Data Science, Predictive Analytics & Big Data Analytics Solutions. Service Presentation

Transcription

1 Data Science, Predictive Analytics & Big Data Analytics Solutions Service Presentation

2 Did You Know That According to the new research from GE and Accenture*: 87% of companies believe Big Data analytics will redefine the competitive landscape of their industries within the next three years. 89% of enterprises believe that companies that do not adopt a Big Data analytics strategy in the next year risk falling back and losing their market share. Increasing profitability (60%), gaining a competitive advantage (57%) and improving environmental safety and emissions compliance (55%) are the three highest industry priorities in implementing Big Data and Machine Learning initiatives. * - How the Industrial Internet is Changing the Competitive Landscape of Industries. 2

3 Data Science Virtually every industry now has access to more data than would have been imaginable even a decade ago. Businesses today are accumulating new data at a rate that exceeds their capacity to extract value from it. Data Science is a new field emerging at the intersection of the fields of software development and statistics, data engineering and business analytics, and even design. At its core, Data Science involves using automated methods to analyze massive amounts of data and to extract knowledge and insights from them. 3

4 Descriptive and Predictive Analytics 4

5 Predictive Analytics The purpose of Predictive Analytics is to forecast what might happen in the future, automatically classify the objects or predict some outcomes based on present data. Predictive analytics uses data to determine the probable consequences of an event, or the likelihood of a situation occurring. Predictive Analytics = Advanced Analytics + Decision Optimization Statistics Machine Learning Algorithms Artificial Neural Networks Visualization Data Storages and Data Processing Scoring Algorithms Rules Engines Recommendation Engines Optimization Algorithms 5

6 Key Advantages of Predictive Analytics Accuracy Predictive Analytics uses data to discover an optimal decision-making engine for your problem. As you collect more data, the accuracy increases automatically. Automation As new data comes in, the output interest can be automatically estimated. This allows users to embed Predictive Analytics directly into an automated workflow. Velocity Predictive Analytics can generate answers in a matter of milliseconds as new data streams in, allowing the systems to react in real-time. Scalability As your business grows, Predictive Analytics easily scales to handle the increased data rates. Most Analytics processes run in parallel and enable good scalability. 6

7 Why Predictive Analytics Matter Finance and Banking At a leading global financial services company, one rogue trader created $2 billion worth of losses. This incident could have been prevented with Predictive Risk Models. Retail and E-commerce Retailers miss out on $93 billion in sales every year e ause the do t have enough stock to meet customer demand. The Analytics allows you to work out an effective financing strategy and skyrocket your sales. Marketing and Sales Currently 48% of firms are using predictive analytics, which enables them to gather and analyze u stru tured data. It s esse tial to keep up with the competition. Travel and Booking Analytics benefits include revenue & profit optimization, a reduction of up to 30% in loyalty program drop-out rate, and a boost in promotions/offers acceptance rates. Healthcare and Life Sciences The healthcare industry spends $250 to $300 billion on healthcare fraud per year, while Predictive Analytics allows you to notably reduce these expenses. Telecommunications In TELCO, five billion subscribers demand personalized offerings that match their lifestyles. Using Predictive Analytics, you can greatly enhance the user experience. 7

8 Predictive Analytics can Transform your Business Finance and Banking Credit scoring Fraud detection Risk analysis Client analysis Trading exchange forecasting Travel and Booking Demand forecasting Price optimization Price forecasting (for dynamically changing prices) Retail and E-commerce Demand forecasting Price optimization Recommendations Fraud detection Customer segmentation Healthcare and Life Sciences Increase of diagnostic accuracy Identifying at-risk patients Insurance product cost optimization Marketing and Sales Market and customer segmentation Price optimization Churn rate analysis Customer lifetime value prediction Upsell opportunity analysis Sentiment analysis in social networks Other Object recognition (photo and video) Content recommendations (movies, music, articles and news) And more 8

