The Journey to Insight and Action

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There's no doubt that most companies are seeing massive increases in data assets, both internal and external. Beyond social content, enterprises encounter data explosions from business activities such as M&A, outsourcing initiatives, and highly diversified partner and supply chain programs. Healthcare, utility and telecom industries generate enormous stores of machine-generated data related to customer activities as well as to operational performance and 'product' pricing. GPS data continues to yield more and more customer and market insights for certain industries. And it's all quite overwhelming for many companies.

Midsized organizations are aware of the growing importance of extracting more value from data already in enterprise repositories, as well as continuously looking to external data sources. But to extract value from data, it's not about the data. The key focus should be on connecting data and analytics processes to what matters to the business, whether it's operational improvement, overall high performance, enhanced customer experiences, or innovative product development.

To help make the connection to desired outcomes, midsized organizations must understand the vital relationship between data and business processes, and must define the business cases and problems to be solved that have strategic and business-critical purposes. Value will be achieved from data when organizations identify processes and problems that impact revenue, business agility, competitiveness, and overall positive performance.

Strong data management initiatives are essential to ensure reliable, unified and timely data for key systems and processes. Such data must conform to how information is used by the business. Technologies play a crucial role in data management, but organizations must resist the temptation to think of data management as just a technical or IT initiative. Data management delivers best when it is directly tied to business opportunities, goals and objectives.

New processes must now come into play to understand how to:

• Validate data sources to ensure trustworthy and relevant information for analytics
• Verify and correlate analytics results with other intelligence sources to secure the right path for decision-making
• Use analytics results for deriving insights and reach decisions more quickly
• Take action on decisions in an agile and timely fashion while managing risk

 

The journey to continuously improved decisions and actions doesn't really start with data – it starts with what the organization wants to accomplish with its data assets. But the right data, business processes and analytics results can propel companies to better achieving goals and sustaining competitiveness. To make the journey to agility and high performance requires strong data management initiatives that combine analytics processes with cross-functional collaboration and practices that inject intelligence into corporate DNA for all purposes.

 

                      

 

 

 

 

 

 

 

 

 

  

 

 

 

 

 

 

Source: Accenture, 2012

 

 

Image source: Real Food Rules

 

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I've been compensated to contribute to this program, but the opinions expressed in this post are my own and don't necessarily represent IBM's positions, strategies or opinions.

 

About the author: Julie Hunt understands the overlap and convergence of many business processes and software solutions that once were thought of as "separate" – and how this impacts software Vendors and Buyers, as well as the strategies that enterprises implement for how technology supports the business and its customers. Julie shares her takes on the software industry via her blog Highly Competitiveand on Twitter: @juliebhunt For more information: Julie Hunt Consulting – Strategies for B2B Software Solutions: Working from the Customer Perspective

Comments

[Richardveryard]

To complete the journey, there needs to be a round-trip (feedback loop) from action via business outcomes to improved insight. We can only assess the quality of a decision when we see its results, and this gives us a new perspective on the value of the insight that contributed to the decision.

Ultimately it is this feedback loop that will allow data to be properly used and appreciated.
#orgintelligence

[Julie Hunt]

Hi Richard - thanks for adding your excellent comment.

I totally agree - most, if not all, analytics processes have iterative aspects. Besides assessing outcomes of decisions and actions, the analytics process itself frequently requires iterative testing and experimentation to find the optimal data sources and methods for analysis and interpretation. I would also include correlation with other intelligence sources as a frequent iterative process.