It takes some measure of skill to convert the vast amounts of data that organizations store and access into insightful information. It has time, money, and risk costs, but if done well, it can boost performance and support growth for years to come.
Data analytics may appear to be a challenging, unattainable process for many firms, especially small and medium-sized ones. It might be helpful to think of it as “management accounting” which offers a better, more detailed overview of corporate operations and boosts performance by enabling data-driven decision-making.
Instead of producing basic information from a point of sales (POS) system, data analytics uses the information stored in various systems within an organisation to draw insights that you don’t get from a standard report.
Data analytics employs the information held in many systems within an organization to draw insights that you don’t get from a normal report rather than producing basic information from a point of sale (POS) system.
Find below some of the best practices and approaches to using data analytics in your business:
Identifying data sources and systems
It is often easier to decide which datasets are irrelevant, rather than trying to find the right ones. When firms employ data analytics for the first time, they frequently struggle to identify the best datasets and sources to use. Determine the issue(s) your company is attempting to address or the question(s) you need to address, as a first step. It will be simpler to identify and disregard any irrelevant material if the topic at hand is clearly understood. This is especially beneficial for businesses with a lot of data. It is best to isolate any inaccurate aspects of the analysis until you fully address the issue.
In addition to identifying the right data to use, your business will have to ensure the right technology systems and processes are in place to handle enterprise-level data analytics.
Getting the most out of your data and systems
While having a wealth of data and the most up-to-date systems for analysis is beneficial, it’s crucial to ensure that you’re maximizing its value for the company. Consider your needs for the data. Do you need to make decisions right away? If not, real-time data feeds can quickly become unneeded and unmanageable. Try to align the frequency of data collection with your organization’s and your decision-making requirements. According to your business plan, for instance, you might only wish to update a specific dataset once every three months.
You should make sure that your organization has robust cybersecurity methods and processes in place if you want to make the most of your technology and protect important data.
Ensuring accuracy and data integrity
Assuring the integrity of your data is a crucial step in maximizing its worth. Analyses are utterly unreliable and won’t help you make better decisions if you don’t have proper facts. Conduct routine data cleanse to ensure data integrity by searching for and cleaning up any areas of inaccurate data. Take a step back and re-evaluate your procedures if you discover that there are significant portions of erroneous data. Focus on fixing the highest-value datasets first to prioritise and organise seemingly unmanageable projects.
Steps to success for data analysis
Businesses tend to want to jump right into the data without having a plan too frequently. Before you move forward, you must first grasp the information you wish to obtain from your data and your overall goals, as stated above.
Once your objectives have been set, the next stage is to begin collecting data. Depending on your organization and your needs, you might only want an annual data capture or live data stream to make choices in real-time. Make a plan to obtain the information you require. Your datasets should ideally come from a variety of internal and external sources, including point-of-sale systems and cloud accounting software, industry trends and third-party data sources).
For many businesses, data analytics may fall outside of their internal capabilities. Whether this is due to a lack of resources, knowledge or both. Seeking support from external data analytics experts can alleviate confusion, and ensure you have the right systems and processes in place.