The term “Big Data” has become wildly popular over the last decade. And rightly so. Businesses everywhere have realized the true potential of the data sitting in their systems. Analyzing this data to make well-informed decisions has become the backbone of any successful organization.
However, the same businesses are in a catch-22 situation, especially when they face the huge challenge of integrating data from multiple data marts.
Data Integration Strategy: Why is it Done?
Contrary to popular belief, data integration is not only about extracting bits and pieces of data from one place; it is also about transforming and loading before loading it into a destination, a process more commonly known as ETL. It is at the heart of every enterprise data integration strategy.
In effect, a data integration plan creates a data warehouse of high-value information that can simply not exist in the source system. Data transformations repurpose the existing data enhancing its value, both in its interim state and in the form of analytics derived from this data.
Last but not the least, enterprise data integration is about collaboration. It is about bringing technical IT professionals and non-technical business personnel in one place, unifying the data stored in each system to generate highest-quality information that benefits both stakeholders.
Needless to say, it is imperative for businesses to define an enterprise data integration strategy for their systems. Only then will they be able to generate real business insights for their operations.
Robust Data Integration: Which System to Integrate and Why?
Depending on the size of an organization, specialized teams will likely use purpose-built, independent systems to create and save data. Each stakeholder will utilize their own database to store data, which often does not correlate with another system within the same company. Each small mound of data in an isolated system creates data silos.
This means the key stakeholders (C-Suite), who need 360-degree visibility of the company in order to make strategic decisions, will have little or no knowledge of the data stored in isolated systems. This may result in poor decisions that could’ve been easily avoided if the management was aware of the foreseeable risk.
The ideal situation for a business involves complete integration of independent systems into one master data warehouse. But we don’t live in an ideal world.
Primarily, the following systems need to be integrated to create a holistic overview for a company:
- Financial systems: Financial planning and budgeting is key to business sustainability. When generating company-wide dashboards, every part of the financial system must be appropriately represented, and hence integrated. This would typically involve accounts payables, accounts receivables, general ledger, planning and budgeting, and consolidation systems.
- Sales and Marketing systems: Sales and marketing are key revenue generation functions of a company. Data silos may be created in the form of sales management systems, CRM, inventory management system, manufacturing and distribution systems, depending on the business model. Integration across all fronts empowers businesses to take strategic business decisions confidently.
- Human Resource systems: Last but not the least, the human resource systems hold information about the people who make it all happen. These primarily involve applicant tracking system, payroll, leave management, manpower planning, and basic administration modules. Without adequate visibility, human resources can be thrown off track, derailing the progression of the business as a whole.
How to Sync Data? Processes & Framework
Bringing data together from isolated systems is a lengthy process. Before jumping to integration tools and management strategy, the following items need to be addressed.
- First things first, the following questions need to be answered.
- Who will be involved in the process?
- What data systems require integration?
- When will the data integration occur? (ETL or ELT)
- Where is the data warehouse created? (On-premise or on cloud)
- Why is the data integration process being done?
- How to carry out the process? This involves defining the data integration strategy and framework to be used.
- Data Governance: To ensure the authenticity of data being extracted, transformed, and loaded, data governance needs to be established. This ensures everything is being tracked – who created the data, why was it created, who saw it, who edited it, how was it transformed (if at all), and how did it end up where it did. It is also referred to as establishing data lineage – a way to ensure the data remains trustworthy.
- Data Quality: This is a means to identify incomplete, duplicate, inconsistent, and incorrect data lines so that these can be excluded from analytics. While data governance tracks the source of data, the data quality processes verify its eligibility to participate in data transformation. This process is also known as data cleansing.
- Data Security: Data is powerful, even in the wrong hands. Integrated systems data is even more powerful given the amount of information and visibility it contains. Data security includes measures taken to ensure unauthorized access to data assets can be avoided. In part, data security is ordained by government rules and regulations to maintain privacy of everyone involved with a business.
Choosing the Right Data Integration Tool
Once you’ve done your homework, the next step is to pick the right tool. There are several out-of-the-box code-free data integration management solutions available in the market. Here are a few pointers that can help you pick the perfect one for your enterprise:
- Data Profiling and Validation: Get visibility into the type of data you have, its structure, quality, and integrity. The more you know about the type of data you have, the better you can decide how to use it. You should also be able to set custom rules for data cleansing and quality validation. This gives you complete control over data usage.
- Workflow Automation: In this era, automation is the key. Data integration engages resources and takes time. You need to schedule these activities at off peak hours to ensure minimal operational losses. Not only this, to ensure consistent data integration, you should be able to automate the process so that it takes place with minimal human intervention.
- Code-Free: The most important factor for the selection of an integration tool, however, pertains to the code-intensive nature of interfacing. If it requires copious amounts of codes to execute data integration and you do not have the kind of resource available within the company, you probably wouldn’t be able to do so effectively. You should ideally look for alternatives that offer drag and drop mapping functionality, eliminating the need for technical know-how in order to operate a data integration tool.
Data Integration Planning Checklist
When you’re on your way to mapping out an enterprise data integration strategy and selecting an appropriate tool, use the following checklist. These items ensure all your business needs are taken care of before, during, and after integration plan execution.
- Define Project Scope: This would typically answer the who, what, when, where, why, and how of data integration.
- Analyze Systems and DB: Examine compatibility between data assets and data transformation requirements to create master data in the way you need it.
- Analyze Operational Database vs Data Warehousing options: Do you need real-time data management or your integration solution is primarily built to tackle historical data?
- Ensure Data Quality: identify data cleansing requirements to ensure only high-quality data is fed into the system. Likewise, analytics will likely be high quality as well.
- Prepare Data: raw data may not necessarily be usable in its current state – identify how to make it analysis-ready with appropriate transformations.
- Map Data: Figure out how to eliminate data silos and ensure all pieces of data are fed into a single database where appropriate relationships are built to ensure data consistency.
- Data Refining: Identify how you intend to convert data into usable information (insights).
- Encourage Business Engagement: Data integration, for its apparent “techie” nature, may seem like a job for the IT professionals. However, its true value is revealed when appropriate representation from different business units are involved in the process.
Data integration is imperative for business success. From tackling data silos to creating valuable business insights, the integration framework can work wonders. The way data integration strategy is built, the tools which are used, and the process which is followed plays a pivotal role in determining the success of integration.