Data is the greatest source of competitive advantage available to businesses today. The largest companies in the world like Amazon, Facebook, and Google, have achieved dominance by collecting, managing, and utilizing their data. Fortune 1000 companies are eager to follow their lead, with 99% of them reporting investment in data science and AI, according to a recent report by NewVantage partners.
But many companies are having difficulty trying to achieve their data goals. Less than half of the companies in the study above have found success in key metrics, such as driving innovation with data, managing data as a business asset, or creating an overall data-driven organization.
Some of the common hurdles they face when trying to become more data-driven includes:
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The Composable DataOps model helps businesses address each of these concerns. It provides a holistic set of tools and processes for ingesting, managing, orchestrating, and operationalizing data throughout your entire organization.
Though the DataOps model is relatively new, it builds on ideas that have been driving major business efficiency gains for decades, such as agile development, DevOps, and lean manufacturing. Here’s a quick overview of each of those terms and how they contribute to DataOps.
There are many ways that Composable DataOps draws inspiration from DevOps. For example, DataOps practitioners want to minimize the end-to-end cycle time of transforming raw data into business intelligence, which is similar to how DevOps practitioners strives for continuous integration and continuous delivery (CI/CD). Other parallels include the use of automated testing and version control.
Composable DataOps approaches your data in a similar way. By viewing your data lifecycle as a pipeline, then analyzing and applying optimizations at each step and process of that pipeline, it helps to improve end-to-end efficiency and the total satisfaction of your end users.
The Composable DataOps model isn’t just a collection of best practices drawn from these established frameworks, though. It builds on these foundations, adding new elements specific to the needs of data science teams that want to achieve repeatable, efficient workflows.
Automated Data Pipelines
The concept of automation is central to the DataOps methodology. Data that’s been ingested into your systems must be tested and processed before data consumers can use it to build analytics or machine learning applications. Historically, that has meant coding isolated scripts (e.g., in Python), which is both time and skill intensive.
The Composable DataOps model replaces that manual work with intuitive automation capabilities. It gives engineers and end-users the resources to build and modify automated data pipelines using low-code or no-code interfaces, allowing them to perform integration, aggregation and other transformations with no scripting expertise or input from expensive subject matter experts (SMEs).
According to the Harvard Business Review, 77% of business executives report that adoption of analytics initiatives is a “major challenge.”
By automating extract, transform, and load (ETL) tasks, quality checks, data reporting operations, and other time- wasting processes, the Composable DataOps pipeline empowers your team to confidently scale up the number of data sources you’re using to achieve greater insight, while tearing down data siloes and providing a more efficient and repeatable process.
Embedded Enterprise AI
The data ecosystem of most large and midsized businesses is complex. In most scenarios, this complexity makes properly ingesting and managing data a complex and time-consuming task; in others the complexity is so overwhelming that it completely undermines the data science or analytics effort.
Composable DataOps platforms embed enterprise AI and machine learning capabilities deep into the data management workflow, allowing data engineers to achieve a level of speed, consistency, and accuracy that would be impossible through automation alone.
One area of particular importance is master data management (MDM). Composable DataOps tools read and identify incoming data to find potential quality problems, then apply the appropriate tags and annotations in real-time.
AI has many other applications within the Composable DataOps framework, for example in streamlining data-driven workflows that involve large sets of unstructured data or paper documentation. Extracting relevant data from paper invoices, contracts, and financial statements with machine learning algorithms relieves your staff of one of the most tedious and time-consuming data management tasks, allowing them to focus on higher value work.
Composable Architecture
Another key component of the Composable DataOps approach is composability. Composable design architecture divides systems and applications into independent, flexible microservices that your team can deploy and combine as they need to create powerful pipelines and applications.
A composable architecture has several benefits:
The first is greater agility. Communicating through APIs, each part of a Composable DataOps workflow functions independently, with its own set of dependencies and libraries. This empowers your team to choose a technology stack that best meets each part of the data pipeline and add new workflows without affecting existing ones.
Another is scalability. Analytics and machine learning applications must be ready to ingest and process an ever-growing amount of data. Composable architecture allows your team to provision compute and storage resources to meet the demands of those applications as needed, so you can make granular provisioning decision as needed.
Now that we understand what the Composable DataOps approach is, we can explore what benefits a business can expect from implementing Composable DataOps tools and methodologies.
Streamlined Data Governance and Compliance Efforts
Under the best of circumstances, businesses struggle to achieve and maintain compliance with the latest regulations. But growing data volumes and new regulations like GDPR and CCPA have exacerbated the technical and operational hurdles to starting or expanding a data science program.
