23-12-2018 Door: Lyndsay Wise

Supporting Speed to Insight with Strong Data Integrity

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Successful analytics requires strong data management. It also requires speed to delivery to ensure timely and valuable insights. However, developing analytics that take both into account can be easier said than done. Surveying my many years of experience as an industry analyst and consultant, I can safely say that many organizations unfortunately take a piecemeal approach to their analytics investments.

The promise of interactive dashboards and visualizations drive initial interest in analytics beyond reporting and spreadsheets with their user-friendly features. But the complexities involved in consolidating multiple, disparate, and diverse data sources is frequently overlooked until after the solution is in place, creating a gap between time to implementation and time to value.

Longer time to value can be avoided by gathering data management requirements during any analytics evaluation phase, irrespective of whether an organization is new to business intelligence or expanding its current environment. By implementing data management as part of an analytics initiative, organizations can ensure:

• Better collaboration between IT and business units as requirements from both are needed
• The creation of a data management framework that takes continuous quality and integration into account
• Trusted information inputs and valuable business outputs
• Effective analytics and quicker time to value
• Tighter value chain between data acquisition and information delivery

But how is this achieved and what does strong data management mean for organizations?

Supporting continuous change and automation
Selecting a data management solution requires several considerations. But the most important ones, beyond features and solution capabilities, are level of automation and the ability to meet changing data requirements. Automation quickens processes and supports shorter time to delivery. Adding requirement changes as analytics environments become more complex means that automated data management can deliver the inputs needed to gain analytical insight faster. Ensuring overall and continuous data quality using an automated approach helps organizations build trust and deliver accurate insights to business users.

Both of these considerations should be incorporated into what an organization hopes to achieve on the business side as well. In too many cases, businesses focus their efforts on developing the right metrics but do not consider how data requirements will affect the outcomes of those defined metrics. Developing strong data management with a focus on delivering continuous, automated data integrity provides a framework for organizations that allows them to take advantage of faster speed to insight.

Enhancing speed to insight
Speed to insight depends on the ability to access valid and reliable data in a timely manner. Delivering quick time to value means taking the right data management processes, ensuring their automation, and creating analytics environments that are relevant and aligned to broader organizational goals. Organizations can no longer afford to reevaluate data quality after putting analytics tools into the hands of decision makers. This creates a lack of trust in the system and fails to provide the value promised by business intelligence insight. Consequently, as organizations begin to understand the intrinsic value of data, they will develop alignment between how information assets are managed internally with how they are delivered to create analytical insights and develop action-oriented outcomes.

Lyndsay Wise

Lyndsay Wise is the Solution Director, Professional Services Division for North America, responsible for helping customers align their business strategy successful data management. Before joining Information Builders, Lyndsay worked as an industry analyst for 12 years, founding WiseAnalytics in 2007 and covered research areas related to data visualization, analytics, BI in the cloud, and implementation strategies for mid-market organizations. She provided consulting services as well as industry research into leading technologies, market trends, BI products and vendors, mid-market needs, and data visualization. In 2012, Lyndsay wrote Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize ROI to help provide organizations with the tools needed to evaluate open source business intelligence and make the right software decisions.

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