Although cognitive computing, which is many a times referred to as AI or Artificial Intelligence, is not a new concept, the hype surrounding it and the level of interest pertaining to it is definitely new. The combination of hype surrounding robot overlords, vendor marketing and concerns regarding job losses has fueled the hype into where we stand now.
But, behind the cloud of hype that is surrounding the technology currently, there lies a potential for increased productivity, the ability to solve problems deemed too complex for the average human brains and better knowledge based transactions and interactions with consumers. I recently got a chance to catch up with Dmitri Tcherevik, who is the CTO of Progress, about this disruption and we had a healthy discussion which led to the following insights.
Cognitive computing is considered a marketing jargon by many, but in layman terms it is used to define the ability of computers to replicate or stimulate human thought processes. The processes behind cognitive computing may make use of the same principles as AI, including neural networks, machine learning, contextual awareness, sentimental analysis, and natural language processing. However, there is a minute difference between both of them.
Difference between Cognitive Computing and AI
Both AI and Cognitive Computing may look extremely alike, but like we mentioned above there is a small difference between both methods.
Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they’re looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm. And, we all know that a self-driven car isn’t giving suggestions to the driver, it’s responsible for all the decisions in driving.
Secondly, cognitive computing is not responsible for making decisions for humans, instead it is responsible for complementing or supplementing our own cognitive abilities of decision making. AI in medicine would be all about making the right decisions pertaining to a patient or the preferred mode of treatment, and minimizing the role of the doctor. Cognitive computing, on the contrary, would be more focused on achieving evidence that could supplement the human expert into making more flawless medical diagnoses.
Emerging Use of Cognitive Computing in Industries
We can gauge the success of cognitive computing and the development through the opportunities it has across industries. Cognitive computing is currently in a research phase, where research is going into properly implementing the technology in the fields deemed appropriate for its use. One can assess the opportunities for cognitive computing by looking at industries and industry specific scenarios where cognitive computing could make a big difference.
Customer services
Companies offering customer services deal with a lot of data which they have to accommodate with large processing requirements and are required to be efficient and flawless in advising customers to the right outcome. With so much happening, one can think about the opportunities for cognitive computing in this specific industry. At a consumer level, we can take the aid of robo-advisors that assist staff in advising new customers about what they can do and how they can go about creating a new account. There is also the concept of automated document processing that will limit human involvement and the flaws that come with it to a large extent. According to Dmitri: ‘Customer services are up for disruption, and the use of chatbots while booking airplane tickets or checking your insurance claim will go a long way in the future.’
Healthcare
Whenever we talk about Big Data, Machine Learning, AI or Cognitive Computing, the services that will be rendered through these technologies in healthcare always spring to mind. Human healthcare is certainly not at 100 per cent efficiency nowadays, which is because of the fact that there are certain flaws in the process. These flaws can be eradicated by giving machines the cognitive abilities required for going through a report and forming a basic judgment regarding the condition of any patient. The results can then be communicated to humans through a virtual display.
Industrial IoT
Most of the Industrial IoT giants that we have in industries such as car manufacturing, transportation, etc., have implemented exemplary data collection methods. These data collection methods do their job well, and hand over the necessary input to their patron organizations. Now, when the data is collected and stored off, the real challenge of anomaly analytics arises. Despite having stringent data collection and storage facilities, these firms don’t know what to do with their data and how to find actionable results.
The biggest problem facing businesses in today’s myopia is that only 20 percent of all problems or anomalies that occur are predicted and understood beforehand. This means that around 80 percent of the problems that businesses face are unpredicted, and the business is not prepared to handle them because of below par anomaly detection.
The Cognitive Anomaly detection is different from the traditional method, as it is a machine and data-first solution. The future for cognitive anomaly detection is seemingly bright, and it is now the time to move from a research phase to deployment.
How to Move to Deployment
The deployment of cognitive computing requires adhering to a certain set of levels for achieving the desired aims. The levels that should be used for proper deployment of the technique include:
With cognitive computing gaining center stage, it is expected that the concept will develop over time and will be implemented over numerous industries. Industrial IoT is expected to benefit a lot from cognitive computing as it can be used for deriving meaning out of the data they work with. In short, cognitive computing is currently leading the wave of the future as it holds the key to not only making healthcare, AI and Industrial IoT better, but also providing human thought processing and behavior that was needed here.
The post Cognitive computing: Moving from hype to deployment appeared first on Big Data Made Simple - One source. Many perspectives..
