The Components of Effective, Data-Driven Decision-Making

By Barton Goldenberg

While there is no shortage of Data analytics opportunities within an organization, succeeding at implementing Data analytics is no slam-dunk.  The following seven requirements need to be in place as the key components of effective data driven decision making:

1. Data Analytics Strategy & Goals:  Why is the organization embarking on a Data analytics effort – what are the goals, what problem are we trying to fix?
   
2. Quality Data:  Does the organization have the necessary data in place to perform required analysis and is the data quality where it needs to be?  Take an inventory of available customer data and its quality.  Where possible, make corrections to alleviate data quality issues.  This can be facilitated by tapping into the large selection of valuable customer and industry data sources including the use of 3^rd party data sets and overlays.  Creativity in data acquisition is paramount.
3. Meaningful Data Analysis:Performing meaningful data analysis cannot be left to either the business or the IT side of the organization.  It requires a collaborative effort between both sides.  On the one hand, the business side needs to fulfill the role of the ‘creative’ marketer to determine how to secure more real-time data coming from brand loyal customers who are often sensitive to privacy issues.  On the other hand, the IT side needs to fill the role of the ‘technical’ marketer to harness more real-time data coming from external sources, leverage the new data tools, and deliver meaningful business insights coming from the data analysis.  The focus should be on the analysis and resulting business insights and not the manipulation of the data. If required skill sets do not currently exist within the organization, consider engaging external subject matter experts.
4. Well-Trained Managers/Executives: I continue to be amazed by how few executives have grabbed the “data analytics bull by the horns” and put it to use. Perhaps this is an issue of training managers and executives on how to use data-driven insights to make better business decisions.  In best-in-class organizations, data-driven decision-making is a core value.  Make data insights actionable.
5. Meaningful Measurement and Monitoring: Set metrics at the outset and measure these metrics carefully throughout the data analytics initiative.  This is important for creating baselines and benchmarks to be applied as the organization’s data analytics efforts expand.
6. Implementation of a Benchmarking Program: Data-driven decision-making is not a one-off event. With several customers, we are into second-, third-, or even fourth-generation data models that use both quantitative and qualitative data to provide deeper insights.  Start with low-volume data analytics trials.  Document what worked and what did not.  Continue to benchmark results so that the organization realizes incremental gains.
7. Tight Integration with Social CRM efforts: As tight integration with your organization’s Social CRM efforts is a very crucial requirement, I will discuss the importance of tighter integration with Social CRM efforts in greater detail within my next blog post.

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Recognized as a leading “customer-focused” author, his latest book, The Definitive Guide to Social CRM, is hailed as the roadmap for Social CRM success.  He is a long-term columnist for CRM Magazine and speaker for CRMevolution and frequently quoted in the media.