementation of a traditional sales intelligence platform bears fruit.

10. JasperSoft

The Jaspersoft Business Intelligence Suite offers a number of ways for end users to perform interactive analysis. For the most casual user, this might involve simply changing a filter setting on a report to view a different slice of data. For a data analyst this could mean writing powerful, multi-dimensional expressions.

11. Looker

Looker is a data-discovery platform that helps companies make better business decisions through real-time access to data. Data, no matter the size, can be analysed within Looker’s 100% in-database and 100% browser-based platform. Looker analytics integrate with any SQL database or data warehouse, such as Amazon Redshift and Greenplum.

12. Microsoft BI platform

Microsoft Business Intelligence platform include Analysis Services, Integration Services, Master Data Services, Reporting Services, and several client applications used for creating or working with analytical data. This section of the SQL Server Setup documentation explains how to install these features. Analysis Services and Reporting Services can be installed as standalone servers, in scale-out configurations, or as shared service applications in a SharePoint farm. Installing the services in a farm enables BI features that are only available in SharePoint, including PowerPivot for SharePoint and Power View, the Reporting Services ad hoc interactive report designer that runs on PowerPivot or Analysis Services tabular model databases.

13. MicroStrategy

From local spreadsheet data to enterprise data systems to cloud-based data, MicroStrategy provides effortless access to all business data from one place. Use data connectors that are optimized for each source, and allow queries to reach their greatest performance potential. Connect to one source or many, separately or in combination. Gain the pure play advantage of superior R&D focus on strong technology partnerships and high speed analytics.

14. MITS

MITS, an established leader in reporting and business intelligence solutions for the Wholesale Distribution market, is growing and we need a BI Solution Developer to join our team. Are you passionate about helping businesses make proactive.

15. OpenI

OpenI provides a web-driven interface to build and publish interactive reports from OLAP data sources. Going beyond that, OpenI aims to provide consolidated analysis from all the key data components of an intelligent application. Our key goal is to take away the complexity of creating and publishing reports for business users. OpenI does this by providing a clean, intuitive interface to connect to different types of data sources, and to publish web-based interactive reports. If you want to build web-based intelligent applications that interact with your OLAP data sources.

16. Oracle BI

Oracle BI is a comprehensive collection of enterprise business intelligence functionality that provides the full range of business intelligence capabilities, including dashboards, full ad hoc, proactive intelligence and alerts, and so on. Typically, organizations track and store large amounts of data about products, customers, prices, contacts, activities, assets, opportunities, employees, and other elements. This data is often spread across multiple databases in different locations with different versions of database software.

17. Oracle Enterprise BI Server

Oracle Business Intelligence Enterprise Edition 11g is a comprehensive business intelligence platform that delivers a full range of capabilities including interactive dashboards, ad hoc queries, notifications and alerts, enterprise and financial reporting, scorecard and strategy management, business process invocation, search and collaboration, mobile, integrated systems management and more. OBIEE 11g is based on a proven web service-oriented unified architecture that integrates with an organization’s existing information technology infrastructure for the lowest total cost of ownership and highest return on investment.

18. Oracle Hyperion System

Oracle acquired Hyperion, a leading provider of performance management software. The transaction extends Oracle’s business intelligence capabilities to offer the most comprehensive system for enterprise performance management. The acquisition of Hyperion extends our business intelligence product strategy. Customers are increasingly using performance management and business intelligence together. Hyperion adds complementary products to Oracle’s business intelligence offerings including a leading enterprise planning solution, world-class financial close and reporting products, and a powerful multi-source OLAP server. Coupled with Oracle’s BI tools and pre-packaged analytic applications, the combination redefines business intelligence and performance management.

19. Palo OLAP Server

Palo is a memory resident multidimensional (online analytical processing (OLAP) or multidimensional online analytical processing (MOLAP)) database server and typically used as a business intelligence tool for controlling and budgeting purposes with spreadsheet software acting as the user interface. Beyond the multidimensional data concept, Palo enables multiple users to share one centralised data storage.

20. Pentaho

Pentaho addresses the barriers that block your organization’s ability to get value from all your data.  Our platform simplifies preparing and blending any data and includes a spectrum of tools to easily analyze, visualize, explore, report and predict. Open, embeddable and extensible, Pentaho is architected to ensure that each member of your team — from developers to business users can easily translate data into value.

21. Profit base

Profitbase SIM is a full scale financial planning and simulation tool for budgeting and forecasting where Profit & Loss, Balance Sheet and Cash Flow statements are fully integrated. SIM enables management to simulate business scenarios and immediately see the financial impact. SIM delivers a wide selection of standard reports, graphical charts and features seamless integration with Profitbase Studio and WebPlan.

22. QlikView

The QlikView Business Discovery platform delivers true self-service BI that empowers business users by driving innovative decision-making,Develop, enhance, re-engineer, maintain and support QlikView applications to create robust services around business requirements to inform business decision-making and Understand all the data that the business holds and create sustainable reporting solutions ensuring the accuracy of the data.

23. Rapid insight

Rapid Insight is a leading provider of business intelligence and automated predictive analytics software. With a focus on ease of use and efficiency, Rapid Insight products enable users to turn their raw data into actionable information. The company’s analytic software simplifies the extraction and analysis of data, enabling clients ranging from small businesses to Fortune 500 companies to fully utilize their information for data-driven decision making.

