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Posts Tagged ‘Data mining

What is the hottest trend in artificial intelligence right now? Machine Learning is the right answer! Thanks to technological advances and emerging frameworks, Machine Learning may soon hit the mainstream. Because of new computing technologies, Machine Learning today is not like Machine Learning of the past. While many Machine Learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development. Every single day it’s become clear that Machine Learning is already forcing massive changes in the way companies operate. Every Fortune 500 company is already running more efficiently — and making more money — because of Machine Learning. But how this “phenomenon” helps business bring money and attract new and new customers?

Problems that can be easily solved using ML

Every single business some time or other can face with definite problems. But there are some kinds of business problems Machine Learning can prevent if not handle at all:

Email spam filters
Some spam filtering can be done by rules (IE: by overtly blocking IP addresses known explicitly for spam), but much of the filtering is contextual based on the inbox content relevant for each specific user. Lots of email volume and lots of user’s marking “spam” (labeling the data) makes for a good supervised learning problem.

Speech recognition
There is no single combination of sounds to specifically signal human speech, and individual pronunciations differ widely – Machine Learning can identify patterns of speech and help to convert speech to text. Nuance Communications (maker of Dragon Dictation) is among the better known speech recognition companies today.

Face detection
It’s incredibly difficult to write a set of “rules” to allow machines to detect faces (consider all the different skin colors, angles of view, hair / facial hair, etc), but an algorithm can be trained to detect faces, like those used at Facebook. Many tools for facial detection and recognition are open source.

Credit card purchase fraud detection
Like email spam filters, only a small portion of fraud detection can be done using concrete rules. New fraud methods are constantly being used, and systems must adapt to detect these patterns in real time, coaxing out the common signals associated with fraud.

Product / music / movie recommendation
Each person’s preferences are different, and preferences change over time. Companies like Amazon, Netflix and Spotify use ratings and engagement from a huge volume of items (products, songs, etc) to predict what any given user might want to buy, watch, or listen to next.

Here is enumerated not all but just a few problems that can be solved. And with the course of time this list will only expand.

Industries that already use ML in action

Most industries working with large amounts of data have recognized the value of Machine Learning technology. The adoption of Machine Learning is likely to be diverse and across a range of industries, including retail, automotive, financial services, health care, and etc. By gleaning insights from this data – often in real time – organizations are able to work more efficiently or gain an advantage over competitors. In some cases, it will help transform the way companies interact with customers.

Retail industry
Machine Learning could completely reshape the retail customer experience. The improved ability to use facial recognition as a customer identification tool is being applied in new ways by companies such as Amazon at its Amazon Go stores or through its Alexa platform. Amazon Go removes the need for checkouts through the use of computer vision, sensor fusion, and deep or Machine Learning, and it’s expected that many shopping centers and retailers will start to explore similar options this year.

Financial services
Banks and other businesses in the financial industry use Machine Learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.

Health care
Machine Learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment. Machine Learning can be used to understand risk factors for disease in large populations. For instance, Medecision company developed an algorithm that is able to identify eight variables to predict avoidable hospitalizations in diabetes patients.

Oil and gas
Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective, and many others thing that you can do using ML. For example ExxonMobil, the largest publicly traded international oil and gas company, uses technology and innovation to help meet the world’s growing energy needs. Exxon Mobil’s Corporate Strategic Research (CSR) laboratory is a powerhouse in energy research focusing on fundamental science that can lead to technologies having a direct impact on solving our biggest energy challenges.

Government
Government agencies such as public safety and utilities have a particular need for Machine Learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine Learning can also help detect fraud and minimize identity theft. Chicago’s Department of Public Health is early adopter. It used to identify children with dangerous levels of lead in their bodies through blood tests and then cleanse their homes of lead paint. Now it tries to spot vulnerable youngsters before they are poisoned.

Marketing and sales
Websites recommending items you might like based on previous purchases are using Machine Learning to analyze your buying history – and promote other items you’d be interested in. This ability to capture data, analyze it and use it to personalize a shopping experience (or implement a marketing campaign) is the future of retail. PayPal, for example, is using Machine Learning to fight money laundering. The company has tools that compare millions of transactions and can precisely distinguish between legitimate and fraudulent transactions between buyers and sellers.

Transportation
Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of Machine Learning are important tools to delivery companies, public transportation and other transportation organizations. In some cases, mathematical models are used to optimize shipping routes. By honing in on excessive driving routes, drivers can see a reduction of nearly one mile of driving every day. For a company like UPS, a reduction of one mile per day per driver would equal a savings of as much as $50 million a year in fuel.

