Observations

CIO Priorities for 2013 from 2,053 Industry Leaders

Posted on by Bill Freedman

Every year a Gartner survey summarizes global CIO priorities, and every year I take a very close look at the findings.

The most recent survey was conducted in the fourth quarter in 2012 and included 2,053 CIOs. These individuals span 41 countries and 36 industries. I like this annual survey because it is a well designed study into the priorities driving US$3.7 trillion of spending on information technology and personnel.

CIO Priorities: the Findings

Top 10 Business Priorities

Ranking

Top 10 Technology Priorities

Ranking
Increasing enterprise growth

1

 Analytics and business intelligence

1

Delivering operational results

2

Mobile technologies

2
Reducing enterprise costs

3

Cloud computing (SaaS, IaaS, PaaS)

3
Attracting and retaining new customers

4

Collaboration technologies (workflow)

4

Improving IT applications and infrastructure

5

Legacy modernization

5

Creating new products and services (innovation)

6

IT management

6

Improving efficiency

7

CRM

7

Attracting and retaining the workforce

8

Virtualization

8

Implementing analytics and big data

9

Security

9

Expanding into new markets and geographies

10

ERP Applications

10

One of my favorite parts of this survey is that the technology executives are asked about business priorities first. They may be propeller heads at their core, but they understand their primary task is to find ways to align technology with business initiatives and drive strategic results. As a result, top line growth, business expansion, cost control and personnel issues are clearly present in the business priorities. The only item that I’m surprised isn’t explicit ed stated in the business priorities is accelerating product cycles and decision-making.

The technology list is dominated by newer technology that has enough of a track record of delivering disruptive results. The heightened priority suggests that these investments are moving from lab experiments to broad deployment. Cloud and mobile are the talk of Silicon Valley; it’s also found, in my estimation, in 6 of the 10 priorities. Multiyear initiatives where the necessity has out-paced results are also on the list: Analytics, Security, Virtualization and ERP.

Large budget items like desktop hardware, software and support, which in many cases are the largest portions of annual budgets are not strategic topics in this years survey. Likewise, vendor relationships and outsourcing aren’t a priority this year as they’ve been in the past.

Takeaways

  • Its going to be a good year for technology in general as top line growth leads the list of priorities
  • Its not just that CIOs are spending on cloud and mobile, their organizations are benefiting from these technologies
  • Enabling agility from the bottom-up is a big opportunity. From mobile and cloud, to analytics and virtualization, and ERP and CRM, technologies that provide productivity leverage across the organization will be easiest to justify
  • Infrastructure investments won’t be slighted. Organizations will strive to move quickly, but with a strong foundation. Security, scalability and maintainability will be built into to major initiatives. This is a correction to previous years where organization were burned by having to spend on remediation and refactoring to fix mistakes of moving too fast.

What do you think? Comments welcome.

Analytical Rigor Trumps Big Data

Posted on by Bill Freedman

The Silicon Valley brain trust, from VCs to entrepreneurs to business executives, are all agog with the relatively new phenomenon of big data. It’s clearly an important technology trend at the intersection of internet’s ability to generate massive amounts of data and cost efficiencies involving the storage and processing large data sets.

Big Data

Image by Rachel Jones of Wink Design Studio using: Tagxedo.com.

Funds are flowing into many big data start-ups which are creating powerful systems and tools for enabling new types of decisions aided by big data collection, analysis, workflow and communication. Established tech companies are building connectors to big data systems. And, not to be left out, mainstream businesses are launching internal big data analysis projects.

The big data excitement is clearly a new phenomenon. Its roots come from applied mathematics, forecasting and econometrics. Statistical analysis has looked as data samples, built models, tested hypotheses and simulated outcomes for many years. Businesses have tested everything from pricing proposals to demographics to feature lists with applied math. Weather forecasts and evaluating baseball talent routinely use applied mathematics. Google’s successful business strategy is a triumphant use of applied mathematics.

What’s changing is that technology makes it efficient to collect the entire universe rather than samples. Size has its advantages. Analysis of larger samples improves accuracy. Collecting large samples quickly speeds decision-making.

But this is where we need to be careful. I believe that, in most cases, more value is created through rigorous analysis rather than collecting larger samples. While the cost of data collection is lower than ever, the labor pool capable of rigorous analysis remains fixed in size. Yes, these brains are increasingly working with larger data samples or the full universe.  Their tools and intellect are data size independent.

Invest in Analytical Rigor

Before jumping head-long into big data, I recommend investing in the brain power needed to do rigorous analysis. Analytical rigor is hard…and it takes a major investment of time, personnel and leadership to accomplish. This is a very different effort that making capital investments in computer hardware, software and processes. Creating a culture of analytical decision-making has paid off for Google and the Democratic National Committee.

Anyone can buy big data tools (heck, anyone can download Hadoop, R and many other big data management and analysis tools for free) and tell their investors they’re using big data. The missing piece that is the commitment to rigorous analysis, to building a team that has the brain power needed to collect the right data, to building valid predictive models that enable profitable decisions.

Once your organization excels at analytical decision-making, expanding into big data is a no-brainer. If you choose to invest in big data without having a foundation analytical rigor, results will likely be illusive.