This article aims to list all top BI (Business Intelligence) products available on the market. It should help interested users to compare and select the best solution for their needs. According to the list of best business intelligence tools prepared by experts from FinancesOnline the leading solutions in this category comprise of systems designed to capture, categorize, and analyze corporate data and extract best practices for improved decision making. The more advanced the system is, the more data sources it will combine, including internal metrics coming from different company departments, and external data extracted from third-party systems, social media channels, emails, or even macroeconomic data. Ultimately, business intelligence software helps companies gain insight on their overall growth, sales trends, and customer behavior.
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. icCube
icCube is a SaaS end to end BI platform, specialized to be embedded in your application. Deploy it on premises, in the cloud or make use of one of their managed services and enjoy a short time to market for custom feature requests. It integrates seamlessly with any application because of the on-the-fly-authentication and authorization (up to cell level), the ability to connect and combine any custom data source, direct access to Java and R, a web based dashboard builder and the ability to graphically design widgets from scratch. Basically, icCube is the dream for any software developer who needs to provide predefined dashboards or a solid web based self service BI solution, to their end-users.
4. 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.
5. 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.
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.
7. 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.
8. 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.
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.
10. 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.
11. 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.
12. 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.
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.
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
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.
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.
27. 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.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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.
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.
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.
35. 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.
36. 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.
37. 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.
38. 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.
The post Top Business Intelligence (BI) tools in the market appeared first on Big Data Made Simple - One source. Many perspectives..
As the business and commercial world expands to include more digital platforms and content, so will the data requirements and capabilities necessary to run the industry. It’s no surprise, then, that many businesses are focusing on deploying their own data center services and systems. Even in cases where those systems and platforms won’t be available externally, there may be a valid reason to have internal access within the company and among the workforce.
Of course, while the benefits of having your own data center and related hardware are vast, the costs are not always as manageable. This is not just in terms of building and deploying the necessary hardware, but also operating a data center, which takes massive amounts of energy, manpower time to develop and maintain.
Did you know, for instance, that in 2010 there were more than 8 million data centers operational worldwide, and they were responsible for up to 10 percent of global power consumption? Those are some hefty power consumption ratings. Imagine business owners trying to take on that burden themselves, via their own properties, systems and locations.
It would be a mess, that’s for sure.
That’s why colocation solutions have been introduced, and rightly so have also flourished in the current market. However, with an increase in popularity and deployment also comes a boost in innovation and evolved processes. More importantly, the technology and hardware involved improve as time goes on and providers perfect their setup or installation models.
It begs the question: How can existing location or acting colocation managers forge their own path to success through new technologies? What evolving or up-and-coming technologies should you focus on serving to your tenants?
Technologies to Implement
IoT
IoT has grown considerably, placing a huge demand on modern data centers. Over the last five years, traffic to big data facilities that support IoT have grown fivefold, and is expected to surpass 1.6 zettabytes by 2018.
Things are happening so fast, that the old data center model from years past is no longer sufficient. Smart technology from the consumer and enterprise markets now taps into the cloud and remote systems to offload and access stored data. Colocation providers can aid by offering upgraded data solutions to customers, and support modern IoT strategies. Data aggregation, analyzation and the management of that data are incredibly important in IoT.
Big Data
Quite frankly, big data is integrated with almost all the other technologies discussed here. It’s more about the facilitation, collection and streaming of data than it is the servers and systems required to prop up such a platform.
For example, in retail, companies might use big data systems to report trend and web activity stats. Another system would analyze said data and pull out usable information that can be deployed as part of future marketing and business strategies. The data itself — massive in size — is the driving factor of this setup.
Of course, big data systems require cloud computing, remote storage hardware and solutions, and software-as-a-service platforms. Colocation comes into play, because the involved businesses and parties may want to leverage a big data system without housing the hardware in-house. Colocation allows them a more affordable, more manageable off-location solution.
Edge Computing
Edge computing does not replace cloud computing — in fact, it actually complements the technology. It refers to when a system or platform handles all the data processing on the edge of a network. This is opposed to remote processing power performed from the cloud or a central data location.
Generally, it is used in the industrial IoT space where devices in use capture, analyze and deploy streaming data. For example, consider a smart traffic light that analyzes data of traffic in the area and then reroutes based on collected info.
It is both speedier and more reliable than cloud computing, simply because all the analyzation and processing is handled locally.
Cloud Computing
Similar to edge computing, cloud computing is done remotely, at least regarding most of the processing and data storage. It’s all handled via a central data warehouse or data system designed for this very purpose.
