Increasing amounts of data and the corresponding, nearly universal, recognition of the value it can provide to an organization have led to a growing investment in data analytics initiatives. When undertaking such initiatives, it is important to plan for the potential roadblocks that may stall or even prevent successful project completion. The best way to combat this is to understand them and prepare in advance.
In this blog post, we will walk through the four common barriers organizations often experience when undertaking data analytics initiatives and present some ideas that may help you plan for and prepare to overcome them.
Organization-wide commitment
Project Buy-In
Gaining project buy-in is a barrier faced by both public and private sectors when launching a data analytics initiative.
The application of data analytics within state government is fairly new. This means that projects must gain buy-in across constituents, government officials, and governmental agencies for successful initiation.
To mitigate the political concerns raised by constituents and government officials, often the best course of action is to start with a data analytics project that will impact a significant and meaningful social cause. If individuals understand the opportunity to impact citizens lives, it becomes easier to rally support.
Within private sector organizations, value must also be realized. Select a project and showcase how it will create a return on investment to achieve buy-in. Pick something that the organization really values or drives a direct improvement to the bottom line.
Data Sharing
Data analytics projects sometimes require data from a variety of sources, owners, and systems. Some groups or organizations may be hesitant to share information, whether it be across departments in the private sector or from agency to agency in the public sector. In both scenarios, it is not uncommon for individuals to fear that data comparison will fuel competition or point out mistakes or shortcomings. To overcome this, it is important to have executive leadership set the precedent and to be clear about the purpose and intended use. Know that after the first project success, it’s common for these objections to completely evaporate as individuals recognize the value data analytics can deliver.
Legal
When using data protected by state and federal laws or organizational policy, you have to be sensitive to both the written and unwritten provisions for control and exclusions for its use.
First and foremost, it is imperative to understand the laws, statutes, and regulations that are at play. By doing so, the project will not mistakenly use data for a purpose that is not allowed.
When these laws are in place, it can make data analytics projects more difficult as information is not as easily accessible. The best course of action is to creatively find ways to use the data on hand. By engaging with the data and taking an innovative approach, you can make data fit together to enable a positive outcome despite regulations.
The State of Indiana successfully overcame legal concerns by working with our team to ensure all regulations were met and security was maintained.
Financial
Big data projects can have rewarding results, but often require specialized resources and technology in order to transform data into relevant and actionable insight. With this comes a level of financial commitment that can create barriers to organizational buy-in. This can be challenging when there is uncertainty about the specific value a data analytics initiative will provide. It is impossible to know exactly where the data will lead you!
The best approach is to start small and prove the value on a meaningful, but thoughtfully scoped project. Convince future stakeholders of the transformative power of data. With this buy-in comes an understanding of how the results of a data analytics project will achieve the original objectives.
Technical
Finally, because the technology enabling many data analytics projects is new, there may be technical barriers to completion. In many cases, organizations lack the technology and technical resources in house.
People can easily become so enamored with the technology that they fail to keep perspective in regard to their purpose. When working through a data analytics initiative, start by defining the problem you want to solve and steer clear of formulating results you hope the project will produce. Define the problem, find technology that will enable you to get to work, iterate, and continue to drive forward to ensure the technology does not become a barrier.
Even more important than technology is the technical talent to help you drive your data initiative to completion. Technical expertise is essential, but it must be combined with internal subject matter experts who can bridge knowledge gaps that will inevitably exist. If you lack the technical talent in house, consider finding a trusted partner to work alongside rather than expending the effort to bring resources up to speed.
Conclusion
Before undertaking a data analytics initiative, remember to plan and prepare in advance for the concerns that may present themselves along the way. For more information on how to overcome the barriers you are facing, connect with our data analytics team.