Adopting DataOps methodologies allows businesses to flip how they perceive those compliance challenges. Instead of compliance enforcement as a tedious process that hinders data science projects, it allows them to proactively embeds security and compliance controls into their workflows with automation and AI, so that compliance becomes a seamless, foundational part of their efforts.
This not only reduces organizational complexity, but it also empowers their data scientists to embark on ambitious projects with confidence that the right policies and protections have been applied. Here are some of the other ways Composable DataOps helps businesses maintain regulatory compliance:
A study from 451 Research shows that 66% of respondents to their survey say that greater security and compliance is the top reason to begin implementing DataOps tools and strategies.
Improve Data Utilization and Quality
Low-quality data is one of the biggest hurdles for businesses that want to become more data driven. According to the Harvard Business review, poor quality data collectively costs businesses $3 trillion every year; similar statistics from Gartner show that data quality issues cost the average midsized company $12.9 million every year.
By proactively streamlining and automating the ETL process, and applying data quality assurance checks, Composable DataOp platforms ensure the accuracy, relevancy, and completeness of new data assets with minimal input from your team. In addition, the Enterprise AI capabilities of some DataOps platforms can also help clean existing data, fix manual data entry errors and missing data fields, and automatically address data unification tasks, helping your company achieve a reliable single source of truth (SSOT).
Self-Service and Data Democratization
By implementing Composable DataOps tools and practices, companies discover that they can eradicate many of the barriers that may have prevented data analysts from doing their jobs more efficiently.
Most Composable DataOps platforms provide an intuitive visual workspace that allows data professionals to interact with and customize data pipelines, without the need for programming knowledge. Strong data governance coupled with ease-of-use features allows non-engineers to design powerful data workflows, drawing data from on-premise and cloud sources, transforming it into the desired format, and loading it into analytics or related applications.
Replacing cumbersome scripts with automation and self-service accelerates the analytics process, enabling data consumers to work within their domain of expertise — statistics and analytics — without risking miscommunication with your data engineers, while also allowing data engineers and scientists to focus on other, more complex work.
Strengthen Cross-Team Collaboration and Data Orchestration
Data science teams are often complex, with engineers, business analytics, database administrators and managers, often from disparate business groups or divisions, trying to unify around a set of goals and initiatives.
Under previous data management paradigms, the assets of one department rarely makes it into the analytics workflows of another without a significant investment of time and resources. Naturally, this has a dampening effect on the enthusiasm for ambitious, cross-departmental projects. If inefficient collaboration becomes chronic and damages analytics outcomes, it may also undermine the data science initiatives altogether.
By standardizing the way that data is managed across your entire network, and giving each of your teams a common language, toolset, and set of processes they can use to share ideas, resources, and insights with each other, the Composable DataOps model provides companies with everything they need to achieve a high level of coordination.
Uncover “Dark” and Underutilized Data
As discussed above, enterprises are drowning in their own data, and a lot of that data is going to waste. Gartner calls the data assets that businesses collect and store but fail to use for other purposes “dark data.” Forrester research has estimated that dark data accounts for around 73% of all enterprise data.
The problem of dark data is a particular problem for companies with legacy environments and processes or heterogeneous systems that don’t communicate well. In these environments, it’s easy for valuable data assets to fall through the cracks, which is a major missed opportunity.
The Composable DataOps framework provides transparency and control of all data contained in your network. This visibility will dramatically reduce the number of data dark spots in your systems and help you maximize the value of your company’s data assets.
Composable Analytics, Inc. builds software that enables enterprises to rapidly adopt a modern data strategy and robustly manage unlimited amounts of data. Composable DataOps Platform, a full-stack analytics platform with built-in services for data orchestration, automation and analytics, accelerates data engineering, preparation and analysis. Built with a composable architecture that enables abstraction and integration of any software or analytical approach, Composable serves as a coherent analytics ecosystem for business users that want to architect data intelligence solutions that leverage disparate data sources, live feeds, and event data regardless of the amount, format or structure of the data. Composable Analytics, Inc. is a rapidly growing data intelligence start-up founded by a team of MIT technologists and entrepreneurs. For more information, visit composable.ai.
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Composable’s mission is to enable every organization to become a digital master by adopting a disciplined and intelligent approach to Data Operations and Enterprise AI. The Composable DataOps Platform, built on years of technology development at MIT and the US Department of Defense, now enables leading organizations to build data-intensive Enterprise AI applications.
