The Dilemma of Data Ownership

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Data is the new currency in the digital economy. Businesses that are able to leverage data could create winning products and services, expedite their delivery, and optimize operations to leave competition in the dust. Recognizing that data is a strategic asset, businesses are in a race to uncover insights embedded in data they own.

Data ownership is more than ownership. Beyond claiming possession of data, business leaders need to take a broader perspective on their approach to gather and maintain useful data, and adopt a culture to use data in meaningful ways.

Discerning noise filter

More data does not guarantee better insights. Attempts to capture as much data as possible for just-in-case purposes can backfire.

Consider an online form for customers to submit a product return request. The retailer would have captured all the information for the sale transaction. It is superfluous to have the customer key in any information other than the original invoice number and the reason for the return. An elaborate return form is a turnoff.

In this case, the retailer needs to determine the essential data it needs to process the return and analyze the cause. Requesting for more than what is meaningful simply annoys the unhappy customer.

For the business, the owners of product returns include the marketing, purchasing, and warehouse teams. As a group, they need to assess the causes for returns, process them as quickly as possible, and resolve the issues. Data that helps to expedite the work would enable them appease customers and minimize future issues.

Effortless data capture

Data entry consumes time and effort. Poorly designed user interfaces dampen productivity leading to inaccurate data and misleading analytics output.

The main role for a field service technician is to complete a job efficiently and move on to the next job. For purposes of good accounting, the technician is requested to enter an appropriate account code to bill the work order to. If he needs to find the right one from a long list of accounts, he likely would pick an account closer to the top even though it is not the correct one.

For the technician, his work for that job is complete. But for the accountant, an incorrect account code skews cost analysis. This would impact variant analysis and budget allocation for the next fiscal year.

The owners for proper job order booking include the technician and the accountant. To make the account selection easy for the technician, the accountant needs to figure out a simplified approach for the technician. Failure to do so doesn’t help the technician or the cost analysis.

Commitment to sustain data quality

Sporadic data entries don’t provide complete data for analytics. Irregular and inconsistent use of an application is an adoption issue that impacts data quality.

A customer relationship management application (CRM) is meant to consolidate all customer related data in one repository for instantaneous data lookup. When account managers are not committed to enter all customer interactions and potential upsell opportunities, for instance, into the CRM, workarounds are necessary to bridge the data gaps.

On the other hand, when account managers use the CRM consistently, the marketing event coordinator could rely on the CRM data for finalizing event invitation lists. This saves time from having account managers to prepare their own list for the same purpose.

Despite the power modern technology has, adoption rate and commitment for consistent use are crucial for maintaining data quality. Individuals ought to own the tasks and hold themselves accountable. Without conscious effort to do their work wholeheartedly, it is a real challenge to maintain data currency for reliable insights.

It is also important to note that technology leaders and their staff need to take ownership of working with business teams to create tools that are user friendly. User centric designs help to facilitate higher adoption and quality of data input from the get-go.

Habits and culture

To build data as a strategic asset, there needs to be coherence in capturing useful data, storing them for easy access, and conducting analyses to guide decisions and actions.

This coherence is reflected through commitments across the organization to step up and accept accountability for the work performed. Instead of focusing on fulfilling the technical needs, efforts are invested upfront to filter noise and communicate the essence of data ownership.

When team leaders, managers, and senior leaders acknowledge their roles in helping employees understand the significance of data in every task, a shift in mindset develops over time. The alignment establishes a solid foundation of useful data, ready for mining for insights.

Habits leads to a data-driven culture where there is better collaboration and morale because everyone is committed to data quality and use for informed decision-making.

In the exciting world of artificial intelligence, a trustworthy data source lends itself to reliable output for decision-makers.

Data ownership with organization-wide discipline avoids amassing data that have little use and delivering unreliable insights. Beware of garbage in and garbage out. To minimize wasted efforts and poor technology investments, it is necessary to take a reflective approach to clearly identify purposeful data and develop a data-driven culture. By amplifying the commitment to build quality data and use analytics for impactful insights, businesses would position themselves to remain relevant and competitive.

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