How data analysts can help CIOs bridge the tech talent shortfall

Becoming a data-driven organization requires more than just implementing technology and hiring AI talent. IT leaders, follow this foundational advice
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how big data aids digital transformation

Digital transformation has increased interest in AI, ML, and DevOps talent to revolutionize the customer experience through automation and personalization.

While these positions are essential, it may not make sense for every company to prioritize them, especially those that don’t already have a cohesive data strategy. While digital progress and tech investments will continue, leaders must first prioritize how their company manages its data.

Data: The foundation for innovation

Data is typically a company’s most significant competitive advantage as it delivers deep insights that drive advancements and modernization. According to a recently released report, 67 percent of digital leaders believe data and analytics provide a competitive advantage and will drive tech investment priorities in the near future.

While many in senior leadership claim to be data-driven in their decision-making, most companies have gaps in implementing new technology to help them access that data and business insights. A survey of business executives found that 92 percent of respondents were working with AI/ML, but 61 percent didn’t have a data strategy to support it.

[ Also read Digital transformation: 6 lessons for CIOs. ]

Worse, increased digitization has created more data that, in many cases, is not actionable without intervention. To put this valuable information to work, companies need to invest in hiring data professionals.

Here are a few considerations to keep in mind when bringing in talent for these roles.

1. Establish a data-driven vision for the organization

CEOs and CIOs are critical to driving the cultural shifts needed to become a data-driven organization. Leveraging better data insights without top-down buy-in from senior management isn’t possible. Yet, according to a survey of data and AI leaders, more than nine out of ten respondents feel that the primary challenges to becoming data-driven involve culture, people, and processes.

Before you implement new technology and/or hire data professionals, your organization must shift its culture from being data-hesitant to being data-driven. It’s possible to do this using internal resources, but many organizations find that bringing in an external consultant will drive quicker results.

In either case, success depends on setting a vision and a strategy for what the data-driven organization will look like. This vision can be achieved through the following:

  • Stakeholder feedback: Input from different stakeholders will help paint a holistic picture. This input should come from members of the C-suite, technology and digital leaders and key staff members, and other employees who rely on the data today.
  • Strategy and tech alignment: A strong data strategy must align with the larger business strategy. As part of this alignment process, it may help to revisit the company’s mission and goals and its technology roadmap.
  • Current state analysis: This requires digging into existing systems and team structures to understand strengths and weaknesses and identify gaps and opportunities.
  • Documentation: The organization’s data needs to be documented so that it’s clear to everyone impacted.
  • Measurement: It’s important to identify what metrics should be measured, how to tie them back to the business objective, what tools are needed to track the data, and the frequency of reporting.

2. Ensure the organization is pulling clean data

Business analytics are only as good as the data they’re using. Given the wealth and complexity of data, it’s easy to understand why leaders are often overwhelmed in their attempts to access better analytics and insights. This is where data professionals can help.

Data scientists and analysts are statistics, math, databases, and systems experts. They are especially adept at looking at historical metrics, recognizing patterns, pulling in market insights, and identifying outlier data to ensure the best points are utilized. They’re also able to organize vast amounts of unstructured data, which is often very valuable but difficult to analyze, by leveraging conventional databases and other tools to make the data more actionable.

These professionals enable all frontline or executive leadership employees to apply insights and make better business decisions.

3. Determine the right data skills needed in the organization

If you’re looking to invest in data transformation, start with some basic questions: What is the goal? What problem or issue needs to be solved?

Once you’ve identified the pain points, tech teams can work backward to determine the ideal solution and what kind of talent best fits the required roles. A clear vision from the CIO provides a roadmap for the recruitment team to build a candidate pipeline.

[ Related read Data scientist: A day in the life ]

If you’re looking to invest in data transformation, start with some basic questions: What is the goal? What problem or issue needs to be solved?

It’s also important to look at the attributes of the data scientists and analysts themselves. In addition to having technical skills, data professionals with a background in programming, data visualization, and machine learning are also highly valuable.

On the non-technical side, they should have strong interpersonal and communication skills to relay their findings to the tech team and those without a tech or math background. It also helps if they have business experience that allows them to translate the data findings and how they can be used as a competitive advantage.

4. Use data to drive tech recruitment strategy

In addition to having the insights needed to improve the customer experience, companies with a data-first commitment should be able to hire tech talent that can deliver on these digital products and services. An organization that makes hiring decisions based on accurate and meaningful data can better identify the actual tech skills needed and where there are gaps.

There’s a growing movement to incorporate more AI into recruiting. But AI depends on quality data to survive – algorithms need it to learn, and patterns need it to form. By incorporating complete and accurate data, AI can automate many administrative and tedious HR processes, enabling recruiters to spend more time on the people side of the business. It can refine job descriptions, for example, and quickly scan resumes to identify the best-fit candidates for any given role. And it’s a great asset for developing internal training programs, identifying employee career paths, and instituting other retention programs.

Becoming a data-driven organization requires the right data strategy and an understanding of the company's tech, tools, and personnel. Business leaders need to shift their focus from investing in new technology to investing in its technology people. Hiring data professionals who can identify, collect, process, and analyze the vast amounts of data being collected is critical for successfully implementing all the solutions that come with AI, ML, and other advanced technologies.

[ New research from Harvard Business Review Analytic Services identifies four focus areas for CIOs as they seek more flexibility, resilience, and momentum for digital transformation. Download the report now. ]

jason_pyle_harveynash
Jason Pyle is President of Harvey Nash USA. He spends much of his time fostering relationships with digital, procurement, and technology leaders to develop customized solutions for their unique needs.

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