The one who owns the data owns the customer
Our life and world around us is gradually moves into digital sphere. The volume of customer data is growing exponentially. There are new sources that help to understand better not only the client, but also his/her environment, lifestyle and psychological profile.
Data collecting is no longer enough!
It is important to create a unique offer not only faster than your competitors, but also within the appropriate context, which sometimes lasts no more than a few hours.
Data collecting is no longer enough!
It is important to create a unique offer not only faster than your competitors, but also within the appropriate context, which sometimes lasts no more than a few hours.
Returning investment on data projects is getting harder and harder
It took us more than 100 problem interviews and 100,000 hours in analytical solution projects to understand:
The same old problems are in every project related to business process analysis, customer data, or hypothesis testing:
The same old problems are in every project related to business process analysis, customer data, or hypothesis testing:
Long and Winding
Complex multi-level architecture and intricate planning processes
prevent businesses from getting data from sources quickly
prevent businesses from getting data from sources quickly
Expensive
High cost of integrators' services. Growth of capital and operating expenses with the growth of data volume amid the degradation of productivity.
Many of the ideas remain unimplemented
Many of the ideas remain unimplemented
Complicated
Data loading and processing tasks require highly qualified specialists, which are not enough on the market.
9 out of 10 big data projects fail
9 out of 10 big data projects fail
Obsolescence
The data update time is days and weeks behind the actual changes in the customer profile and business processes.
Low efficiency of marketing companies and decision-making on outdated data
Low efficiency of marketing companies and decision-making on outdated data
In-memory data computing technologies and in-memory data grid
The first real-time analytics platform
Gridfore Intelligent Analytical Platform (GRIP) is an analytical platform to work with real-time data, which contains all the necessary components and provides support for the full implementation cycle without using additional tools
Great opportunities for a big business
GRIP allows you to quickly collect the necessary data, test hypotheses, implement online scenarios for marketing, business processes monitoring or fraud identification
Banks
Scoring to assess the creditworthiness of individuals and SMEs
Operational reporting for internal units and customers with online cash
Marketing to use a "hot" customer profile and segment change control
Anti-fraud in real time using predictive models
Operational reporting for internal units and customers with online cash
Marketing to use a "hot" customer profile and segment change control
Anti-fraud in real time using predictive models
Retail network
Assessment of cash flow reduction based on the analysis of business processes efficiency in cash logs
Rapid identification of massive failures in real time with forecasting the development and the evaluation of cash losses
Internal fraud. Identification of misuse of loyalty cards, cash drawer and check reversal
Rapid identification of massive failures in real time with forecasting the development and the evaluation of cash losses
Internal fraud. Identification of misuse of loyalty cards, cash drawer and check reversal
Telco
Billing and processes. Data collection and preparation of traffic and call Analytics (CDR) and cell tower logs (XDR)
Counter outflow by means of customer behavior analysis and the usage of predictive models
Increase customer loyalty with the use of proactive reporting on client's behavior when you call the call center
Counter outflow by means of customer behavior analysis and the usage of predictive models
Increase customer loyalty with the use of proactive reporting on client's behavior when you call the call center
BDaaS. Data monetization
GRIP helps you transform your data into Big Data-As-a-Service and turn it into additional revenue sources without revealing personal data of a client
Scoring services
On the basis of your own customer data, you can generate scoring estimates that help, for example, to assess its income level, the number of members in the family and creditworthiness
Request propagation
Using REST services, the access to the business model is provided without disclosing the original data and personal data of clients
Client data protection
By transferring calculated metrics only, your data as well as your customers' data are 100% protected and it will not be transferred to a third party
6 advantages instead of 4 disadvantages
Creating GRIP, we thought primarily about business tasks
QuickApplication language. All tools are in one window. Reduced code allows you to focus your attention on the task than on technical nuances.
SimpleThe DSL application language is as simple as SQL. The analysts working with data and system administrators will need no more than 3-4 days to learn it.
CheapIt does not require Hi-End equipment. It works with x86 and RISC architecture. The cost of equipment is low while the performance is high.
RelevantIn-memory calculations allow you to use predictive models in real time, update data marts of any complexity, and volume and track events
PowerfulStoring data in RAM speeds up their processing by 1000 times. Computing power is not limited by horizontal scaling
ReliableMultiple data reservations are made automatically in the cluster nodes. Crash recovery takes just a few minutes.
In-memory processing (IMCG)
Nowadays it is the most effective technology, designed for distributed data processing. It provides deployment, resource management, service, and execution processes. It works in close conjunction with IMDG in GRIP. It ensures the processing of data in the place of their storage, eliminating the displacement between the cluster nodes, thereby increasing overall performance
In-memory data storage (IMDG)
Technology of data processing and storage in memory. It allows you to increase the speed of data processing by more than 1000 times. It ensures data integrity at a high level. It increases the concurrency, minimizes network exchange and reduces the number of locks. It also withstands the OLTP load well, unlike big data solutions based on Hadoop stack.
Scale-out
It is a real hot horizontal scaling with redundancy. Using IMDG, you can use scalable data partitioning (Data Partitioning) in a cluster. The limit of computing power and storage capacity can be only the financial capacity of a client.
Gridfore Orchestrator
Domain-Specific Language
Domain-specific programming language is developed on the basis of Groovy for specific applications. Unlike General-purpose language, it allows you to reduce the development cycle and the amount of code. Using DSL, an analyst or a developer can focus more on a specific task than on its technical aspects.
Extract-Transform-Load
ETL machine provides simultaneous connection to any number of sources: databases via JDBC, local or remote (FTP/SFTP) files (including archives), Kafka and Hive. It supports various strategies for loading and overloading data, taking into account business periods such as incremental and full. It supports the work with transactions and data marts creation in real time.
Complex Event Processing
Handling trigger events of any complexity: from simple conditions to prediction models. It is embedded in already written ETL scripts using the GO component. It supports asynchronous (delayed) execution. After triggering the event, it can send messages to other ETL processes or to external services (Kafka, REST, SOAP).
beta
Data Quality
Supports data quality control within a single line or set of data. Simple logical checks, the checks based on complex business rules and predictive models (trend control) can be used. The check results are used to generate an error report based on a custom template and then it is sent by e-mail.
beta
Machine Learning
A machine learning component designed to solve Data Science problems that is to use and learn the models in real time. It simplifies the process of training and deploying the models in an industrial environment. It allows you to use models from third-party systems through the PMML format support. The developed models can be used together with the CEP and DQ components.
Maximally simplified business delivery process
Extensive experience in building DevOps approaches used in more than a hundred of our projects helped us to find a convenient and understandable for business solution, which does not require deep IT involvement. Work with all components takes place in one window
Convenient development environment
Our solution uses JetBrains IntelliJ IDEA, the most functional and convenient integrated development environment (IDE) on the market. JetBrains supports all existed repositories, such as GitHub, CVN, BitBucket and Beanstalk, which guarantee your code perfect safety.
Quick removal of the code and putting it into production
The process of transferring the code to other environments, including productive ones, is already automated with the help of Gradle project build system and JFrog Artifactory repository. All you need to do is to run an appropriate task.
Automation of typical tasks
Gradle also allows you to automate common user tasks such as running calculation processes, tests, publishing models, etc.
Self-test
Your code will be automatically checked before being published into other environments Functionality testing will be started automatically after the publication. All this significantly reduces the percentage of errors
Super Reactive Implementation
The advantages of our platform is our crown jewel
4 weeks
From the first day of work till the first MVP
3 months
To launch an industrial full-fledged prototype based on real case studies
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