24. SAP business intelligence

Predictive analytics give your decision makers the insight they need to predict new developments, capitalize on future trends, and respond to challenges before they happen. SAP’s market-leading combination of real-time business intelligence (BI) and predictive analytics make it easy for you to extract forward-looking insights from Big Data, harness the power of R, and create stunning data visualizations with ease.

25. SAP BusinessObjects

SAP BusinessObjects Analysis, edition for Microsoft Office is an Office add-in that allows multidimensional ad-hoc analysis of OLAP sources in Excel. It also allows, Excel workbook-based application design and creation of BI presentations in PowerPoint. It perfectly connects to SAP NetWeaver BW and SAP HANA.

26. SAP NetWeaver BW

Quickly Capture, store, and consolidate your vital information with our real-time data warehouse platform. Tightly integrate your warehousing capabilities for a single version of the truth, decision-ready business intelligence, and accelerated operations.Supercharge your data warehouse environment with SAP Business Warehouse powered by SAP HANA.

27. SAS BI

According to Forrester, SAS has not only been a market leader in advanced predictive analytics, but also a provider of a formidable BI platform. Customers select SAS for its well-integrated, one-stop platform, a significant part of which is its BI capabilities. SAS provides scalability, excellent data integration, multiple query languages, internationalization, customization through a rich set of APIs, advanced analytics tools, MDM, performance management, and reporting and querying. SAS ranks eighth on number of Forrester BI inquiries. Recent market survey data indicates that 14% of corporate customers depend on SAS for their BI needs.

28. Silvon

Business Intelligence solution provider Silvon Software, Inc.to bring a powerful, web-based business analysis software interface to retailers. Under the terms of the agreement, RPE will market Silvon’s Viewer interface for Performance Analysis by IDEAS, a client-server BI application for JDA Software Group’s Merchandise Management System. This new optional interface for Performance Analysis by IDEAS will provide many added features for today’s mobile professionals.

29. Solver

The solver in excel is part of an analysis tool known as “what ifs analysis”. You can use solver to ascertain an optimal value in one cell known as the “target cell”. Basically, solver is used for a group of cells that are directly or indirectly related. Constraints can also be applied to minimize the value that can be used by Solver. This article will provide step-by-step guide on how to use solver to find solution to a business problem.

30. SpagoBI

SpagoBI supports the real-time monitoring, analysis and presentation of business data and processes. You can keep business processes under control by constantly monitoring their state.SpagoBI allows you to go further than this: you can detect inefficiencies and bottlenecks in your business processes, promptly react to events requiring quick decision making, as well as discover new business opportunities hidden in your own data.

31. SQL Server Analysis Services

Server Analysis Services platform, build high performance analytical models (multidimensional and tabular) that can be used for interactive data analysis, reporting, and visualization. SQL Server provides a comprehensive analytical and modeling experience to support rapid solution prototyping and support for the largest enterprise-grade solutions.

32. Style Intelligence

Style Intelligence is business intelligence software for dashboards, reporting, visual analysis, and data mashups. It blends enterprise strength with a small, 100% Java footprint. Unlike traditional BI platforms, Style Intelligence does not require specialized BI skills or consultants to implement or use. It delivers maximum self-service that is both end-user and IT-friendlier than other BI solutions.

33. Syntell solutions

Syntel’s Technology Outsourcing services deliver value and provide solutions that transcend platforms. Leverage Syntel’s expertise in managing business processes, systems and platforms in order to reap the benefits of an innovative and collaborative outsourcing partnership. Syntel understands your pain points and offers a set of distinctive services that enhance your operations across the applications and IT environments. Syntel designs a client-specific strategy to achieve your desired objectives, and our services help you create a strategy based on the value to your business.

34. Targit

TARGIT fights all unnecessary clicks that only make your life difficult. TARGIT BI Suite has a very unique and intuitive user interface you have to see it to believe it! You will experience an integrated and ready-to-use set of tools which enables you to create intelligent dashboards, revealing analyses and insightful reports in fewer clicks than with any other Business Intelligence solution on the market. TARGIT will accelerate decision making, increase operational awareness, and improve performance across the organization. TARGIT BI Suite is so easy to use that all employees can follow trends, create all types of analyses, and make decisions.

35. Vismatica

Vismatica by IronRock Software is powerful data visualization solution geared toward small to medium businesses. Dashboard development tools make up the core of this system, but Vismatica also empowers you to create powerful data collection forms and conduct data analysis. It can be deployed on premise or over the web as a hosted solution. Vismatica comes with additional features for sharing documents and designing web applications.

36. WebFOCUS

The WebFOCUS Business Intelligence and Analytics platform empowers everyone in your organization to make smarter, more confident decisions. WebFOCUS extends to your customers and partners, too, giving them easy access to analytic apps and tools from any browser or mobile device.

37. Yellowfin BI

Data to dashboards Yellowfin delivers a brilliant analytical experience. Our interface is more than beautiful it provides all the data discovery features that you will ever need. All this whilst providing a fine balance between the ease of use business users require and the governance needs of enterprise IT.