9 Finance and Banking Credit scoring Customer retention Other examples Credit scoring can be implemented through lots of different approaches. However, Machine Learning is worth special attention. Its regression algorithms, that are tested using real data, let financial institutions use their history of successful and unsuccessful loans in order to better predict if a customer is creditworthy or not. This allows banks and other credit institutions to make their rules much more sophisticated and effective, so as to decrease the percent of overdue and default loans. Data analysis with Machine Learning algorithms estimates the time when present customers are likely to leave by using the past data based on previous customer behavior. The proper action performed at the right time increases the chances of this customer staying with your business. Financial fraud detection Cross-sell and up-sell Risk analysis Client analysis Financial trading 9

10 Retail and E-commerce Recommendations Demand/sales forecasting Big Retail and E-commerce businesses use Recommender Engines to increase their sales. Recommendations can be general or personalized for a particular user. Promptly implemented recommendations improve cross-sales and boost revenues. Both Retail and E-commerce businesses can benefit if they know which products will be of interest to their customers in the future. Customer demand for a particular product might be seasonal.. Many factors are taken into account in order to develop a good forecast, which assumes inventory planning and optimization, price changes, discounts and marketing campaigns for particular groups of products. Time Series forecasting techniques are also used as a part of the Machine Learning process. Other examples Price optimization Fraud detection Many more 10

11 Marketing and Sales Market/customer segmentation Sentiment analysis in social networks Other examples Market segmentation allows businesses to divide their customers into groups based on different criteria geolocation, demographics, behavior, etc. Although these types of segmentation are useful in themselves, their combinations might be much more beneficial. Sophisticated clustering and ensemble algorithms used for the segmentation reveal great insights for marketing and sales, and predict new customer values based on their seg e t s features. Sentiment analysis enables marketing specialists to see the market response to a campaign or a new product/service. The main purpose of sentiment analysis is to monitor the social networking activity and visualize the changes in market attitude and loyalty to the brand or product. This has become achievable with different complex Natural Language Processing algorithms. Price optimization Product Ad optimization Churn rate analysis Customer lifetime value prediction Upsell opportunity analysis 11

12 Travel and Booking Price forecasting Recommending the best hotels Airline prices change frequently and it is difficult to determine if toda s ticket price is optimal, or if it s better to wait to make a purchase. This is where Time Series forecasting methods can be of utmost importance, as they are used to foresee and save usto ers money. Thus this feature is essential for the travel agents who sell airline tickets. Obviously, travel agencies that book hotels benefit from recommending the best options to their customers. From a technical perspective, this can be done using Clustering and Regression analysis. Other examples Demand forecasting Price optimization Many more 12

13 Healthcare and Life Sciences Biological image processing Computer Vision algorithms can be used for automatic analysis of different types of images produced by modern medical devices: radiograms, MRI pictures, ultrasonography and many others. This significantly helps doctors with disease diagnostics and enables more informed decisions regarding the patie ts treatment. Sound recognition Digital Signal Processing, and classification algorithms that work on top of DSP, can be used in sound analysis and recognition. For example, snoring or apnea can be detected with sleeping patients. It allows healthcare personnel to determine if the treatment is effective, even when they are not near the patient. Other examples Predicting disease evolvement Predicting hospital readmissions Motion recognition Bioinformatics and Biostatistics 13

14 Other ML Applications Motion and image recognition for smartphones Automatic information retrieval from texts Motion recognition based on smartphone sensors, such as accelerometers, gyroscopes, GPS trackers, opens a wide variety of possibilities for building applications, especially in Fitness and Healthcare. Smartphone cameras in turn feature image recognition, which provides the opportunity to develop dozens of applications for different domains. Natural Languages Processing algorithms allow extracting valuable information from unstructured text arrays. For example, important facts and text topics can be automatically retrieved for a faster analysis by a human or to be saved in a structured format for storage and later use. Other examples Collaborative filtering Content recommendations Sentiment analysis Speech recognition Advanced search Machine translation 14

