When it comes to building a data strategy, plans are useless but planning in invaluable, said data management authority and Associate Professor at Virginia Commonwealth University Peter Aiken.
Aiken held a workshop titled, “Building a Data Strategy” during Data Summit Connect 2021. A data strategy should be concise, actionable, and understandable by business and IT, he said.
The annual Data Summit event is being held virtually again this year—May 10–May 12—due to the ongoing COVID-19 pandemic.
Aiken taught attendees how to apply data strategically in support of strategy. Trying to write a good (much less perfect) data strategy on the first attempt is generally not productive.
By refocusing on crawl, walk, run approaches to using data strategically, data is able to keep up with agile, evolving strategies.
This approach will contribute more to three primary organizational data goals than other efforts: Your organization’s data; the way your people use data; and how your people use data to achieve your organizational strategy.
The data strategy must provide the why, how and what the organization is doing, he said. The military definition of strategy is a plan or policy designed to achieve a major overall aim. For data organizations should focus on a pattern in a stream of decisions.
Aiken provided several examples of different strategies including how Wal-Mart offers every day low prices, how Napoleon conquered some of his enemies, and more. Any strategy that ends up on a shelf is not useful, he added.
“Maybe it has a use but we have to look at it relative to what we are trying to accomplish,” Aiken said.
The data strategy should comprise of the highest level data guidance available. It should focus on data activities on business-goal achievement. It provides guidance when faced with a stream of decisions or uncertainties.
Data strategy most usefully articulates how data can be best used to support organizational strategy. This usually involves a balance of remediation and proactive measures, he said.
Forming a strategy helps the organization’s data program over time increase capacity and improves focus from reactive to proactive.
“Getting better at strategy will help your approach,” Aiken said. “Planning is everything.”
As a topic, data is complex and detailed, he explained. Data is taught inconsistently and not well understood. The focus is largely on technology with the business impact ignored.
Today, data is the most powerful, yet underutilized and poorly managed organizational asset, according to Aiken.
Data scientists are spending too much time preparing the data for someone else to analyze it. Hidden productivity bottlenecks are holding companies back. Improving productivity must be part of the complete solution, Aiken noted. Every data scientist needs a data engineer.
The business needs and existing capabilities the business can do need to be meshed together before creating strategic data imperatives.
Data strategy is implemented in two phases: prerequisites and iterations. Architecting is used to create and build systems too complex to be treated by engineering analysis alone. Engineers develop the technical designs.
The organizational strategy should be constructed so that the data strategy is made a bit more important than the IT strategy, he said.
CDOs need to rise to the challenge of changing the status quo if they expect to lead the business in making data a strategic asset.
The CDO agenda should be to develop the first version of an organizational data strategy, and then inventory data assets to decrease data ROT. Finally, they should monetize the organization’s data.
There are seven deadly data sins that need to be eliminated, Aiken explained. This includes:
- Not understanding data-centric thinking
- Lacking qualified data leadership
- Not implementing a robust, programmatic means of developing shared data
- Not aligning the data program with IT projects
- Failing to adequately manage expectations
- Not sequencing data strategy implementation
- Failing to address cultural and change management challenges
Identify the primary constraint keeping data from fully supporting strategy, he said. Exploit organizational efforts to remove this constraint. Subordinate everything else to this exploitation decision. Elevate the data constraint and repeat these steps to address the new constraint.
There needs to be a balance between business value and integrating new capabilities.
Register here now for Data Summit Connect 2021 which continues through Wednesday, May 12.