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Confirmation Bias

Confirmation bias refers to the need to prove a hypothesis and therefore to lean heavily on data that might lead this way. Confirmation bias acts to skew results in that the analysed data doesn’t actually represent the full picture of the scenario. For example, a data collection may want to prove that Twitter users were more engaged with a TV show while it was on air – and may neglect to take into account that the greater cumulative engagement occurred in the days after viewers had had a chance to digest the episode. So recommendations could result in companies producing show-related online materials at the wrong time. One of the most galling examples of confirmation bias occurred after the 2016 US Presidential election, where polls were gathered based on a Clinton win, ignoring evidence that might prove otherwise.

Availability Bias

The availability heuristic is just one of a number of phenomena that affect decision-making in daily life, that most people are unaware is even taking effect. Essentially, availability bias refers to the way in which people make decisions based only on information readily available to them. For example, a data collection may discover that respondents spend time looking at a website’s blog – and will use this information to develop the blog in order to convert to a sale or returning customer. However, the availability bias may cause other factors to be neglected due to the information that the blog is successful being the only piece relied on. For instance, the blog could be successful but could create very little engagement, meaning solely developing the blog would create no conversions. Value Walk’s article on behavioural finance helping stock market investors includes the image below, which outlines the availability heuristic in simple terms – and shows how the perspective needs to be shifted to take into account all the information available. The blog would be the small yellow circle, and the rest of the website would be the larger blue circle in the example.

Source: JamesClear.com

Selection Bias

Selection bias refers to the sample the data has been collected from being unrepresentative of people on the whole. Imagine a console game has collected data on how long players spend on the game and then begin to use this in their game development. The data only looks at existing users, and doesn’t take into account factors that might convert a non-user to a fan of the game.  For example, a survey found that Xbox gamers were overestimating the prevalence of the “red ring of death” console fault due to the likelihood of those who had experienced it to complete the survey.

Confounding Variables

One of the most dangerous biases results when a correlative relationship between two variables is actually only true when combined with an overlooked confounding variable. Confounding variables cannot be separated from the variables that lead to the correlation. For example, a data collection may discover that a commercial for a children’s theme park that airs during prime time on a children’s channel, which is broadcasting a show about the theme park itself leads to website check-ins. As the scientist cannot state empirically that it is either the commercial or the TV show itself leading to the higher rate of check-ins, the data would be impacted by a confounding variable. Ensure that all data collected can prove a relationship between two variables without being influenced by anything external.

Data collection can be time-consuming and unruly – but completing it successfully can pay dividends for a business, especially with the impact of big data. Biases can be mitigated against to ensure that the statistical recommendations have a low margin of error.

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Think about it: personal statistics, business revenue, healthcare statistics, even credit history—these aspects of our lives are quantified and reduced to numbers in a database or spreadsheet, somewhere. Moreover, much of this data never even makes it to the printed page. Reports and presentations of the past were printed out on paper or written out via accounting ledgers. But this is no longer. Rather, information is stored in what’s known as “the cloud,” and to someone from the 19^th or 20^th century, this language would make no sense whatsoever.

From chatbots and interactive social media pages to consumer-created products, surveys, and perks, marketing is becoming more personalized than ever before. If customers are strategic and aware of their value to retailers, they will be able to successfully navigate the newly ubiquitous nature of online data mining and customized marketing campaigns to their own advantage.

1. Interactive Customer Service

Through platforms such as chatbots and social media pages, companies can capture quite a bit of information from customers, from the types of questions asked to which products are of the most interest to those customers. If companies are savvy, they will incorporate the conversations that customers have with their service agents and use it to better understand what customers want, so they can tailor future services to what customers most avidly want and need.

Maryville University writes, “By understanding the consumer’s pain points, purchase preferences, and shopping habits, specific marketing campaigns can speak directly to the intended audience.” Since any information about buying habits or customer preferences is bound to come up, during a customer service agent’s conversation with a customer—either online or in person—such information should be utilized as valuable feedback that could help a company improve the customer experience or “buyer’s journey” for future fans of the same product.

Some people even prefer to connect via online chat or messaging apps, while making a purchase — 26 percent, to be exact, according to eMarketer. Of course, the extent to which chatbots are able to access each customer’s user-generated data is dependent upon the technology that is used — such as what page they are on, what they clicked, and the nature of their searches.

2. Personalization

Companies like Netflix and Amazon are personalizing customer experiences by recording which movies their customers watch and recommending similar films, based on other users’ data and watching patterns. Moreover, users are paying attention: apparently, 35 percent of all sales are generated by Amazon’s recommendation engine. And Netflix’s recommendation algorithm is the most powerful and successful on the market.

Interestingly, health care is no stranger to this increasing pattern of data collection. According to George Zachariah, hospitals can benefit from the use of data analytics in five major ways: administrative costs are reduced; clinical decision-making is supported; abuse and fraud are minimized; coordination of patient care improves; and patient wellness increases. Hospitals and medical clinics have been forced to maximize their efficiency, whenever possible, due to ongoing state and federal budget cuts, so data analytics will likely continue to be implemented, in healthcare IT.

