Throughout the past three years, there’s been a lot of chatter about data – specifically big data and business intelligence (BI) data.
Big data is defined by three key characteristics: 1.) a large volume of data, 2.) an array of data types, and 3.) an urgency to process the data quickly.
BI data refers to the process and tools used to analyze data, usually in the hope of helping businesses make more informed decisions.
For example, Amazon’s Dash Buttons, which allow consumers to order household products with the click of a portable button, have proven more successful as data-gathering rather than profit-building tools. The Dash Buttons allow Amazon to see the bigger picture of people’s buying habits and in turn, cater more resources to meeting their audience’s demands and improving the online shopping experience.
What types of data are businesses collecting? What tools do they use to analyze it?
To answer these questions, we surveyed 291 employees who use BI data analytics tools for their jobs.
- Data analytics users look to BI tools primarily for statistical analysis (25%), data management (22%), and predictive analytics (13%).
- Businesses collect and analyze the accessible data types more than unstructured forms of data, such as IoT and social networks data.
- 65% say internal data is more valuable than external data.
- 83% say structured data is more valuable than unstructured data.
- 65% say business systems data is more valuable than IoT and social networks data.
- Before selecting a BI analytics tool for your company, take the following three steps. 1.) Define the degree of data analysis you want from the solution, 2.) identify the level of technical expertise available on-staff, and 3.) determine whether you want to integrate existing platforms with the tool.
- Create a clear, detailed data strategy before investing in BI tools for your company.
- Establish robust metrics to track and prove your data’s value to company executives.
Our study included 291 employees who use BI data analytics tools for their jobs. We eliminated respondents who said they only use spreadsheets or free web analytics tools. In this study, 54 percent of respondents work at large organizations with more than 500 employees. 35 percent work at companies with more than 1,000 employees.
Top Tasks Businesses Use BI Tools to Complete
Sorting through vast amounts of data is not an easy task, especially with the increasing prevalence of unstructured data, such as text, music, and images. Companies began using BI tools to help process and manage the data they generate. In fact, widespread use of BI tools is fairly recent, with 71 percent of users adopting the solutions after 2012.
BI data users charged with analyzing their company’s data primarily use BI tools for statistical analysis (25%), data management (22%), and predictive analytics (13%).
The even distribution of tasks businesses use BI data tools to complete shows that the solutions’ analytical capabilities are useful and interrelated.
“Once you start doing some of these tasks, you inevitably end up doing others because they’re also useful.”
— Dean Abbott, Co-founder and Chief Data Scientist, SmarterHQ
Because of the continuum of capabilities and skills required to use BI tools, there’s a trend toward using multiple solutions to analyze BI data, rather than relying on one tool for everything.
“There are different tools that are stronger matches for different tasks, so it’s not unusual for organizations to use multiple tools.”
— Dean Abbott, SmarterHQ
It makes sense for businesses to seek out best-of-breed tools for each task they want to accomplish.
“I don’t think a lot of the products – unless they’re embedded in applications – are mature enough to provide end-to-end functionality. The choice is always, do you want one tool that can do everything you need to some degree, or do you want to get best-of-breed of all things and bring them in?”
— Carl Paluszkiewicz, Director of Customer Value, Denologix
For example, if a manager wants a better way to visualize her team’s business metrics, it is better for her to explore BI solutions, such as Tableau, that specialize in data visualization, rather than SAS, an advanced analytics solution.
Breaking it Down: What are the Different Types of Data?
Structured Data – data that can be stored in a database or spreadsheet, is easily searchable
Unstructured Data – data that cannot be stored in a database, such as text, images, or music
Internal Data – data generated within a company, such as sales quotas, revenue, marketing outputs, and human resources.
External Data – data generated from outside a company, such as information collected about consumers from surveys or on social media.
Business Systems Data – data a collected as part of a business process, such as financial and medical records
Internet of Things (IoT) Data – data collected from sensors, such as heart rate monitors, manufacturing equipment, and weather stations.
Social Networks Data – data about users’ behavior online, such as when they visit your website, what pages they interact with the most, and what they buy.
Types of Data Businesses Use the Most
BI data analytics users focus on internal data (70%), business systems data (59%), and structured data (58%). The trend demonstrates that data analytics users still focus on more conventional types of data – “old school data” –despite the industry’s increasing emphasis on newer types of data, such as unstructured, social networks, and IoT data.
Why do businesses focus on internal, business systems, and structured data, as opposed to unstructured social networks, and IoT data?
The first three types of data are structured – easily put in a database and searchable – while the other types of data need to be organized into a structured form before being analyzed.
“Newer types of data are more difficult to use because that data isn’t in a user-friendly form. All the old-school data is in a structured form, so you can put it in the database, apply algorithms, and get value from it much quicker.”
— Dean Abbott, SmarterHQ
Comparing the Value of Different Data Types
65 percent of BI data analytics users believe that internal data is more valuable than external data.
Internal data’s popularity is a result of its accessibility. It is data pulled from within the business, such as performance metrics. On the other hand, gathering external data takes more effort.
However, if a company wants to grow its audience, it needs to collect and analyze external data – demographics, characteristics, buying habits – so it can target potential customers better.
83 percent of BI data analytics users believe structured data is more important than unstructured data.
Unstructured data is more difficult to work with because existing BI tools assume data is structured. To analyze unstructured data, analysts first must convert it into its structured form, an extra, often time consuming task.
