Professionals and entrepreneurs across organizations frequently overlook great analytic opportunities because of a misconception that the business is too small. While large organizations like Apple and Amazon have stunning examples of analytic insights, their size isn’t the important factor. Analytics are useful for small business too.
Analytics work with smaller data sets
If you follow analytics stories, you’ve probably heard that these tools are data-hungry. One common recommendation of advocates for using modern analytic tools is to collect as much data as possible.
Sure, developers train the latest artificial intelligence (AI) models using billions of words and hundreds of gigabytes in datasets. However, AI systems are the latest analytic developments and still have many problems.
Data analytics has over a century of statistical and methodological development that doesn’t require massive data. Instead, you can perform most analyses with as little as a few hundred observations.
Of course, the more data you have, the better. But don’t let that stop you from leveraging the data you already have. Your data are worth their weight in gold!
Analytics work with smaller budgets
OpenAI spent at least $100 million to build ChatGPT. They spend billions now to develop upgrades and keep it all running.
But you don’t have that kind of dough in a small organization. For most small- to medium-sized businesses, the idea of spending even $100 thousand might seem like too much.
Fortunately, you don’t need to spend even that much money to begin reaping the rewards of a solid analytics program. By starting small and choosing your efforts carefully, you could launch an analytics program for just a few thousand dollars.
And when I say start small, I mean really small. Don’t plan on hiring analysts right away. Instead, save your money by hiring experts as contractors.
Let the contractors help design a plan to incrementally build your analytics program. With this method, you keep better control of your cash and can focus on maximizing ROI for your investment.
And, like other aspects of organizational growth, you don’t want to over-expand your analytics. Grow your program carefully and with intention to keep your budget intact. You can read more about finding budget for analytics here.
Analytics work with smaller staffing
Amazon employs thousands of data scientists across the company, and they should. The scope of Amazon’s data infrastructure and systems across business units, especially Amazon Web Services (AWS), is massive.
For small organizations, working with analytics might seem out of reach without a strong staffing model. You might think an analytics team would need at least three positions to cover data scientist, analyst, and engineer.
Yet, this is an area where your size as a small organization can benefit you. Many aspects of your data infrastructure and analyses scale down as the organization size gets smaller.
You don’t need to hire large teams of analysts, engineers, database people, and super-expensive data scientists. As I said, you can even start by hiring contractors to help build and maintain your systems.
When ready to bring your analytics in-house, you can choose one of a few routes. First, you could make an outside hire of one person if you have enough revenue and work to keep them going. Second, if you’d prefer not to bring in an outside person, you could opt to train existing staff. You can read more about training your staff for analytics here.
Regardless of your route, your contractors can help transfer operations to your new staff. If funds allow, you might have your in-house staff and contractors work together temporarily to ensure a smooth hand-off.
Easier to Implement Data-Driven Culture
There are many ways in which you can implement analytics to great success in a small organization. However, there are also aspects of small businesses that can help overcome the challenges of analytics. Remember, analytics are useful for small business.
Creating a data-driven culture in your organization is easier when you have fewer staff to onboard. Sure, you may still encounter some staff who are data-resistant. However, overcoming their objections and integrating them into a new culture is often easier on a smaller team.
With a smaller team, you’ll identify bottlenecks and processes that don’t integrate well with your new analytics culture. Adjusting your processes and approaches to bring workflows and analytics into alignment is easier while they are still small and easy to observe.
Additionally, incorporating your team in developing a data-driven culture and integrating workflows has benefits, too. By letting your team have input into the organization’s growth and culture, you can form stronger bonds of loyalty.
Ultimately, it is far easier to create a new culture in a smaller organization than in a large one. You won’t have to fight against a strong institutional memory for how it was before your data-driven culture.
Easier to Integrate Data Sources
Integrating data sources is another area where being a small- to medium-sized organization is beneficial when standing up analytics. For large organizations, data integration can be a nightmare; but not so for you.
Technology platforms are increasingly siloing data within proprietary apps. For large organizations, a lot of resources goes into managing and integrating data flows to use analytics.
However, as a smaller organization, you have the benefit of having fewer applications and data sets lying around. The sheer number of integrations you need to implement is smaller than in larger companies.
Fortunately, applications like Zapier and Make.com make these integrations easier. Additionally, many technology platforms are also developing APIs that facilitate easier integration.
If you integrate your data sources while your organization is still small, you’ll save yourself many headaches down the road.
I recommend you start by building a data warehouse. Set up automated systems to update the warehouse data periodically. Then build your analytics to work off the warehouse. This is a simple way to get started, and your analytics contractor can help put this in place.
Easier to Manage Data Quality
A common complaint among leaders across organizations of all sizes is that their data quality is lacking. Fortunately, as a leader in a small organization, you can easily correct this.
There are several sources of poor data quality that are common across organizations:
- Non-standardized data collection tools
- Not forcing collection of critical fields
- Poor training on data collection tools
Non-Standardized Data Collection Tools
The most flexible way you can create a data entry form is to make everything a text field. However, this also means that literally anything can be typed into any field.
Date fields could contain names. Address fields could contain dates. Numeric fields could contain text.
Developing standardized data collection forms, with documentation about what belongs in each field will help resolve this. Including data validation rules (e.g., date fields contain only dates in a specific format) on the form is also important.
For public-facing forms on websites, keep your data collection efforts as simple as possible. Only collect the data you need and make the fields easy to understand. I recommend using closed-ended question types rather than open-ended text boxes.
Not Forcing Collection of Critical Fields
For critical fields in any form, you should require the field to be completed before letting the form be submitted. This helps ensure the completeness of your data.
Where possible, you want to minimize user data entry. If you have data across multiple databases, develop data management code to join the data together. If there is a way for you to calculate what you want, do that rather than asking the user to fill in another field.
Poor Training On Data Collection Tools
Finally, perhaps the larges cause of poor data quality is having staff with poor data collection training. If your staff will be responsible for entering data into a system, provide training to ensure they use it properly. Such a system could be a CRM, an inventory system, a contact list, or many others.
When staff are confused about what they should enter into a system, they often take one of two approaches. They either enter nothing or guess what belongs in each field. Neither approach works particularly well, especially if your form isn’t standardized.
Give your staff documentation on what belongs in each field. Document any decisions they need to make to determine the right information. Provide examples for them to follow. Then monitor your staff and provide corrective feedback for mistakes.
Following these several suggestions will help dramatically improve your data quality moving forward. You can read more about how to improve data quality here.
Conclusion
Analytics are useful for small business. Leaders standing up analytics programs in any organization will face challenges. However, you should recognize that these challenges are no easier or worse for small organizations. As discussed, several challenges large companies face are mitigated in a smaller company.
Analytics aren’t just for Amazon, Google, Apple, and Meta…they are for organizations of every size. And if you’ve been thinking about leveraging your data to build a better business, now is a great time to start.
