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How to Extract More Value from Your Data

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March 17 2021 |
2 minute read

Increasing the value of your data is easy with master data management

Last year was a year of tremendous change for businesses and consumers, as they adjusted to the realities of life during a pandemic. Many consumers turned to apps to track diet, fitness and other personal goals with incredible detail, accuracy and consistency, often connecting them to wearable technology that captures data automatically (e.g., heart rate or steps taken).

For many, the process of collecting more data increased the likelihood of success. This is because data becomes increasingly trustworthy – and valuable – as it becomes more consistent and complete. By providing greater transparency into quantified data, apps make it easier for users to track progress against a goal. For instance:

  • Reading more books was a goal for me; last year I read 13,500 pages vs. 4,000 the prior year, using an app to track my progress and calculate the number of pages read.
  • A friend used an app to count his steps; he logged 5.3 million steps (or 2,428 total miles) in 2020, losing 51 pounds.

Yet, it’s important to note that the amount of data collected doesn’t automatically translate into success. Someone who regularly inputs data into a weight loss app – but is underestimating caloric intake or overestimating the number of calories burned in a workout – will likely struggle to reach their weight loss goal.

Which begs the question: What exactly makes data valuable? Knowing what to look for can help you reach your personal goals, as well as your business goals.

How to improve the value of your business data

According to authors Thomas H. Davenport and Jeanne G. Harris of the classic, Competing on Analytics, there are seven data characteristics that can be improved to enhance the value of data, and all of the characteristics emphasize data quality over data quantity.1

Seven characteristics to increase the value of data

1. It is correct
2. It is complete
3. It is current
4. It is consistent
5. It is in context
6. It is controlled
7. It is analyzed

Competing on analytics








These data characteristics are important for chief data officers and other data leaders to consider when adopting big data analytics and business reporting and visualization tools. These new technologies can drive organizational engagement and reveal new insights that benefit the business; their effectiveness, however, is limited by the trustworthiness of the data being used. If data is knowingly incomplete or without context, for instance, then the analytics and reporting are less likely to add value to the organization.  

The good news is that all seven characteristics – including analysis – can be managed and performed via a master data management solution. The transformative value of master data management is relevant to many different businesses across industries. With master data management, companies can increase data value (and therefore its trustworthiness), enabling them to better understand their business and improve their performance.

An important aspect of data value is data transparency. It includes both internal and external visibility of the data that’s collected and used by an organization. Internal data transparency brings data into a single source and eliminates silos, enabling the creation of a trusted and complete data foundation. By establishing a trusted digital business hub, companies can further increase data value by providing internal teams and systems with visibility and access to the data through better data workflows and governance.

External data transparency ensures that you are linking and sharing appropriate, complete and trusted data to your customers so they can become more informed about what you are offering in order to make the right purchasing decisions. External transparency also includes sharing complete, timely and correct data to your partners – such as retailers, marketing firms, agencies and industry data sources – to ensure your business is being represented in the marketplace consistently.

With a vision and plan to increase your companies’ data value, being mindful of the seven characteristics will enable you to set a foundation of better data and get greater results from your analytics. 

1 Davenport, Thomas H.; Harris, Jeanne G; (201&) Competing on Analytics - The New Science of Winning. Boston, Mass. USA: Harvard Business Review Press, pg. 230.

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Brian Cluster is Stibo Systems’ Industry Strategy Director for the consumer packaged goods and retail industries. He has more than 25 years of experience collaborating on strategy, delivering analytics and developing business plans and digital transformation. At Stibo Systems, Brian is putting his varied industry expertise to good use, providing direction and strategy for field teams and helping to drive customer value for master data management solutions. He is a frequent contributor to The Consumer Goods Forum, and his articles have been published in Consumer Goods Technology, Multichannel Merchant, Total Retail, Footwear News, and Center for Data Innovation, among others.

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