15 Essential Takeaways Predictive Analytics provides abundant opportunities for enterprise evolution and new product development. Even if your company or product already employs this technology, it presents such a wide range of value propositions that there will always be a new frontier in which to deploy it. Through systematic learning from the o pa s experience, and applying what's been learned, it becomes possible for you to determine the way your enterprise will evolve. If business is a u ers ga e, Predictive Analytics is the tool to guide you through. Begin your analytical transformations with one of these four high-value initiatives: 1. Grow, retain and satisfy your clientele 2. Notably increase operational efficiency 3. Streamline and improve sales & financial processes 4. Effectively manage risk, fraud & regulatory compliance 15

16 How AltexSoft Can Help

17 Data Science from AltexSoft AltexSoft is an innovative software R&D company that provides full-cycle custom development solutions and IT consulting services. Predictive Analytics is one of our main areas of expertise. Our professional team consists of specialists in many different fields, including mathematicians and technology experts, who encapsulate advanced mathematical and statistical expertise to extract predictive knowledge. When deployed in existing processes, this knowledge assists them in improving outcomes. AltexSoft gives companies the power to discover deep analytic insights, predict future trends, make recommendations and reveal untapped markets with potential customers. We contribute to both the research and the actual development of a solution or product appropriate for customer business needs. 17

18 Approaches AltexSoft offers the implementation of Data Science (DS) and Predictive analytics solutions, including Big Data solutions. These solutions can be used for a o pa s internal purposes (company data dissemination) and as products and services built for o pa s customers. Already using Data Science/Predictive Analytics? AltexSoft can analyze your existing approach and algorithms, make improvement recommendations and implement them. New to Data Science/Predictive Analytics? AltexSoft can analyze your business and offer beneficial Predictive Analytics applications. A comprehensive portfolio of advanced analytics gives you clear, immediate and actionable insights into your current performance, and the ability to predict future outcomes. 18

19 Predictive Analytics Workflow 19

20 Machine Learning Expertise Algorithms and Models Regression models Decision trees and random forests Artificial Neural Networks, RBM and Deep Learning Support vector machines Hidden Markov models a d ore Problem Fields Classification and regression Clustering Recommender Systems Time series forecasting Computer vision Digital Signal Processing Natural Language Processing a d ore 20

21 Technology Expertise Programming Languages and Tools Data Storage Software Engineering R, RStudio Python, scikit-learn, SciPy, NumPy Matlab, Octave Java, Mahout, Lucene C/C++, C# Azure Machine Learning RDBMS: MS SQL Server, Oracle, MySQL, etc. NoSQL: MongoDB, Redis, Azure Tables, etc. Software Architecture Data storages Design Cloud computing: Amazon EC2, MS Azure, etc. Parallel computing 21

22 Our Experience Airlines Price Prediction Algorithm Approach: Alte oft s Data Science team was given a challenging task, to create an algorithm that predicts whether airline prices will go up or down over the next seven days. This is a Time Series forecasting problem. After the problem research, data analysis and a lot of experiments, the team came up with an algorithm that uses ARIMA for forecasting. Along with several additional data filtering, munging technics, and ensemble voting, it gave a correct prediction up to 85% cases based on validation testing. Benefits: Creation of better retention programs to increase customer loyalty via additional features to predict the prices Tools and algorithms: R (modeling), ARIMA, Ensembles, C# (implementation) 22

23 Our Experience Sound Recognition for Healthcare Approach: The main goal of this algorithm was to detect the target sounds in the nightly recording of a person sleeping via iphone or an Android smartphone. The input data is a sound recording, which means that Digital Signal Processing has to be used to convert the digital signal into data that can be processed by the algorithm. The main challenge in this process was the correct algorithm of candidate events extraction. In order to classify the extracted candidate events on true and false signals, several learning algorithms were tested. These learning algorithms were taught using a specially created dataset. Benefits: Operative determination of the patient and clinical information needed to better promote wellness or manage diseases Tools and algorithms: Python (modeling), Objective-C, SciPy, scikit-learn 23

24 Our Advantages Combination of PhD and Master of Science degree specialists and skilled software engineers Good understanding of customer business needs and challenges The Usage of state-of-the-art algorithms, research techniques and tools 24