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Smart Data Distancing: Can We Apply Pandemic Lessons Learned?

The ongoing COVID-19 pandemic has made the term “social distancing” central to our daily conversations. There have been guidelines issued, media campaigns run in prime time, new 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 through the noise!

And this might provide clues as to what data scientists could do to garner enterprise attention towards the importance of better data management. How?  Read on.

While many enterprises kick-start their data management projects with much fanfare, egregious data quality practices can hamper the effectiveness leading to disastrous results. In a 2016 research study, IBM estimated that poor quality data costs the U.S. economy around $3.1 trillion dollars every year.

And poor quality data affects the entire business ecosystem—salespeople chase the wrong prospects, or at the wrong times; marketing campaigns do not reach the target segment clearly; and delivery teams are busy cleaning up flawed projects. The good news is that it need not be this way. And the answer is what could be called, “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 it is devoid of data corruption or mishandling.

The recent COVID-19 pandemic has forced governments and health experts to issue explicit guidelines on basic health etiquette—washing hands, using hand sanitizer, keeping social distance. At times, even the most rudimentary facts need to be repeated 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

  1. Establish clear guidelines based on global best data management practices for internal or external data lifecycle process. When accompanied with a good metadata management solution which includes data profiling, classification, management, and organizing diverse enterprise data, it 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 its accuracy and consistency. A data-driven culture will ensure the who, what, when and the 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 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 the technology.
  7. Always choose ethical data partners. Don’t take your customer for granted.

How to navigate your way around third-party data

The COVID-19 crisis has clearly highlighted how prevention is far better than 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 a third-party data, risks also increase many times. Enterprises cannot ensure if a third-party data partner/vendor follows proper data quality processes and procedures.

The questions that should keep you up 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—and not only that customer, but perhaps many more as a result of fallout. 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 (or data custodian) in 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 established within an organization. The data steward determines the fitness levels of your data elements, establishment of control, and evaluation of vulnerabilities. They are a crucial player in the trenches in managing any data breach. As a conduit between the IT and end-users, a data steward offers 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 for 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, generally, that corporate data is typically valued at 20-25% 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 result in creating regulatory compliance issues and impacting 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 lots of technology solutions and platforms available. To that end, 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 that 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 the step in the right direction today with Smart Data Distancing!

 

REFERENCES
Sowmya Kandregula

Sowmya Kandregula

Sowmya Kandregula is a seasoned Data Management professional with 15 years of experience in Data Governance, Data Privacy, Data Security, Regulatory Compliance, Metadata Management, Master Data Management, Service Management & Agile Project Management. Sowmya’s proficiency is developing cohesive solutions for large and complex projects spanning multiple divisions and/or platforms. He may be reached at sowmyateja@gmail.com.

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