Problem to address
As with database-driven applications, spreadsheets are even more widespread in both usage and wide-ranging in functional application. Spreadsheets are both the most under-, and over appreciated tools in the toolbox of any information analyst. Used for both information analysis and information management, spreadsheets give a nice challenge to organizations aiming to structure their data governance processes.

From Good Data Governance perspectives, there is unfortunately almost nothing worse than document-driven data management, like spreadsheets. As the documents themselves fulfil multiple roles in one single point of failure. Data container, metadata container, data integrity measures are all encapsulated within the same single file.
Very often data security/integrity measures are circumvented by users by simply making a copy of a spreadsheet document file, or even worse, copying and pasting arbitrary ranges of data from worksheets into different spreadsheet documents, and then continuing to work on those copied rows which have lost any reference to their original source sheet.

The dynamic nature of a spreadsheets data structure is however also a very valuable asset for organizations, as they can rapidly and easily remodel the data structure of their information system into the changing needs of their business. So while regulated organizations, or any organizations that puts strict requirements on data-integrity, should consider getting rid of user-definable data-formats such as spreadsheets, that kind of flexibility must be retained to enable agile implementation of continuously evolving work processes.

How does Aranei solve this?
Aranei by design is completely flexible in its data structure, allowing for any organization to add/delete/restructure data on demand. This allows organizations to retain the flexibility of a spreadsheet, but with strong change control measures enforced by the Aranei platform. And because of the fully automated Aranei test framework, change is much less of a burden on an organizations QA department as any change on any part of Aranei is automatically added to the test and validation framework.
Many database-driven systems adhere to their own main data structure and allow little customization, or only small extensions of existing data tables and no ability for newly added tables / relations / constraints.
Even when a database system does allow for far-reaching customizations, maintaining a validated state of such a system is usually a very manually intensive process of re-running operational and performance qualification test scripts upon each update.

Aranei implementation process
The flexibility of the Aranei data model allows for a one-on-one re-implementation or migration of any existing spreadsheet-driven application. This unique approach allows for a very iterative implementation process of Aranei where spreadsheets are imported one at a time, with full data integrity verification as well as data normalization evidence reported through the migration tooling.
By adopting this approach, an organization can gradually remedy the data ailments of spreadsheet-driven information systems, while maintaining their flexibility thanks to the dynamic data management capabilities of Aranei.

Problem to address
Contract Research Organizations as well as Sponsors of Clinical Trials often build up decades worth of trial data, usually stored in archived Trial Master Files together with their accompanying metadata, scripts for data analysis, medical images, and a vast array of other artifacts.

From Good Data Governance perspective, there is unfortunately almost nothing worse than document-driven data management, or having to manage several separate sources of data simultaneously.

Researchers and their teams often create archives of their own data, metadata and other artifacts during the course of their projects and trials. These archives aren’t always created in the most structured manner. During the course of a study or project artifacts need to be subdued to proper change control as well, given the time-, and financial pressure under which studies and projects are performed it is not surprising that frequently data governance and data integrity aren’t top of mind priorities. Yet when archiving the results of any study or project, and even more, in making the results of studies and projects available sustainably for future reference, having a good data governance process in place is essential.

How does Aranei solve this?
Aranei can help here by allowing study directors or project managers at a very early stage in their projects to create and maintain their own data spaces within an Aranei instance. Then connecting all other relevant data processing tooling or data-exchanging interfaces to Aranei and making sure there is a Single Source Of Truth in the Aranei data store.

Aranei offers out of the box a state of the art OpenAPI compliant REST interface, or even ODBC access to data within the data store, making post-processing data by third-party data analytics components very straightforward.
Additionally, as Aranei is the Single Source Of Truth for any (meta)data of any study or project, the time-series (or temporal) database concept implemented by Aranei provides fully transparent insight into the state of any record in the system at any point in time during the execution of the trial, study or project.

Problem to address
Many organizations want to be able to access data from within their data warehouse for analysis through third party analytical tools. Traditional proprietary database-driven application licensing models are usually quite restrictive in allowing such access. Let alone that access to data within those applications is often not the most straightforward endeavor as often data models aren’t optimized for re-use other than from within the own legacy application. Aranei allows for direct data access out of the box using its state of the art OpenAPI compliant REST API or by providing ODBC access to the database directly.
Being able to connect data analysis platforms for machine learning based anomaly detection on imaging is one example of extending the Aranei core functionality by interfacing with third-party image processing platforms and clinical data management systems.
A very common scenario where this Single Source Of Truth is invaluable is in Chain Of Custody tracking and tracing applications. Whether in the life-sciences industry tracking (human) sample materials, or in the manufacturing industry keeping track of process stability and quality performance indicators, both cases are addressed by using the Aranei data management platform.