Retail personalization is also influencing the healthcare wearables market, with many customers taking charge of their health by utilizing apps like Google Fit or smart devices like Fitbits to monitor their own heart rates and activity levels. Moreover, due to rising costs for direct care, many are going one step further and turning to telemedicine and genetic testing to determine their ancestral risk for conditions like heart disease or breast cancer. Much of this independent research and treatment is taking place because healthcare has become so expensive and out of reach for so many. As a result, the retail marketplace is wasting no time in filling the gaps.

3. Consumer Credit

If you don’t realize your financial data and credit history is easily obtainable by banks, credit card companies, lenders, and potential employers, perhaps you’ve been practicing hermitage in a mountain cave somewhere deep in the wilds of Idaho. Yes, people with loads of financial power and cultural capital have the power to learn all about your financial history at the push of a button. Unfortunately, the world of data analytics has a big hand in how much credit you’re likely to have access to, at any given time.

So what can you do to alter your financial data records and improve your credit score? Well, first you should focus on paying off old debts and keeping current accounts up to date, payment-wise. If you’re unfamiliar with the current state of your credit report, you’re legally entitled to a free credit report if you are denied credit for any reason. It’s also fairly easy to obtain your credit report online for a small fee, so be sure to look into that, as well.

One way to rebuild your credit is through opening a new credit card account and paying off the balance right away. As long as you remain up to date on payments, you’ll be able to reliably rebuild your credit, which will help your credit rating. Just remember not to take on too much debt. If you’d like a relatively surefire way to get approved, apply for a secured credit card account. Secured credit cards require a deposit to open an account, and your spending limit is determined by your security deposit, rather than your credit score. Mind the fine print, however, as some SCCs come with extremely high late or annual fees.

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Do you work in marketing? If so, how has data analytics influenced the way you do business? Share your experiences in the comments section, below.

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For example, supermarkets have club cards and store cards that allow people to earn points through shopping. The data produced by such cards will help those supermarkets target their customers with discounts and offers that are more likely to appeal to their customers. It also allows them to second-guess what people will buy in the coming months so that the supermarkets may adjust their stock levels and prices accordingly.

In this example, a supermarket chain would be foolish to sell this information to another supermarket, but what about smaller manufactures? A manufacturer of toiletries may like to know when the next sales surge is going to happen so that it may also adjust its prices and output accordingly. The supermarket could easily sell relevant information to the manufacturer without it affecting the supermarket’s business in a negative way.

Trading To Just One Company

A straight sale from your company to another company is the easiest way to monetize your data, just like the example above with the toiletries company. However, it may be a smarter move to sell subscriptions to your data. For example, you could release your data every week, but charge a quarterly fee for it. Such information would help companies who do not have the means to gain such data by themselves, or companies that cannot afford (or don’t want to) pay bigger research companies. Julie Petersen, the founder of the writing blog says, “Back in the old days, we used to sell fiction-based magazines, and we automatically rejected authors who didn’t have an idea that could be turned into a series. Big data is not a one-sale issue; it is something you can sell again and again.”

Creating A Tool That You Sell On Mass

Apps and desktop software are easier than ever to build, and there are thousands of app developers out there that would happily build an app or program that uses your data as its chief database. The data doesn’t have to have anything to do with your products. It could focus on customer activity. It could focus on anything from how many people visit your website at 11am, to how long people spend choosing their next pair of shoes.

Build A Service That Uses The Information

Think more along the lines of the many eBay tools and services that exist because third parties have used eBay as a chief database. Do people want to know when the best time is to buy your product? Do they want to know what the best offers are in play? Are there people out there who could get a good deal by seeing what you and your competitors offer? In addition to creating a service using your data, you could also help promote your own products at the same time. If you want inspiration, then research into Amazon.com services provided by third parties, and you will see everything from email-update services to Chrome extensions.

Using The Information To Sell Affiliate Products

You could sell your data to other companies to help them sell their products, or you could use your information to sell affiliate products from other companies. Take Rotten Tomatoes as an example, they use their big data to help make more effective affiliate sales. They host a series of up-to-date adverts about the most recent movies, and they also feature affiliate adverts based on what the user looks at and how long the user engages with each page. They use their big data, drawn from user-website interactions, to help increase the chances of people clicking on their affiliate adverts.

Selling As Part Of An Information Product

We all know that a lot of what you read on the Internet is either semi-true or not true at all. What is true seems to be based on which website is the best at SEO (Search Engine Optimization). Yet, there are some companies and academics out there who genuinely want to know the truth. They write books and journal entries with information they buy from other academics, research teams and companies. You could be one of those companies.

Even Infographic makers may be interested in your data. Did sales from a product line increase the day after your competitor launched a big product? Did sales drop after a big headline that there was a recession? You can already see the headlines in the journals and information products, such as “Recession causes 26% drop in sales” or “X product sales surge after product Z was released.”

Using And Selling Are Closely Linked

When you think about data monetization, don’t just think about selling your information like you sell apples at the side of the road. Consider how the information may be used, and how that “use” may be monetized.

For example, back in the days when DVDs still existed, a DVD merchant built an online tool that tracking the cost of different DVDs with different competitors. That information was valuable to the DVD merchant, especially since the tool was linked directly into the company’s pricing system so that prices could adjust automatically according to the prices the company’s competitors had. That same company started licensing the tool to other websites as a way for website users to find the cheapest DVDs, and one of the first DVD price-comparison tools was born.

