Companies often find themselves squeezed between outdated business infrastructure, a shifting regulatory environment and the advanced technology of tomorrow. This regularly occurring situation leads many companies to question and lose confidence in the validity of their data because there are so many external and technological variables influencing the collection and measurement of data.
Data is essential to develop informed, customized business strategies that improve performance. In order to capitalize on the countless benefits of data and overcome skepticism, enterprises must establish data confidence by improving their data management capabilities. Without confidence in the data, business leaders tend to resist using analytic analysis to support business decisions and revert to instinct and experience.
The internet revolution has sparked a data evolution, and businesses are struggling to cope with the volume, velocity and variety of data. Adding to this burden are the market’s expectation for increasing speed in process execution and evolving regulatory standards.
Customers and investors expect immediate responses for every information request, and they are becoming more aware of their personal use of data and how that data is used and shared by companies.
For example, one common complaint is, “Why can’t your company do this if these large corporations have been doing it for years?” Customers are expecting enterprises to know them better than ever, yet customers also have high expectations about the level of control they should have over their personal data and security. Regulators in many countries and across all industries are also taking an interest in data. They are setting expectations on where it is stored and how it is used as well as establishing penalties if data is compromised.
The key to establishing data confidence is through successful data management. Without data confidence, businesses will not be able to comply with the demand for customization, especially with increasing security concerns and regulatory requirements. For businesses to thrive in the digital age, they must achieve optimal data management levels that far exceed previously accepted standards.
Pathways to Achieving Data Confidence through Optimal Data Management
The data confidence roadmap has two major pathways – 1) the pathway to operational excellence and 2) the pathway to unlocking the hidden value within the data.
Pathway 1: The operational basics
To accomplish optimal data management, it’s essential to first bring three basic operational elements together — data stewards, data management and data technology. These groups must work together to achieve operational excellence by improving data management capabilities at the conceptual, logical and physical levels.
Guiding work at all levels requires a clear data strategy and data governance model as this sets the tone and direction for the data management, which drives organizational policies and procedures used to attain data confidence.
Core elements of this pathway are data quality and data operations. Coordination with data stewards across business units is critical for success, as data quality is frequently a function of the business process that drives its generation. Businesses must establish “fit for purpose” definitions and then measure data quality against those definitions.
Also critical to success are the architecture and infrastructure that support optimal data management. This pathway is considered successful when the elements of modern data management are embedded within the organization and data quality processes are complete, managed and properly leveraged. Once the operating model is in place, companies are one step closer to data confidence.
Pathway #2: The power of data
After achieving operational excellence, an organization is ready to take the advanced pathway toward unlocking the hidden value of data. A primary component of this approach is the data lake architectural pattern, where a wide variety of data is stored and then curated for analysis.
Successful data curation is dependent on the governance and management foundation established in the first pathway, as well as a strong infrastructure that ensures the security and quality of the data.
Part of the data-curation process also includes using transcription, recognition and natural language processing to streamline the processing of unstructured data. Statistically, most of the data in an organization is unstructured, so leveraging these techniques will increase the volume and variety of the data being processed. If done effectively, this will also increase the information and insights that can be gained from the data, helping business extrapolate trends relevant to consumers, which in turn, builds data confidence.
Unlocking the true value of data will enable companies to understand invested audiences on a new level. By following these pathways, companies will be better equipped to address data confidence concerns that are preventing them from reducing customer friction, adjusting to modern expectations, adapting to regulations and, ultimately, implementing new technologies for the overall benefit of the company.
(About the author: Michael Goodman is senior director, data and analytics practice lead, at NTT Data Consulting, Inc.)
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