How does Aranei solve this?
The value of Aranei here is found in its ability to mimic/implement any data structure or model that is required to support and supplement the third-party systems domain-specific database schema in ways that allow for the client organization to build out their datasets in a dynamic and flexible manner.
By connecting tools like Jupyter Notebook, as well as leveraging well known and widely used open-source libraries like NumPy, SciPy or the R data analysis language, the Aranei data management platform is augmented with industry standard and cutting-edge data analysis and processing technology.
Yet by keeping Aranei in the role of the primary data management platform allows for a harmonized cyclical data aggregation and analysis process, where the results and outcomes of the third party tools analysis are again stored within the Aranei data store to maintain a Single Source Of Truth of all relevant data, analysis results and follow-up thereof.

Problem to address
Aranei can not only be used for one-on-one migrations of applications, but it’s also capable of consolidating, aggregating and harmonizing data from various separate sources.
From Good Data Governance perspectives there is unfortunately almost nothing worse than separate silos of data, or even document-driven data management, like spreadsheets. As the documents themselves fulfil multiple roles in one single point of failure. Data container, metadata container, data integrity measures are all encapsulated within the same single file.
Unfortunately, it’s often all but unavoidable that separate silos of data come into existence, especially as different laboratory / clinical disciplines work with different information systems, different equipment, different analytics tools, etc. Keeping track of the individual records, data types and information entities is in such cases a daunting task, let alone keeping track in which workflow states any of those records/entities is currently being processed.
Transforming such an intertwined bundle of raw data into a single pane of glass overview can be very challenging.

Not just implementing a harmonized overview of data from different sources poses a challenge, as various semantic differences between systems can cause problems for aggregating and correlating data. Also having to maintain coherent change control between separate systems or data sources to ensure changes don’t cause escalating semantical data issues, or inconsistencies, in a harmonized data store is a time consuming and complex issue which causes QA departments many challenges.

How does Aranei solve this?
Aranei by design is completely flexible in its data structure, allowing for any organization to add/delete/restructure data on demand. And because of the fully automated Aranei test framework, change is much less of a burden on an organizations QA department as any change on any part of Aranei is automatically added to the test and validation framework.
Whether those sources are database-, or spreadsheet-, or external API driven makes no difference for Aranei. Client organizations using Aranei can be confident and proactive into identifying data sources to merge into the Aranei Single Source Of Truth data store.
This unique approach allows for a very iterative implementation process of Aranei where spreadsheets, or other data sources are imported one at a time, with full data integrity verification as well as data normalization evidence reported through the migration tooling.
For organizations that are subject to frequent organizational changes, in example during mergers and acquisitions or during hand-over and import/archival of vast amounts of trial or study project data, the flexibility of Aranei is of great help to ensure information modelling can be done on an as-needed and on-demand basis while maintaining a validated state through the highly sophisticated and fully automated test and validation framework that immediately upon any change in the applications’ data structure or functionality runs a full test and qualification suite on the whole application as well as on the database and API.
The value proposition of Aranei lies within the approach of making changes as cost-effective and efficient as possible, allowing any client organization to focus on their organizations informational needs instead of having their business development to be held back by the inflexibility of an information management system or the price of doing manual tests and validations.

Problem to address
For any application development cycle, a considerable amount of development time is spent selecting, trying out and integrating technology components into a coherent software stack.
Businesses often spend a lot of time and effort dealing with technical debt building up over the history of any app development lifecycle.
Considerable time and effort goes to waste on re-inventing the proverbial wheel by developers fixating on solving technical problems instead of delivering business value.

How does Aranei solve this?
The nature of the flexible data model, and the extensibility of the standards-compliant Aranei open-source User-Interface components allows for development agencies to focus on creating business process value adding modules where it matters and not have to worry about the basics of the foundational technology stack on which they’re building their customers’ information systems.
Aranei already combines a compelling package of best of breed and state of the art open-source modules and components, readily integrated and immediately available to use.
Proven technology to facilitate focus on business logic.
Sharing development cost for generic re-usable components by using Aranei as base framework allows an development agency to focus on adding value where it matters most for their customers.
With Aranei the basics of both the app infrastructure as well as deployment methodology are already covered.
Aranei already has many generic and re-usable components integrated and available like data-entry/edit forms, notifications, dashboard widgets, etc.
Focus on modelling the data schema optimally, while Aranei takes care of the REST (API and ReactJS frontend).

Problem to address
Changes in application behaviour, or even updates of an otherwise functionally unchanged application, is often a costly endeavour as computerized system validation strategies often require full reruns of manual operational / performance qualification test scripts.

How does Aranei solve this?
As Aranei implements an advanced automated testing framework on both database, API as well as User-Interface level, this testing framework can not only be leveraged within Aranei itself, but also by organizations struggling with keeping their application landscape in a validated state.
For Aranei, one of the main business value propositions OpenNovations is championing is a fully automated test and qualification suite that gets generated together with the Aranei REST API and User-Interface.
This test and qualification suite is also usable on other web-applications than the Aranei User-Interface, as long as the application follows common web standards.
For client organizations, this offers the ability to continue using (and developing/changing) their existing applications, but at the same time leveraging the unique capabilities of Aranei test and validation frameworks in maintaining the validated state of a system and with that ensuring continuous inspection readiness on any stage in an applications operational lifecycle.