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Growth Roadblocks: Barriers That Prevent Data Science Teams’ Maturation

In the modern marketplace, businesses across all industries are clamoring to gain a competitive edge over other companies in their sector. Even a seemingly slight advantage can help organizational leaders make wiser decisions, capitalize on emerging growth opportunities, and protect business continuity. 

While there are many methods by which business leaders seek to gain this edge, one of the most pragmatic approaches is to leverage data science. Launching a data science program and curating a team of talented scientists and analysts can be an exhilarating experience for any business. 

However, this initial optimism can quickly wane when business leaders attempt to put their plan into action. Far too many adolescent data science initiatives either fail entirely or never mature.

Fortunately, you can avoid these pitfalls by familiarizing yourself with, and eliminating, common data science program maturation roadblocks. The team at Rolai has compiled a list of several such roadblocks and divided them into four primary categories:

  • Data challenges
  • Business confidence shortfalls
  • People and skill shortages
  • Tool and platform deficiencies

After outlining each of these maturation pain points, we provide you with solutions that can help you tap into the power of data science and fuel organizational growth. 

Data Challenges

For many organizational leaders, the mere idea of attempting to harness massive amounts of data can appear to be an insurmountable task. That is why we elected to tackle data challenges right out of the gate in this list of growth roadblocks. 

The data-related challenges that you are most likely to encounter in your own data science program include the following:

Failure to Launch 

Largely influenced by the turbulent events of 2020, businesses across the globe have been adopting data science technologies at a feverish pace. At the start of 2020, the global data science platform market was approximately $4.7 billion. By 2030, the market will be valued at approximately $79.7 billion, according to Allied Market Research.

Allied Market Research also found that much of this growth is being fueled by small to medium-sized businesses (SMBs). Despite these figures, some business owners are still hesitant to step into the data science domain. 

While every organizational leader must contend with a unique set of reservations and concerns, many of these individuals are hesitant because the data analytics program will be imperfect.

Although these concerns are founded in fact, a company with an imperfect data science program will still have access to far more valuable insights than a business with no such program. So what does this mean for your business? 

It’s simple — start your program, even if you are a bit hesitant. Do your research, put a plan in place, and act on that plan. Doing so will pay dividends, even if you have to use a little trial and error along the way.

Misconceptions About the Need for Big Data

Another major data challenge is centered around another misconception held by some business owners. This misbelief is that data analytics only works if you have access to “big data.” 

In case you are not familiar with the term, big data refers to massive amounts of data that require highly sophisticated technologies to process. As you might imagine, the more data you have, the richer the insights. 

Once again, this misconception is founded in truth: big data yields better insights than smaller or narrower sets of information. However, starting with small data sets will still yield actionable intelligence that you can use to guide decision-making. 

Fears About Data Security

Businesses are right to be concerned about data security. Cybercrime has steadily been on the rise over the last several years. In 2021 alone, the FBI received nearly 850,000 complaints of cybercrime. There were also approximately $6.9 billion in reported losses that year.

Some leaders fear that by compiling all of their organization’s data into an analytics platform, they may somehow be making themselves more susceptible to cyberattacks and all of the headaches that come with an encroachment onto their network. 

Although there is no way to reduce the risk of cyberattacks to zero, businesses can take steps to protect vital information. One creative and effective approach involves “data tokenization.” In addition to using tactics like data tokenization, businesses should partner with a cybersecurity firm or hire in-house professionals.

A Lack of Business Confidence

A data science team cannot be successful unless leaders support them. If the organizational leaders’ actions and behavior demonstrate a lack of confidence in the team or the program, meaningful results will be hard to come by. Here are a few ways in which a lack of business confidence can manifest itself:

No Top-Level Buy-In

The first business confidence challenge is closely linked to the data-related issue described under the “Failure to Launch” header. The difference in this scenario is that organizational leaders started a data science program and began hiring personnel to run it. However, they did not follow through.

To avoid this challenge, make sure that top-level personnel understand the value proposition of a data science program. While you may be fully sold on investing in a data science team, you cannot assume that your counterparts feel the same way. Some statistics suggest that they may not.

For instance, in a survey of CEOs, LinkedIn found that 50% of respondents want data analytics professionals to work directly under them in the organizational hierarchy. But 56% of CEO participants stated that they did not trust the validity of their data. 

Distrust of Black Box Solutions

The next business confidence-related data maturation challenge centers around the concept of control and who has it. Specifically, some business leaders are hesitant to adopt so-called “black box” solutions. 

Black box solutions do not provide companies with a glimpse into the inner workings of a technology or platform. The platform is provided via the software as a service (SaaS) model, which means that businesses pay the developer a recurring licensing fee in exchange for access to the technology. 

Most black box solutions are not inherently bad. They can be valuable assets to your business, especially if they are created and managed by a talented team of developers. 

The “black box” restrictions simply prevent your staff from making modifications to the tech that would adversely impact performance. You can still integrate these technologies with your other software and reap the benefits of the SaaS service model. 

