Explore the benefits of the Esplores platform

applied to real use cases

Connect all your data, explore and perform checks

No technology barriers

With more than 20 data connectors, Esplores enables you to connect all your data from one platform, regardless of the technology of the database engine.

Easy data navigation

Once connected, the data may be navigated with point-and-click operations, without knowing the architechture or the syntax of the database engine.

Seamless integration

The platform act as a data federator, allowing interactions between the different sources, defining joins and drills without writing a single query and with no replica.

Easily generate new fields, calculating or enriching your data on the fly: the databases will not be touched.

Data governance

Locate your data, compare them and create reports on the fly.

The advanced features allow you to reconcile archives or check existance of the same data object across your entire database list.

Use case: connect branches to headquarters

Reconcile data across the branches

After connecting all the branches, select the fields to check across your databases and choose a key, then run the algorithm to discover which data is up to date and which needs to be reconciled.

Cross search your customers

With all branches connected, Esplores makes it easy to compile a list of all your customers, find in which branch they are present and in which they are not.

Also check if the HQ is missing the data and add the information where needed.

Analyze your data, find insights and apply transformations

Auto tagging fields

Esplores can find personal data and GDPR information in your data with the automated GDPR detection algorithm.

Confidency is calculated using statistical models and helps spotting errors in your data and explain the tagging results.

Define custom domains

Create new tagging rules to discover specific data in all the databases.

You may also define custom masking operations to maintain the desired level of confidentiality for specific fields.

Unstructured data

Find defined domains also in document files

like PDF, Office documents, etc

Transfer a subet

Optionally transfer a subest of your data for your developement or test environment using the Subsetting module.

Use case: mask personal information of production data in your developement environment

Discover personal data

In the first phase, run our Data discovery algorithms on the data in the production DB, for identifying and locating personal data, GDPR data or custom domains.

Apply masking on the fly

The masking process can be performed on demand to align or update the masked DB.

Use the on-the-fly feature to preview the masking or give access in read only mode to production data for technical operations by non authorized engineers.

Run persistent masking

The masking algorithm keeps consistency across tables and databases, without the need to replicate data redundantly.

Write only the data you need to be persistent.

Esplores APIs allow to schedule the pre-configured process from anywhere.

Apply complex analysis and visualize results easily

Advanced statistical functions

Run ready-to-go statistics on a data table like: regression, clustering, correlation and table or field profiling.

Powerful dashboarding

Visualize your data, even from different sources and interact between each other with a click of the mouse.

Machine Learning

Link tables across different sources using Join Recommender with Deep Learning.

Apply your Machine Learning algorithms and re-define the results in Esplores for further analysis.

Continuous monitoring

Define specific checks and run your analysis continuously on constantly updating data.

Use case: forecast and churn prediction models made easy

Forecast sales or consumption rates

Combine information from different sources (e.g. weather history and sales history) and run an algorithm on the past data to obtain prediction with new data (e.g. weather forecast). Then visualize all data in a dashboard.

Filter by date or sale type with a click, zoom and go to the fully detailed data table for further analysis.

Predict customer churn

Gather all customer traits to make a persona, then combine all information with past history KPIs.

Run an algorithm to cluster the personas and make a prediction. Visaluze the prediction in a insightful dashboard.

Filter by the personas traits with a mouse click and open the full list of customers at need.

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By ticking the above box I agree and consent the terms of the Privacy Policy consenting Esplores processing and disclosing my data and communicating with me according to the policy.

CHECK OUT OUR PUBLICATIONS.

We often publish new content.

REQUEST YOUR DEMO.

By ticking the above box I agree and consent the terms of the Privacy Policy consenting Esplores processing and disclosing my data and communicating with me according to the policy.

CHECK OUT OUR PUBLICATIONS.

We often publish new content.

REQUEST YOUR DEMO.

CHECK OUT OUR PUBLICATIONS.

We often publish new content.

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