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Smart Data Distancing – A Necessity or a Myth?

Applying some habits from “social distancing” to data could have a variety of benefits for any organization

As an adage goes, “Desperate situations call for desperate measures”. The driving force for many new opportunities arise from unforeseen circumstances.

The term “social distancing” has become a cynosure of many conversations, with forced adoption of new habits. Guidelines issued, media campaigns run on prime-time TV, hashtags created, and memes shared highlight how social distancing can save lives. When young children are talking about it, the message has cut across the cacophony!

The adoption of social distancing may hold a clue about what data scientists could do to develop an enterprise attention towards the importance of better data management. While many enterprises kickstart their data management project with much fanfare, egregious data quality practices can hamper the effectiveness and lead 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 – sales folks 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 need not be this way. And the answer 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 the data is devoid of data corruption or mishandling.

Occasionally, even the most rudimentary facts need to be repeated multiple times so they become accepted practices. For instance, the coronavirus pandemic forced governments and health experts to issue explicit guidelines on basic health etiquette – washing hands more often, wearing a mask, and using hand sanitizer.

Likewise, enterprises 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 accepted data management best practices for internal and external data lifecycle process. When accompanied with a good metadata management solution which includes data profiling, classification, management, and organizing diverse enterprise data, guidelines 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, standardization for all inbound and outbound data.
  3. Build centralized data asset management practices 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 an organization’s 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 their accuracy and consistency. A data-driven culture will ensure that the who, what, when and the where of an organization’s data are captured and will help bring transparency in complex processes.
  6. Don’t focus only on existing data that is readily available; implement the best processes for creating or capturing new and useful data. Responsive businesses create a successful data-driven culture that encompasses people and process as well as technology.
  7. Don’t take customers for granted – treat them and their data well. Always choose ethical data partners.

Navigating around third-party data

Prevention is better than cure. Applying the same logic, risks also increase greatly when enterprises rely on a third-party data.  Unfortunately, enterprises can never be fully confident that a third-party data partner/vendor follows proper data quality processes and procedures.

The questions that should keep a manager’s lights on at night are:

  • Will the 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 the data partner have an adequate security response plan in case of a data breach?
  • Will a vendor agreement suffice in protecting the enterprise’s 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 their data integrity is compromised, an organization stands to lose vital business data. However, taking a few steps can help reduce or avoid the risks:

  • 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 advances 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 data stewardship in a data management and data governance framework is often overlooked. The hallmark of a good data governance framework lies in how best the role of the data steward has been etched and fashioned within an organization. The business data steward determines the fitness levels of a set of data elements, establishes control for that data, evaluates vulnerabilities, and remains on the frontline in managing any data breach. As a conduit between IT and the end-users, a business data steward offers their subject area a transparent overview of its critical data assets so decision-makers can act with confidence in the data. 

Unlock the benefits of Smart Data Distancing

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

Gartner says corporate data can be valued at 20-25% of the enterprise value. Businesses should learn to monetize and use data wisely. Organizations can reap the benefits of their historical and current data by harnessing and linking the data sets to new business initiatives and projects. Data governance based on smart enterprise data offers 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 to its bottom line. Not having a properly defined data management framework can result in regulatory compliance issues, which affect a business’ revenue.

Enterprises can see the value of data in driving better outcomes and may rush to establish robust data governance initiatives. There are many technology solutions and platforms available, but 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. To accomplish that, the organization’s data must 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!

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Sowmya Tejha Kandregula

Sowmya Tejha Kandregula is an experienced data management professional leading data governance/metadata management/data privacy/data quality/data integration projects at a variety of Fortune 500 businesses. Sowmya’s recent emphasis has been focusing on data demands, including a changing landscape of privacy laws, increased movement of data onto the cloud, and a greater dependency on quality governed data for machine learning and Artificial Intelligence (AI) solutions.

Sowmya conducts seminars, webinars, and training sessions for aspiring information management professionals on a pro bono basis. To date, Sowmya has mentored over 800 professionals across the globe.  He also serves on the advisory panel of various professional and non-profit associations.

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