Unwillingness to Commit or Invest

Starting any program, especially a data science one, requires the business to invest time and resources. The organization must recruit and screen data science professionals, purchase the right technology and provide staff training on these new resources. Otherwise, the data science program will never mature. 

When you’re considering whether to invest in optimizing your data science program, make sure to examine the potential long-term benefits. Also, remember that many companies you are competing against are probably already using data science to gain an edge. If you are not, your business will find it difficult to keep pace.

People and Skills Shortfalls

After you have invested in the right technologies and committed to launching a data science program, your biggest hurdles will be related to staffing your team. Some of these people and skill-related shortfalls include the following:

The Elephant in the Room: The Talent Shortage

According to a 2020 MicroStrategy survey, 95% of employers reported difficulties finding skilled analysts and data scientists. The meteoric growth of the data science industry has led to a significant shortage of workers. 

Those who are available fall on one of two extreme ends of the spectrum. They either have limited experience or a wealth of skills and talent — and salary demands to match. 

Since many businesses have no in-house nurturing program to upskill the less experienced candidates, they are forced to fork over premium pay to attract top applicants. 

While there is certainly nothing wrong with paying data science professionals a salary that aligns with their experience and skills, tying up too much capital in staff can stunt a new program’s growth. 

Retention Challenges

Well-paid data science professionals with high job satisfaction will probably stick around a while. However, if you opt to hire up-and-coming data scientists and analysts, there is a good chance that they won’t stick around long. 

According to data compiled by GlobeNewswire, only 2% of 2021 data science professionals had been with their current employer for five years or more. 

Researchers also found that most data science professionals will only stay with their current company for approximately 1.7 years before moving on to another employer.

Inefficient Hiring and Onboarding Protocols

You can boost employee morale, reduce involuntary turnover, and minimize voluntary attrition by investing in employee training programs. Additionally, you will need to provide data science professionals with clear pathways for advancement and offer them competitive salaries. 

Despite these efforts, some of your analysts and data scientists will still elect to move on from your company. Therefore, you must streamline hiring and onboarding protocols. This approach will help you quickly fill essential vacancies and staff your data science team adequately. 

Tool and Platform Deficiencies 

Technology is the final hurdle your company will need to overcome to develop a mature data science team. The wrong technology can undermine the efforts of even the most talented data science professionals. Conversely, a great set of tech and tools can give your staff precisely what they need to be successful. 

The primary tool and platform deficiencies you are likely to encounter include:

Cobbled-Together Software Solutions

Is your business using a cobbled-together software stack that does not communicate effectively? If so, you are setting your data science program up for failure before it gets off the ground. 

Without easy access to your organization’s data, your analytics professionals will never be able to provide accurate intelligence to guide your decision-making process. 

When your platforms are too diverse and disjointed, they create data silos. A data silo occurs when one platform contains information that cannot easily be shared or accessed via other software. When data silos exist, you will experience information inconsistencies across your organization. 

Transitioning to a centralized data analytics solution that can integrate with your other technology will remedy this shortcoming. It will eliminate data silos and provide your team with the information to perform high-level data analysis. 

Poor/Non-Existent Collaboration

You must eliminate collaboration and communication barriers if you want your data science team to reach its true potential. They must be able to freely collaborate with other departments within your organization. 

When everyone is on the same page and working towards a common goal, your data science professionals can provide powerful insights.

To improve collaboration, provide your staff with the right training, tools, and software. A great software solution should make collaborating easy and enjoyable. 

Disconnect in the Data Engineering Pipeline

Data must be transformed into a digestible format promptly. Otherwise, you may miss out on the chance to use the information to capitalize on fleeting or time-sensitive opportunities. 

Investing in a comprehensive enterprise management solution can create a streamlined data engineering pipeline. Such a platform will help you facilitate better collaboration and monitor the progress of high-priority projects. 

How Rolai Can Help

The truth is that some of the aforementioned challenges will not be resolved anytime soon. However, you can circumvent many data science maturation roadblocks by leveraging valuable resources such as those provided by Rolai.

Rolai is committed to helping our academic and business partners thrive in the data-centric modern marketplace. Not only do we want to provide enterprises like yours with cutting-edge solutions, but we also want to offer a unified suite of technologies so that you can simplify your array of software. 

With those goals in mind, we have created not one but two solutions your organization can use to catapult its data science program up to current standards. 

The first offering is geared towards upskilling and educating your staff. Cumulatively, this solution has over 50 case studies and more than 60 courses designed to teach your staff valuable data science skills. While you may not be able to find the talent you need in your applicant pool, you can create and nurture it with Rolai. 

Our second solution is a robust platform that can provide real-time information about the state of your organization. It also includes administrative management tools so that you and other organizational leaders can always keep a finger on the pulse of your business.

If you are ready to make the previously mentioned data science maturation challenges a distant memory, schedule a demo of Rolai’s solutions today.

September 14, 2022