Data may be the key to accelerating strategic decision-making, yet without a seasoned bench of expert talent, data is just another underutilized asset with a limited ability to impact the business, let alone the bottom line.
Despite the momentum and dollars funneled to data initiatives, the lack of available talent with specialized expertise in areas such as data modernization, data science, data storytelling and visualization, AI, and advanced analytics is hampering many companies’ ability to wring value out of their investments.
According to the IDG “2021 State of the CIO” report, companies are having a particularly hard time building this expertise internally, second only to handling cybersecurity. Given the shift to cloud and the focus on agile delivery, there is also a scarcity of other experts needed to successfully deploy these data solutions. According to 25% of the respondents to the survey underlying the report, they need professionals who are versed in cloud services and integration competencies along with devops/devsecops/agile processes.
Companies are striving to modernize data and computing infrastructure in support of a hybrid, multicloud world and transition to modern apps, AI, and machine learning-infused workloads. Simply put, they need technical and business analysis skills to support those efforts.
Organizations are desperate for data-savvy experts, but they also need talent with experience in change management and business skills — competencies that can be lacking in a traditional IT lineup. In fact, the IDG “2021 State of the CIO” survey found that technology integration and implementation skills were in great demand, cited by 47% of the respondents. At the same time, more than one-third of the respondents said they need soft skills in areas such as change management (36%) and strategy development (34%).
“A lot of organizations have data professionals, but they have a very focused role, whether that’s on a particular business use case or database,” explains Glyn Bowden, chief technologist, Advisory and Professional Services AI and Data Practice for HPE GreenLake. That singular focus can impede the ability to get creative with data sets or experiment with new ways to bring different data sets together. “It can translate into a lack of imagination on how to solve certain data problems, because the expertise just isn’t there to gain new insights,” Bowden says.
A multipronged approach
To combat the ongoing skills gap and encourage a data-driven approach to business, companies need a multipronged plan to shore up expertise and address key competency gaps. The need for specific skills will vary from organization to organization, depending on the requirements and where they stand on the maturity curve. Technology leaders need to start by determining the organization’s current status while conducting an in-depth skills assessment to identify possible gaps.
Although there is no one-size-fits-all approach or holistic road map for addressing the data skills shortage, there are some proven steps for building internal competencies and leveraging outside assistance. These include:
Cultivate internal talent. To promote data competency at all levels, it’s important to define the project and desired outcomes first and then survey the company ecosystem for possible candidates. Individuals who understand the business but don’t necessarily know how a data schema works can still have a significant impact on advancing data analytics goals.
It can also be useful to pair up internal candidates to take advantage of specialized skills. Teaming up business users with data professionals, for example, can help enhance enterprise data skills while developing new use cases for existing data.
Mine new talent pipelines. Universities, business incubators, and local startups can offer a fresh stream of talent that is either formally trained in data skills or ripe for learning (with the right training). Partner with universities with existing data science programs, or get involved in developing new curriculum — both types of activity can help develop budding data science and analytics resources. Such partnerships have many other benefits as well.
Consider data science as a service. Consider advisory services where data scientists and other expert talent can be secured on a project-by-project basis or as part of an ongoing “as-a-service” partnership such as what is offered with HPE GreenLake. Aligning with the right services partner delivers a variety of benefits, including augmenting your team with much-needed expertise, helping design business cases, creating a future road map for data and analytics, and helping internal users understand the value of data.
An outside partner can also take over ongoing maintenance of the complete service, data architecture, and IT stack, tending to everything from security to data governance and continuous improvement. This frees up the IT organization and the enterprise at large to focus on business outcomes instead of data operations.
From there, it’s important to create training programs that nurture data proficiency as well as data literacy for a varied audience and at a variety of levels, including:
In-the-trenches users who understand the day-to-day impact data can have on strategic business goals but need upskilling in how to understand context and how to best leverage that data
Data engineering and data science experts who can leverage AI and analytics to produce new data products; create new, meaningful insights; and extrapolate other use cases for data
Talent with data management competencies in areas such as data performance, security, and governance
“These [data management] skills are still relevant, but we can start seeding them with people closer to the business rather than closer to infrastructure,” Bowden explains.
Organizations should recalibrate from treating data as a single-use commodity collected for a specific reason to a more all-purpose mindset. “Typically, expertise gets built up around how to use specific data for a specific reason,” Bowden says. “Instead, organizations need to think of data in its own context rather than the application it was initially bound to.”
When aligning with a consulting and services partner, consider breadth of experience in different industries and with different business problems. “Look for consultancies that can take on new uses cases without having to rearchitect, reengineer, or refactor existing data and applications,” Bowden says. “Having consultants who understand how to build and scale platforms is a huge advantage.”