Top 6 Roadblocks Derailing Data-driven Projects
Reposted from CIO.com. Authored by Bob Violino | Writer
Data is what drives digital business. Consider how strategically important it has become for companies to leverage advanced analytics to uncover trends that can help them gain decisive insights they might not otherwise possess.
But data-driven projects are not always easy to launch, let alone complete. In fact, enterprises face several challenges as they look to leverage their information resources to gain a competitive advantage.
Foundry’s recent Data & Analytics Study looked into why organizations have difficulty making good on the promise of data-driven projects and revealed several key roadblocks to success. Here are the top six reasons data initiatives fail to materialize and deliver, as revealed by the research, along with tips from IT leaders and data experts on how to overcome them.
1. Lack of funding for data initiatives
Funding can be hard to come by for any technology initiatives, particularly in an uncertain economy. This certainly applies to data projects. These undertakings might be competing with a host of other initiatives in need of financing, so it’s important for IT leaders and their data teams to present a strong business case for each project, and to not make them overly complex.
2. Lack of a clearly articulated data strategy
Oftentimes there isn’t a clear understanding within enterprises of what data is available, how the data is defined, how frequently it changes, and how it is being used, says Mike Clifton, executive vice president and chief information and digital officer at Alorica, a global customer service outsourcing firm.
Companies need to create a common language among stakeholders in advance of establishing any data-driven projects, Clifton says. “If you don’t have a solid foundation, budget and funding are too unpredictable and often get cut first due to a lack of clear scope and achievable outcome,” he says.
3. Technology to implement data projects is too costly
Making the challenge of getting sufficient funding for data projects even more daunting is the fact that they can be expensive endeavors. Data-driven projects require a substantial investment of resources and budget from inception, Clifton says.
“They are generally long-term projects that can’t be applied as a quick fix to address urgent priorities,” Clifton says. “Many decision makers don’t fully understand how they work or deliver for the business. The complex nature of gathering data to use it efficiently to deliver clear [return on investment] is often intimidating to businesses because one mistake can exponentially drive costs.”
When done correctly, however, these projects can streamline and save the organization time and money over the long haul, Clifton says. “That’s why it is essential to have a clear strategy for maximizing data and then ensuring that key stakeholders understand the plan and execution,” he says.
In addition to investing in the tools needed to support data-driven projects, organizations need to recruit and retain professionals such as data scientists. These in-demand positions typically command high levels of compensation.
4. Other digital transformation initiatives took priority
Digital transformations are under way at organizations in virtually every industry, and it’s easy to see how projects related to these efforts could be given a high priority. That doesn’t mean data-driven projects should be put on the back burner.
5. Lack of executive buy-in or advocacy for data initiatives
If senior executives are not sold on data-driven projects, their chance of success will likely diminish because of lack of adequate funding and resources.
To get executive buy-in, technology leaders must be able to articulate from the beginning what the outcomes of data projects will be and align them to business priorities or pain points, Clifton says. Ironically, all digital-related deployments depend heavily on data to achieve benefits, “so whether or not the executives realize it, they are funding data initiatives,” he says.
6. Lack of appropriate skill sets
The technology skills shortage is affecting nearly every area of IT, including data-driven projects.
Hiring talent with the specific technical experience needed to lead and manage data-driven projects “is a challenge in this competitive job market, but it’s key in ensuring you have the right skills in place to successfully implement the projects,” Clifton says. “Without the right skills and expertise up front, companies can start a project and then run into issues where the team is unable to quickly and effectively identify and resolve the problem.”
Data scientists, data stewards, and data forensics experts are becoming mainstay roles, Clifton says, whereas data architects were the higher-end skills most needed in prior years.
Read the full article at: https://bit.ly/3YyL4L2