If you have to sell your data as more of a commodity and you cannot find a way to create a “use” and then sell the “use,” then try to find a way to license your information or build a subscription service. Even selling email lists can be done on a subscription basis where your buyers get an updated email list every four months.

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A Partnership with Massive Potential Impact

Today, sharing economy platforms use smartphone and web applications to communicate between the service provider, the platform, and the customer. But what if smartphones could communicate directly with devices that help provide the services—without the involvement of the device’s owner? This kind of automation could make the sharing economy even more efficient and user-friendly.

Examples of Possible IoT Partnerships

Some companies are already imagining what these partnerships might look like. GigaOM created a concept for an IoT smart locking device, called “Lock-Bot” which would allow guests to access the key to the unit securely, and return it at the end of their stay—with notifications to the property owner that the transaction had been completed. While the locking device failed to reach its Kickstarter funding goal, it’s an indication of the direction the sharing economy is heading.

Another example is the dream of automated rental car transfer. Instead of locating a rideshare driver through a smartphone, people could actually find the rental car nearest to them (similar to the ZipCar model, which has small lots dispersed over large cities), open the locks with a Bluetooth beacon, and drive away, with the GPS recording distance in order to charge the user’s credit card.

Less Human Involvement

Of course, AirBnB hosts still have to clean their rental units (or pay another person to do so) and Uber still needs actual human drivers behind the wheel—for now. As more devices come online, and artificial intelligence achieves greater sophistication, however, even these interactions will likely become unnecessary, allowing for seamless sharing and more freedom on the part of the home or car’s owner. This will enable people to generate a passive income stream with assets they already own, either as a primary or secondary source of income.

Will Public Opinion Slow Things Down?

As with any new technology or idea, the IoT is going through some growing pains, and is still working to gain public acceptance. Now that the sharing economy has become mainstream, more people are comfortable with the idea of staying at someone’s apartment over a hotel, or taking a rideshare instead of a taxi. This is especially true of the Millennial generation, which will drive the majority of spending in the near future. Trends indicate that Millennials prioritize experiences over consumer goods, an attitude that is perfectly suited to an evolving sharing economy that encourages maximum use of a smaller number of goods. This generation is also used to having the convenience of smart devices close at hand, meaning the learning curve of integrating the IoT into the sharing economy will be less significant overall.

The Labor Force is Always Evolving

Of course, there are some concerns about how the IoT will affect the sharing economy, namely that it will cut out many of the jobs that have been created by sharing goods and services. Driverless cars are far along in their development, and ride-sharing services have already begun adding automated vehicles to their fleets (though the use of these is limited by pertinent laws). However, automation has always played a role in our evolving labor force, and the probable shift to IoT devices in the sharing will simply create shifts in the labor force that are completely normal. Sharing your car would have been unthinkable in the 1960s, but today, it’s part of our economic future.

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Realizing the potential financial value found in your data has a multitude of benefits (and not just for hackers). Only recently has it become possible to gather such fine-grained information about costs, profits, operations, and customer behavior, and use them for company gain.

Simply put, data monetization is utilizing data to it’s utmost potential to turn it into currency. To properly accomplish this as a company, one must dedicate much time and effort into analyzing varieties of data and pursuing potential markets. The ability to monetize data effectively can be a competitive advantage in the digital economy to increase and manage business cash flow.

There are three major approaches that businesses use to monetize their data.

Upgrade Internal Business Procedures

Using data to ensure the operational processes with the business are improving is crucial.

1. Stop Revenue Leaks

Using elementary systematics, companies can identify patterns associated with the codes and procedures so client/patient invoices can be flagged for potential errors or false charges. This also helps businesses improve their ROI (return on investment) collections; in other words, this identifies the right person to contact, a responsive channel, and the time a contact will likely produce positive results.

2. Embrace a New Business Model

Growing businesses evolve, and there is always enough room for improvement. Big names like Uber, Facebook, and Amazon are constantly revising their business models to stay ahead of the competition, bringing about relevance and a source for new revenue.

Wrap the Product

This is a creative task in which companies will closely research the problems their customers are experiencing, and find ways to solve these problems with data and analytics.

1. Infer Customer Satisfaction

It isn’t uncommon for businesses to use surveys and social media to gain a firmer understanding of their level of customer satisfaction. They crosscheck the data from a variety of sources to conclude a satisfaction level based on a set list of product factors.

2. Minimize Churn

The churn rate, also known as the rate of attrition, is the percentage of subscribers to a service who discontinue their subscriptions to that service within a given time period. For a while companies used a narrow set of data to determine how they can best serve their customers. Now companies have compiled a full-bodied approach of incorporating the data with more intelligent tools, and data science to determine when customers will likely churn, why they are likely to churn, and what the company should do to preempt it.

Selling Information

Many businesses believe that their data holds inherent value, and can become a gateway to new revenue. Selling data can be profitable, but is heavily cautioned as the hardest way to monetize data mainly because it requires a unique business model that many companies do not have set up yet.

1. Rethink Value

Companies hold a unique selling proposition that will inevitably change over time, if it hasn’t already. More businesses are running on software, and in turn, more of them will see their data assets expand. This is a playground to reimagine the value provided to customers, and continue their value chain.

