Core features
More than 200 features
Ready to use exploration, consolidation, calculation and statistics functions right at your fingertips
Reduce data preparation activities by a factor of 10
Go quickly up and running, connected with all data technologies without deep knowledge of the SQL syntax.
Take advantage of the special features
of Esplores, like Join Recommender and Data Hub to increase the speed of data analysis and data preparation.
Data Federation
Connect all the databases of a holding’s companies without replica or the need for a single repository, link data even if from different technologies and run complex analysis like registry alignment or cross comparison.
Analyze your data using advanced statistics and machine learning
Run ready-to-go statistics on a data table like: regression, clustering, correlation and table or field profiling.
Link tables across different sources using Join Recommender with Deep Learning
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Esplores as an end-to-end platform
With more than 20 data connectors, Esplores can be integrated with your existing data in a simple way, regardless of the technology, the volume or the variety, making the benefits offered by countless features immediately available without requiring duplication or replication of the data.
Esplores has features for each step of the data analysis workflow
Combine data from heterogeneous sources to gain insight
Get insight form combining data from multiple sources in a matter of days / weeks compared to months
Eliminate expensive and rigid DWHs
Get insight form combining data from multiple sources in a matter of days / weeks compared to months
Perform complex calculations
Relieve enterprise applications from complex calculations
Introduce Machine learning and Artificial Intelligence in business processes
Check the quality of your data
Quickly identify data that do not match rule / description
Find the location of your data
Where are these data located?
What attributes do they have in the different locations?
Which db contain these data?
Impressive dashboarding
Build interactive dashboards that put together data from different sources and interact between each others with a click of the mouse.
Choose between 30 different objects and 70 different types of charts
Analyst
Intelligence Analyst
The Intelligence Analyst plays a crucial role in the collection, analysis and interpretation of information, supporting strategic decisions in various contexts, both public and private. This professional figure is essential for ensuring the security and operational effectiveness of organizations.
Analyst Companion
Esplores is the ideal platform that accompanies the Analyst by providing a set of tools and functions that accelerate the process of quantitative, qualitative analysis and interpretation of the information gathered, then to make the best decisions in assessing risks and opportunities.
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Esplores can collect information in over 100 languages and translate it into your preferred language.
The workflow of an analyst inside the Esplores Analyst platform
Through its countless connectors, drivers and APIs, Esplores gathers information from any data source. Internet “search”, social media, Youtube videos, transcripts, databases of various forms and natures, documents in different formats (PDF, XLS, DOC, etc) are only some of its sources.
Esplores, with its Discovery Engine & A.I. algorithms is able to correlate all external and internal company data. With Named Entity Recognition one may create a representation of the relationships between things, people, facts, organizations, companies, public institutions, and places as a function of the research.
Esplores can interface with other software and platforms to retrieve other types of data useful for analysis.
Features like alerts based on tags or events, scheduled searches and automatic summarization enables the anlalyst to focus only on data insights.
The index of all Internal and External data with different Search Algorithms (Phonetic , Fuzzy, Similarity, etc...) allows to search easily through the entire recorded data
The NER representations can speed up the correlations creation.
Named Entity Recognition tree view
The multilevel Whiteboard gives the possibility to the analysis manager to connect the set of all the researches and all the Analysts insights in a single whiteboard
Whiteboard is the space where the Analyst organizes final and analyzed data.
GDPR & Data Masking
Discover domains in your data
Out-of-the-box domains (Name, Surname, Birthdate, VAT#, etc) ready for discovery in every database.
Add your custom domain (customer id, product code, etc) to the discovery process.
Fine tuned masking
Toggle masking on the fly without writing on the database, based on user profiling.
Run persistent masking on the whole databse, when writing is enabled.
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Esplores provides, in a single suite, all the features for managing a Data Masking project combined with functions for managing Data Discovery and Data Analysis projects.
Preview the masked data with the on-the-fly function, then persist the masking
The completeness of Esplores' analysis and management functions leads it to be a solution that follows the process of Data Masking Projects, which are typically characterized by the following Management Flow:
- Definition of personal data domains ("out of the box" the main domains used in the projects are already present)
- Definition of Masking Algorithms (possibility of customizing existing algorithms and creating new ones)
- Entire Customer Data Base Profiling
- on-the-fly masking for the purpose of testing the results and managing the databases in production
- Persistent Masking for managing development, debugging, and testing (non-production) environments
Subsetting
Build your subset
Compose a query from the source db choosing the fields and using custom WHERE conditions.
Check the query results in realtime.
Cross technology destination
Run the subsetting workflow without limiting the destination database to one technology: from any to any.
Specify the operations on the destination db with its SQL syntax.
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Extract smaller subsets of your data to fill your dev environment
In research communities (for example, earth sciences, astronomy, business, andgovernment), subsetting is the process of retrieving just the parts of large files which are of interest for a specific purpose. This occurs usually in a client-server setting, where the extraction of the parts of interest occurs on the server before the data is sent to the client over a network. The main purpose of subsetting is to save bandwidth on the network and storage space on the client computer.
Source: Wikipedia
Four steps Subsetting from start to end
- Apply selection rules to your source
- Read the subset from source
- Apply before and after custom operations to your destination
- Write the subset to destination
Text Processing
Text and OCR extraction
Extract text from all the office documents (DOC, XLS, PPT) file formats and many others (PDF, ZIP, DWG, DICOM).
Perform high confidence OCR on text-as-image with language recognition.
Data discovery on unstructured data
Identify personal data or any other domain within unstructured documents.
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Apply full text search to your documents
Create an index of all the text within your documents with minimum storage impact, theen run complex search patterns within milliseconds.
Textual data can be extracted from any format. OCR will do extraction on images
Search within your index by category or by search term. Enable term similarity when the search is not satisfactory.
Write the search query with jolly characters, surround phrases with double quotes for exact match or choose character distance between terms.
Natural Language Processing
Generate your model
Submit a dataset of texts (corpus) to train an NLP model for your needs.
Extract insightful data.
Use knowledge for different applications.
Get new texts and insights
Crawl the web or your databases searching for information semantically.
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Semantic classification of texts using Machine Learning
Wether you have a structured database with unstructured fields or a set of documents with descriptive texts you may process your data for semantic classification.
Our team will train an NLP model specifically configured for your needs that can be used inside Esplores or as a service
The trained NLP model can be used to find insights or classify new texts.
Our web crawler can scout the net for new information to be submitted to the Natural Language Processing model and find best articles.
AI Data Extraction
Extract from any documents
Using our text processing engine, you will be able to extract data from any document type containing with text.
Data from images
Our OCR workflow can manage different kind of information contained in images, extracting texts ang tabular data.
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Extract structured data from unstructured documents
Submit your templates to our team to get a customized pipeline of extraction.
Using a combination of Computer Vision techniques, Machine Learning algorithms and OCR tools we can extract the information from all types of documents and obtain structured data.
Use the dashboarding module to generate insightful reports of the extracted data, then search and export when needed.
Include our model in your workflow using APIs or manage directly the information in the Esplores Data Exploration Module. You may even create a Dashboard for analyzing the extracted data within the dashboarding module of Explores
Use the dashboarding module to generate insightful reports of the extracted data, then search and export when needed.
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