Data is like fruit and veg – It must be fresh and healthy to be valuable!

And don’t forget your data five-a-day!

The time is ripe for a new data analogy

You’ve heard it before – “data is the new gold”, or “data is the new oil” or the one I prefer “data is the new water”, but what about data is like fruit and vegetables?

And I don’t’ mean rotten to the core and only fit for the compost bin. No - It is because data is a raw material that can be cultivated, harvested, and processed to produce something valuable - once it is kept fresh and healthy.

I’ve not found anything that uses that analogy and I don’t claim to have created the term but the idea arguably bears some fruit!

Harvest for the world

Just as fruits and vegetables are harvested from fields and orchards (not to forget vineyards), data is collected from various sources such as sensors, data repositories and online interactions. The quality of the data collected can be compared to the ripeness and quality of the fruits and vegetables that are picked.

In the same way that fruits and vegetables can be transformed into different forms such as juices or soup, data can be analysed and transformed into different formats such as graphs, charts, or predictive models. The end product is only as good as the quality of the raw materials and the expertise used to transform the data. Fruits and vegetables require care and attention throughout their growth and harvesting process, similarly data must be carefully collected, processed, and analysed to ensure its quality and usefulness.

Just as chefs use fruits and vegetables to create appetisingly delicious dishes for us to consume, data scientists use data visualization techniques to create visually appealing and informative graphics that help to convey the insights and knowledge gained from the data analysis. This involves creating plots, charts, and graphs that are easier to understand, digest and interpret. It also incorporates analysing the data using statistical methods, visualizations, and machine learning models to identify patterns, trends, and relationships to reap valuable insight from the data.

Protect the fields of gold

A farmer must protect their valuable crop from threats such as adverse weather, pesky insects and other pests, disease, and fungus data needs diligent care and protection.

Data must be protected from pesky cyber threats such as hacking, malware, and phishing attacks. This means implementing rigorous security measures such as firewalls, encryption, and access controls to prevent unauthorized access to the data. In the same way as crops can be damaged by disease and fungus, data can be damaged by privacy breaches that compromise the personal information of individuals. Data managers must comply with data protection regulations such as GDPR and CCPA to protect personal data and prevent privacy breaches, in the same way that farmers must put the appropriate controls in place to protect their crops.

Just as crops can be damaged by human error such as overwatering or over-fertilization, data can be damaged by human error such as accidental deletion, succumbing to a phishing email or incorrect entry. Data managers must have procedures in place to prevent such human errors through training programs and data validation checks.

Crops can be damaged by bad weather and natural disasters and without appropriate resilience measures in place, data can be damaged by power outages, fires, floods, and other disasters. Data managers must have backup and recovery procedures in place to ensure that data is recoverable in the event of a disaster with a known and rehearsed data recovery point and time.

Preserve data’s best before date

Elton John sang of “rotten peaches, rotting in the sun” and of course data can go bad and lose its usefulness if not consumed when fresh and relevant. Fruits and vegetables need to be stored properly to preserve their quality, and the same is true for data. Data managers must ensure that data is stored in a secure and organized manner to prevent loss, corruption, or unauthorized access. Just like fruits and vegetables have a limited shelf life and can spoil if not consumed or stored properly, data can become outdated or irrelevant if not managed and updated regularly.

Data has a shelf life or "best before date," after which it can become obsolete or unreliable, leading to inaccurate or biased analysis. Clearly, outdated or inaccurate data can be just as harmful as spoiled food, leading to incorrect conclusions or flawed analysis. Customer data may become outdated, leading to incorrect evaluation of customer behaviour or market data may become obsolete, leading to inaccurate market insights for key business decisions. Data governance policies, such as data quality management and data lifecycle management must ensure that data is maintained and updated regularly and validated for accuracy, completeness, and relevance to ensure the data's "best before date" maintains its usefulness.

Manage your data five-a-day

Just as we think about taking our five-day of fruit and veg, we must also think about the organisation’s healthy five-a-day data diet. Here are five important daily principles to regularly (daily) check for the best data nutrition:

1.??????Data must provide insight to enable business value. Data must be valuable in providing the insights needed to steer the business strategy and tactics. It must enable leaders to answer the fundamental questions:

a.??????Are we headed in the right direction? (Aligned with strategy)

b.?????Are we moving at the right pace? (Progressing performance)

c.??????Do we know where we might be going wrong? (Ability to diagnose)

d.?????What does the data tell us we could be doing better (Insights to what we didn’t know)

2.??????Data must be high quality, clean and fresh. ?Regularly check the processes to ensure data accuracy, completeness, timeliness and consistency continue to be effective. This includes data cleaning, validation, and verification procedures. Monitor the performance of automated processes to cleanse data on a regular basis – are processes accurately removing duplicates, errors, and inconsistencies as expected? Ensure that data is regularly updated, particularly for time-sensitive information such as customer or inventory data. Confirm the effectiveness of processes for validating the accuracy of new data and for removing outdated data.

3.??????Data policies are clearly defined and understand. Policies might be defined but are they clearly communicated and understood and called upon when needed? Are they kept up to date regularly enough to be aligned with the organization's goals, values, and regulatory requirements? Is data governance working to ensure data is managed properly, stored securely, and used ethically? Keep a heartbeat on checking these questions and don’t assume that just because the policy is defined, it is working effectively and understood across the business.

4.??????Data is always secure and protected from threats and risks. Validate security controls are consistently and effectively working to mitigate vulnerability threats and to ensure sensitive data is fully protected from unauthorized access, modification.?This includes data encryption, access controls, and user authentication protocols. Monitoring and alert systems to detect data issues or anomalies, such as sudden changes in data volume, quality or performance should be frequently tested. Validate your backup processes and you understand and have tested your recovery point and recovery times and use immutable storage.

5.??????Data is transparent and accessible at the point of need. Make sure data continues to be easily accessible and understandable by all relevant stakeholders, while also ensuring that sensitive data is protected. This includes providing appropriate data access, data literacy and awareness training to stakeholders.