As organizations attempt to make their services more accessible to a wider audience, they experience heightened administrative challenges. These are driven by customer demands for high quality and speedy service delivery through internet-based technologies.
Therefore, not only do these organizations encounter massive data generated by customer-facing systems in use, they need to have internal systems in place to handle their workflows.
This all quickly escalates into a multitude of disparate data sets that leave teams struggling to convert them into actionable insights in a timely manner. In that respect, a data fabric can be a resonant answer to this problem, so let’s discuss it in depth, and how best to apply it.
What is Data Fabric?
Data Fabric is the combination of architectural components involved in delivering management functionality across various end points for distributed data, such as that in hybrid cloud environments. It helps to standardize the collection, storage, processing and retrieval of data with the aim of improving the user experience.
There are a number of other terms and concepts you might come across worth discerning from a data fabric. These include data virtualization, data mesh and data lake/warehouse.
A data lake, is simply the central point where all data is converged, and not the entirety of the applications and other infrastructure used to access and effect changes to the data.
A data mesh also differs from a data fabric in that it’s a platform that is less reliant on centralization of data, instead focusing on enabling processing and other functions to be run while data is still in its dispersed state, based on each data set’s respective domain.
On the other hand, data virtualization enables you to access data, apply commands to it and conduct analysis without necessarily overhauling its structure, moving it around or paying much attention to some of its technical attributes like the format, location and more.
How does Data Fabric work?
In order to truly appreciate the potency of Data Fabric, we ought to understand the inadequacies in legacy data management systems. These commonly include:
Data silos; When data is siloed, pieces of information that are enlightening when viewed side-by-side are instead constrained in separate stores. Any new data that is generated through a computation as simple as an addition or a percentage calculation takes longer to realize.
Leaders are bound to make decisions without the full picture in mind. Data silos promote partial ignorance in various sects and also slow down the process of reaching a consensus.
Replication; While having multiple copies of your data can be advantageous, many legacy systems aren’t well-equipped to facilitate replication in a non-redundant manner. For instance, they can’t make adjustments to transactions in near real-time to reflect changes in a deal.
The result is a scenario in which you have multiple contracts, payroll sheets, quotations and other kinds of data that you don’t need, but you’re storing and have to sort as you work.
Latency; As you start querying your databases more frequently and making changes to your data, your existing management systems will be tested. Legacy systems will start having massive delays between the time a request is made and the time a result is presented.
The same goes for any changes you make. You’ll find yourself looking at data versions that haven’t yet changed according to your commands, or have only partially changed.
Not only will you take more time to make information available to those who need it, there might be discrepancies in what you’re communicating and what your colleagues are viewing.
Remember that the data fabric model combats the adverse effects of data movement on your data management strategy. Data Fabric allows you achieve central governance while spreading management-related capabilities to various devices in your ecosystem.
By doing so, it helps you save time that would otherwise be spent copying data before you can use it. Additionally, data fabric mitigates the complications associated with nuanced permissions for accessing and utilizing specific pieces of data.
Key Components of Data Fabric
A typical Data Fabric is made of a number of basic components such as:
Augmented Data Catalog; This is a data catalog fortified by intelligent techniques such as machine learning so that previously manual tasks such as metadata discovery, categorization, population, enrichment and curation can be executed automatically.
Persistence Layer; This is basically the layer between the application interface and the actual storage, which maintains the state effected by a user, encapsulating changes in the characteristics of data and ensuring that they remain visible.
Active Metadata; Typical metadata as we know it serves the role of describing the data. Active metadata goes a step further by continuously taking note of the actions taken within your system, the actors and the outcomes to maintain an accurate description of your data.
Knowledge Graph; A knowledge graph describes various entities within your ecosystem and their relationships. With such a network in place, you can easily determine which entity is sending or receiving information for tax purposes, stocking, forecasting, supplying and more.
Insights and Recommendations Engine; This component serves as a filtration tool, usually relying on machine learning algorithms to spot patterns in user activity data and suggest actions that bring you closer to a desired outcome.
Data Preparation and Data Delivery Layer; The preparation layer transforms raw data into a processed data for a particular application while the delivery layer works on how to output processed data in a plain manner, where it should be relayed, the timing and more.
Orchestration and Data Ops; When you combine an orchestration solution with Data Ops, you’re able to control various actions and processes tied to different data pipelines and analytics tools within your ecosystem from one place.
Subsequently, you don’t have to zoom in on point integrations and custom scripts along each data pipeline since you can use a low-code or no-code approach to designing workflows.
What D&A leaders need to know about Data Fabric
Data and Analytics teams play an important involvement in maintaining an organization’s competitive advantage. However, many organizations still run with each department sticking to analyses that are related to their specific objectives.
In a world with increased reliance on big data, one team’s ability to derive actionable insights from a data set won’t be enough if they can’t quickly juxtapose it with other teams’ findings. This is where data fabric can be beneficial to D&A leaders.
With components such as data ops orchestration tools, leaders can quickly address the inter-departmental administrative bottlenecks that hamper human-conducted data analyses by simplifying the creation and maintenance of new data pipelines.
For example, the people in HR can quickly understand the implications of a setback within a product development team, and so will the marketing department. Consequently, it will be easier for every eventuality to be portrayed in a manner that business leaders understand.
Such capabilities are reinforced by the creation of knowledge graphs that offer a clear map of how entities benefit from the particular data sets that each one disperses. This data also becomes more helpful as the associated metadata is changed from a passive to active state.
