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5 key factors holding small businesses back from joining the “data revolution”

data analytics for small businesses

“For small and medium businesses, using data is like trying to rescue a piano from a burning room — it’s nice to have but I have higher priorities.” -Small Business Owner

We are living in a time where data is revolutionizing almost every aspect of human society. Businesses, for example, are now expected to use data to target their core customers down to individual addresses and provide these customers with personalized promotions to maximize consumer spending.

However, despite all this hype around the commercial sector leading the “data revolution,” for most small and medium-sized businesses (SMBs), using data feels like it’s making life harder, not easier.

On this article, we will give you an overview of 5 major obstacles and unpack one challenge at a time — and provide an action checklist for solving each challenge.

Without further ado, here are the 5 challenges.

1. Data Interpretation“

Bar Chart from Zepto

The most common problem we found plaguing SMBs is interpreting and making sense of the data they have in their organization. Modern technology makes it possible for all SMBs to capture data to some extent. Unfortunately, merely having the data doesn’t mean they can use it to improve their business.

Here are some specific data interpretation challenges that we heard from SMBs:

*Understanding what kind of business insights their data can provide to them

*Understanding the value and ROI of implementing analytics tools in their organization

*Choosing the right metrics to track

*Placing analytics results in a business context and converting them into action items

*Doing sanity checks of the analyses to make sure they are accurately answering the right questions

Now I know what you’re thinking. Can’t all of these problems be solved just by hiring data analysts or consultants who know what questions to ask and how to answer them?

Yes, that may be true for a big brand. But the problem for SMBs is that data analysts tend to be prohibitively expensive, and leadership will often prioritize filling the “must-have” positions first — operations, sales, product managers, whoever is bringing in the revenue to keep the lights on — before they hire a data scientist.

To make things more difficult, not having a data analyst in-house means that smaller businesses have a harder time knowing how to properly make an informed decision when hiring for data analysts.

2. Data Collection

Many tools have emerged in recent years to help businesses with web analytics (Google Analytics, Mixpanel), customer relationship management (Hubspot, Salesforce), and e-commerce (Shopify, Woocommerce). Even though these tools have solved some of the more pressing data collection problems, some specific needs remain unmet.

Instead of complaining about having no data at all, most companies commonly face data collection challenges in a few specific areas. These areas include:

*Collecting qualitative data about their customers

*Converting these qualitative data into quantitative data

*Collecting accurate data about customer behavior on their website and on their product

*Verifying that the data collected is clean, standardized, formatted correctly, and accurate

*Overall, data collection needs have become more sophisticated, emphasizing more on quality, rather than quantity.

3. Data Integration

Most SMBs use at least 3 SaaS tools to collect data across their businesses (e.g. Facebook Analytics + Google Analytics + Operation Database). Some companies use up to 15 tools at the same time, because SMBs are using more and more tools that only good at one kind of task, making these tools talk to each other becomes a daunting challenge.

In order to capture valuable information such as a customer’s complete journey from awareness to revenue, companies have to pull data from all of their platforms and merge them together.

However, there are major challenges preventing SMBs from integrating their data, including:

*Lack of structured guidelines and procedures for data management

*Technical inability to connect various data sources together via databases or API connections

*The lack of bandwidth to set up a logical, holistic data infrastructure

*The huge time cost of reformatting data so they are compatible for integration

*The main two barriers to solving these problems among the SMBs are (1) lack of management bandwidth to manage all data sources, and (2) lack of technical human capital to construct a sound data infrastructure ahead of time.

The biggest benefit to data integration, of course, is to be able to answer bigger business questions more accurately, which brings us to the next data challenge.

4. Analytics Automation

Even for companies with integrated data infrastructure and analytics expertise, data analytics can still be an extremely resource-intensive effort.

This is because data analytics tasks are extremely explorative, and analysts have to slice and dice various metrics across many different dimensions such as demographics, time, and product categories.

With each dimension added to the analysis, the effort of analysis increases exponentially, with perhaps hundreds of variables needed to uncover only a few important insights. Without automation, sometimes it is physically impossible for analysts to conduct all these analyses within their time constraints.

Common automation challenges SMBs face are:

*Lack of tools to automate tedious data cleaning and repetitive analysis processes during analysis

*Lack of tools to efficiently advise analysts on what analyses to focus on for actionable insights

*Lack of automated reporting mechanism post-analysis

The primary barrier preventing those two challenges from being resolved is the lack of analytics tools on the market that automates data cleaning and basic exploratory analysis. Without those tools, it will take even the most experienced analyst a lot of time to uncover insights, especially when you don’t know where to look or where to start.

5. Analytics Adoption

If we were to describe the previous four challenges as technical problems, the last one is very much human. To many SMB owners, even though they might know data can create value for them in some abstract way, they either don’t have enough time or bandwidth to leverage their data, or they don’t see the short-term benefits.

Next to data interpretation, the adoption problem is probably the most difficult one facing SMBs in their path to joining the “data revolution.” Here’s what was holding back the SMBs we worked with so far:

*Inability to see the immediate value of data, which means indefinitely pushing back analytics to some point in the future that will never come

*Fear of long-term commitment to expensive analytics tools

*Fear and frustration with the time cost of setup and implementation

*Feeling like they could be using their time more efficiently by focusing on something more urgent, like operations or sales

*For many SMBs, data analytics is a nice-to-have, not a must-have. They don’t see that it is exactly because they are more constrained on time, money, and bandwidth that they need data even more than larger enterprises because data-driven decisionmaking is how they can optimize their limited resources. Data is how they can make each dollar stretch further.

However, with a lack of transparent and low-commitment ways to see the value data analytics can add to their businesses, SMBs will remain stuck in outdated business practices and hesitant to fully adopt the “data revolution” of the large enterprise sector.