Organizations should adopt a collaborative approach to information governance to speed up time-to-insight and fuel productivity.
According to Accenture’s C-suite survey, 84% want better insight into customer needs, while 61% want faster decisions. An environment with easy access to the right data and seamless data exchange can solve both needs.
However, companies are cautious about open access to data.
According to HBR, less than 25% share data and intelligence effectively within teams, business units, or key partners.
The cautious approach to access and data sharing is understandable with a meteoric rise in data breaches as more organizations collect, store, and share large volumes of data.
In 2022, fintech giant Block (previously known as Square) suffered from a data breach that leaked over 8 million customer records. The reason for the leak was a former employee without the right access credentials downloading sensitive reports.
Such breaches can severely damage a company’s reputation and lead to hefty fines. For instance, a failure to report a breach (like Block’s incident) within 72 hours can lead to a GDPR penalty of up to 2% of the annual revenue.
Moreover, a company suffering from a data breach can experience an almost 10% decline in revenue for up to six months. Recovering all that lost revenue can take up to two years, putting the company further behind its competition.
Yet, getting meaningful insights faster requires an environment with open data exchange, and that’s why finding a middle ground between democratizing data access and enforcing security is a must.
In most large, data-intensive enterprises, the data generated is scattered across apps, teams, systems, and doesn’t go into a single repository — leading to chaos. Questions such as “where is the data on the previous month’s sales from XYZ region?” or “where’s that document Alex (a team member) shared?” aren’t uncommon. Chaos leads to a lack of visibility and control over how data is accessed and consumed.
Define data access, management, storage, and sharing. A solid framework lays the foundation for a collaborative workspace that seamlessly lets data teams discover, validate, discuss, and share data.
An example is the Five Safes Framework, a model adopted by several Australian government agencies and organizations like the Office of National Statistics (the UK). Each ‘safe’ is an aspect of risk that emerges when accessing data:
Data chaos ensues when manual data sharing methods, such as mailing spreadsheets or executing batch processes, are used. Such techniques require you to extract, copy, and move data manually, leading to duplicate and redundant data, besides exposing it to unauthorized access.
In an ideal scenario, data won’t get copied or moved. Instead, those with proper permissions will access it from a centralized cloud-based location. Moreover, since the location is central and on the cloud, it’s possible to maintain a single, real-time version of all data – a single source of truth.
Besides enabling data access and sharing seamlessly, i.e., data democratization, a cloud-based centralized data repository can also facilitate:
The next question – how can you find such a platform, and what capabilities should you look for?
Since several companies are reluctant to openly share data because of the risk of data breaches, it’s essential to adopt a platform where security is part of the design and not an afterthought.
For example, the platform shouldn’t be able to store, assess, or control your data. Such an approach ensures that everything from data storage and management to access permissions is transparent, monitored constantly, and under your control.
Besides a security-first approach to design, the platform should also continuously test for flaws or vulnerabilities by encouraging security experts to report such instances.
For example, Google has Vulnerability Reward Programs (VRPs) for products such as the Chrome browser, the Android OS, and the Google Play Store. Such bug bounty programs offer payouts for reporting security flaws.
In addition to a security-first design, end-to-end transparency in data management, and a bug bounty program, here are some more evaluation criteria to consider:
Needl.ai is a cloud-based data platform that integrates multiple data sources and channels to serve as the single source of truth for all of your data. Each data stream is called a feed.
Needl.ai doesn’t just compile your data, but also lets you set up intelligent workflows that can segment, prioritize, and share data in real-time. For example, you can create data feeds and segment them using keywords, filters, tags, and data sources. These data feeds update continuously in real-time.
The crux of Needl.ai — the USP — is a privacy-first architecture and collaboration-centric design. Here’s how that works:
A key tenet of a privacy-first architecture is control. You maintain complete control and ownership of your data. You can add, sync, share, and delete your data whenever you want. Our commitment to data privacy and security is central to how we operate.
That’s why we leverage AWS best practices on data security, ensure compliance with data regulations, and use an OAuth 2.0 protocol (token-based authentication) to connect to third-party data providers.
Lastly, we don’t see, retain, or sell any data. We have an ad-free business model and our revenue comes from subscription fees, not through ad sales using your data.
Interested in learning more about Needl.ai’s data storage and privacy policies? Check out our Privacy Policy.
A single source of truth is effective only when you can use it to engage with your data and collaborate with your team. Needl.ai lets you share data from your feeds, documents, emails and more with your colleagues using multiple platforms.
You can also:
Data security, privacy, and integrity are at the heart of Needl.ai’s infrastructure and design. Here’s how we ensure that:
So, if you want to democratize data access and simplify collaboration around data, why don’t you take Needl.ai for a spin?
Sign up for free right now!
