data governance enterprise architecture Archives

How much does your business invest in reputation management? It’s likely no one in the organization knows for sure because every interaction – in person, online or over the phone – can affect your firm’s reputation. The quality of the goods and services your organization provides, the training it gives employees, and the causes and initiatives it supports all can improve or worsen its reputation.

Reputation management has always been important to businesses, but because information flows so quickly and freely today, reputations are more fragile than ever. Bad news travels fast; often much faster than businesses can respond. It’s also incredibly hard to make bad news go away. Social media and search engines crushed the concept of the news cycle because they make it easy for information to circulate, even long after incidents have occurred.

One of the fastest ways to see your organization’s reputation suffer today is to lose or expose sensitive data. A study in the U.K. found that 86 percent of customers would not do business with a company that failed to protect its customers’ credit card data.

But data theft isn’t the only risk. Facebook may not have even violated its user agreement in the Cambridge Analytica scandal, but reputations have a funny way of rising and falling on perception, not just facts.

It’s estimated that Walmart, for example, spent $18 million in 2016 and 2017 on advertising for retrospective reputation management, after suffering from a perception the company was anti-worker, fixated on profits, and selling too many foreign-made products.

Perception is why companies publicize their efforts to be good corporate citizens, whether it means supporting charities or causes, or discussing sustainability initiatives that are aimed at protecting the environment.

When you are perceived as having a good reputation, a number of positive things happen. For starters, you can invest $18 million in your business and your customers, instead of spending it on ads you hope will change people’s perceptions of your company. But good reputation management also helps create happy, loyal customers who in turn become brand advocates spreading the word about your company.

Data permeates this entire process. Successful reputation management shows up in the data your business collects. Data also will help identify the brand ambassadors who are helping you sell your products and services. When something goes wrong, the problem might first appear – and be resolved – thanks to data. But what data giveth, data can taketh away.

A big part of building and maintaining a good reputation today means avoiding missteps like those suffered by Facebook, Equifax, Uber, Yahoo, Wells Fargo and many others. Executives clearly grasp the importance of understanding and governing their organization’s data assets. More than three-quarters of the respondents to a November 2017 survey by erwin, Inc. and UBM said understanding and governing data assets is important or very important to their executives.

A strong data governance practice gives businesses the needed visibility into their data – what they’re collecting, why they’re collecting it, who can access it, where it’s stored, how it’s used, and more. This visibility can help protect reputations because knowing what you have, how it’s used, and where it is helps improve data protection.

Having visibility into your data also enables transparency, which works in two ways. Internally, transparency means being able to quickly and accurately answer questions posed by executives, auditors or regulators. Customer-facing transparency means businesses have a single view of their customers, so they can quickly solve problems, answer questions, and help align the products and services most relevant to customer needs.

Both types of transparency help manage an organization’s reputation. Businesses with a well-developed strategy for data governance are less likely to be caught off guard by a data breach months after the fact, and are better positioned to deliver the modern, personalized, omnichannel customer experience today’s consumers crave.

The connection between data governance and reputation is well understood. The erwin-UBM study found that 30 percent of organizations cite reputation management as the primary driver of their data governance initiative.

But data governance is more than protecting data (and by extension, your reputation). It is, when done well, a practice that permeates the organization. Integrating your data governance strategy with your enterprise architecture, for example, helps you define application capabilities and interdependencies within the context of your overall strategy. It also adds a layer of protection for data beyond your Level 1 security (the passwords, firewalls, etc., we know are vulnerable).

Data governance with a business process and analysis component helps enterprises clearly define, map and analyze their workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

For example, many businesses today are likely keeping too much data. A wave of accounting scandals in the early 2000s, most notably at Enron, led to regulations that included the need to preserve records and produce them in a timely manner. As a result, businesses started to store data like never before. Add to this new sources of data, like social media and sensors connected to the Internet of Things (IoT), and you have companies awash in data, paying (in some cases) more to store and protect it than it’s actually worth to their businesses.

When done well, data governance helps businesses make more informed decisions about data, such as whether the reward from the data they’re keeping is worth the risk and cost of storage.

“The further data gets from everyday use, it just sits on these little islands of risk,” says Danny Sandwell, director of product marketing for erwin.

All it takes is someone with bad intentions or improper training to airlift that data off the island and your firm’s reputation will crash and burn.

Alternatively, your organization can adopt data governance practices that will work to prevent data loss or misuse and enable faster remediation should a problem occur. Developing a reputation for “data responsibility” – from protecting data to transparency around its collection and use – is becoming a valuable differentiator. It’s entirely possible that as the number of data breaches and scandals continue to pile up, firms will start using their efforts toward data responsibility to enhance their reputation and appeal to customers, much in the way businesses talk about environmental sustainability initiatives.

A strong data governance foundation underpins data security and privacy. To learn more about how data governance will work for you, click here.

Previous posts:

You can determine how effective your current data governance initiative is by taking erwin’s DG RediChek.