Data fabric will do more than bringing efficiency to human-driven organization-wide analysis. Insights and recommendations engines means that your tech stack will be more efficient in drawing conclusions by itself, which humans would take much longer to achieve.
In addition, it will suggest effective solutions based on the findings, which helps to close the ideation gap and put your teams ahead of timelines thanks to more accurate forecasting.
Ultimately, every functional team will know how best they can contribute to solving a problem while also staying within the boundaries of solutions that make business sense.
Managing a Data Fabric Model
To get the most out of a Data Fabric model, here are some aspects to stay on top of:
Integration; This is the process of identifying all the disparate sources of data and enabling users to access data from a central point where it is converged and consistent. In the case of the data fabric model, the integration options at your disposal go beyond Extract, Transform, Load (ETL), virtualization and replication to include streaming and change data capture.
Security; Firms adopting a data fabric model can institute firewalls, Transport Layers Security (TLS), IPSec and the Secure File Transfer Protocol to keep both at-rest and in-flight data encrypted. Also add dynamic access control policies as cyber attacks evolve in nature.
Security teams should eliminate secrets and keys both manually and automatically from any resources where they don’t need to be and use features like PrivateLink for AWS and Azure.
Monitoring; The crucial aspect of the data fabric model starts with having a blueprint of what data should look like at various stages.Ensure that all data makes it to the appropriate dashboards, and that there are notification systems in place to let you know when certain data points are represented the wrong way or you have figures that defy the logic at hand.
Optimization; Data optimization is all about applying data quality tools to rectify any anomalies, redundancies, and inconsistencies discovered in data during monitoring.
What are the Benefits of a Data Fabric Model?
A data fabric model can benefit an organization in numerous ways such as:
Efficiency
With machine learning and other intelligent techniques, various manual processes can be automated, be it something as simple as updating metadata or something more complex like analyzing inter-departmental productivity. This saves time while also improving accuracy.
Democratization
A data fabric model makes it easier for different team members to access the data they need when making decisions and serving customers, while also making the functionalities commonly applied to it more accessible in the long run.
Scalability
The data fabric model accommodates more integration options, simplifying the addition of new entities into the fold while simplifying the creation of new workflows that data will be channeled through. This makes efforts such as opening new business branches much easier.
Integration
As integration is performed while factoring in the continuous changes in an organization’s data and the users interacting with it, data delivery is sped up and conducted in a manner that is more resonant with the concerned parties at a given time.
Control
A data fabric model enables leaders to make specific capabilities available at different endpoints while also restricting access to certain resources based on more than just an employee’s role but also the evolution of their contribution to a particular project.
Everyone is aware of what they need to know at the right time, and also empowered to act on it in a timely manner, which limits data security breaches and other malicious actions.
Agility
The data fabric model improves business agility, first by enabling the relevant parties to get a better picture of the impediments to success both in the present and future.
Leaders are also supported by a tech system that simplifies the addition of new applications, on-boarding new users and bringing them under your governance.
Ultimately, adding a new worker, a piece of software or a database, and a workflow are all made easier and in many cases, adjustments down the road are partially or fully automated.
Data Fabric & Data Democratization
A data fabric model can promote data democratization through:
Discovery; The data fabric model offers individuals more visibility into which entities are handling data that is pertinent to their roles and also facilitates direct links to these sources.
Exploration; Data Fabric allows users to get a visual representation of a firm’s data and understand various domains, volumes and levels of completeness among other characteristics. Thus have a better picture of the resultant relationships to be managed.
Experimentation; In addition to enabling thorough exploration, users can conduct numerous tests on the data, to confirm or disaffirm any assumptions about the correlations and causations that exist in the data. The result is a better understanding of how different members’ roles can be simplified through changes in the way they interact with data.
Orchestration; A data fabric model can bring simpler visual design to the creation and adjustment of workflows, an approach that is more inclusive to team members that aren’t proficient in coding. Thus have easier time applying recurring commands to data.
Data Fabric Use Cases
Data Fabric can be used in a number of cases such as:
Catalog and Discovery; Data Fabric can bring more collaborative capabilities such as enabling you to single out faulty data sets and point colleagues to them with further instructions.
Lineage and Governance; With a data fabric model, you can automatically identify sensitive data and create user groups based on their need to interact with that data, while also setting policies to govern the ways in which they can act on the data.
Quality and Profiling; A data fabric model enables an organisation to automatically generate data profiles, detect anomalies, implement custom quality checks and collaborate on fixing issues along the way, all through a neat data quality dashboard in the process.
Data Fabric Success Stories
The data fabric model has boosted performance in various organizations such as:
SocialCops
This is an intelligence startup in India that built Disha, a national data platform bringing data from 42 government schemes and 20 ministries into a single dashboard and helping to bring LPG connections to 70 million below-poverty-line women under the Ujjwala Yojana scheme.
Along the way, the startup mapped each of the 6.4 lakh villages, adding population and income level information amongst other details along the way.
The data fabric model has also been adopted by Atlan for a public Fortune 500 company to automate the master data process, track their finance approvals and audit logs and also to automate a 6-year old manual workflow in the process.
Conclusion
All-in-all, the data fabric model transcends the goal of speedy accessibility of data. It is well-poised to address several challenges that different people who interact with an organization’s data encounter. Data Fabric also does all this in a continuous manner, steadily reducing the need for users to keep doing a lot of maintenance, governance and other tasks on their own.
Gerald Ainomugisha is a freelance Content Solutions Provider (CSP) offering both content and copy writing services for businesses of all kinds, especially in the niches of management, marketing and technology.
