Mark Waller liked Bill Schmarzo's blog post 5 Steps to Building a Big Data Business Strategy

Mark Waller liked Bill Schmarzo's blog post 5 Steps to Building a Big Data Business Strategy
  1. Mark Waller liked Bill Schmarzo's blog post 5 Steps to Building a Big Data Business Strategy
    Mark Waller liked Bill Schmarzo's blog post 5 Steps to Building a Big Data Business…

“The problem is that, in many cases, big data is not used well. Companies are better at collecting data – about their customers, about their products, about competitors – than analyzing that data and designing strategy around it.”  

How can this still be the case? I mean after 5+ years of experience with Big Data, have we not learned a darn thing? We get the following observation from no less than the Harvard Business School:

The new attention being given to data today is because suddenly, everywhere, it’s become much cheaper to measure,” says John A. Deighton, the Baker Foundation Professor of Business Administration at Harvard Business School. “Used well, it changes the basis of competition in industry after industry.”

Used well, it [big data] changes the basis of competition in industry after industry!” What more does one need to say? In spite of its game-changing opportunity, organizations still have not gotten Big Data right.

This is consistent with what I observe on my many travels and conversations across a multitude of different organizations. I would estimate that less than 2% of these organizations really know what they are doing to exploit the potential value of big data to power their business models.

I think the biggest problem is the focus on creating a big data strategy is as soon it’s completed, it’s outdated. New unknown-potential data sources emerge, new hardware innovations drive new capabilities, new data management tools and techniques evolve, new open source advanced analytic tools pop out of universities, new edge analytic architectures become scalable, etc.

The growing wealth of “monetizable” data (social media, mobile, IOT, wearables, images, photos, video) and the absolutely astounding availability of advanced analytic tools (many of them open source such as MADlib, Mahout, H2O, OpenAI and Google’s powerful Tensorflow) are obliterating your big data strategy before your expensive consultants can even get the leather binding in place.

Too many organizations are making Big Data an IT project instead of making big data a strategic business initiative that exploits the power of data and analytics to power the organization’s business models.

Figure 1: Change Your Big Data Focus to Change Your Big Data Results


Build a business strategy that incorporates big data. Build a business strategy that uncovers detailed customer, product, service and operational insights that can be the foundation for optimizing key operational processes, mitigating compliance and cyber-security risks, uncover new revenue opportunities and create a more compelling, more differentiated customer or partner experience.

Build a business strategy that exploits the power of data and analytics to exploit changes in market demands, customer expectations, competitive moves, commodity prices, student debt, stagnating salaries, the underemployed, political trends, decline of the manufacturing middle class, fashion trends, Chicago Cubs winning the World Series after 108 years…yes, and many of these changes are sudden and unpredictable.

So how does one build this business strategy that exploits the power of big data?

Here is our 5-step approach:

Step 1: Start with the Business Initiatives

How can you transform the business if you don’t understand what’s important to the business? How can you transform the business if you don’t intimately understand what the business is trying to accomplish, why, and the desired business outcomes? Understanding the organization’s key business initiatives is the key to identifying the supporting decisions (use cases), analytics, data, and underlying big data architecture and technology requirements.

Invest the time upfront to envision how the growing bounty of internal and external data coupled with advanced analytics might impact the organization’s most important business initiatives. Brainstorm with the key business stakeholders the decisions that they are trying to make and envision how predictive analytics, prescriptive analytics and ultimately cognitive analytics can help the organization to accelerate, optimize and continuously learn from those decisions.

Step 1 requires intimate engagement between the Business and IT stakeholders. This is not something that IT does alone and then “presents” the results to the business stakeholders in some monthly “alignment” meeting. If the business stakeholders are not leading this effort, then the effort is doomed. Welcome to the 98% who just don’t get it.

Step 2: Identify and Validate Supporting Use Cases

Step 2 involves taking the decisions captured in Step 1 and clustering or grouping the decisions around common subject areas. These clusters become the key business “use cases” that support the organization’s key business initiative (see Figure 2).

Figure 2: Capture and Validate Top Priority Use Cases


The use case documentation should capture both the sources of business value as well as the potential implementation risks (“eyes wide open”). Tie the use case back to the organization’s key financial goals and assess the impact of the use case on each of financial goal. Estimate the financial impact and Return on Investment (ROI) if the use case is successfully executed over the next 12 months. Focus on the “4 M’s of Big Data”: 

Step 3: Prioritize Use Cases

Step 3 may be the most difficult step because it requires organizations to do two things that they don’t like to do: prioritize and focus. Prioritizing and focusing are not popular concepts for many organizations because of politic…

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    10.07 / 21:26
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