3 Reasons Why Now Is the Time to Implement Data Orchestration
Now is the Time to Implement Data Orchestration
Mark your calendar. 2023 is the year that data integration died. It has been 30 years since the number one vendor in data integration was founded. The next ten will be the demise of data integration, with modern technology replacing a generation of legacy systems. There are three reasons why data orchestration will emerge as the replacement for what has become a very splintered market.
REASON #1: It is necessary. The need for a data integration replacement stem from 2022 EMA research showing that the average enterprise maintains at least 6-8 different data integration or data movement technologies. The splintered market includes individual platforms for data integration, replication, preparation, API integration, streaming data, and messaging, along with separate platforms for data lakes or cloud technologies. Every additional platform adds additional cost, complexity, and constraint, and most of the modern platforms lack built-in governance and rich metadata services. Data orchestration has the potential to replace multiple traditional platforms with a single solution.
REASON #2: Itis unprecedented. Data management has remained relatively unchanged for almost25 years. This is the first time in 30 years that new technology innovation threatens to replace traditional data management platforms. We have seen three waves of change, culminating in this unique opportunity. The big data wave showed us the importance of semi-structured data, making data integration tools obsolete. The cloud wave showed us the importance of distributed computing and universal access to data, making way for new tools. The data orchestration wave redefines how data products are delivered in the modern world. Now is the time to make the shift to data orchestration.
REASON #3: It is urgent. Organizations that used to operate independently are now part of complex business ecosystems with significant relationships with suppliers, customers, partners, and investors. Orchestrating these complexities is vital to the success of the business. Since the speed of business will continue to move faster and become more complex, it is urgent to align business orchestration with data orchestration to address today’s needs and to prepare for a faster future. Now is the time to make the shift to data orchestration.
The Requirements for Unified Data Orchestration
Unified - Disrupting Decades of Single-Purpose Tools
Major shifts in data and analytics the last three decades brought several major shifts in data and analytics technologies, from data warehouses and data lakes, to managed services and SaaS, to data centers and the cloud. Every new shift was addressed with a new tool for data integration. The result is a plethora of diverse data management tools. To address the issues created by legacy data management approaches, data orchestration must be unified. It must support all data types, at all latencies, for all use cases, in all locations.
All data types. Unified Data Orchestration addresses the needs of all different types of data, with the ability to combine diverse data types, including both structured and semi-structured data. For example, e-commerce platforms produce JSON files with rich text and digital images. These data formats must be easily combined with structured data without having to use multiple tools. The combination of multi-structured data provides richer insight and a more complete context for the insight being mined.
All latencies. Unified Data Orchestration processes both streaming and batch data, with the ability to combine data from both latencies in a single data pipeline. For example, a call center technician needs access to historical data and real-time transactions to help a customer who calls in for guidance seconds or minutes after making a new purchase. The connection of multi-latency data lays the foundation for immediate, intelligent responses to business events as they occur.
All use cases. Unified Data Orchestration convers a broad range of use data use cases, including data collection, movement, replication, CDC, integration, quality, governance, transformation, analysis, and observability, making it the perfect platform for the unification and consolidation of legacy data management platforms. For example, data coming from a network of devices in the Internet of Things requires cleansing of noise from the data, extraction of critical data, transformation to a consistent format, and integration with historical data for context. The amalgamation of multiple use cases in a single platform makes data pipeline automation and optimization more accessible for small and medium enterprises.
All locations. Unified Data Orchestration. Unified Data Orchestration provides access to data everywhere, including SaaS, IoT, cloud, multi-cloud, on-premises, and hybrid data storage configurations. It makes it the platform for modern data ecosystems where data moves in and out of systems in all directions. For example, smart cars capture data from on-car sensors. That data can be stored, prepared, and analyzed in the cloud, with insight feeding into SaaS engineering platforms for product design or automating actions within the vehicle. Singular orchestration from collection to automation operationalizes machine learning and other actionable information with minimal effort.
Orchestration - Disrupting Deficient Data Management Tools
The evolution of data over the last 30 years has spawned numerous new technologies and even more opportunities for the exploitation of data. However, as new opportunities emerged, more purpose-built tools were built to address new data types, data storage, and data location. The result has been numerous tools designed, marketed, and oversold to do specific tasks. Each individual tool has expanded capabilities, trying to re-architect to meet changing requirements. To address the issues created by single- use case tools, there must be one single data orchestration platform. Therefore, orchestration must be distributed, visual, reusable, automated, governed, intelligent, and centralized, providing quicker access to more accurate data.
1.Distribution. Unified data orchestration combines distributed computing at the core and agent-based software execution to create the architectural foundation for automation, optimization, and centralization.
2.Visualization. Unified data orchestration uses a drag-and-drop design interface to support a low-code or no-code approach to the development of complex data pipelines, providing accessibility to users without experience in data engineering.
3.Reusability. Unified data orchestration separates data pipeline logic from execution, maintaining reusability percentages as high as 80% and ensuring maximum reuse of code as new data platforms enter the marketplace.
4.Automation. Unified data orchestration automates formerly manual and menial tasks in data engineering, enabling data scientists and engineers to focus more time on value creation and innovation.
5.Governed. Unified data orchestration is fully governed and synchronized, covering both data in motion and data at rest. When rich metadata is automatically generated and lineage available for active use, it streamlines the process of delivering on the promise of enterprise data governance and improves compliance measures.
6.Recommendation. Unified data orchestration uses historical data to make recommendations on the next best actions, potential opportunities, and potential risks.