2. Detect Fraud and Piracy

Online retailers use a variety of sites to sell, often including Amazon, eBay, and other online marketplaces maintained by very large storefronts such as Walmart and Best Buy. This process is very data intensive because the pricing, products, and customer types often vary across channels. Sometimes the price variations are so significant they signal potential fraud or piracy.

Many companies still don’t think of their data as an asset they can monetize. They’re overlooking an opportunity in plain sight. Not only can they make better use of their data to enhance products and services for current clients, but new technologies now allow them to take the next step and combine their data with external data.

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1. W3Schools – SQL Tutorial

W3Schools is one of the largest web developer sites that you can find on the internet. The website provides a multitude of tutorials you can use to develop your skills and SQL is one of them. On this website, you can learn how to use SQL in SQL Server, MySQL, Oracle, and other systems. What’s practical about this site is the quiz feature where you can test your progress, identify strengths and weaknesses, and improve the learning experience.

2. SQLBolt

SQLBolt is, essentially, a series of interactive lessons and exercises that are created to help users learn SQL easily. The lessons and topics found on this site are comprehensive and they cover all the important details of using SQL. This resource is particularly useful for beginners with 19 easy but important lectures that you should know before you move on to more complex details. They also started adding intermediate lessons, at this point only 3 are available but we can expect more soon.

3. Vertabelo Academy

Vertabelo Academy provides interactive SQL courses right in your browser. Each course features extensive practice material that you can use to enhance your skills and build confidence. The website offers three types of courses: free, paid, beta (works in progress that you can use for free to practice). Vertabelo Academy teaches you about SQL queries, table creation, and data management.

Using this website is easy; you start each item with instructions and examples and when you’re ready, it’s time to move on to exercises. Here, you also have the opportunity to discuss the course with other users and trade experiences.

4. Codeacademy

Codeacademy is an online platform that provides various free coding courses in programming languages. The site is dedicated to providing an optimal learning experience and you can use it to learn how to manage data with SQL. Codeacademy’s LearnSQL is free and interactive. The platform covers the basics of database essentials including queries, tables, aggregate functions, developing advanced database queries, among other things.

Each lesson is divided into three panels containing a description of the exercise, an interactive SQL command line, and a visual representation of the database schema with the result of the query. Check your knowledge with a quiz and see how far you’ve come. To take the course, you have to register using your email address and Facebook or Google account.

5. Udemy

Udemy is a great online resource with a mission to “help anyone learn anything”. SQL courses on this site are paid, but frequent promotions bring prices down and you can find an ideal course regardless of your budget. What’s beneficial about courses at Udemy is that you can opt for the one that perfectly matches your current skills and SQL knowledge.

6. Khan Academy “Intro to SQL”

Khan Academy offers personalized learning dashboard, a lot of practice exercises, and micro-lectures in the form of YouTube videos. This allows you to study at your own pace and develop SQL skills gradually. Unlike many other resources, you can adapt this one to your needs and preferences.

The entire course contains 5 parts starting with basics and leading you all the way up to more advanced lessons. You don’t have to register in order to watch videos, but if you have some questions or want to take part in discussions, then you will have to sign in.

7. SQLZoo

SQLZoo is ideal for people who prefer extensive personal support and a more thorough approach to lessons. Of course, lessons are interactive and the site is free to use. Here, SQL course comes with live interpreters and interactive exercises for different types of databases. All tutorials come in step-by-step format and you also have the option to use live chat, test your knowledge with a quiz, and the content is available without registration.

8. Stanford University

Yes, THAT Stanford University provides an online self-paced course with video tutorials that you can use to learn basic SQL skills. To get started, all you have to do is to select the course and there are plenty of options including querying databases, SQL advanced features such as indexes and transactions, constraints and triggers, online analytical processing, recursion in SQL, and many others.

9. SQL Problems and Solutions

This unique platform acts as an interactive textbook which allows you to visualize tables and execute queries using a sample database. This tutorial explains the fundamental concepts and constructs of SQL. At the same time, it displays examples for different levels of expertise in order to help you learn better and move on to other lessons. Once you’ve learned all the lessons, put your skills to a test using a sister site SQL Exercises.

Bottom line

If you have ever wanted to learn SQL but didn’t know how then resources from this post will help you out. They cover the basics, move to more advanced lessons and courses, and allow you to test your skills using quizzes and sample databases. These sites only prove that you don’t need to spend a fortune to learn more about SQL, you can do it for free.

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What Is Blockchain?

The blockchain was originally created for Bitcoin, a digital currency, but today a variety of industries are finding uses for this new technology.

A blockchain is a ledger like an Excel spreadsheet, but it has some special qualities that make it potentially game changing. If someone wanted to edit a spreadsheet on most modern networks, they would must pull it from the location where it’s being stored, edit it and then send back the newly edited version to its storage location. While that document is being edited, no one else can access it.

With a blockchain, the document isn’t stored in one centralized location. It’s stored and owned in a distributed way by all the nodes in a network. Everyone in the network can see when changes are being made, and nothing can be altered without the approval of every member of the network.

The Benefits for Big Data

This new method for managing information has several benefits. The process is much more transparent because nothing can be changed without the approval of every server in the network, everyone can see what changes are being made and there is no one authority who controls information.

