The process of data analytics
Data is gathered in all sorts of ways and from various sources depending on the business or organisation concerned: for example, a retail outlet will likely gather copious amounts of data from customer transactions at the point of sale such as:
- What they bought
- In what quantities
- When they bought
- The order value for that transaction
- If they are a new or existing customer
- The effect this order has on stock levels
- Where they found out about the product/offer
…and much more
This data is carefully processed and goes with other similar data from other transactions to build a picture of concerns including shopping trends; response to an offer or the marketing behind it; recording stock levels so items do not run too low or go out of stock altogether; what is or is not selling and so on.
The beauty of data analytics is it can be tailored to meet specific commercial requirements, so answering the question ‘what is data analytics?’ could also be replied with ‘obtaining the particular business intelligence your business uniquely requires’.
If you do not have trained data analysts within your staff, then partnering with experts in data analysis can ensure you make the most of your hard-won data to serve your specific purposes.
The benefits of data analytics
Little benefit can be gained from unprocessed raw data since important trends and metrics can only be uncovered through expert data analytics. To use a pottery analogy: your basic data gathered is like an unformed piece of clay while the data analytics is the process by which the clay becomes useful (turned into a vase for example by a skilled potter).
Similarly, skilled data analytics professionals can take your raw data and turn it into something meaningful in terms of information you can use to improve your marketing, production processes, point of sale performance, customer relations and more based on the information you want to focus on.
Different types of data analytics
There are basically four types of data analysis:
Descriptive – showing what has happened over a given time frame, for example: ‘are sales up on the previous quarter?’
Diagnostic – answering why something happened (or not). For example: ‘did the recent unseasonal heatwave reduce footfall in the stores?’
Predictive – what may happen: the strategic planning side of data analytics where trends and past performance are used to look ahead and plan.
Prescriptive – where a course of action is pointed up based on data analysed: for instance, ‘does extra footfall warrant physical expansion of a retail outlet by a certain time?’
What is data analytics especially useful for?
In short, many business activities benefit hugely from data analytics – especially with detailed data gathering increasing in the big data era.
Marketing – to assess marketing activities and plan future campaigns.
Sales – to help increase sales through learning more about your market and customer.
Customer engagement – understanding how customers tend to interact with your business: social media? Email? Website?
Production – enhancing the efficiency of production through analysis data from machinery such as its runtime, downtime, when its likely to need restocking and so on.
Can data analysis help us plan ahead?
Certainly. A key use of data analytics is in looking ahead based on hard facts rather than guesswork.
Skilled and experienced data analytics consultants will work with you to help you plan ahead based on various factors such as trends in your business.
Predictive analytics can help answer questions such as: ‘what type of products and services do our customers require moving forward?’ Data analysis based on a combination of data gained from past sales and trends, and perhaps from asking your customers via online forms and social media, can help inform your future decision making.
How do we get started in making the most of data analytics?
Talk to skilled data analytics consultants! They can help you from the ground up by advising on data gathering, storage and manipulation methods to ensure your data is easy to access and work with and of the right caliber to conduct meaningful analytics.
A strategic data analytics programme can then be drawn up and also incorporate ‘one off’ analytics projects such as, for example, measuring the effectiveness of a specific sales campaign or special offer in retail stores.