Using satellite systems or other devices, you can tap into a cloud computing network to take advantage of all it has to offer. That includes software, hardware and processing power, stored data and much more.
When deploying a cloud or remote access system, it’s a cheaper option to go through a third party that handles cloud services. The customer — or business, if you will — does not have to buy, install and maintain the hardware related to operating such a system. The cloud provider does, and it’s all stored in the data warehouse.
DCIM
Data center infrastructure management —or DCIM software as its often called — is a competitive and viable solution for colocation providers. It is the system, application and portal used to facilitate the relationship between a colocation provider and tenant. It handles billing as per-usage contracts, secure access and account portals, power reports, maintenance and system monitoring and much more.
Thanks to the tools and processes offered by this software, it can vastly improve efficiency, reduce costs and improve system reliability.
Design Architecture
Simply put, the design architecture of a data center is one of the most crucial aspects of any data system, cloud or local. All content is sourced and passes through the central IT architecture, which includes local machines and computers, remote platforms and systems, and remote data warehouses.
By deploying innovative design architecture that optimizes and speeds up the data facilitation, your business will boom. Colocation providers especially can benefit from improved systems, particularly when it comes to high bandwidth and high traffic setups.
The post How colocation managers can forge their own path to success appeared first on Big Data Made Simple - One source. Many perspectives..
The world and everything in it are in a constant evolution. Starting with things that were merely Sci-Fi concepts just ten years ago such as holograms or smartphones that can be used to scan your body and check your health, everything is evolving. But the most progress was registered in the augmented reality, VR, and artificial intelligence. The amazing speed at which these technologies are developed has made everyone wonder what could be the future use of all these technologies and how they will shape our day-to-day life.
The changes that technology brought to our lives are already visible and a good example in that direction is the smart objects people already use. From smartphones all the way to smart houses that prepare the ambient temperature before you get home, dim the lights while you’re watching a movie or set up the alarm when you go to bed. The big tech companies are getting closer to developing self-driven cars and a lot of other projects that are based on AI. The domains where artificial intelligence could bring significant improvements are diverse, for example, security, health, education, time-management and, of course, entertainment.
Of course, there aren’t only positive aspects surrounding all the technological advances in Artificial Intelligence. The ones that point towards the ethical considerations that are still left unanswered are not few and, no matter how big the benefits from including AI in our lives are, these answers have to be found in order to prevent further complications in the future. The common conception is that, as long as the ethical concerns are resolved and there’s a clear plan for the development of AI, everyone can enjoy the beneficial aspects it brings.
Realistic predictions show that by 2030, we will be surrounded by many different technologies based on Artificial Intelligence. The same predictions show that the improvements brought to the quality of life will be considerable through better health care systems, economic management, and other aspects. But with all the improvements, there’s a downside as well that needs to be taken into consideration. The most affected domain will be the employment system as we know it right now. Since robots and AI will take care of more and more tasks that are being done by human beings right now, what will happen to those humans then? Also, another point that requires a lot of attention and analysis is how the resources will be gained and distributed. It’s clear that the need for regulations that back up equal sharing of resources between individuals is very real. If, for example, someone with a manufacturing company will use AI technology to maximize the efficiency and will put a monopoly on the respective domain, then they will get the most of the revenue in that industry and there will be a clear problem with the distribution of resources and wealth.
Another important aspect regarding how Artificial Intelligence will be present in day-to-day life is related to the Internet of Things. The ability to connect common objects to the internet is excellent. Some experts think that the IoT will help a lot in avoiding accidents such as traffic collisions and in the event of something like that happening, the response time of the authorities will be much shorter. The theory is that, by removing the human factor, errors will also be removed and everything will function smoothly.
There are a lot of applications for AI and Internet of Things and, probably, the most important one could be saving our planet. Through AI-powered technology we can register a series of breakthroughs that can help deal with global warming, cure diseases, travel in time and space, etc. When thinking about medical applications for AI, perhaps the most appealing one is that of a personal medical record and body scanning to detect and prevent illnesses.
In terms of free time, AI can bring a lot of changes by taking over the simple, time-consuming tasks that will allow human beings to have more time for hobbies, art, and a wide range of activities that can lead to a better community.
The bottom line is that AI is definitely finding its way into our lives and the way things will evolve from this point further is only up to how prepared people are to embrace technology and understand the positive outcomes of it. Nobody can predict with accuracy what exactly life will be like 30 years from now, and only time will tell.
The post The future of Artificial Intelligence – What will it look like? appeared first on Big Data Made Simple - One source. Many perspectives..
