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Prove and Scale – Steps Three and Four on your Data & Analytics Transformation Journey

In last week’s blog – Establishing the Strategic Framework For Your Transformation Journey – I wrote about how each organization must put in the effort up front to articulate their data and analytics strategy and propose an architecture(s) that address both business and IT needs for now and the future. 

Where the IDENTIFY step created the “master list” for realizing data and analytics benefits, the ESTABLISH step began building on that plan, starting with your most pressing needs

If you’re like most people, you’re chomping at the bit to get going, to roll up your sleeves and work with the tools and applications you’ve selected or trying out other products/services you've identified to get the job done. That’s what Step Three is all about—proving the architecture and strategy work.

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Step Three: Prove Value through Pilots and Proofs of Concept

In this step, the IDENTIFY and ESTABLISH steps come to life. This is where the implementation plan is enacted and proven. There are some simple rules to keep in mind:

  • Keep the goal in view: Organizations typically excel at measuring well-understood data—but when they get preoccupied with metrics, they often lose sight of the ultimate goal: to grow, improve and transform the business. Keep your eyes on the prize…and don’t be distracted by today’s limitations or traditional thinking. For many, replicating the past processes is enough—if it ain’t broke, don’t fix it. That, however, will likely not get you anywhere near your digital transformation goal.  
  • Focus on developing pilot projects and proof-of-concept tests to validate the ideas and requirements of identified projects. Define the expected returns. Experimentation can trigger a greater understanding of the project’s broader usefulness.  You may quickly discover that an assumption you made in the architecture isn’t really the right approach to increase business value.  For example, sales teams might benefit from a personalized mobile experience that lets them ask questions of the data by voice in real time rather than rely on reports sent to them on a daily or weekly basis.  HR executives might want to analyze data in context of their role and blend data from many sources to understand both employee satisfaction but also costs to acquire and retain key employees.   
  • Consider quantitative and qualitative results in evaluating the impact of any use case. It’s important to listen to one’s instincts—sometimes a leap of faith is the right course.  While it’s best practice to measure value gained as well as costs saved, you sometimes may need to deploy something that can’t quite be measured yet.
  • If a project is failing to meet its stated aim, then pause. Examine the pilot project. And, if necessary, adjust and reshape goals. That may also mean it’s time to move on to another effort. 

There are bound to be some hits and some misses. For those hits, the next step is to scale it up for broader enterprise use. 

Step Four: Scale Your Successful Pilots and Expand to Additional Use Cases

When the initial use cases are achieving their desired outcomes, it’s time to scale these pilots more broadly, and expand data and analytics into other areas of the organization, applying lessons learned from the initial journey.

  • Continue to manage original use case projects to maintain and extend their value. Continual evaluation and assessment helps keep processes running smoothly. New technologies may open up additional access and analysis paths. Products are updated far more frequently than in the past, so new capabilities may appear that you should try and exploit.
  • Establish a center of excellence where skills, expertise and knowledge can be shared and used to benefit all. By sharing best practices and lessons learned, businesses can improve big data efforts more quickly and achieve greater long-term value.   There are lots of models for these centers, so don’t think that a one-size-fits-all model is the only way to proceed. 
  • Reuse or reference results and efforts from previous successes rather than starting from scratch each time. Imitation is the sincerest form of flattery.

Continuously Repeat the Prove and Scale Activities

You’re now firmly on your transformation journey. In my experience, this isn’t a strictly linear process. You cycle through the PROVE and SCALE steps on a regular basis with new data, new use cases, new capabilities, new requirements. The sign of a well-performing data and analytics initiative is that you are always improving on what you do.

Let me know your thoughts and leave a comment below. 

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