Blockchain has been used for financial purposes because it makes it much more difficult for mistakes and fraudulent transactions to occur.

Every transaction that occurs on a blockchain network is encrypted and time stamped. They can’t be tampered with once they’ve been created and often occur automatically, which enhances security even further. Since the worldwide cost of cyber crime is expected to grow to $2 billion by 2019, this is extremely important.

Blockchain leads to higher quality data. In a more traditional network, there are multiple points at which someone could enter information incorrectly, files could become corrupted or other issues could occur.

With this new technology, there is no single weak spot that can damage a document. There is one document that’s shared by every node. If one user makes a mistake, it won’t be recorded because the other servers will override it.

Because every node in a network shares information, it will also be much easier to access it. With today’s big data technology, it can often be difficult to get a full picture of the available information because it has to be collected from multiple locations.

Blockchain networks allow every member of a network access to all data, which allows everyone to get a clearer picture of data and reach better analytical conclusions.

Implementing Blockchain

While blockchain is quite an exciting technological development, and this is expected to be a big year for the technology, it will likely take a while for it to be implemented. It will require new infrastructure and retraining staff to use the technology.

Of course, implementing blockchain also means convincing those in charge that investing in the technology is worth it. There is an upfront cost, but implementing it could result in long-term financial savings. It’s been estimated that blockchain could save financial companies up to 30% of their back-office costs, which comes out to about $16 billion across the industry.

To implement blockchain, a company should explore which areas the technology should be used for to get the most customer benefit. Smaller companies will then look to an outside company to implement it for them, or, if they the resources, build it internally. A budget, implementation schedule and training program for using the technology must then be established.

Thus far, only a few innovative companies in a few industries have adopted blockchain technology. By starting to prepare for the new technology now, any company can be ahead of the curve.

Experts believe that blockchain could be a gamechanger in the world of tech, which affects almost all industries, and preparing for the change now could help any business become a leader in tomorrow’s world.

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So let’s get going with this shorter list. Also, check out the infographic image midway through this article and feel free to download and keep it in your pocket.

Algorithm: A mathematical formula or statistical process used to perform an analysis of data. How is Algorithm is related to Big Data? Even though algorithm is a generic term, Big Data analytics made the term contemporary and more popular. (Bonus: Here’s a pickup line on your date, ‘You show me your algorithms and I’ll show you mine. …. Ok, Ok, I’ll stop! No more cheesy jokes)

Analytics: Most likely, your credit card company sent you year-end statement with all your transactions for the entire year. What if you dug into it to see what % you spent on food, clothing, entertainment etc? You are doing ‘analytics’. You are drawing insights from your raw data which can help you make decisions regarding spending for the upcoming year. What if you did the same exercise on tweets or facebook posts made by your friends/network or your own your company. Now we are talking Big Data analytics. It is about making inferences and story-telling with large sets of data.There are 3 different types of analytics and let’s discuss them while we are on this topic.

Descriptive Analytics: If you just told me that you spent 25% on food, 35% on clothing, 20% on entertainment and the rest on miscellaneous items last year using your credit card, that is descriptive analytics. Of ​course, you can go into lot more detail as well.

Predictive Analytics: If you analyzed your credit card history for the past 5 years and the split is somewhat consistent, you can safely forecast with high probability that next year will be similar to past years. The fine print here is that this is not about ‘predicting the future’ rather ‘forecasting with probabilities’ of what might happen. In Big data predictive analytics, data scientists may use advanced techniques like data mining, machine learning, and advanced statistical processes (we’ll discuss all these terms later) to forecast weather, economy etc.

Prescriptive Analytics: Still using the credit card transactions example, you may want to find out which spending to target (i.e. food, entertainment, clothing etc.) to make a huge impact on your overall spending. Prescriptive analytics builds on predictive analytics by including ‘actions’ (i.e. reduce food or clothing or entertainment) and analyzing the resulting outcomes to ‘prescribe’ the best category to target to reduce your overall spend. You can extend this to big data and imagine how executives can make data-driven decisions by looking at the impacts of various actions in front of them.

Batch processing: Even though Batch data processing has been around since mainframe days, Batch processing gained additional significance with Big Data given the large datasets that it deals with. Batch data processing is an efficient way of processing high volumes of data where a group of transactions is collected over a period of time. Hadoop, which I’ll describe later, is focused on batch data processing.

Cassandra, a beautiful name, is a popular open source database management system managed by The Apache Software Foundation. Apache can be credited with many big data technologies and Cassandra was designed to handle large volumes of data across distributed servers.

Cloud computing: Well, cloud computing has become ubiquitous so it may not be needed here but I included just for completeness sake. It’s essentially software and/or data hosted and running on remote servers and accessible from anywhere on the internet.

Cluster computing: It’s a fancy term for computing using a ‘cluster’ of pooled resources of multiple servers. Getting more technical, we might be talking about nodes, cluster management layer, load balancing, and parallel processing etc.

Dark Data: This, in my opinion, is coined to scare the living daylights out of senior management. Basically, this refers to all the data that is gathered and processed by enterprises not used for any meaningful purposes and hence it is ‘dark’ and may never be analyzed. It could be social network feeds, call center logs, meeting notes and what have you. There are many estimates that anywhere from 60-90% of all enterprise data may be ‘dark data’ but who really knows.

