3 Biggest Challenges for Data Integration

Next Story

5 AI Predications for 2019

Developing a big data strategy is a far from simple task – but it’s one that needs to be completed sooner rather than later if you want to remain competitive in the coming years. Over time, companies have collected and compiled a huge volume of data. From the very first moment they started recording transactional data right up to the present day, the amount of data has been mounting up. Add to this the increase in usage of modern technologies, networks and services – mobile phones, sensors, social media, etc. – and the scale, volume and variety of data they’re now dealing with is potentially astronomical. Now, this vast amount of data needs to be strategically utilized to enable companies to extract actionable insights that were previously concealed. This requires a solid big data strategy – and data integration is one of its most important elements.

Simply put, data integration involves combining data from two or more disparate sources into a single, unified view, enabling the centralized analysis of the combined datasets to unlock the insights contained within. In today’s data-driven economy, data integration is more crucial than ever, as everything from operations to customer satisfaction and business competitiveness depends on an organization’s ability to merge diverse datasets and extract value. Moreover, as more and more organizations pursue digital transformation initiatives, their ability to access and combine data from multiple sources becomes even more critical.

Data integration usually takes place in a data warehouse, and requires specialized software to host large data repositories and extract, amalgamate, and then present the information in a unified form. However, even with modern tools, there are number of challenges that are likely to be encountered when embarking on an integration project. Let’s take a look some of the biggest.

  1. Heterogeneous Data

One of the biggest challenges that will likely crop up during the integration process is dealing with data in heterogeneous forms. Most organizations collect data from multiple locations – customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, etc. – with different data types stored in different formats. However, a single integration platform might not support such data heterogeneity – it must all be homogenized for accurate and effective analysis.

To overcome this challenge, it’s essential to have an awareness of heterogeneous data formats from the outset, and so a detailed evaluation and analysis of the characteristics of the various data formats must carried out in the early stages of the project. Next, the database developer must convert the information into a format that the integration platform can handle. Though this can be a major and time-consuming exercise, there are automated data transformation tools now available to minimize obstacles.

It is also worth considering whether you need to integrate all types of data. Many organizations will find that they have a number of datasets with very little in common. In such cases – and especially due to the fact that data homogenization can be difficult, time-consuming, and expensive – business value may in fact be maximized by analyzing certain data sets separately, as opposed to integrating everything for the sake of it.

  1. Poor Quality Data

Data integration projects will only ever be as good as the data an organization starts with. As such, data quality is a top concern in any data integration strategy. Any impurities in the data will result in poor insights and ultimately decisions being drawn from it.

This can be a compounding problem. When inconsistent or even invalid data is used to draw insights, the faulty analytical data will be passed downstream, allowing for even more inconsistencies to emerge, and eventually resulting in a disastrous and ineffective big data environment with all datasets effectively corrupted.

The issues surrounding data quality persist throughout the entire lifecycle of any data integration system. As such, best practices in quality assurance must be established from the outset of the project, with roles and responsibilities clearly defined to ensure that both the development phase and ongoing use of the system is kept free of all bad data.

  1. Scalability

Data integration projects can grow exceptionally quickly due to the massive inflow of data from multiple sources into a single system. When this happens, many organizations can be caught out by the fact that pretty soon the demand for both additional storage capacity and processing power is going to increase.

Organizations need to anticipate the extent of growth in the big data environment before selecting an integration solution. They might also do well to consider a piecemeal approach, where they look at each data point individually, evaluate their respective value within the overarching big data strategy, prioritize, and then integrate them one at a time. For example, let’s say an organization wanted to merge data from three separate applications – a CRM system, a product database, and a merchandise management system (MMS). The data within each could be broken down into individual datasets, such as financial data, sales data, and customer information. These could then be prioritized and integrated one at a time, allowing the organization to scale the operation gradually.

In any event, the need for additional storage will emerge at some point. As such, cloud-based and hybrid solutions provide the answer for many organizations, as they offer the scalability to deal with growing data demands. According to Gartner, 20% of large organizations are already beginning to implement hybrid integration platforms to meet their scalability needs. Solutions such as Actian DataConnect11 provide a comprehensive and powerful hybrid integration solution that enables organizations to quickly and easily design, deploy and manage integrations on premise, in the cloud, or in hybrid environments with no limits on data types or volumes.

(Video source: youtube.com)

Final Thoughts

The challenges of implementing a dig data strategy are very real – but so too are the benefits. You will need to do your research, and choose your integration solutions wisely, for the complexities involved when attempting to combine multiple sources of data that were each created in isolation are many and varied. As such, successful integration requires deep knowledge, expertise, and comprehensive planning. When executed effectively, the advantages of creating one cohesive, reliable, resilient and secure source of information outweigh the strains, and will provide your organization with a significant competitive advantage in the increasingly data-driven business landscape.

The following two tabs change content below.

Terry Brown

Terry is an experienced product management and marketing professional having worked for technology based companies for over 30 years, in different industries including; Telecoms, IT Service Management (ITSM), Managed Service Providers (MSP), Enterprise Security, Business Intelligence (BI) and Healthcare. He has extensive experience defining and driving marketing strategy to align and support the sales process. He is also a fan of craft beer and Lotus cars.