Four mistakes SMEs are making with their data — and how they can get it right

SMEs data

Hyper Anna co-founder and chief executive officer Natalie Nguyen.

Australian businesses are falling short in taking up data analytics, but not through want of trying.

The underlying issue is there’s a lack of conversation about how businesses can actually use data correctly.

In reality, simply looking at snapshots of customer numbers or demographics data will only do so much. Instead, companies need to know how to interpret their data over time, and how to pull insights from combining different data sources to truly reap the benefits for their business.

Here are four of the most common ways businesses tend to use data incorrectly — and what they can do to get it right

1. Implementing the wrong technology

Often, businesses can get caught up in discussions about data science and AI, and end up implementing data analytics tools merely for the sake of having the technology. This can be troublesome if they don’t have the right strategies in place, or haven’t taken the time to consider if the tool they’ve just adopted is actually right for their needs.

Selecting the right technology should never be rushed, and businesses need to remember that new technology won’t completely transform them from the inside out without a proper plan.

For many smaller businesses, adopting advanced data tools with all the bells and whistles can actually present more challenges than they solve, as these platforms often require highly tech-savvy employees, or entire IT and data teams to lead data requests and interpret results.

Most of the time, SMEs don’t require advanced tools, and rather, need a simple platform that allows staff to import data quickly, and gather insights without a tonne of admin time.

2. Overlooking training and education

It’s a good idea to consider tools that your entire staff can use, from managers to junior employees. This helps people solve more problems, faster.

A simple tool with a conversational feature that allows team members to search for data results without building complicated queries can really open up data analytics to people, regardless of job function or title.

Democratising data like this will ensure all people in the company can problem solve quickly and independently.

And in order to do this, it’s essential that team members are trained and familiarised with new tools and processes. Introducing new tech without proper company-wide training can result in the product not being used to its full potential, or not being used at all.

Ensuring all staff can comfortably and proactively use new data tools is key to ensuring specific job roles or departments, with the most experience in unique day-to-day operations, are armed to best solve relevant business problems.

For example, marketing specialists at startup hub Fishburners used data analytics to measure the effectiveness of lead generation campaigns, with data indicating word-of-mouth was the strongest lead generator. This prompted the team to quickly focus on increasing networking events, to fuel lead generation.

3. Failing to look beyond the topline

Getting the most out of your data involves looking at the right details to get to the bottom of the bigger picture. Looking at the right details can support strong and effective action plans.

For example, a business can use data to see that cancellation request rates are rising, but without looking at the right details, won’t see why. Without looking at corresponding data, these numbers do little to help businesses figure out how to solve the problem.

In this example, if the company looks at how trends in multiple data sources correlate, they can start to see if the cancellation requests are a result of issues with a product, perceptions of customer service or price points.

Data might also indicate the majority of cancellation requests come from customers based in the same geographic location. This could signal there’s a competing service in the area that people are switching to, prompting the company to look at competitive marketing tactics.

Alternatively, the cancellations in this area could correlate with an ongoing decline in household income, indicating the price point may no longer be in reach for the people in this area. This knowledge can help the business develop nuanced and targeted strategies to quickly and effectively curb cancellation requests.

Here, using data to dig into the details, rather than just sticking to topline insight, can help your organisation make transformative change.

4. Failing to take action fast enough

Once data reveals problems in the company or answers to key questions, moving quickly is essential. For example, your data might reveal that a particular online customer base is diminishing over time and is likely to drop off due to raised prices, or rising interest in a rival product.

It’s critical you put a plan in place as soon as you have this data, to re-engage the customer. Taking too long to respond to data can hold businesses back, and diminish the value of data analytics tools in the business altogether.

When you’re a growing company with a lot to lose, it’s essential you’re using your data right, and investing in the right data processes.

As larger companies increasingly move towards data, we should spend less time validating the need for SMEs to adopt data, and more time talking about how they can make the most of their data insights. This can help SMEs make use of their data in the most useful and appropriate ways.

For time-poor small business owners, it’s especially important to make the most of data tools, so you can enjoy actionable insights without depleting resources.

NOW READ: Even data noobs can reap the benefits of analytics: Here’s where to start

NOW READ: Data analysis startup Kapiche bags $1.75 million, and eyes $2 billion valuation in the not-too-distant future

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