Change is the only constant in today’s business environment. Whether you’re growing your business, entering new markets or undergoing a merger or acquisition, your business - and its data - is evolving and expanding.

Proper data quality management ensures improved customer service and relationships, more efficient operations, lower costs and more revenue-generating opportunities - all of which can contribute to Return on Investment (ROI).  As a result data quality management has become a critical part of IT management.

Data management often overlooked

As an organisation evolves over time, its ability to manage existing data and to incorporate new data management demands grows in importance. Data cleansing becomes fundamental to business’ continuity in order to preserve the integrity and quality of data for migration when deploying new systems or retiring legacy systems.

There is a similar need during mergers or acquisitions where data integrity should be an early part of the due diligence process. It simply ensures that the companies can easily continue with business post-merger. But you would be surprised at how often this is overlooked.

The downside was illustrated recently when the Bank of Scotland was fined £4.2m following a failed merger with Halifax mortgages. The resulting damage was not just limited to the size of the fines. Company reputation and executive credibility also suffered in the process.

Data Integration

Going hand in hand with data quality is data integration. Data integration is the art of making things that are not designed to work together - work together. Data integration projects often occur because of time-sensitive business initiatives like the acquisition of one business by another. Other examples of data integration projects include:

  • Application-to-application (A2A) or enterprise application integration (EAI) - the process of linking such applications within a single organisation together in order to simplify and automate business processes to the greatest extent possible, while at the same time avoiding having to make sweeping changes to the existing applications or data structures. In the words of the Gartner Group, EAI is the “unrestricted sharing of data and business processes among any connected application or data sources in the enterprise.”

  • Business-to-business (B2B) integration, particularly important within the supply chain - automating the way a business connects, communicates and transacts across extensive global supply and demand chains can have a massive impact on the overall cost of doing business.

Good data versus bad data

In an organisation, there will be both ‘good’ and ‘bad’ data. ‘Good’ data connects with business people and processes to create powerful solutions that support a specific set of business use cases. ‘Bad’ data quickly results in inadequacies in business processes that rely heavily upon data, which can result in incorrect business decisions being made.
Whether business data is ‘good’ or ‘bad’ is a result of how the business has integrated the sources and consumers of information within the company and how it has created a data architecture. A data architecture is composed of models, policies, rules or standards that govern which data is collected, and how it is stored, arranged, integrated, and put to use in data systems and in organisations.

Good Data: how the business can benefit

Recent advances in technology are creating opportunities for data architects, business analysts, programmers, and business users to collaborate in order to address business challenges with greater speed and more flexibility. As a result, innovative ways to manage a company’s information architecture that were once out of reach are now possible. However, many of these advances are relatively new and are not widely used yet. For instance, data integration tool and process advances have made a more agile development process possible. Such tools and processes work by examining data from the existing data flow with an inference engine, which then guides the user to rapidly reconstruct the mapping.

Traditional approaches to reverse-engineering existing data maps are either entirely manual or rely on reports from old transformation engines. Data mapping is where two distinct data models are created and a link between the models is defined. It works as a unique model to determine relationships within a certain field of interest. This is the fundamental first step in establishing data integration of a particular concern or interest.

Often companies don’t plan from the outset for a technology infrastructure that is flexible and scalable enough to support data quality processes throughout the enterprise. If planned well, data quality management practice can:

  • Refine data - examine and correct the business’ transactional data automatically and implement rules throughout the business in real-time

  • Cleanse and analyse Data - enable organisations to use business rules and reference data dictionaries to analyse and standardise free-form text data elements

  • Profile data quality - give organisations the tools to implement business rules and reference data to analyse and rank according to completeness, conformity, consistency, duplication, integrity and accuracy

Data architecture and data integration working in unison

Widespread business benefits are possible when companies make their data architecture and data integration projects complementary components of their overall information framework. These benefits amass because projects run more efficiently as everyone in the organisation works in unison toward a common goal. The key benefits of a business whose data architecture and data integration projects work in unison include:

Common best practices and shared assumptions can be communicated

  • IT and business units can communicate better with each other

  • IT staff focus on activities that contribute to the health of the business

  • Business keeps pace with a high rate of change

  • Ability to experiment with new business models becomes practical

  • Better management of a variety of local business needs

  • Able to more successfully outsource data mapping and other data processes

  • Costs are reduced through shorter time to market and greater staff productivity

  • Strategic and tactical business requirements are balanced

In order for a business to succeed, the pursuit of new process efficiencies needs to be pervasive throughout the enterprise. Potential new business value can be found in some unlikely places and one of those sources of new business value is the abstract world of data architecture.

Mikko Soirola is VP of IT integration specialists Liaison Technologies