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Your path to becoming a data-first enterprise by integrating structured and unstructured data from different sources in your organization.
With digital transformation, a sharp focus has come on disparate data sources. More CIOs are realizing the need for data integration and data agility.
Most data gets accumulated in segmented silos, warehouses, data lakes, databases, even spreadsheets.
It is becoming increasingly complex to manage disconnected sources of data, especially when budgets are restricted and data teams are expected to do more with less.
With the volume of data rising every single day, it is essential we address these challenges. The two approaches to solving the data integration problem is discussed in detail in the sections below.
Who needs data integration?
Data integration isn’t just for large enterprises. At this point, just about every organization can benefit from a data integration strategy, because every single business needs data to compete effectively.
Most enterprises use multiple applications to support their business, such as CRMs, accounting applications, and asset management systems and even standard spreadsheet software.
Data is locked in silos in each of these applications, which can result in disconnects and miscommunications between processes. If important decisions are based on misinformation resulting from these disconnects, the results will be detrimental to a business’s success.
Focus on data integration, not application integration
Organizations must integrate all data sources, including clouds, applications, servers, data warehouses and the likes. This not only maximizes the value of existing infrastructure investments but also streamlines the data warehouse, reduces workloads, IT backlogs and puts valuable data at business users’ fingertips.
Enterprises can solve the data integration complexity through application integration and system orchestration. Yet in terms of achieving quick insights or integrating data for answering business questions, this is not an optimal solution.
Deep application integration is not ideal for flexibility, modularity and composability. It is also time-consuming, can cause business disruption and often adds complexity to data warehouses.
A cloud-based analytics platform brings the right data to the right user in a single environment. This approach amalgamates data into automated pipelines, while also achieving governance, control and actionable insights.
Cloud-scale integration
A good cloud integration platform seamlessly integrates data from – applications, systems or sources across the entire business or beyond -and transforms it to be used more effectively. The cloud platform should ideally turn data integration into a one-stop-shop that provides certain unmatched features.
- Integration and automation: Prevent dark data by automating the ingestion, cleaning, and blending of data from any source—cloud, data warehouse, legacy systems, user desktops—without the need to build new data warehouse. Data can also be fed into any business intelligence or artificial intelligence/machine learning platform for further analysis.
- Governance and control: IT can give users the data they need for analysis—in the tools they want to use—with granular access rights. Plus, IT retains control with access and data-flow monitoring capabilities built to the best security standards and compliance certifications.
- Performance. Ensure that data and processes flow at speed with parallel processing functionality. Also, provide users with self-service capabilities to quickly request dataset access with a few clicks.
A cloud integration platform can help organizations dynamically integrate data from thousands of sources and systems. With a single source of truth, businesses can finally derive value from a vault’s worth of data.
3 Steps on the data integration roadmap
Organizations must combine all its data regardless of where it resides in order to obtain any value from it. That requires connecting data sources, shining light on dark data, processing and cleaning data in real-time and automating analytics environments.
So, where should one start from? The answer lies in building a data integration roadmap.
Let’s look at 3 crucial pieces of advice from technology experts.
1. Put context around data
The first step in the data integration roadmap is understanding what data you have. This involves looking at the volume, breadth, velocity and integration requirements of different data sources. While you are at it, shine light on dark and unstructured data. Also, limit the number of locations where unstructured data is stored. This saves your organization from legal or regulatory risks.
When you begin to discover and catalog data, the process puts context around it. So, it is important that you understand the current state of your data as well as the desired outcome. Hence, map out where you are on your data integration journey and where you want to go.
2. Define roles and objectives
Define your objectives and identify the problem you are addressing.
Try asking questions like,
- Who is managing the data stakeholder?
- What story with the data are we trying to tell?
- Who is responsible for developing and elaborating the business case?
The next step is to start dropping your data story into a framework that addresses risks and the data life cycle.
Lastly, think about data as a complete life cycle—from acquisition to insightful analysis. Don’t abandon data; ensure it’s integrated into the total data picture. If you’re only analyzing 10–15% of your data, 85–90% of your business insights are still not being realized.
3. Look for the right data solutions
Explore solutions that best fit your business. It is important that you build a system that can anticipate and respond to a changing environment as well as evolving technology. So make sure you have a culture that adapts to change, thereby helping you stay ahead of the evolving data landscape.
Furthermore, a data integration roadmap should take into consideration emerging technologies. Next-generation data integration platforms will guide organizations to automatically discover different data types and claim granular access controls for downstream data consumers.
Your data integration roadmap should be driven from ‘state-of-the-art’ data infrastructure, as opposed to an ‘infrastructure you are struggling with.
Summing up
The ultimate goal of a data integration roadmap should be deriving data value. If you don’t know what your data means, if it isn’t benchmarked properly or it’s just plain confusing, then both your business and your customers will be lost.
To learn more about data integration, we encourage you to speak to our in-house data warehouse experts.
