September 26, 2022

Want to boost the ROI of data? Invest in DevOps and use innovation metrics that don’t focus on money

[ad_1] Splunk’s State of Data Innovation report identifies 9 strategies data leaders use to turn…


Splunk’s State of Data Innovation report identifies 9 strategies data leaders use to turn information into action and ideas into reality.

Image: mokee81, Getty Images/iStockphoto

Everyone is talking about how data is driving business strategy but only a few business leaders have figured out how to turn analysis into action. Splunk’s State of Data Innovation 2021 report found that only about 9{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} of companies are using data to drive innovation. 

Only 9{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} of the 1,200 business and IT leaders surveyed were data leaders. Those companies have these markers of success:

  • Developed eight products or services in the past year attributed to data innovation
  • Are almost twice as likely to say these products have opened up new markets
  • Have increased employee productivity in the last year
  • Are nearly twice as likely to be directly monetizing data

More than half of the survey respondents (56{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2}) landed in the beginner phase of data maturity with the remaining 35{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} in the followers category. 

How do they do it?

The report identified nine strategies that leading companies use to turn data into measurable positive outcomes:

  1. Get data to developers fast: Leaders are four times as likely as beginners to have accelerated data delivery over the last year.
  2. Prioritize among conflicting goals: Data leaders are more likely to use data to pick innovations that will be well received by customers.
  3. Observability is cutting edge: Leaders are ahead of the pack in using edge computing and observability. 
  4. Dig deep into data: Go beyond the basics and incorporate network data, application/transaction performance data, sensor/IoT data and physical/virtual server data.
  5. Incentivize innovation: Offer seed funding or budget for employees to test new ideas, create dedicated time for working on those ideas and tie bonuses to nonfinancial innovation metrics.
  6. Create innovation metrics that don’t focus on money: Measure the number of ideas generated, hypotheses tested or patent applications filed. 
  7. Make innovation someone’s job: Your leadership team should include a chief customer officer, chief data officer, innovation officer or a cloud architect.
  8. Push harder to implement DevOps and DevSecOps: More than 79{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} of leaders are using these approaches extensively.
  9. Invest in data innovation: Leaders spend up to 50{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} more of their tech budgets on data-centric solutions and staff.

SEE: Report: SMB’s unprepared to tackle data privacy (TechRepublic Premium)

Common barriers to data innovation

Splunk identified cross-team collaboration as the single biggest blocker of innovation. Survey respondents listed this challenge as the most common gating factor to testing and implementing new ideas. As the report authors note, “Innovation breaks normal processes, workflows and mindsets, so make it possible (and appropriately prioritized) for groups to get behind new possibilities and work cross-functionally effectively.”

The survey also found that these are the most common data maturity inhibitors:

  • Lack of a classification system that defines most or all data
  • No comprehensive data aggregation
  • No measures of data quality
  • Little progress on automatic data monitoring
  • Sufficient training and tools for employees

Survey methodology

Splunk used these six measures to gauge leader/follower/beginner status among survey respondents:

  • Data definition: Classifying and tagging data with metadata that supports access and use.
  • Data aggregation: Consolidating the data from an enterprise as a whole and in such a way that allows different business silos to access each other’s data.
  • Data quality: Measuring how accurate, complete, consistent and deduplicated data is.
  • Data investigation skills: Ensuring that employees have the right skills to query the organization’s data to answer business questions.
  • Data investigation tools: Ensuring that employees have the right tools to query the organization’s data to answer business questions.
  • Data monitoring: Automating queries to capture ongoing and real-time answers to important business questions.

Leaders excelled at all six factors, followers had mastered three to five and beginners had figured out two or fewer. The report authors noted that previous research has found similar numbers with leaders representing 9-11{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} of the sample with the beginner group ranging from 50-60{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2}. This suggests that about 10{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} of all larger companies are at the leading edge of digital transformation while about half have barely begun.

Enterprise Strategy Group conducted the survey in June which includes respondents from 10 countries from North America, Western Europe and Asia Pacific. About 84{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} of respondents work at companies with 1,000 or more employees and 16{26a7e12d73bf173349c48f5f5de6cf664a59afbbee642b337fd293804c6597c2} work at mid-sized companies of 500-999 employees. Eighty-three percent are senior IT and business decision makers. 

Also see


Source link