On behalf of the Californian business software provider Veritas Technologies, the research company Vanson Bourne surveyed 1,500 IT decision-makers in 15 countries worldwide (including Germany and Switzerland) at the end of 2018 about their challenges and successes in dealing with company data.
The survey confirms: Ineffective data management is a significant burden on organizations, impacting efficiency, productivity and profitability. Companies estimate that they lose over two million US dollars each year due to the daily struggle with data management challenges. What's more, IT decision-makers waste two hours a day searching for relevant data.
The importance of data quality in master data management cannot be overestimated. Almost all IT managers (97%) state that the daily challenges of data management have a major impact on their organization. 38% state that their company's strategic decisions are delayed due to ineffective data management processes. Missed new revenue opportunities were cited by 35% of respondents, while 34% each complained of limited cost savings and a slowdown in the development of new products and services.
Ineffective data management characterized by silos also has a long-term impact. 95 percent of companies report such long-term consequences. These include increased operating costs (39%), impaired employee productivity and efficiency (36%), lack of agility (35%), loss of competitiveness (29%) and increasing customer dissatisfaction (25%).
In view of the clearly negative effects of ineffective data management, the question arises as to what the particular challenges of day-to-day data management are. There are too many different data management systems to manage, say 40 percent.
Almost as important are rising costs that make data management difficult (39 percent) and too many complex data sources that are difficult to analyze (38 percent). 34% of respondents say they lack the right skills/technologies to exploit the potential of data.
IT managers consider ensuring data compliance (83 percent), the visibility and control of data (81 percent), the speed and reliability of data access (80 percent) and the simple sharing of data across business functions (80 percent) to be particularly in need of improvement.
Unattainable? Not at all! Companies that have implemented effective data management initiatives report competitive advantages. In terms of increased data compliance and reduced data security risks, 38% say "We are already experiencing this" and a further 43% say "We are already experiencing this, but could do more".
Other realized benefits include reduced costs (23/49 percent), using data to drive new revenue/market opportunities (25/46 percent), increased productivity (27/44 percent), fewer data silos to achieve greater data efficiency (25/43 percent) and increased customer satisfaction (25/45 percent). In addition, IT leaders believe there is significant financial justification for making their organization's data management functions effective: They expect an average ROI of 2.18 for every dollar invested.
An active approach to data management is required
The latest figures from Vanson Bourne show once again that data management problems are a global phenomenon - and that they have a significant negative impact. What's more, data quality problems are not going to disappear by themselves, nor can it be a case of companies simply having to learn how to deal with them. On the contrary, they tend to become more extensive as companies' data volumes continue to grow and data needs to be integrated into different IT infrastructures, notes Christopher Tozzi from Syncsort.
However, it is also often the case that "data quality in the context of timeliness, accuracy and completeness of data is not the real issue at all, but rather the quality of the information that describes the data (i.e. the metadata)," says Monica Richter, Chief Content Officer at Dun & Bradstreet. But information is the basis for business decisions. This is where we approach the relationship between data quality and business outcomes. Many organizations struggle, according to Gartner, to propose a program to sustainably improve data quality.
Effective engagement (of business units) and funding can be limited for a number of reasons. One of these is that there is no clear link between improving data quality and business outcomes (although this can be demonstrated, as Vanson Bourne's findings show). It is therefore crucial to develop compelling business cases that link data quality improvement to key business priorities. "Data and analytics leaders need to understand their organization's business priorities and challenges. Only then will they be in a position to build compelling business cases that link data quality improvement to key business priorities," said Ted Friedman, vice president and analyst at Gartner. Ironically, Friedman adds, one of the main reasons for unsuccessful data quality improvement business cases is that they focus on data quality. To be successful, business cases need to address the key components required to achieve business objectives, such as financial performance, operational performance, regulatory and compliance, and customer experience.
Anyone concerned with the interdependency of data quality and business results will not be able to avoid master data. The need for clean, coded, standardized and expertly mastered master and reference data content that is seamlessly integrated internally into methodologies, processes, workflows and platforms - as well as externally between companies, value chains and in all market ecosystems - is already huge today - and it will continue to grow. The decisive keyword is data interoperability. This refers to the ability of independent, heterogeneous systems to work together as seamlessly as possible in order to exchange information in an efficient and usable manner or to make it available to the user without the need for separate agreements between the systems. Master data plays a key role here.
Find out more about the benefits of standardized data and centralized data management in our blog post "Return on investment of MDM projects".
