Data Analytics for Small Business
A Key to Competitive Growth
There are a lot of buzz words out there like Digital Transformation and Data-Driven. What is consistent across them all is the importance of data in the future growth of your company.
The truth is that digital solutions do provide a business advantage, and as companies move to use these solutions there is a large increase in the volume of data created. Eventually, all firms reach the point where data becomes key to their continued success. So how can small businesses use data analytics?
Many firms are shocked at how difficult the transition to leveraging data analytics is. Wherever your company is on its data journey, this transition is made easier by preparing your company to make the most of its data.
With small business analytics, resources are tight, and doing everything that a large company can do is impossible. Fortunately, the 80/20 principle is at play, and most of the benefit comes from a fraction of the total possible effort.
By making a few smart choices and establishing a few procedures, even a small business can begin leveraging its data.
The Advantage of Starting Small
The growth in data has impacted companies of all sizes, yet few companies have truly become data-driven and embraced data as a strategic advantage (Hey Google!).
This is because large, established companies have ingrained policies, procedures, and structures. Successfully retraining thousands of people, changing hundreds of processes and hundreds of systems can be an impossible task.
When a company is smaller, the investment of time and money is a fraction of what is needed later when you become larger and more established. In a world where data is a strategic asset, it is better to grow into it than attempt a large-scale transformation later.
Unfortunately, the data analytics industry has a lot of big terms that mean simple things, which comes from the business arising within large corporations. For a small company, these words can sound daunting, but the concept behind them is what is important.
By implementing a few simple things into your company, you will be ready to get the most leverage out of your data. There will be immediate returns to this investment and will enable a company for more advanced analytics in the future.
Start with Your Data Strategy
Whether your company has a formal strategy or not, you probably have a good idea of what is most important to your success. Your strategy is the set of decisions and actions you plan to take to achieve your business goals.
These business goals will lead to the critical success factors or KPIs you think are needed to achieve those goals. For your data strategy, you take these success factors and list out the data concepts that you think underlie them. Concepts such as, Customers, Sales, Vendors, Inventory, or whatever else is most important.
Modern digital systems produce large volumes of data; not all this data is equally valuable to your long-term growth and success. You want to be able to focus your resources on the most important things first. You can then build on this foundation step by step with iterative, incremental development. We consider this to be the most cost-effective approach for small businesses.
Your data strategy is the set of decisions and actions you plan to take around these critical pieces of data that support your business goals, and the order that you will address them. It can be a simple one-page ordered list or a multi-page document describing Principles, Goals, Objectives, Metrics, Roles, and Responsibilities, and Data Management.
The main point is that your company has a plan for its data that includes what data is most important, how the data will be managed, and how you are going to make use of it.
Follow That with Data Management
What is data management?
In a large enterprise data management is a comprehensive set of practices, processes, procedures, and systems that allow the organization to control its data resources. It covers the entire lifecycle of the data from data creation through to data consumption.
For a small business, there is too much stuff in data management for you to be able to do it all. Most large corporations do not do all possible data management activities. So, your Data Strategy should lay out what data management practices your company intends to use.
Within these practices and procedures, certain parts provide the most benefit for a small business immediately and as you grow. These are Data Governance, Data Quality, and Data Modelling.
By itself, data governance is a comprehensive set of practices that treats data as a strategic asset. The goal is to ensure confidence in the data for its use.
The important place for a small business to start is to assign responsibility and accountability for data to people within your company. This aspect is called Data Stewardship.
Then you regularly check in with them regarding the data that is their responsibility. For example, this could be limited to the data creation step at a specific location or across a wider span of the business.
The point here is that people are assigned to pay attention to the data as if it were an asset and to make them responsible for the status and condition of that asset. If there are problems with that asset then you go to them to fix the problem, or they are the person responsible to ensure the problem is fixed.
Closely aligned with data governance is data quality. One of the best definitions of data quality is “fit for use,” meaning that the data is complete, consistent, reliable, and accurate enough to be useful to the data consumer.
Data Governance is concerned with the big picture of data as an asset and Data Quality focuses on the specific condition of the individual records.
This has several aspects that are important for any business to better leverage their data for results. Throughout the data lifecycle, various activities that ensure data quality need implementing.
At the Data Creation step, the people creating the data must be aware of its significance for the business and the data consumers. They need the training to ensure the data is properly created. The data capture system also needs to correctly secure all the required data and provide that to other internal systems that come to query that data for downstream use.
In the Data Integration step, you need to ensure the correct and secure movement of data. Meaning that no data is lost or changed accidentally, that it is tracked from source to target, and any data transformations or calculations are performed correctly.
Finally, at the Data Consumption step you again need to ensure that any data transformations or calculations are performed correctly. That display aggregations are correct and consistently applied under various filter conditions.
The final aspect of data management that a small business should start with is data modelling.
Data modelling is the process of creating a model for the data to be stored in a database. It represents the data objects you identified in your data strategy, and other required supporting data objects.
Data captured in your source systems are usually complex structures with obscure names that people in your company will have trouble understanding. In the Data Integration step, when pulling source data for later data consumption you build a data model that better represents the business for your data consumers.
Many small companies start data consumption without doing any data modelling, and it does work for a while. At some point, they reach the point where the data analysis that they built starts to fail because they are based on the original source tables. They become too large to process or too cumbersome to change.
Building a good data model will provide a strong basis for all data consumers to get the most out of the data. With a data model representing your business to your data consumers in business terms, they can achieve more with your data.
Across all businesses, digital solutions have become more and more prevalent. These solutions have created an abundance of business data for all companies to utilize.
Small companies have an advantage compared to most large companies because they can grow into using data more effectively. By implementing a few practices in your company, you will be ready to get the most leverage out of your data.