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How Time is Ltd. Protects Your Data Privacy

September 10, 2020
min read
We provide the first privacy-minded collaboration and productivity analytics in the world. Data privacy is at the core of what we do.

Working with company internal data often involves making important ethical choices. Many datasets that we use are about people, identity and their behaviour. We must treat such data with humanity and respect: integrity is paramount.

Almost all of our competitors do not aggregate data, and their analysis focuses on the individual. We don’t believe that this is an ethical way to help people with productivity - it directly contradicts our internal core principles.

Our platform prioritises privacy. Our core principles are adhered to at each stage, with a privacy layer that aligns with the good practices of privacy restricted companies. We believe that this should be the norm in the people analytics industry.

If you are working in a heavily restricted and high-risk industry, you might take advantage of the additional pseudonymization layer, like our clients from the banking industry. We have built an open-sourced pseudonymization service for internal G Suite and O365 data that anyone can leverage for internal insights.

We take our core privacy principles seriously

We have known since the beginning that storing all data from the Google and Microsoft APIs would be against our internal privacy rules, so our data storage and data pipelines are designed according to these principles:

  • We adopt differential privacy. We do not show individual names, surnames, email or any other identifiable information in the output analysis. All data is aggregated on a team level. The data we need is statistical in nature, so it is valuable to us when aggregated by department or team. Even if the probability distribution is on the whole correct, individual cases may well be wrong. We do not, therefore, analyse individuals separately unless we are explicitly asked to do so, and only do so in compliance with privacy rules. This usually occurs when CxO or top management are curious about their own productivity, and its correlation with collaboration.

  • We drop sensitive information immediately. We remove all textual data as soon as it is received from the API. We don’t store sensitive information. Our analysis is built around communication direction and frequency, without the use of textual data.

          We remove:

                              - Email content - Subject & body

                              - Slack / MS teams - All text messages

  • We do not store or analyse personal events. We remove single-person events before they are even stored. Personal events might contain individual and sensitive information or it might be a time blocker (a method which we highly praise!). In both cases, it’s your personal time and it stays yours.
  • We use pseudonymized data for all data science and data insights. Our data scientists and machine learning engineers use data that does not identify individuals, which means that we can glean new insights and test new data pipelines without compromising privacy. All raw data is encrypted at rest and in transit, it is never used internally for any experiments or development.

Read our Privacy page to understand how we handle company data and privacy of individuals.

Our additional pseudonymization layer

Our privacy layer aligns with the companies we work with. But if stricter anonymisation is required, then all data is pseudonymized in accordance with privacy laws. Restricted industries include, but are not limited to, the banking sector. In these cases companies cannot use internal data or provide direct API access to 3rd parties, for any kind of analysis, without cloaking the data. We substitute any identifiable data, therefore, with a reversible constant value before use. 

Building an anonymization layer internally for each use case is a time consuming and intricate task, especially given the continual development of APIs. Making sure to remove all fields that might contain confidential information is a tedious job. It is a burden we know too well!

We have, therefore, built an open-source pseudonymization service that can be easily deployed on cloud or on-premise directly in the client infrastructure. This service provides anonymized versions of the G Suite and O365 API endpoints. This anonymization service removes all textual data and all personal data from the API requests.

The anonymization service can be configured and deployed by the client without our intervention and any confidentialities, like API tokens and configuration, stays on the client side also. Your IT and security is in your full control. Configuration allows you to set the level of anonymization of internal and external email addresses, which are part of the PII (Personal Identifiable Information). You can choose to:

  • Anonymize external email addresses - i.e. everything before @ ->
  • Anonymize external email address and domain - i.e.ANON_912DE@34A655H
  • Anonymize internal email addresses - ie.e. everything before @, like

All textual and sensitive fields are removed by default. You are in charge of your data and access without relying on any 3rd party services.
Read more about our Anonymization service on our Github page. Contact us if you need help with the deployment, or if you have any questions about how this works.

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Get ethical analytics today

A modern working environment is one which thrives on collaboration, and business leaders are using data to help their teams reach full potential. Get your company’s data today. We provide full productivity analysis and are the first to offer you this ethically. See how your team performs and communicates on a dashboard that helps you identify areas for growth. Improve your productivity now. 

Time is limited, and we give you more of it so you can work on what matters.

At Time is Ltd., we measure digital collaboration and productivity, without ever sacrificing employee privacy. We provide an advanced analytical SaaS platform that delivers a holistic view of an organization collaboration patterns. We measure your team’s digital footprint to improve communication, productivity as well as save precious time. Our approach only aggregates meta-data from a variety of data sources, to show how your teams work with your collaboration tools so you can get them more productive and motivated.

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Article by
Ondrej Kuchta
A skilled engineer focusing on building the big data and ML pipeline that deliverers insights about time & productivity to large companies from their internal data sources like calendars, chat, or emails.
Article by
Ondrej Kuchta
A skilled engineer focusing on building the big data and ML pipeline that deliverers insights about time & productivity to large companies from their internal data sources like calendars, chat, or emails.

See other articles

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