Data is Eating the World.
It’s Time for Unified Data Operations.
In August of 2011, Marc Andreessen wrote the famous article, Why Software Is Eating The World in the Wall Street Journal. Fast forward 10 years, and today Data is eating the world.
Why Data is Eating the World?
Yesterday’s software was powered by code. Today’s software, that businesses run on, is powered by code (now including models and algorithms) and, the ever more elusive, data. Not any data, but usable data.
People who run the business also need to make decisions. This decision-making also needs data. Not any data, but usable data.
But there is a huge disconnect between what the data producers create and what is usable data. But what is it? Usable data is data that has been prepared, transformed, curated and ready to be used by the destination systems and data consumers who need it to run the business or make decisions.
Who Makes the Data Ready to Use?
Ready to use data sounds like apple pie. But who is going to do the necessary work?
Oftentimes, it starts with the Data Engineering team. Data Engineers focus on delivering the data to the analytics and operations team. Because most engineering teams run the bulk of their work using scripts and custom code, they can’t offload this work to non-engineers even if they are quite basic and repetitive. The analytics and operations teams, therefore, keep coming back to data engineering teams to tweak scripts and make modifications because their initial requirements had drifted. Number of requests keeps on growing and putting immense pressure on the data engineers who are already oversubscribed.
Data Analysts and Data Scientists on the other hand see very slow response times for their requests and are scrambling to deliver on the innovation projects that they are supposed to work on by experimenting with new datasets.
Is it possible to create a win-win solution for both data engineering teams and data science teams? The answer is yes. The combination of self-service and collaboration can tackle the hardest data challenges in the enterprise.
80% Self-Service + 20% Collaboration = 100% Win-Win
There is a reason self-service analytics tools took over the analytics market from first generation BI tools. Business could not wait for report writers to deliver the BI report. Everyone hated the back and forth and take-a-number-and-wait-for-your-turn culture.
Data Engineering is going through a similar sea change. Businesses can not afford to lose time waiting for data engineering teams to deliver on every small request. In most of the cases, the business teams are capable enough to benefit from a self-service model where they modify, cleanse, transform datasets themselves because no one knows the data better than them. If they are aspects of data that they need help with from a more technical person, they should be able to collaborate with their team. And for really edge-cases, the data engineers can step in and address their challenges.
Time for Unified Data Operations
The combination of self-service, collaboration, and state of the art data platform is at the heart of Unified Data Operations that can help organizations achieve operational excellence with respect to data. It operationalizes data so that operations and analytics teams get the “Ready to Use” data with reliability and speed. Data Engineering is still needed but more and more teams rely on continuous data flows that they can create to feed the systems and tools that need the data. Data users like data analysts, data scientists, and business users can get data for their use in a few hours instead of days. Unified data operations converges many data tasks like data integration, data preparation, data quality, governance, sharing, and monitoring into one seamless experience.
Nexla for Unified Data Operations
Nexla’s approach to DataOps is a step change to alternate approaches out there. Nexla focuses on converging data management capabilities to simplify the life of data users and data engineers. Self-service and collaboration are key tenets for its product design.
At the heart of Nexla is a metadata-driven data fabric architecture that abstracts away the complexities of working with data through logical data entities that are called Nexsets. The no-code interface provides data users a friendly way of working with data and tackling many data engineering tasks themselves. When Nexla ingests data, the first Nexsets are automatically generated. But then, these nexsets can be endlessly transformed, exchanged, and governed to fit the requirements of a wide-variety of use cases. Nexla platform and Nexsets enable:
Nexla platform integrates data from any data sources whether its data at rest or data in motion, data store or an API, SaaS Apps or files in any format. Our universal connectors let you connect any system in minutes and hours as opposed to days and months. Another special feature of our connectors is that they are bidirectional by design to give you the maximum flexibility. Nexla also understands that companies need to adopt cloud-native solutions while maintaining on-prem assets. Nexla deeply connects to the on-prem infrastructure in sharp contrast to simplistic SaaS data tools.
Once you have connected your systems and files, Nexla automatically ingests the data from APIs and streams or files and folders and from that point keeps track of all metadata at record-level. Just by looking at a few records, the Nexla engine is able to infer a number of data characteristics such as schema, data arrival time, APIs called, data for change detection, lineage, validations, audit-log etc. and packages it all as an automatically generated logical data entity called a Nexset. The nexsets can be used as is but are also ready to be combined, transformed, filtered, and enriched infinite numbers of times so that it becomes ready to use, prepared data. This dataflow can then be activated to run to any destination system that’s integrated with our universal connectors.
Nexla’s no code UI enables data users to get access to data and data flows in a self-service manner. They can create the nexsets and data flows themselves or have other users and engineers share it with them. Our philosophy is business teams understand their data the best and with our focus on lowering the technical barrier, they can take care of 80% of their data engineering requests themselves. But for those 20% of edge scenarios, they can phone their data engineering friends who can send help their way all while staying in Nexla.
Nexla’s integration and data sharing features enable a secure way of sending and receiving data between organizations. A data exchange setup this way serves the needs of both data receivers and data senders, thus helping companies embrace data-first approach where every business relationship is also grounded in data collaboration.
In Nexla, continuous monitoring occurs with respect to access to data sources, destinations, nexsets, and data flows. All aspects of data flows are continuously monitored and can be made available in monitoring tools of choice.
Data governance and data sharing go hand in hand. Data users need access to data without any friction but this in a well-governed way. Nexla supports advanced credentialing approaches that allow it to connect to multiple cloud as well as on-prem data systems. By monitoring which user was given access to what data, Nexla can automatically provision the data to the data system of choice.
Incremental optimizations and manual approaches to data have stopped working and you need to think about data operations in a whole new way. You need to get your arms around your data, processes, and culture before it’s too late. Learn from data leaders like Instacart and Poshmark and give your teams access to ready-to-use data so that they can leverage it and take your business to the next level.
Contact us to learn more.