The Australian economy consistently ranks among the most competitive of any nation in the world. In 2018, we placed 14th out of 140 major economies as ranked by the World Economic Forum, continuing decades of high performance. That may make it somewhat of a surprise to find out that the Australian economy is not one made up of giant corporations – and that it is, in fact, dominated by small businesses.
According to government data, 97% of all businesses here have 19 employees or less, which highlights just how vital they are to our success. It also makes it obvious that Australian small businesses must be doing their work very efficiently to produce such success on the global stage. In recent years, much of that efficiency can be chalked up to technology.
In fact, digital transformation is something of a specialty for Australian SMBs, with surveys reflecting that a full 80% of them have already begun the process. The one area where they've struggled, however, is in making the transition toward being data-driven in their operations and decision-making. To help, here's a simple, three-step process any SMB can use to become more data-driven.
Understand the Data You Already Have
The first step an SMB must undertake to become data-driven is to take careful stock of what data sources they already have at their disposal. In most cases, the average business already has a rich variety of data to mine for insight. Typical sources include:
- Sales Data
- Departmental Reports
- Inventory Data
- ERP Data
- Transaction Histories
- Customer Service Records
Depending on the needs and goals of the business, these pre-existing data sources may already be enough to start working on creating a basic analytics programme and start using the data to make business decisions.
Build a Small Analytics Team
SMBs generally don't have the resources to build out large analytics group, but the good news is that they don't have to. Today, with standardization increasing in analytics and visualization tools, it's no longer necessary for companies to hire full-fledged data scientists to build out their data mining operations. Instead, small businesses can target candidates that are just starting out in data science, because they'll have all of the skills needed to manage a fledgling data programme. By staffing up in this way, an SMB can acquire candidates with the right skill set that will continue to learn and tailor their growth to the company's specific needs.
Ask the Right Questions
The most important part of making the transition toward being data-driven is to develop an understanding of what questions available data can answer. It's critical for decision-makers to know the limitations of data analytics, understand what to expect, and agree on how to measure results beforehand. For example, it's critical to make sure that any insight that comes from your data satisfies these three basic requirements:
For an insight to be of any benefit to a small business, it must suggest specific actions for the business to take to achieve a certain goal. It shouldn't merely point out deficiencies in current operations without pointing towards a solution.
If data insight is meant to provide actionable advice, it must also lead to measurable results. For that reason, the data used to create a hypothesis or to suggest an action must be tied to specific key performance indicators (KPIs) that will help measure efficacy.
Insight stemming from SMB data should also contain inherent time limitations to the actions they suggest. They can't be open-ended or vague in how long they'll take to be effective. Otherwise, measuring success becomes difficult (or impossible) and there's no way to determine the ROI of your data programme.
Analyze and Decide
With data sources in place, analysts on board, and clearly defined goals, an SMB should be ready to start putting its data into action as a key part of its' decision-making process. All that's left is for all stakeholders to start making data-driven insights a part of their deliberations when they look for ways to enhance business performance. That part of the process can be the most challenging – since it's a somewhat new way to operate that many resist (either intentionally or otherwise). With management buy-in, however, that's a problem that should be easily overcome, leading to a complete transformation of the organization into a lean, mean, data-driven machine.
About the Author
Andrej is a dedicated writer, digital evangelist and a freelance writer. He is a contributor to a wide range of business and technology-focused publications, where he may be found discussing everything from neural networks and natural language processing to the latest in smart home IoT devices.