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Driving Digital Transformation through Data Integration
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Driving Digital Transformation through Data Integration

Amid rising competition and significant disruption, enterprises generating business value via data will surpass their competitors. Businesses unlock intelligence through the intentional integration of data, which helps in augmenting productivity and overall efficiencies. Additionally, it helps in making strategic decisions to deliver actionable insights that propel the company forward.

The ultimate way to ensure it is sustainable is by moving beyond point-by-point, one-off integrations. Instead, businesses need to continuously strive for comprehensive and genuine interoperability among their enterprise systems, which necessitates a shift toward a culture of data.

Back in the day, integration meant combining two systems to maximize data coordination between the isolated points. However, industry leaders are increasingly exploring this space and discovering that data integration is more than that. In fact, it is an integral part of the digital transformation model. Therefore, a unified integration strategy needs to be in place for businesses running an extensive digital transformation strategy in an agile way.

Read along to know how data integration powers digital transformation and deeper insights.

Data integration: meaning and types

The process of taking data from different sources and combining them to achieve a unified, single view is called data integration. The consolidated data offers users continuous and consistent access to their data. Furthermore, it delivers a clear picture of important performance indicators, market opportunities, customer journeys, etc. The types of data integration are:

1. Data Consolidation

Data consolidation means bringing data together physically from multiple separate systems and creating a version of the combined data in a single data store. The objective of data consolidation is to decrease the number of data storage locations. Data consolidation is supported by ETL technology.

ETL gets data from various sources, transforms it into an understandable format, and transfers it to another database. The process of ETL cleans and filters data and even transforms it before applying business rules.

2. Data Propagation

It refers to the use of apps to copy data from one location to another. It can be done asynchronously or synchronously, and it is event-driven. Typically, two-way data exchange is supported by synchronous data propagation between the target and the source. EDR and EAI technologies support data propagation.

3. Data Federation

Data federation is a form of data visualization. It utilizes a virtual database to create a data model for heterogeneous data from several systems. All the data is brought together and viewed from a single access point. Data federation is supported by the EII technology, and it uses data abstraction to offer a unified view of the data. Then, the data is presented and assessed in various ways through apps. Data federation and visualization are apt workarounds for situations where data consolidation is not cost-effective or might cause too many compliance and security issues.

4. Data Virtualization

Data virtualization utilizes an interface to offer a unified and near real-time view of data from disparate sources with multiple data models. You can view data from one location, but it is not stored in that particular location. Virtualization retrieves and interprets data, but it does not provide a single access point or uniform formatting.

Building Data Foundations for Digital Transformation

1. Upskilling

Successful digital transformations require expertise and unique skill sets, including technical skills. Therefore, enterprises must identify and recruit individuals with specialized data skills. Moreover, companies must invest in training their employees to hone their skills or acquire new skills required to grow.

2. Cross-collaboration working

When it comes to incentives and goal setting, there’s no one-size-fits-all solution. To achieve organizational objectives, you need to promote a cross-collaboration working environment where all the teams engage and work together to build data infrastructure. Digital transformation doesn’t occur throughout an organization. Therefore, we need a mechanism to foster and support cross-functional work.

3. Technology

Tools, technology, and platforms activate the integral processes needed to drive growth and spur innovation through data. Implementing the right technology to embed data in key business functions impacts the overall success of your data strategy.

4. Understanding types of data

Some of the important use case activities differentiating organizations are fueled by how first-party data sources are managed. Businesses also need to consider how to incorporate the right governance practice to make sure their data is responsibly handled, well-structured, and compliant.

The different types of data are first-party, second-party, and third-party data. First-party data is the data collected directly from the readers or customers, and second-party data is information collected from another entity’s first-party data. Third-party data refers to a company buying data from outside sources like websites and platforms.

Benefits of data integration in digital transformation

1. Customer Experience (CX)

In the overall reporting, every touchpoint needs consideration when it comes to assessing data results. If any touchpoint is missed, the results will be inaccurate, and the business decisions will be skewed. You can prevent this from happening by utilizing data integration and having an avenue to merge and gather data from various sources. You can collect data from various touchpoints like social media, website, mobile, advertising, eCommerce, POS, and so much more. When you have a complete view of your audience, their pain points, and needs, you can improve the overall customer experience and know exactly what your customers are seeking.

2. Business Intelligence

Business intelligence or BI is a set of operations that help to bring out useful information from the available raw data. It helps companies make better decisions, identify data clusters, predict analysis and manage the business processes. Furthermore, it supports the development of better communication and effective collaboration between teams. And, it even supports decision-making pointers.

Data undergoes necessary manipulation at the data warehouse. After that, valuable data is obtained and it is held under various BI tools. Finally, decision-makers make use of the data to make in-depth analyses and extract relevant information.

3. Master Data Management

Master data management or MDM properly functions without integration, but integration is one of the main challenges for implementing MDM solutions. It is tricky to merge lists to create a master record because the same customer may have different contact numbers, names, and addresses across different databases. Therefore, a standard database won’t be capable of combining the data and solving such differences. Businesses need to use sophisticated data integration capability to pick up nicknames, typing errors, or alternate spellings. Through data integration, companies can understand the variations in a customer’s name, address, etc.

MDM solutions involve a wide range of transformation, data cleansing and integration processes. For every merged data source, MDM starts the required process to collect, identify, transform, and even repair data.

4. Data Integrity

Before data is useful for helping business decisions, it needs to go through several changes and processes. First, data needs analysis in raw form for analysis that can augment business processes.

Today, data is no longer static, but it is primarily digital. That’s why it gets transferred regularly to other systems and is often altered and updated. The updating and alteration of data may happen several times. This is how data integration and data integrity are related closely. The quality of your data integration software will impact data integrity.

As data is being replicated or transferred between regular updates and new types of data are collected, it must remain unaltered. But, data integrity might sometimes get compromised due to human error, errors occurring during data transfer, hacking or other cyber threats, compromised hardware or physical compromise to devices.

Data won’t be of much value without integrity and that’s why you need to take steps to prevent it from becoming compromised or corrupt. Corrupt data can become very harmful to your business and may even damage your reputation and credibility. Also, the loss of potentially sensitive data is a massive danger that no organizations want to go through.

Therefore, that’s why you must use the right integration platform. The platform must allow you to easily and securely merge data and provide all the updated information.

5. Data Visualization

Data visualization tools must be pre-built to be most efficient. They must be able to collect and integrate data from various sources. Thanks to data integration, the opportunities are limitless for businesses to explore their market and growth.  You can merge data into a visual format to make it more comprehensive and understandable. As a result, organizations can analyze the data more accessible and make strategic decisions.

Conclusion 

From analytics to business processes, everything is directly or indirectly dependent on data. Therefore, ushering in a full-blown digital transformation is impossible without deploying correct data integration techniques. Every organization must have access to a complete and unified format of data that aids them in decision-making and propelling the growth of the business.

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