Data Cooking Explained

So what is “Data Cooking”? We believe the process of utilising your business data can quickly get complicated.
Small businesses are being left behind when it comes to utilising the data they generate in a timely manner.

A recent survey suggests only 45% of small businesses are using analytics. Plus another study suggests 81% of data used is stale.

This means only 8.5% of small businesses are using analytics in a timely manner.

Most reporting options available are usually bundled in with a program or only a fragment of what the business does.

Rarely do these reports combine all aspects of the business which leaves someone to piece multiple spreadsheets together in the hope to track growth and highlight any concerns.

At Data Cooking, we’re working to simplify this process and help small businesses connect their data in a way that is simple and easy to use.

Our process combines years of experience in business, data intelligence, and design to provide affordable and straightforward reporting.

The data cooking process can be adopted by any business with or without our assistance.

The Data Cooking Blueprint:

This blueprint is a recipe on how to work through the process of creating a report structure that can be built upon further. Creating a dynamic tool allows for additional reports to be built without needing to start from scratch.

We like to call the following your ‘data cooking kitchen’. Similar to a kitchen it follows a simple process. You place your order, we then create the recipe, source and clean the data, process the data, cook the data by combining different elements, test the output is correct, present it nicely and finally hand it over.

Here is a breakdown of the blueprint we utilise and you could also implement into your business.

Plan . Design . Collaborate

The first step to any good reporting structure is to understand the business. Locate any gaps of information/metrics might be present. Discuss potential options on requirements for reporting. Review any existing reports and explore potential on how to expand and go deeper.

Plan - Identify the data required for the outputs. Understand where it comes from how it gets updated. Ascertain if there are options to acquire the data regularly, if necessary.

Design- List the outputs required and suggest metrics based on the data sets that will be used. Discuss if there are any accessibility requirements, such as mobile use, scheduled emails, etc.

Collect . Inspect . Store

Map out how to obtain the data from the sources needed. This could be from on-site sources or from a external platform.

Store- Where the data will be located when it is utlised. This approach can take many forms based on business requirements and services used.to

Data Pantry

A data pantry is an area specific to the data it represents. One way to look at a data Pantry is like a table of data. The data is funnelled into the pantry with some processing along the way. This may include removing any sections that aren't relevant, and designating the formatting or the data (number, text, date etc.) The frequency of how often the pantry data will be updated will be noted for future use. Data pantries typically contain raw data unless pre-prep us required. Additional Data Pantries can be added over time for more complex reporting.

Data Prep Stations

For each area or speciality of data a prep station will be created. A prep station exists for each data pantry. Typical operations for the data prep station would include calculations such as sum, average, percentages etc. These then can be used in multiple reports. Having calculations run through these prep stations ensure report building becomes a much more efficient process. These pantry/prep stations can also run calculations with other pantry/prep stat stations once a connecting link has been established.

Report Building

The finalised reports can be handed over to the business to begin using. Provide comprehensive instructions on how to interact with the report will ensure the best experience. Gather feedback from the business on design and results to make any adjustments that may arise. Once completed any additional reports requirements can start back at the consultation / planning stage. Once the base platform has been established, adding additional data / requirements should be straightforward.

Testing

How to obtain the data from the sources this could be on site data or from a external platform. Store- Where the data will reside in order to be used whether it needs to be download and added to another platform or taken where its resides. This will be the party of data for a specific metric.

Consume Reports

Analyst Insights

Having an analyst review the reports can have huge benefits for your business. Analysts look for anomalies and KPI's within the data. These may not be in areas of the business that you would typically look in. Providing an analyst with a task to locate specific metrics can also be quicker than trying to locate yourself. Within Data Cooking our analyst can have quick additions made to the reports to locate the answers efficiently.

Conclusion:

Reports in general can be time consuming to both create and maintain. Starting a new report may not lead to any time saving. Connecting different reports together can also have its own challenges. What the data cooking blueprint aims to solve is the time is takes to create a report and also provide the ability to expand the depth of reporting without requiring large amounts of resources (time and money).

If you’re interested in discussing how Data Cooking could help your business turn your data into answers book a call here:

If you’ve got any questions we’d love to hear from you, contact us below and we’ll get back to you shortly.

Data Cooking =
FIND (MAX [ Profit ]) SEARCH (Expense/Trends) SUM (Sales) AVERAGE (Customer+Spend) POWER (Business*Growth) REPLACE (Slow>Fast) FILTER ( ADD (Efficiency) - TIME REDUCE (Hours COUNT PRICE ( PROPER ( Forecasting ) YIELD ( TRUE (Results - Doubt )

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Data Cooking =
FIND (MAX [ Profit ]) SEARCH (Expense/Trends) SUM (Sales) AVERAGE (Customer+Spend) POWER (Business*Growth) REPLACE (Slow>Fast) FILTER ( ADD (Efficiency) - TIME REDUCE (Hours COUNT PRICE ( PROPER ( Forecasting ) YIELD ( TRUE (Results - Doubt )

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Data Cooking Explained