An incalculable amount of data exists: Humans create more than 2.5 quintillion bytes of data each day. Without the right tools and talent to assess it, you’ll lose key insights that can help expand your business.

This explosion in the amount of new data helps explain two trends: The rise in use of business intelligence (BI) software and growing demand for data scientists.

Gartner predicts that by 2020, the number of roles using BI and analytics tools will grow at twice the rate—and deliver twice the business value—of roles that don’t use these tools. But does having a data scientist on staff lead to data-driven decision-making?

To find out, GetApp surveyed nearly 500 business leaders in the United States. (You can learn more in our methodology section.) We wanted to know what data-driven decision-making looks like in small businesses today.

Key findings

  • There is a positive correlation between how confident respondents are in their ability to use data to make decisions and how impactful they feel data is on their business.
  • Companies using BI tools rate the decisions they make based on data as more impactful.
  • Companies that employ data scientists are much more confident that they have the right data and insights to make decisions.

Employing a data scientist gives business leaders more confidence in their data-driven decision-making. But strong data scientists are not easy to find: most U.S. cities have a data scientist shortage.

That’s because data science expands beyond the tech industry: It affects everyone from coffee shop owners to sales teams. So, it’s great to know that data scientists are worth the hype, but what should you do if you can’t afford one right now?
 

GetApp’s recommended actions:

  • Outline how data science will improve your business at strategic and tactical levels.
  • Invest in BI tools to manage large datasets
  • Up-skill your staff by providing training on data-driven decision-making.

Most business leaders say they have the right data

Data scientists play a key role in businesses of all sizes. They perform a delicate dance between business stakeholders, decision modeling, and data management. It’s tough to overstate the value of someone who combines the skills of a statistician with the brain of a business analyst.

But before you bring a data scientist on board, you must have enough data for them to work with. The amount and quality of data in a business both play a big role in finding the right insights.

So, we asked the small business leaders in our survey if they have the right data to make decisions that will scale their businesses The overwhelming majority said yes:

Chart: Do you feel you have the right data to make business decisions?

Our respondents showed clear confidence in the data they have and in their ability to analyze it. Along with the two in three who said they have the right data to make business decisions, 81% said they are “highly confident” or “mostly confident” in their ability to make decisions based on data.
 

GetApp’s advice: Build a data science strategy first

Confidence in your business data is the best groundwork for a data science strategy. You should complete this strategy before making more investments in data-driven decision-making.

If you don’t understand the essence of data science, you can’t use data in a tangible way. Some call this “paying the data science vanity tax because they’re doing data science for the sake of saying they’re doing data science.”

To avoid the tax, revisit your core business model. If you built a roadmap when you first started, revisit it. Knowing how your data fits into the big picture will help you use it more effectively.

Does BI software boost business’ data efforts?

33 years after its creation, an estimated 750 million people still use Microsoft Excel. Its “Power Query” feature lets users clean data, while vlookups, filters, and conditional formatting help find missing items.

Our survey’s respondents also favor Excel: 30 percent use it to collect data, and 45 percent use it to analyze their data.

Which tools does your business use to analyze data?

We see why Excel remains popular. Respondents cited data visualization, predictive analytics, and custom dashboards as the most helpful data points for making key decisions: Excel has all of these.

But when it comes to finding insights in large datasets, Excel isn’t enough on its own. Our research found that companies using BI tools rate their data-driven decision-making as more impactful.
 

GetApp’s advice: Use BI software to manage large datasets

Companies with large amounts of data have a huge competitive advantage. It’s why a few tech companies—Google, Facebook, Amazon—have a monopoly on machine learning: The more data you can give an ML system, the more effectively it can train itself.

When it comes to managing large datasets, BI software offers more prowess. It’s also more effective at real-time collaboration, security, and scalability than using Excel on its own.

If you’re using Excel, you don’t need to stop cold turkey, or even give it up altogether. Instead, shop for BI tools that integrate with Excel.

Power BI is the obvious choice since both tools fall under Microsoft’s umbrella. If you’d rather extend your search beyond Microsoft, you can search for data visualization apps that integrate with Excel.

Are data scientists worth the hype?

Data scientists are in such high demand that they rarely have to hunt for jobs. LinkedIn co-founder Allen Blue told the Knowledge@Wharton network that most data scientists already have plum roles.

That demand isn’t on pace to slow down: Blue added that, taken together, data science and machine learning (ML) represent five of the 15 fastest-growing roles in America today.

GetApp’s survey respondents know how essential it is to have employees devoted to data: Half of them reported having two or more people in roles devoted entirely to working with/analyzing data.

Does anyone at your company work exclusively with data?

Companies that reported having such employees also showed much more confidence that they had the right data and insights to make big decisions. It’s encouraging to see half of small business leaders following best practices for data: Employing someone to watch the ongoing source, history, and context of your data is a key step to increase your data’s quality.

But hiring a skilled data scientist comes with a big cost. Experienced data scientist command salaries ranging from $140,000 in Phoenix to $100k in Dublin. Add these high price tags to the fact that data scientists are in such high demand, and it might seem impossible to hire the right person.
 

GetApp’s advice: Upskill your staff to become citizen data scientists

If you’re not ready to splurge on a data scientist, you can create a team of citizen data scientists in the meantime. According to Gartner (available to clients), “Citizen data scientists are ‘power users’ who can perform both simple and moderately sophisticated analytical tasks that would previously have required more expertise.”

Citizen data scientists don’t replace data scientists. Rather, they use BI techniques like counting, summaries, and roll-ups to find predictive insights from data. Although they often lack the coding or statistical skills of a data scientist, they might build data pipelines, visual drag-and-drop models, and more.

Gartner predicts that by the end of this year, citizen data scientists will produce higher volumes of advanced analysis than skilled data scientists. The most cost and time-effective way for your business to benefit is by up-skilling your current staff.

To do this, start by identifying colleagues in your business with the right skillset to lead data science efforts. Since “citizen data scientist” isn’t a job role in itself, you’ll need to reward these employees for the analytics efforts they show in their respective roles. By building a process to reward leadership in your BI projects, you can learn what works and what doesn’t while empowering your employees.

Want more help with data-driven decision-making?



Methodology

In April 2019, GetApp used Amazon Mechanical Turk to survey 488 business leaders in the U.S. Respondents were required to be self-employed, employed part-time, or employed full-time to take the survey. Respondents also had to work in a business with 500 or fewer employees.