Click to learn more about author Sowmya Tejha Kandregula.

The ongoing COVID-19 pandemic has made the term “social distancing” a cynosure of our daily conversations. There have been guidelines issued, media campaigns run on prime time, hashtags created, and memes shared to highlight how social distancing can save lives. When you have young children talking about it, you know the message has cut across the cacophony! This might give data scientists a clue of what they can do to garner enterprise attention towards the importance of better Data Management.

While many enterprises kickstart their Data Management projects with much fanfare, egregious Data Quality practices can hamper the effectiveness of these projects, leading to disastrous results. In a 2016 research study, IBM estimated that bad quality data costs the U.S. economy around $3.1 trillion dollars every year.

And bad quality data affects the entire ecosystem — salespeople chase the wrong prospects, marketing campaigns do not reach the target segment, and delivery teams are busy cleaning up flawed projects. The good news is that it doesn’t have to be this way. The solution is “smart data distancing.”

What is Smart Data Distancing?

Smart data distancing is a crucial aspect of Data Management, more specifically, Data Governance for businesses to identify, create, maintain, and authenticate data assets to ensure they are devoid of data corruption or mishandling.

The recent pandemic has forced governments and health experts to issue explicit guidelines on basic health etiquette — washing hands, using hand sanitizer, keeping social distance, etc. At times, even the most rudimentary facts need to be recapped multiple times so that they become accepted practices.

Enterprises, too, should strongly emphasize the need for their data assets to be accountable, accurate, and consistent to reap the true benefits of Data Governance.

The 7 Do’s and Don’ts of Smart Data Distancing Are:

1. Establish clear guidelines based on global best Data Management practices for the internal or external data lifecycle process. When accompanied by a good Metadata Management solution, which includes data profiling, classification, management, and organizing diverse enterprise data, this can vastly improve target marketing campaigns, customer service, and even new product development.

2. Set up quarantine units for regular data cleansing or data scrubbing, matching, and standardization for all inbound and outbound data.

3. Build centralized data asset management to optimize, refresh, and overcome data duplication issues for overall accuracy and consistency of Data Quality.

4. Create data integrity standards using stringent constraint and trigger techniques. These techniques will impose restrictions against accidental damage to your data.

5. Create periodic training programs for all data stakeholders on the right practices to gather and handle data assets and the need to maintain data accuracy and consistency. A data-driven culture will ensure the who, what, when, and where of your organization’s data and help bring transparency in complex processes.

6. Don’t focus only on existing data that is readily available but also focus on the process of creating or capturing new and useful data. Responsive businesses create a successful data-driven culture that encompasses people, process, as well as technology.

7. Don’t take your customer for granted. Always choose ethical data partners.

How to Navigate Your Way Around Third-Party Data

The COVID-19 crisis has clearly highlighted how prevention is better than a cure. To this effect, the need to maintain safe and minimal human contact has been stressed immensely. Applying the same logic when enterprises rely on third-party data, the risks also increase manifold. Enterprises cannot ensure that a third-party data partner/vendor follows proper Data Quality processes and procedures.

The questions that should keep your lights on at night are:

• Will my third-party data partner disclose their data assessment and audit processes?
• What are the risks involved, and how can they be best assessed, addressed, mitigated, and monitored?
• Does my data partner have an adequate security response plan in case of a data breach?
• Will a vendor agreement suffice in protecting my business interests?
• Can an enterprise hold a third-party vendor accountable for Data Quality and data integrity lapses?  

Smart Data Distancing for Managing Third-Party Data

The third-party data risk landscape is complex. If the third-party’s data integrity is compromised, your organization stands to lose vital business data. However, here are a few steps you can take to protect your business:

• Create a thorough information-sharing policy for protection against data leakage.
• Streamline data dictionaries and metadata repositories to formulate a single cohesive Data Management policy that furthers the organization’s objectives.
• Maintain quality of enterprise metadata to ensure its consistency across all organizational units to increase its trust value.
• Integrate the linkage between business goals and the enterprise information running across the organization with the help of a robust Metadata Management system.
• Schedule periodic training programs that emphasize the value of data integrity and its role in decision-making.

The functional importance of a data steward in the Data Management and governance framework is often overlooked. The hallmark of a good Data Governance framework lies in how well the role of the data steward has been etched and fashioned within an organization. The data steward (or a custodian) determines the fitness levels of your data elements, the establishment of control, and the evaluation of vulnerabilities, and they remain on the frontline in managing any data breach. As a conduit between the IT and end-users, a data steward offers you a transparent overview of an organization’s critical data assets that can help you have nuanced conversations with your customers. 

Unlock the Benefits of Smart Data Distancing

Smart and unadulterated data is instrumental to the success of Data Governance. However, many enterprises often are content to just meet the bare minimum standards of compliance and regulation and tend to overlook the priority it deserves. Smart data means cleaner, high-quality data, which in turn means sharper analytics that directly translates to better decisions for better outcomes.

Gartner says corporate data is valued at 20-25 percent of the enterprise value. Organizations should learn to monetize and use it wisely. Organizations can reap the benefits of the historical and current data that has been amassed over the years by harnessing and linking them to new business initiatives and projects. Data Governance based on smart enterprise data will offer you the strategic competence to gain a competitive edge and improve operational efficiency.

Conclusion 

It is an accepted fact that an enterprise with poor Data Management will suffer an impact on its bottom line. Not having a properly defined Data Management framework can create regulatory compliance issues and impact business revenue.

Enterprises are beginning to see the value of data in driving better outcomes and hence are rushing their efforts in setting up robust Data Governance initiatives. There are a lot of technology solutions and platforms available. Towards this endeavor, the first step for an enterprise is to develop a mindset of being data-driven and being receptive to a transformative culture.

The objective is to ensure that the enterprise data serves the cross-functional business initiatives with insightful information, and for that to happen, the data needs to be accurate, meaningful, and trustworthy. Setting out to be a successful data-driven enterprise can be a daunting objective with a long transformational journey. Take a step in the right direction today with smart data distancing!