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The Biggest Data Analytics Challenges of 2020

Industry Advice Analytics

The year 2019 saw some exciting changes in both the amount of data generated globally and the various applications of that data across industries. This continued evolution of big data and the analytics industry has led to impactful new technology, business practices, and careers for those in the field. However, the rapid and consistent level of advancement has also brought with it a new set of challenges that will come to define the sector in 2020.

Below, we explore what those challenges are, their potential to impact the future of data, and how aspiring data professionals can find a lucrative career in analytics as this field continues to evolve.


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Data Analytics Challenges in 2020

1. The Need for More Trained Professionals

Research shows that, as of 2018, humans are creating 2.5 quintillion bytes (or 2.5 exabytes) of data per day, and the past two years have seen even greater increases in the number of streams, posts, searches, texts, and more used to generate this massive amount of information daily. Yet this increase in the quantity of data being generated isn’t expected to plateau anytime soon. By 2025, it is now predicted that humans will create an astounding 463 exabytes of data daily.

From artificial intelligence to supply chain management, applications of this incredible amount of data are limitless—if there are enough professionals trained to handle it.

Thomas Goulding, professor for Northeastern’s Master of Professional Studies in Analytics program, says that the biggest analytics challenge of 2020 will be a lack of qualified data analysts with the tools and training needed to work with this massive amount of information.

“The big challenge we will face in analytics will be having enough qualified professionals to support the industry need,” Goulding says. “The demand for analytic talent is really far outstripping the ability of the education system to produce it.”

This shortage is due to a myriad of factors within the industry. One possible explanation Goulding offers is that many universities in America are seeing an increase in international students studying data while domestic numbers decline. While the increase in students of any citizenship studying data is beneficial during this global shortage, these students are often choosing to return to their home countries after graduation, only furthering the gap between qualified professionals and available roles in the U.S. What’s more, these graduates are often restricted from employment in certain U.S. organizations, only further exacerbating the shortage of domestic graduates in this industry.

Learn More: What Does A Data Analyst Do?

“There are literally thousands of openings [in analytics] that are currently not going to be filled anytime in the near future,” Goulding continues, and his estimation isn’t far off; Burning Glassdoor Labor Insight reports that there are approximately 394,715 graduate-level jobs available for those with the proper advanced training—an increase of over 32 percent in the last two years alone. Without individuals to take on these open roles, however, Goulding emphasizes that advancements in research and the applications of analytics will come to an unnecessary standstill.

Facing This Challenge in 2020

In order to bridge this talent gap, Goulding recommends anyone with an interest in data consider honing their skills and increasing their career potential with a graduate degree in analytics from a top university like Northeastern.

An advanced degree provides aspiring analysts with the tools they need to be successful in this evolving field. From practical skills such as programming and statistics to professional or “soft skills” like communication and presentational abilities, graduates leave with the tools and experiences they need to not only fill one of the many open roles in this industry, but to thrive in it.

2. Bridging the Gap Between Executives and Predictive Data

Organizations across sectors use data to inform their decisions every day. Retailers, for example, might determine how much of a product to stock based on past sales of that item. Similarly, insurance companies can use collected information on a client’s past experiences to determine whether or not it’s in their best interest to cover them. Even healthcare organizations might use data in this way to track a patient’s entire medication list in an effort not to prescribe contraindicated doses.

Yet, while all these organizations have embraced this use of data, they are only scratching the surface of its potential. Many organizational decision-makers are unaware of analytical advances that allow them to make predictive rather than descriptive use of their collected data, and those that are aware might not have the technical understanding to fully appreciate the potential of this change.

This is the second-largest challenge that Goulding sees facing the analytics industry: a gap between the new ways and speed at which massive data can be processed to inform decision-making, and the level of data-expertise needed to inform those decision-makers

“There’s a culture change that has to happen in corporate America, where data must now become an increasingly strategic ally to corporate decision-making,” Goulding says. “Executives at the senior-level really need to understand the strategic value of their data so that they’re willing to trust in the judgments that come from their analytics teams.”

Digging In Deeper: This process of making predictions about the future based on an established set of data from the past is known as predictive analysis. Through the use of various statistical modeling tools, analysts can utilize the massive amounts of data that have been collected over the last decade to make informed decisions about what’s to come. This is an incredibly valuable tool for businesses hoping to stay ahead of changing trends.

Facing This Challenge in 2020

Goulding sees a few paths to overcoming this challenge and making predictive data a part of the decision-making process at the executive level. First, he believes that “getting adequate professionals trained” in these new forms of analytics will help to start shedding light on the potential of these practices.

From there, he believes it will be up to the data analysts within larger organizations to find ways of demonstrating the strategic value of predictive methods to senior leadership. By showing executives how data can help them “answer board-level questions with analytics,” analysts will be able to exemplify the true power of data. These questions may include:

  • What is the competition doing?
  • What are the risks or threats in our industry?
  • What are the opportunities we have not yet capitalized on?
  • Where should we go next (in terms of market, location, etc.)?

An analyst that can help answer these questions with data will be able to demonstrate to executives the true strategic value of these tools. However, while most analysts may have the technical training to carry out the technical analysis, the most valuable will have the communication, presentation, and data visualization skills needed to effectively share the value of their analysis with leadership teams. This, Goulding identifies, is a significant opportunity for young professionals entering the industry.

For this reason, programs like Northeastern’s master’s in analytics, have incorporated courses in data visualization, presentation, and communicating with data alongside technical ones to develop analysts who are ready to tackle this challenge head-on.

3. Data That Isn’t Harmonious

While the increase in available daily data is positively impacting many aspects of data analytics, there are some downfalls to the increased quantity. For example, Goulding explains that while the data we’re collecting is extremely valuable once it has been properly processed, it is not easy to manage in its raw form.

“The data that we have isn’t what I might call harmonious,” he says. “We have so many diverse sources of data being generated in so many different formats that it’s not easily integrated. Getting all that data together and into a single format that can be easily rationalized is going to be a major challenge now and for the foreseeable future.”

While the act of rationalizing data is nothing new to a data analyst, it is the amount of time, energy, and resources that businesses will need to put toward this process that is the new factor in the coming decade.

Today, “90 percent of an analyst’s time is working with data to get it integrated and harmonized in a way that’s useful,” Goulding says, and since this work is only going to increase the more data humans produce, future analysts need to ensure they have the necessary skills and training to handle it.

Facing This Challenge in 2020

Analytics training programs that are abreast of challenges like these in the coming decade have developed their curriculum to embrace this type of large-scale integration early on.

In Northeastern’s Master of Professional Studies in Analytics program, for example, students are able to practice working with large-scale data sets from corporate partners and government research organizations. As a result, they learn to “overcome [any] challenges they encounter as if they were a professional within that company, and work to actually answer a question of real value to an employer,” Goulding says.

This type of real-world experience is vital for analysts hoping to hone these essential skills and prepare for the realities of the data analytics field in 2020.

Interested in landing a role in analytics? Learn how to break into the industry with our custom e-book, or explore how a Master of Professional Studies in Analytics from Northeastern can help set you on a path toward success in this ever-evolving field.

 

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