Yes, better decision-making, improved customer experiences, and ultimately better company outcomes are all being aided by the growing flow of data across departments like marketing, sales, and HR. However, there are significant security and compliance problems.
In this article, three fundamental guidelines for the safe integration of data are presented, followed by a discussion of why.
Democratizing access to data: An important caveat
There are a staggering number of no-code and low-code technologies available on the market today for transferring, sharing, and analysing data. Data visualisation applications, extract, transform, load (ETL), and extract, load, transform (ELT) platforms, iPaaS platforms, and databases as a service may all be utilised by non-technical professionals with less administrative control.
Additionally, as organisations are increasingly using SaaS apps, the need for self-serve integrations will probably continue to rise.
Numerous such applications, including CRMs and EPRs, include sensitive client information as well as payroll and invoice data. Since the access levels to them are often tightly restricted, there isn't much of a security issue as long as the data remains inside of them.
However, there is what we may refer to as "access control misalignment" whenever you transfer data out of these settings and feed it to downstream systems with entirely different access level rules.
For instance, employees working with ERP data in a warehouse might not enjoy the same degree of trust from business management as the original ERP operators. Therefore, you face the risk of exposing sensitive data by merely connecting an app to a data warehouse—something that is increasingly becoming required.
This might lead to a violation of laws like the GDPR in Europe or HIPAA in the United States, as well as SOC 2 Type 2 certification requirements and stakeholder confidence.
Three principles for secure data integration
How can we stop sensitive data from being sent unnecessarily to systems farther down the line? How can it be kept safe in case it has to be shared? How can any damage be minimised in the event of a possible security incident?
The three guidelines listed below will answer these inquiries.
Businesses may reduce the risk of data breaches by separating the tasks of data storage, processing, and visualisation. Let's use an example to demonstrate how this works.
Assume you run an online retailer. All of your inventory, customer, and order information is kept in your primary production database, which is linked to your CRM, payment gateway, and other apps. You make the decision to recruit your first data scientist as your business expands. They naturally request access to datasets including all of the aforementioned data in order to create data models for, for example, how the weather affects the ordering process or what the most popular item in a certain category is.
Giving the data scientist immediate access to your core database, however, is not particularly practical. Even if they mean well, they could export private client information from that database to a dashboard that only authorised people can see. Running analytics queries on a production database can also cause it to become so sluggish that it becomes unusable.
Clearly defining the types of data that must be analysed and copying that data onto a secondary warehouse made especially for analytics workloads, such as Redshift, BigQuery, or Snowflake, utilising different data replication strategies are the two solutions to this challenge.
By doing this, you can both stop sensitive data from reaching the data scientist and provide them with a safe environment that is entirely independent from your production database.
Use data exclusion and data masking techniques
Because they completely stop the flow of sensitive information to downstream systems, these two methods also aid in separating issues.
In reality, the majority of data security and compliance concerns may be resolved as soon as data is taken out of apps. After all, why transmit customer phone numbers from your CRM to your production database if there is no compelling need to do so?
Data exclusion works on a straightforward principle: If you have a system in place that enables you to choose subsets of data for extraction, such as an ETL tool, you can simply choose not to choose the subsets that include sensitive data.
Of course, there are times when it's necessary to collect and exchange sensitive data. Data hashing and masking are useful in this situation.
Consider the scenario where you need to determine the health ratings of your clients and the only logical identification is their email address. In order to achieve this, you would need to transfer this data from your CRM to your downstream systems. You can mask or hash it after extraction to keep it secure throughout. Despite rendering the sensitive information itself illegible, this maintains the information's uniqueness.
With the use of an ETL tool, data exclusion and data masking/hashing may both be accomplished.
In addition, since ETL tools let data to be hashed or disguised before being fed into the destination system, they are typically seen as being more secure than ELT tools. Consult this in-depth analysis of ETL and ELT tools for further details.
Access vs. security: Not a zero-sum game
Businesses will be forced to share data more often while also maintaining its security as time goes on. Fortunately, attending to one of these requirements does not entail ignoring the other.
A safe data integration strategy may be based on the three aforementioned principles in any size of organisation.
Then, replicate the data into a secure sandbox environment after first determining what data may be shared.
Second, avoid include sensitive datasets in pipelines if feasible, and be sure to hash or mask any sensitive data that must be extracted.
Third, ensure that your company's operations and the tools in your data stack have robust logging systems in place so that, in the event of a problem, you can limit damage and conduct a thorough investigation.