The General Data Protection Regulation (GDPR) is a regulation intended to strengthen and unify data protection for all individuals within the European Union (EU). It also addresses the export of personal data outside the EU. The primary objectives of the GDPR are to give citizens and residents back control of their personal data and to simplify the regulatory environment for international business by unifying the regulation within the EU.

According to research firm Gartner, Inc., this regulation will have a global impact when it goes into effect on May 25, 2018.  Gartner predicts that by the end of 2018, more than 50 percent of companies affected by the GDPR will not be in full compliance with its requirements.

To avoid being part of the 50 percent that may not be in compliance one year from now, organizations should start planning today. Gartner recommends organizations focus on five high-priority changes to help organizations to get up to speed:

1. 1. Determine Your Role Under the GDPR
      Any organization that decides on why and how personal data is processed is essentially a “data controller.” The GDPR applies therefore to not only businesses in the European Union, but also to all organizations outside the EU processing personal data for the offering of goods and services to the EU, or monitoring the behavior of data subjects within the EU.
   2. Appoint a Data Protection Officer
      Many organizations are required to appoint a data protection officer (DPO). This is especially important when the organization is a public body, is processing operations requiring regular and systematic monitoring, or has large-scale processing activities.
   3. Demonstrate Accountability in All Processing Activities
      Very few organizations have identified every single process where personal data is involved. Going forward, purpose limitation, data quality and data relevance should be decided on when starting a new processing activity as this will help to maintain compliance in future personal data processing activities. Organizations must demonstrate an accountable ground posture and transparency in all decisions regarding personal data processing activities. It is important to note that accountability under the GDPR requires proper data subject consent acquisition and registration. Prechecked boxes and implied consent will be largely in the past.
   4. Check Cross-Border Data Flows
      As of today, data transfers to any of the 28 EU member states, as well as 11 other countries, are still allowed, although the consequences of Brexit are still unknown. Outside of the EU, organizations processing personal data on EU residents should select the appropriate mechanism to ensure compliance with the GDPR.
   5. Prepare for Data Subjects Exercising Their Rights Data subjects have extended rights under the GDPR, including the right to be forgotten, to data portability and to be informed (e.g., in case of a data breach).

Having poor quality data has several impacts on an organization and could hinder your efforts to being in compliance. Visit Service Objects’ website to see how our global data quality solutions can help you ensure your contact data is as genuine, accurate and up-to-date as possible.

According to a recent article in Information Management, nearly two-thirds of CIOs want to hire Chief Data Officers (CDO) over the next year. Why is this dramatic transformation taking place, and what does it mean for you and your organization?

More than anything, the rise of the CDO recognizes the growing role of data as a strategic corporate asset. Decades ago, organizations were focused on automating specific functions within their individual silos. Later, enterprise-level computing like CRM and ERP helped them reap the benefits of data interoperability. And today, trends such as big data and data mining have brought the strategic value of data front and center.

This means that the need is greater than ever for a central, C-level resource who has both a policy-making and advocacy role for an organization’s data. This role generally encompasses data standards, data governance, and the oversight of data metrics. A CDO’s responsibilities can be as specific as naming conventions and standards for common data, and as broad as overseeing enterprise data management and business intelligence software. They are ultimately accountable for maximizing the ROI of an organization’s data assets.

A key part of this role is oversight of data quality. Bad data represents a tangible cost across the organization, including wasted marketing efforts, misdirected product shipments, reduced customer satisfaction, and fraud, tax and compliance issues, among other factors. More important, without a consistent infrastructure for data quality, the many potential sources of bad data can fall through the cracks without insight or accountability. It is an exact analogy to how quality assurance strategies have evolved for manufacturing, software or other areas.

A recent report from the Gartner Group underscored the uphill battle that data quality efforts still face in most organizations: while those surveyed believed that data quality issues were costing each of them US $9.7 million dollars annually on average, most are still seeking justification to address data quality as a priority. Moreover, Gartner concludes that many current efforts to remediate data quality simply encourage line-of-business staff to abandon their own data responsibilities. Their recommendations include making a business case for data quality, linking data quality and business metrics, and above all shifting the mindset of data quality practitioners from being “doers” to being facilitators.

This, in turn, is helping fuel the rise of the central CDO – a role that serves as both a policymaker and an evangelist. In the former role, their job is to create an infrastructure for data quality and deploy it across the entire organization. In the latter role, they must educate their organizations about the ROI of a consistent, measurable approach to data, as well as the real costs and competitive disadvantage of not having one – particularly as more and more organizations add formal C-level responsibility for data to their boardrooms.

Service Objects has long focused on this transition by creating interoperable tools that automate the process of contact data verification, for functions ranging from address and email validation to quantitative lead scoring. We help organizations make data quality a seamless part of their infrastructure, using API and web-based interfaces that tap into global databases of contact information. These efforts have quickly gained acceptance in the marketplace: last year alone, CIO Review named us as one of the 20 most promising API solution providers. And nowadays, in this new era of the Chief Data Officer, our goal as a solutions provider is to support their mission of overseeing data quality.

Data governance has become another trendy buzz-phrase among information technology professionals. Twenty years ago, it was a rarely heard term. Nowadays, there are professional societies, best practices, and annual professional conferences built around it. But what does it mean for you and your business?

According to Wikipedia, data governance “encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization’s data across the business enterprise.^” Properly framed, it involves data quality monitoring strategies, protocols for corrective action, and responsible stakeholders.

Put another way, data governance represents a recent framework for codifying something that has been important for businesses for many years – the quality of the data that drives their operations. This includes marketing leads, orders, customer information, and much more. It recognizes that bad data is not only a cost and service quality issue but something that should be understood and managed at a corporate level.

According to the Center for Innovative Technology, best practices for data governance start with organizational structure, from which specific policies, procedures, and metrics emerge. They recommend a formal data governance committee, reporting to executive management and overseeing the activities of working groups and specific data contributors. The Data Governance Institute’s Data Governance Framework describes this in terms of having a specified Data Governance Office operating between data stakeholders and the actual stewards of this data.

This eventually leads to specific data management tasks such as removing duplicates, validating and improving existing data with data quality tools, performing regular data quality maintenance, and tracking ROI. It has frankly been in the growth and development of such tools that the historical need for data quality has evolved into the profession of data governance. This, in turn, has helped improve data quality for marketing, sales, customer and other data – with immediate, tangible benefits in reducing errors and fraud, along with intangibles such as a strong service brand and satisfied customers.

Finally, here is a closing thought about data governance from the Data Governance Institute, on what they consider to be its most overlooked aspect: “Communication skills of those staff who sit at ground zero for data-related concerns and decisions. They need to be able to articulate many stakeholders’ needs and concerns and to describe them in many vehicles and mediums.” We agree. Policies are important, and tools are important. But at the end of the day, good communication among the stakeholders who work with your actual data is the glue that holds your data quality together.