However, as technology improves, it is easy to imagine the value of unstructured data surpassing that of structured data.
“Eventually, we’ll be able to handle a lot of unstructured social data in an automatic way, instead of converting unstructured data into its structured form. Once we get there, we will see huge adoption. There’s incredible information in text. It’s just difficult to get to now.”
— Dean Abbott, SmarterHQ
65 percent of BI data analytics users believe business systems data is more valuable than IoT (17%) and social networks (16%) data.
Business systems data may seem more valuable because it is a reliable source of data. “It’s what’s available and what the company requires employees to collect,” explains Carl Paluszkiewicz of Denologix.
However, in some cases, data that is less accessible – more difficult to analyze – is more valuable. It has untapped and unknown benefits.
“Today, you have endless sources of data that you can use to predict trends, so the value of this data is increasing. The problem is that you don’t have full coverage of the data. You need a broad range of resources to collect and analyze it.”
— John Keenan, Founder and CEO, Anthem Marketing Solutions
For example, combining social networks data – how a customer interacts with the company’s website – with business systems data – how much money a company dedicates to paid social advertising – allows a company to target its audience better by personalizing what a customer sees on Facebook, Twitter, or LinkedIn.
3 Tips to Help You Select a BI Analytics Tool
After interviewing BI data analytics experts and conducting in-depth client reviews of more than 20 BI solutions, we identified three factors to consider when selecting a BI analytics tool.
1.) Define The Degree of Data Analysis You Want From the Tool
More basic BI solutions are ideal for creating beautiful graphs to display business metrics. Self service solutions, alternatively, enable regular business users who do not have a lot of technical experience, to upload, analyze, and visualize data. Finally, more advanced analytics solutions require businesses to invest in human and financial resources to implement the tool. They demand expertise in statistics and mathematics to track expansive amounts of data.
For example, Clutch groups BI data analytics tools in three different categories: BI, Self Service, and Advanced Analytics.
- BI solutions are basic analytics tools that allow regular users to display business metrics visually.
- Self Service solutions allow slightly more advanced users to upload and analyze data without relying on support from the IT staff
- Advanced Analytics solutions require skilled data scientists to apply advanced mathematics to track massive amounts of data
2.) Identify the Level of Technical Expertise Available On-staff
If a business is adopting a BI analytics solution for the first time, it needs to invest resources into hiring an experienced data scientist or training its current IT staff. Not understanding how to use a data analytics tool diminishes the value of the data collected.
3.) Determine Whether You Want to Integrate Existing Platforms With the BI Tool
If your company uses Microsoft across all platforms, it may make the most sense to choose a Microsoft-based BI solution, such as Microsoft Power BI.
Open source tools, such as R and Hadoop, are another option for companies seeking to analyze their BI data. However, businesses should be wary of some challenges when considering using open source analytics tools.
First, open source analytics tools only save money if you have the human resources to implement them effectively. Using open source tools requires a certain level of technical expertise.
“Tools like R and some of the Hadoop engines are really trendy because organizations think they don’t cost anything. But, the reality is that they cost double because you have to hire smart, capable people to do the work.”
— Laura Squier, Director of Advanced Analytics and Business Development, QueBIT
Second, when you use open source tools, you need to implement a strong internal data infrastructure. If the employee responsible for implementing and maintaining a business’ data leaves, a new person needs to step in to decode the previous person’s process.
“At the end of the day, when the person leaves and the next one takes over, how do you manage the work, the codes, and all the features you thought you invested in – what you thought you got for free?”
— Laura Squier, QueBIT
70 percent of data analytics users surveyed say their company is effective at maximizing the value of the data they collect.
But, what does it mean to maximize the value of your company’s data? Do the survey results demonstrate an honest self-evaluation? BI data thought leaders expressed doubt.
“Even at SmarterHQ, we aren’t maximizing the value of the data we have. There’s so much more we can wring out of our data, but we haven’t gotten to it. We haven’t had time.”
— Dean Abbott, SmarterHQ
- Create a clear, detailed data strategy before investing in BI tools
- Establish metrics to track your data’s value
Establishing a clear methodology for collecting, organizing, analyzing, presenting, and applying BI data allows you to track how data impacts your company.
“What makes data valuable is what you do to it. It’s not the data that you collect that’s most important. It’s the data you create. It’s how you transform the raw data into meaningful new data elements.”
— Dean Abbott, SmarterHQ
One of the most important parts of a BI strategy is metrics tracking. Although skilled data analytics users understand the value and opportunity BI data offers a company, business executives and employees who are unfamiliar with data analysis, may challenge the importance of investing both human and financial capital in BI tools.
“Several years ago, I worked with a very large retailer that implemented a new advanced analytics methodology that could bring value to their business. But, they didn’t measure properly, and when it came time to report the results to the board, the board didn’t understand what they had done. All the work they did and the value they created went out the window.”
— Laura Squier, QueBIT
Some metrics to consider include,
- Data reliability and quality
- Data infrastructure – collection, organization, and availability
- Whether the data meets your business’ challenges
ABOUT THE SURVEY
The study included 291 respondents who use BI data analytics tools as part of their job. 81 percent hold a manager level position or higher.
Respondents worked at companies of varying sizes, with 54 percent representing organizations with more than 500 employees.
The data was collected throughout February 2016.
Published August 16, 2016
Have feedback or questions about this survey? Reach out to Sarah Patrick at firstname.lastname@example.org.