7.Optimization. Unified data orchestration uses complex optimization to address issues like break-fix and resource conflicts when multiple, even thousands of data pipelines are deployed.
Centralization. Unified data orchestration centralizes all administrative tasks for designing, deploying, automating, and optimizing data pipelines.
Platform - Designing the Future of Data
For the last several decades, software features and functions have taken the spotlight, with most organizations making buying decisions based on a set of capabilities Hey deem necessary and advantageous. Product architecture has taken a backseat, with most software architectures mirroring current trends around computing, storage, and networking technology. The result has been a 10-15-yearlifecycle for each new architecture, after which the former architecture renders the software obsolete. To address the issues created by short-sighted architectural decisions, data orchestration takes a platform approach, building an architecture for long-term viability regardless of shifts in infrastructure technology. Therefore, data orchestration architecture must be cloud-first, serverless, elastic, agent-based, secure, boundless, governed, defined, active, and enterprise-ready.
Cloud-First. The domination of the cloud demands that unified data orchestration be developed entirely for the cloud, with consideration that:
Serverless. Unified data orchestration removes the constraints of a server.
Elastic. Unified data orchestration scales up and down automatically.
Agent-Based. Unified data orchestration utilizes agents to provide specific data functionality on top of the distributed architecture.
Secure. Unified data orchestration makes security a requirement; security is built in, not just tacked on.
Boundless. Unified data orchestration knows no boundaries.
Defined. Unified data orchestration is fully defined. When rich metadata stands behind data, analytics, and ML, it increases accuracy, automation, and credibility.
Active. Unified data orchestration is fully activated. When metadata is used actively as part of data services and data sharing, it increases the frequency of data-driven decisions.
Enterprise-ready. The sum of all these modern architectural decisions makes unified data orchestration immediately enterprise ready.
The Seven Benefits of Unified Data Orchestration
Data Engineering Transformation - from cost center to value creation
1.Faster time to value. With data integration taking up 75% of every analytical project, most companies need to take out a construction loan to operationalize insight. They pay for the insight several times before they produce insight that yields a return on their investment. With data transformation, movement, and integration on a single platform, companies can expect to reduce time to value for their analytics and ML by up to50%. Consider what it would be like to produce a single data pipeline that captures all the necessary data, automates the analysis, and delivers insight to both decision-makers without manual intervention.
2.Increased value creation. Iteration is the key to improving analytical accuracy, especially when it comes to predictive and prescriptive models. However, when data engineers, data scientists, and data analysts all use different tools to
prepare and analyze data, the process slows down. By simplifying the delivery of analytics, companies will be able to iterate faster, continually improve analytical outcomes, and create more value using analytics and machine learning.
3.Competitive analytics. Most organizations spend a lot of money preparing data and utilizing machine learning to differentiate their business. However, due to technology and resource constraints, only a small percentage of organizations differentiate themselves based on analytical advancements. Unified Data Orchestration enables companies to combine analytics and ML in new ways to create more competitive analytics in ways beyond their competitors.
4.Accelerated innovation. Innovation is the new oil, and the speed at which a company innovates separates extraordinary companies from the ordinary. Ultimately, the increased speed of innovation cycles gives organizations the ability to dominate and disrupt markets based on accurate intelligence. So, imagine a company orchestrating data on a single platform. Two things will happen. One, innovation takes place in cycles; those cycles will be complete in record time because they no longer must move data from platform to platform to deliver insight to the front lines. Innovation will take place in two different arenas: business and analytical innovation. Organizations that orchestrate will create new business models and deploy new analytical models at a faster pace than the competition.
Data Engineering Efficiency - from 70% of analytical projects to 25%
5.More strategic resource allocation. Every data engineering organization is being asked to do more with less. Successful teams find ways to optimize the use of their time for the greatest analytical return. Data orchestration unifies and consolidates data management platforms, freeing up to 80% of your data engineering team to work on more strategic projects and shifting the focus of data engineering from data preparation to data science. In addition, more meaningful work for data engineers increases their commitment to your organization, reducing churn and increasing productivity.
Resource allocation also flows out to the rest of the organization. From an IT perspective, there will be cost savings from more efficient use of computing and storage resources, especially with Cloud Unified Data Orchestration. Ultimately, business analysts, business users, and executives will save time finding and processing insight to make decisions. The result is better decisions faster.
6.More optimal reuse. There will always be a new data migration. With the speed of innovation constantly increasing, we can expect the next migration to come sooner than the last. Itis Moore’s Law that is applied to data management technology. The unification of data orchestration on a flexible software architecture allows organizations to deploy once and use the code many times, guaranteeing up to 80% reuse of all code in future migrations.
7.More seamless alignment. The latest trend in strategic business management theory focuses on business orchestration, with leading companies creating strategic positions for orchestrators skilled at making several moving parts of the ecosystem work more efficiently together. With all data and analytics orchestrated in a single platform, technical teams more easily align their work with business requirements and objectives, making themselves invaluable to the business.
Make the Move to Unified Data Orchestration
Now is the time to make the move. The technology is in place to meet all the requirements of Unified Data Orchestration. Platforms like PurpleCube AI are already helping organizations centralize all their data pipelines with the industry’s only fully unified data orchestration platform. With its distributed, agent-based, metadata-driven architecture, it is ready for your next set of modern data pipelines or the migration of your most complex and challenging data pipelines.
Because of broad use case coverage, the effort to move to PurpleCube AI is simple. The trial version is easy to download, install, and get up and running on local hardware or a cloud instance with any of the major cloud vendors. Download the trial version today or send PurpleCube AI a message for more information: contact@purplecube.ai