Data lake: When I first heard of this, I really thought someone was pulling an April fool’s joke. But it’s a real term! Data lake is a large repository of enterprise-wide data in raw format. While we are here, let’s talk about Data warehouse which is similar in concept in that it is also a repository for enterprise-wide data but in a structured format after cleaning and integrating with other sources. Data warehouses are typically used for conventional data (but not exclusively). Supposedly data lake makes it easy to access enterprise-wide data you really need to know what you are looking for and how to process it and make intelligent use of it.

Data mining: Data mining is about finding meaningful patterns and deriving insights in large sets of data using sophisticated pattern recognition techniques. It is closely related the term Analytics that we discussed earlier in that you mine the data to do analytics. To derive meaningful patterns, data miners use statistics(yup, good old math), machine learning algorithms, and artificial intelligence.

Data Scientist: Talk about a career that is HOT! It is someone who can make sense of big data by extracting raw data (did you say from data lake?), massage it, and come up with insights. Some of the skills required for data scientists are what a superman/woman would have: analytics, statistics, computer science, creativity, story-telling and understand business context. No wonder they are so highly paid.

Distributed File System: As big data is too large to store on a single system, Distributed File System is a data storage system meant to store large volumes of data across multiple storage devices and will help decrease the cost and complexity of storing large amounts of data.

ETL: ETL stands for extract, transform, and load. It refers to the process of ‘extracting’ raw data, ‘transforming’ by cleaning/enriching the data for ‘fit for use’ and ‘loading’ into the appropriate repository for the system’s use. Even though it originated with data warehouses, ETL processes are used while ‘ingesting i.e. taking/absorbing data from external sources in big data systems.

Hadoop: When people think of big data, they immediately think about Hadoop. Hadoop (with its cute elephant logo) is an open source software framework that consists of what is called a Hadoop Distributed File System (HDFS) and allows for storage, retrieval, and analysis of very large data sets using distributed hardware. If you really want to impress someone, talk about YARN (Yet Another Resource Negotiator) which, as the name says it, is a resource scheduler. I am really impressed by the folks who come up with these names. Apache foundation, which came up with Hadoop, is also responsible for Pig, Hive, and Spark (yup, they are all names of various software pieces). Aren’t you impressed with these names?

In-memory computing: In general, any computing that can be done without accessing I/O is expected to be faster. In-memory computing is a technique to moving the working datasets entirely within a cluster’s collective memory and avoid writing intermediate calculations to disk. Apache Spark is is an in-memory computing system and it has huge advantage in speed over I/O bound systems like Hadoop’s MapReduce.

IoT: The latest buzzword is Internet of Things or IOT. IOT is the interconnection of computing devices in embedded objects (sensors, wearables, cars, fridges etc.) via internet and they enable sending / receiving data. IOT generates huge amounts of data presenting many big data analytics opportunities.

Machine learning: Machine learning is a method of designing systems that can learn, adjust, and improve based on the data fed to them. Using predictive and statistical algorithms that are fed to these machines, they learn and continually zero in on “correct” behavior and insights and they keep improving as more data flows through the system. Fraud detection, online recommendations based

MapReduce: MapReduce could be little bit confusing but let me give it a try. MapReduce is a programming model and the best way to understand this is to note that Map and Reduce are two separate items. In this, the programming model first breaks up the big data dataset into pieces (in technical terms into ‘tuples’ but let’s not get too technical here) so it can be distributed across different computers in different locations (i.e. cluster computing described earlier) which is essentially the Map part. Then the model collects the results and ‘reduces’ them into one report. MapReduce’s data processing model goes hand-in-hand with hadoop’s distributed file system.

NoSQL: It almost sounds like a protest against ‘SQL (Structured Query Language) which is the bread-and-butter for traditional Relational Database Management Systems (RDBMS) but NOSQL actually stands for Not ONLY SQL . NoSQL actually refers to database management systems that are designed to handle large volumes of data that does not have a structure or what’s technically called a ‘schema’ (like relational databases have). NoSQL databases are often well-suited for big data systems because of their flexibility and distributed-first architecture needed for large unstructured databases.

R: Can anyone think of any worse name for a programming language? Yes, ‘R’ is a programming language that works very well with statistical computing. You ain’t a data scientist if you don;’t know ‘R’. (Please don’t send me nasty grams if you don’t know ‘R’). It is just that ‘R’ is one of the most popular languages in data science.

Spark (Apache Spark): Apache Spark is a fast, in-memory data processing engine to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to datasets. Spark is generally a lot faster than MapReduce that we discussed earlier.

Stream processing: Stream processing is designed to act on real-time and streaming data with “continuous” queries. Combined with streaming analytics i.e. the ability to continuously calculate mathematical or statistical analytics on the fly within the stream, stream processing solutions are designed to handle very high volume in real time.

Structured v Unstructured Data: This is one of the ‘V’s of Big Data i.e.Variety. Structured data is basically anything than can be put into relational databases and organized in such a way that it relates to other data via tables. Unstructured data is everything that can’t – email messages, social media posts and recorded human speech etc.

Hope this list was helpful. Please feel free comment with your own additional terms.

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