In modern, data-driven busienss, it’s integral that organizations understand the reasons for bad data and how best to address them. Data has revolutionized how organizations operate, from customer relationships to strategic decision-making and everything in between. And with more emphasis on automation and artificial intelligence, the need for data/digital trust also has risen. Even minor errors in an organization’s data can cause massive headaches because the inaccuracies don’t involve just one corrupt data unit.

Inaccurate or “bad” data also affects relationships to other units of data, making the business context difficult or impossible to determine. For example, are data units tagged according to their sensitivity [i.e., personally identifiable information subject to the General Data Protection Regulation (GDPR)], and is data ownership and lineage discernable (i.e., who has access, where did it originate)?

Relying on inaccurate data will hamper decisions, decrease productivity, and yield suboptimal results. Given these risks, organizations must increase their data’s integrity. But how?

Integrated Data Governance

Modern, data-driven organizations are essentially data production lines. And like physical production lines, their associated systems and processes must run smoothly to produce the desired results. Sound data governance provides the framework to address data quality at its source, ensuring any data recorded and stored is done so correctly, securely and in line with organizational requirements. But it needs to integrate all the data disciplines.

By integrating data governance with enterprise architecture, businesses can define application capabilities and interdependencies within the context of their connection to enterprise strategy to prioritize technology investments so they align with business goals and strategies to produce the desired outcomes. A business process and analysis component enables an organization to clearly define, map and analyze workflows and build models to drive process improvement, as well as identify business practices susceptible to the greatest security, compliance or other risks and where controls are most needed to mitigate exposures.

And data modeling remains the best way to design and deploy new relational databases with high-quality data sources and support application development. Being able to cost-effectively and efficiently discover, visualize and analyze “any data” from “anywhere” underpins large-scale data integration, master data management, Big Data and business intelligence/analytics with the ability to synthesize, standardize and store data sources from a single design, as well as reuse artifacts across projects.

Let’s look at some of the main reasons for bad data and how data governance helps confront these issues …

Reasons for Bad Data: Data Entry

The concept of “garbage in, garbage out” explains the most common cause of inaccurate data: mistakes made at data entry. While this concept is easy to understand, totally eliminating errors isn’t feasible so organizations need standards and systems to limit the extent of their damage.

With the right data governance approach, organizations can ensure the right people aren’t left out of the cataloging process, so the right context is applied. Plus you can ensure critical fields are not left blank, so data is recorded with as much context as possible.

With the business process integration discussed above, you’ll also have a single metadata repository.

All of this ensures sensitive data doesn’t fall through the cracks.

Reasons for Bad Data: Data Migration

Data migration is another key reason for bad data. Modern organizations often juggle a plethora of data systems that process data from an abundance of disparate sources, creating a melting pot for potential issues as data moves through the pipeline, from tool to tool and system to system.

The solution is to introduce a predetermined standard of accuracy through a centralized metadata repository with data governance at the helm. In essence, metadata describes data about data, ensuring that no matter where data is in relation to the pipeline, it still has the necessary context to be deciphered, analyzed and then used strategically.

The potential fallout of using inaccurate data has become even more severe with the GDPR’s implementation. A simple case of tagging and subsequently storing personally identifiable information incorrectly could lead to a serious breach in compliance and significant fines.

Such fines must be considered along with the costs resulting from any PR fallout.

Reasons for Bad Data: Data Integration

The proliferation of data sources, types, and stores increases the challenge of combining data into meaningful, valuable information. While companies are investing heavily in initiatives to increase the amount of data at their disposal, most information workers are spending more time finding the data they need rather than putting it to work, according to Database Trends and Applications (DBTA). erwin is co-sponsoring a DBTA webinar on this topic on July 17. To register, click here.

The need for faster and smarter data integration capabilities is growing. At the same time, to deliver business value, people need information they can trust to act on, so balancing governance is absolutely critical, especially with new regulations.

Organizations often invest heavily in individual software development tools for managing projects, requirements, designs, development, testing, deployment, releases, etc. Tools lacking inter-operability often result in cumbersome manual processes and heavy time investments to synchronize data or processes between these disparate tools.

Data integration combines data from several various sources into a unified view, making it more actionable and valuable to those accessing it.

Getting the Data Governance “EDGE”

The benefits of integrated data governance discussed above won’t be realized if it is isolated within IT with no input from other stakeholders, the day-to-day data users – from sales and customer service to the C-suite. Every data citizen has DG roles and responsibilities to ensure data units have context, meaning they are labeled, cataloged and secured correctly so they can be analyzed and used properly. In other words, the data can be trusted.

Once an organization understands that IT and the business are both responsible for data, it can develop comprehensive, holistic data governance capable of:

Reaching every stakeholder in the process
Providing a platform for understanding and governing trusted data assets
Delivering the greatest benefit from data wherever it lives, while minimizing risk
Helping users understand the impact of changes made to a specific data element across the enterprise.

To reduce the risks of and tackle the reasons for bad data and realize larger organizational objectives, organizations must make data governance everyone’s business.

To learn more about the collaborative approach to data governance and how it helps compliance in addition to adding value and reducing costs, get the free e-book here.