Data Distancing – What and Why?

Desperate situations call for desperate measures. The COVID-19 pandemic has changed our lifestyle. If you switch on the television, chances are 9 out of 10 channels are talking about COVID-19 and how the numbers are going up across the world.

Medical and healthcare experts emphasize preventive measures. Nowadays, the most common term that we hear around the world is “social distancing.” This word that was barely used in mainstream media a few months ago has now become common across the world.

While social distancing talks about preventive measures for individuals, we also have a corresponding term for enterprises called “Smart Data Distancing.”

What is Smart Data Distancing? 

“Social distancing” is a set of measures used to prevent the spread of a contagious disease by maintaining a safe distance from other individuals.

Likewise, “smart data distancing,” is a process used to ensure the integrity of data. Enterprises use well-defined data governance frameworks to identify, create, maintain, secure and authenticate data assets to ensure accuracy and integrity. In short, this process ensures that data is “safe” and devoid of any corruption or mishandling.

Why is Data Quality Important?

With the world moving towards a data driven decision making approach, data assumed the role of the “most invaluable asset” for executive leadership to aid in decision making. Depending on low quality/incorrect data to make decisions can have catastrophic ramifications. Research from IBM shows that low quality data costs the U.S. economy over $3 trillion dollars each year.

Smart Data Distancing Actions:

1. Create a policy framework: Your policy definition should be based on the best data management practices for internal or external data life-cycle 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 aid in new product development.
2. Set up “data cleansing” units: This process is to be used for regular data cleansing or data scrubbing, matching, and standardization for all inbound and outbound data.
3. Build centralized data asset management framework: This is used to optimize, refresh, and overcome data duplication/redundancy issues for overall accuracy and consistency of data quality. As an output, you will have a high-quality data.
4. Create data integrity standards: Usage of stringent constraint and trigger techniques will impose restrictions against accidental damage to your data.
5. Create periodic training programs: A must for all data stakeholders on the right practices to gather and handle data assets while emphasizing the need to maintain its accuracy and consistency.
6. Assimilate Data: Create a process where we can leverage existing data and also define a process of creating or capturing new and useful data.
7. Choose ethical data partners: Don’t take your customer for granted. A challenging but an important step where due diligence and tact are required.

How to Navigate Your Way Around Third-Party Data

When enterprises rely on a third-party data, there is a significant increase in risk. Enterprises cannot be assured if a third-party data partner/vendor follows proper data quality processes and procedures.

The following are the major areas of concern:

• Will my third-party 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 third-party 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?

Managing Third-Party Data 

The third-party data landscape is complex. If the third-party’s data integrity is compromised, your organization stands to lose vital business data. The impact is not limited to one customer, but many more may be affected…

However, here are a few steps you can take to protect your business in such situations:

• 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 Significance of a Data Steward

The hallmark of a good data governance framework depends on how well the role of a data steward has been defined within an organization. Typically, a data steward determines the fitness levels of your data elements, establishment of control, and evaluation of vulnerabilities. Data stewards are crucial players in the trenches in managing any data breach.

As a channel between the IT and end-users, a data steward offers a transparent overview of an organization’s critical data assets that can help in effectual conversations with your customers.

Benefits of Smart Data Distancing 

Typically, many enterprises are content with just meet the bare minimum standards of data governance from a compliance and regulation perspective.

They tend to overlook the priority of having a well-defined data governance policy framework. At times executive leadership is concerned about the return on investment (ROI) for projects, but with the world moving towards data driven decision making, governing your data assets is of paramount importance. Although the results may not be visible immediately, a strong data governance framework will have an everlasting impact for any enterprise.

One of the critical success factors of data governance implementation is the presence of smart and untainted data which signifies clean and high-quality data; the presence of which leads to better decisions for better outcomes.

Gartner says, generally, that corporate data is typically valued at 20-25% of the enterprise value. Enterprises 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 enterprises with poor data management will suffer an impact on their bottom line. Not having a properly defined data governance framework can result in creating regulatory compliance issues and impacting business revenue.

Enterprises are beginning to see the value of data driven decision making 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 culture of being data-driven and being receptive to a transformative mindset.

The objective is to ensure that the enterprise data serves the cross-functional business initiatives with insightful information and for 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!