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Product and Topic Expert
Product and Topic Expert
A better question is, do you have a good data strategy?  Is it a data strategy that resonates with the business?

According the SAP Master Class on Outcome-Driven Data Strategy, conducted by Millar Villar, SAP’s Head of Enterprise Data Strategy and Transformation, here are the 3 reasons why an outcome-driven data strategy is essential:

  • Today’s, business strategies depend more and more on ‘data’ either to digitize a process through automation,   customize customer & employee  experiences,   for growth in  new markets or through acquisition   So, aligning a data strategy to the business strategy - so that the business strategy can be achieved- is the top reason to have a data strategy. A data strategy has to resonate within all levels of the organization. It needs to have meaning and context to the business.

  • A company needs a data strategy to prioritize its work. We all know that the amount of data a company generates and uses is significantly more than before. There will always be more data issues and requirements than resources. The company needs a way to prioritize its data activities on what will bring the most value through the data strategy. The strategy needs to be “living and breathing” and fully aligned with the business priorities yet flexible enough to shift as the business transforms and matures. It can’t just be some words in a document but has to take “life” within the organization.

  • A data strategy outlines all the data capabilities that have to be built to achieve the business outcome. That includes not just DQ management and tools, but business capabilities such as org structure, data acquisition and data network strategy, compliance and ethics capabilities. And to layout a roadmap to develop though capabilities over multiple years. Setting expectations on what can be delivered, at what time frame, cost and executive support requires.

In other words, a good data strategy is not technology focused, it is outcome focused.  A good data strategy is not over-run with buzz words, it is measurable.

You might think data strategy looks like this… This is what great data strategy looks like…
De-dupe customer master data

Sales Effectiveness

Improve cross-sell revenue by 20% with personalized recommendations enabled by a 360-degree view of the customer
Leverage machine learning technology

Customer Experience

Increase customer satisfaction by 15% by leveraging AI technology to automatically propose solutions based on incident data

Supply Chain Efficiency

Fine-tune sourcing via data and algorithms to reduce costs by $10M
Create a data lake

Procurement Decisions

Improve profitability by 5% through cost optimization analysis of supplier selection and rationalization
Harness big data

Operational Efficiency

Increase profitability by 5% by leveraging machine data to do predictive maintenance analysis

Improve the efficiency of their Order to Cash process by 20% through the use metadata to match usage data from sensor to compressor and customer master data in order to streamline and automate customer billing.
Increase speed or agility

Operational Efficiency

Develop self-service data access to support speed, agility and confident in critical business decision making, with 40% less resources.

Use machine sensor data to drive changes in product development processes to become a faster, more agile, and iterative development organization, bringing new product to market 10% faster.
Verify all addresses

Operational Efficiency

Reduce undeliverable package fees and cost of returned mail, saving $1M in fulfillment costs.

Customer Satisfaction

Ensure timely delivery of packages and communication, reducing user support requests by 25%.

Ultimately, a good data strategy is not a series of independent actions.  It is aligned by the business, sponsored by the business, board ready and approved.

So, do you have a good data strategy?

If you’d like to learn more, visit us at strategy.