Big data is a top trending buzzword. But, unlike overused buzzwords such as ‘omnichannel marketing’ or ‘growth hacking’, big data is very underhyped. According to IBM, 62% of retailers report that the use of big data is giving them a serious competitive advantage. Knowing what your customer wants and when they want it can be available at your fingertips with big data; all you need are the right tools and processes in place to make use of it. Let’s explore 7 innovative examples of big data personalization in retail for some inspiration.
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.
The post 7 examples of big data retail personalization appeared first on Big Data Made Simple - One source. Many perspectives..
So you have decided to learn Python, but you don’t have prior programming experience. So you are confused on where to start, and how much Python to learn. These are some of the common questions a beginner has while getting started with Python(for data centric application).
“How long does it take to learn Python”
“How much Python should I learn for performing data analysis”
“What are the best books/courses to learn Python”
“Should I be an expert Python programmer, in order to work with data sets”
It is good to be confused, while beginning to learn a new skill, that’s what author of “learn anything in 20 hours” says. Don’t Panic, I will show you how to get started quickly without becoming a coding ninja in Python.
Don’t make the mistake I did
Before getting started with Python, I had a misconception that for performing data analysis in Python, I have to be proficient in Python programming. So I took Udacity’s intro Python programming course, completed code academy Python tutorials and read several Python programming books. For 3 months(spending 3 hours per day), I was learning Python programming by completing small software projects. Coding was fun, but my goal was not to become a Python developer, but to do data analysis using Python. Then I realized that I was spending more time learning how to develop software in Python, rather than doing data analysis.
After a few hours of research, I found out that I need to learn 5 Python libraries to effectively solve a broad set of data analysis problems. Then I started learning these libraries one after the other.
In my opinion, it is not necessary to become proficient at building good software in Python to be able to productively perform data analysis.
Ignore the resources intended for general audience
While there are many excellent Python books and online courses, I wouldn’t recommend some of them as they are intended for a general audience rather than for some one who wants to do data analysis. Also there are couple of books on “Scientific Programming in Python”, but they are geared toward various topics that are mathematically-oriented rather than being about data analysis and statistics. Don’t waste your time, by taking courses and reading books that are intended for general audience.
Before proceeding further, first set up your programming environment, and learn how to work in IPython notebook.
Learning Pathway
Start with code academy, complete all the exercises in code academy. You can complete the exercises in 20 days, by investing 3 hours per day. Code academy covers all the basic Python concepts. But it doesn’t follow a project oriented approach like Udacity; that doesn’t matter, because your goal is to work on data science projects, not on building software using Python.
After completing the code academy exercises go through this I python notebook:
Python Essentials Tutorials (I have provided the links to download the file in conclusion part)
It consists of concepts that are not covered in code academy.You can complete this tutorial within an hour or two.
Now you know enough basics to start learning Python libraries.
Numpy
First, start learning NumPy as it is the fundamental package for scientific computing with Python. A good understanding of Numpy will help you use tools like Pandas effectively.
I have prepared an IPython note book, that includes the basic concepts of Numpy. The tutorial covers the most frequently performed operations in Numpy, such as, working with N-dimensional array, Indexing and slicing of arrays, Indexing using integer arrays, transposing an array, universal functions, data processing using arrays, frequently used statistical methods, etc.
Pandas
Pandas contain high level data structures and manipulation tools to make data analysis fast and easy in Python.
Tutorial includes working with series, data frames, dropping entries from an axis, working with missing values, etc.
Matplotlib
This is a four part Matplotlib tutorial.
1st part:
First part introduces the basic functionalities of Matplotlib, the basic figure types.
2nd part:
Covers how to control the style and color of a figure, such as markers, line thickness, line patterns and using color maps.
3rd part:
Annotation of a figure- compositing several figures, controlling the axis range, aspect ratio and coordinate system.
4th part:
Covers working with complex figures.
Conclusion
One of the easiest mistakes you can make when learning Python is attempting to learn too many libraries at the same time. When you try to learn everything at once, you spend too much time switching between different concepts, getting frustrated, and move on to something else.
So focus stick on to this process:
Understand Python basics
Learn Numpy
Learn Pandas
Learn Matplotlib
Links to Download:
You can download the files from my github account. The files are in .ipynb format. The files also includes the pictures I have used for illustration.
3) Matplotlib
If you have any queries, feel free to ask in comments.
The post Step by step approach to perform data analysis using Python appeared first on Big Data Made Simple - One source. Many perspectives..
