Levels of Data Strategy

Each level of an organization's data strategy has a compounding impactLearn more about each level below, and ask yourself: is your organization's data strategy hurting or helping?

 
 
 
 

TRIBAL

  • The standard spreadsheet reigns supreme
     

  • Decisions are based on tribal knowledge, meaning the direction and success of the company is placed squarely on the shoulders of a few smart people.
     

  • Each individual group or team has their own data strategy and their own approach to solving data issues. There is little collaboration, and decisions are made based on questionable sources or tribal knowledge.
     

  • Composite perspectives are impossible, which makes using data for a competitive advantage impossible.

NASCENT

  • The business is unable to use data as a competitive advantage.
     

  • Business leaders struggle to demonstrate to customers, shareholders, and partners how they plan to innovate. This is especially true for organizations with a high dependency on spreadsheets.
     

  • Data is stored (captured) but not effectively cataloged or understood. There are long delays in fulfilling data requests. Point solutions to solve data challenges proliferate with little reuse creating costly overhead.
     

  • The business leads to alternative sources of data (shadow IT) and spreadsheet

TRANSITIONAL

  • Despite investment in data analysis and related technologies, the return on this investment has yet to materialize. This is attributed to the complex nature of the business - multiple groups, terms, and technologies hamper efforts to turn data into true insight. Organizations in this category have found some success in areas like consolidated forecasting or financial reporting, but lag in others such as Customer Operations Analysis.
     

  • A concerted investment is made in reporting and analysis. There's growing responsiveness to data requests but the group still struggles with complex logical, physical, and political boundaries.
     

  • A group or individual begins to champion the concept of data cataloging
     

  • Certain groups are efficient at data analysis and reuse (think Customer Analysis, Consolidated Reporting) while other areas are still way behind (operational analysis, employee 360).

  • High confidence in the organization's data translates into greater use. Data-driven decision making is now a core component of how the business operates as analysts are able to bring empirical evidence into meetings.
     

  • The prevalent use of trusted data creates a high level of operational transparency. This transparency enables the business to move with greater speed and agility.
     

  • Advanced organizations have implemented "data production lines" that fully automate the procurements of data for end consumers (both people and downstream applications).
     

  • Advanced organizations are able to integrate the typical business user into this flow so that they have greater involvement and quicker access to information. This means flaws in the process are spotted sooner. Advanced customers capture internal metrics to help them improve efficiencies.

ADVANCED

INTELLIGENT

  • Business leaders are able to demonstrate to partners and shareholders how the organization plans to grow and innovate. "Smart" technologies help businesses increase productivity through automation.
     

  • At the core of the Smart revolution is trusted data that can be leveraged to its full value to enable overall operational excellence. Customers and partners find the business exceptionally easy to work with. The business can adapt to changing market conditions and is better suited to pursue new business opportunities.
     

  • Intelligent organizations take full advantage of "intelligent" data processes where data is fully available to aid decision making. This process is effortless and operates proactively. The business can now use data to predict customer behavior, fraudulent activity, or potential failures.
     

  • Recommendation engines are integrated into business processes in order to make them more efficient. Artificial intelligence is much more valuable as it leverages trusted data and works for improving responsiveness and fine-tuning internal operations.

 
 

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