Have you ever worked with ML? Was it useful for your business? Or maybe you are still thinking about whether it costs to implement Machine Learning in your business? Will it be relevant and defensibly? If you have an answer on at least one question – share with me your experience. We will be happy to discuss it in comments. But if you don’t have an answer, always remember – Big companies are investing in Machine Learning not because it’s a fad or because it makes them seem cutting edge. They invest because they’ve seen positive ROI. And that’s why innovation will continue.

 

Yuliya Poshva

Business Development Manager

E-mail: yuliya.poshva@altabel.com
Skype: juliaposhva
LI Profile: Yuliya Poshva

 

altabel

Altabel Group

Professional Software Development

E-mail: contact@altabel.com
www.altabel.com

I guess you have already read/heard a lot about CRM and BI, so in this article you will not find description what BI and CRM is. Also you will not find such dispute as “CRM vs BI” or “Why BI is not CRM” etc. What, then, is to discuss? 🙂

Let’s imagine BI and CRM in its tandem.

The discipline of business intelligence includes a broad range of functional activities from data mining and statistical analysis to predictive modeling and reporting. So, BI-applications are often positioned as an indispensable tool for decision making at the tactical and strategic levels. As a rule in this case to work with information efficiently we will need enterprise data warehouse, building of which could “seed” at least half of the total budget for BI, in addition analytical models are rather expensive. Under these circumstances, the need of significant investment is one of the most essential and restrictive factors of dissemination of Business Intelligence systems. At the same time, experience shows that the usage of BI-applications can be fully justified at the operational level, where decisions must be taken exactly in real time. In this approach, building corporate Data Warehouse is not critical, and the using of pre-configured models is not necessary, because BI allows to implement arbitrary “point” data depending on the situation. If you don’t mind I would like to illustrate it with a concrete example.

Let’s consider a small example. For CRM-system we will take Oracle Siebel CRM, as for BI-application it will be Oracle BI. To implement CRM for realizing sms-mailing was proposed to use a single sms-gateway. Let’s assume that the frequency of such mailing is quite high, and volume is measured in ten of thousand of sms. Taking into account that the sms-gateway is just a tool of message transfering, you need to monitor constantly the process of mailing considering the timeline plan, “black lists”, the spam load per user, etc. In this case, in spite of the high performance of Oracle Siebel CRM,it is unreasonable to exchange data between the CRM-system and sms-gateway in online, but it`s reasonable to use additional transit system, which would redistribute the load. When you run a marketing campaign such a system would import data from Oracle Siebel CRM and after the campaign would pass results to the CRM-system . But, at the same time, in case any error arise or a failure campaign reaction time for the problem is reduced, you will know this only after the campaign ends and it may adversely affect the relationship with the client. You could solve this problem either using an expensive integration or through the using of BI-application. For example, Oracle BI enables to control the process of distribution and evaluate the results based on the data from the three systems online. Thus, in case of a large number of notifications incoming to the sms-gateway, that a message is not delivered to the recipient, it would be possible to stop the campaign quickly and make changes promptly, rather than waiting for its completion. Furthermore, using BI in this situation allows to correct the results during the campaign.

So the best effect in the marketing process could be obtained from using BI-applications at the operating level. Also effective BI-applications could be demonstrated in other CRM-processes. In sales BI-applications are indispensable in launching new products to market. In the service – when analyzing satisfaction, assessing value of each customer, etc.

In addition, I would like to notice that such tools as Oracle BI enable to cover the problem of business intelligence at the tactical and strategic levels of management effectively. In this case, using of a single tool would provide high-quality synchronization of business goals, set before BI. The previous experience guarantees more effective using of the already proven BI-application.

Thank you so much for your attention and hope this article is of interest to you.

Kind regards,
Elvira Golyak – Business Development Manager (LI page)
Elvira.Golyak@altabel.com | Skype ID: elviragolyak
Altabel Group – Professional Software Development

There is misconception about data mining and data warehousing. Both of them are related to business intelligence tools that are used for turning data into effective knowledge. Many IT professionals use them as synonyms with some differences between the tools. Although the goals of both are related, data mining and data warehousing use different methods and processes for achieving these goals.

Data mining software is one of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
This tool is used by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. This tool allows the companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. That enables them to determine the impact on sales, customer satisfaction, and corporate profits. Also, it enables the companies to “drill down” into summary information to view detail transactional data.
Data warehousing describes the process of building decision, support systems and a knowledge-based applications architecture and environment that supports both everyday tactical decision making and long-term business strategizing. The Data Warehouse environment positions a business to utilize an enterprise-wide data store to link information from diverse sources and make the information accessible for a variety of user purposes, most notably, strategic analysis.

So the two application types are similar because they rely on historical data to drive profitability in the future. However, the methods the two employ are different, and require different skill sets of the analysts that analyze data. Unfortunately they both lack delivering a predictive model 🙂

Thank you for your attention!

Best regards,
Elvira Golyak
Altabel Group – Professional Software Development


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