Revitalizing Historical Data: Why to Prioritize in 2024
Let's face it, when it comes to data sets, sometimes the insights we hope to uncover just never materialize. Suppliers and farmers alike often grapple with this overlooked issue - data sets that become stale and unused. But why does this happen? It could be the challenge of cleaning up existing data formats, the complexity of data silos, the poor quality of the data, or simply a lack of data literacy. Whatever the reason, many companies find themselves with historical data sets that are pushed to the background, leaving valuable information untapped.
Moreover, the repercussions for businesses are undeniable and far-reaching. Neglected data metrics result in decision-making that relies on outdated or incomplete information analysis, leading to overlooked product performance insights and even missed opportunities for revenue generation.
For our team this is incredibly frustrating because we know this is a solvable issue.
Here's what to do about it.
Identify Stale Data Sets in Your Company
Outdated, inaccurate, or incomplete data can pose a significant threat to the efficiency, sustainability, and profitability of the aquaculture industry. Both suppliers and farmers are often sitting on a goldmine of data, though much of it becomes 'stale' over time.
Stale data encompasses information that has lost its value. It may be outdated, inaccurate, incomplete, or simply not actively being utilized to its fullest potential.
Identifying and addressing stale data can be a challenging task, but there are key indicators to look out for.
- Outdated information: Data that hasn't been updated in a long time or reflects past trends that are no longer relevant.
- Inconsistencies: Data that shows discrepancies between different sources or within the same dataset.
- Missing information: Data containing gaps or incomplete entries that hinder analysis and insights.
So what exactly would this stale data look like and what are some common examples in aquaculture?
Sound Familiar? Here Are Common Examples
Stale data can manifest in various forms, often stemming from a range of common issues. These issues include inconsistent formatting during data collection, human errors in data entry, and inaccuracies in data point values.
Here are some common examples you might encounter:
- Formatting inconsistencies: Units of measurement switch between meters and feet, dates are entered inconsistently (e.g., MM/DD/YYYY vs. DD/MM/YYYY), or data formats vary across different sources.
- Data entry errors: Manual data input can lead to typos, transposed numbers, and missing decimal points.
- Value inaccuracies: Sensors might malfunction, or manual readings might be inaccurate due to human error or improper calibration.
- Temporal inconsistencies: Data collection intervals might change unexpectedly, creating gaps or overlaps in the data.
Nevertheless, pinpointing these issues within your data can sometimes be a complex and intricate task.
Let's look at some ways to overcome these moving forward 👇
How to Discover New Value in Old Data
If someone could give you more trends, insights, and direction with your current data (you already have paid to collect) would you do it? Despite the challenges, overcoming stale data is crucial for optimizing operations and achieving sustainable growth.
Simple steps your team can take are:
- Conduct data audits: Regularly assess your data landscape to identify inconsistencies, gaps, and potential errors.
- Foster a data-driven culture: Train employees on data literacy and emphasize the importance of accurate data collection and recording.
- Implement data governance: Establish clear protocols for data ownership, access, and security.
However, expecting every team to dedicate resources to continuously maintain a system of checks and balances for cleaner data may not always be practical.
In fact, we like to challenge companies to reconsider this approach as it is becoming outdated. There are already cost-effective solutions available that can alleviate the burden on your team without adding additional time and stress.
With increasing global competition, rising commodity prices that further inflate product costs, and ongoing challenges with sea lice regulations, there is a strong case for the industry to prioritize the data cleaning at the top of their list in 2024.
And there's an easy way to expedite the process.
AI Data Cleaning & Enrichment: The Path Forward
Gone are the days of manual data scrubbing and tedious error-checking. AI algorithms can now sift through massive datasets, identifying and correcting inconsistencies with lightning speed and superhuman accuracy.
Missing values? Filled in. Formatting errors? Standardized. Outliers? Detected and flagged for further investigation. All this, without needing to build complex in-house systems or divert valuable resources from core operations.
But the benefits go beyond mere cleaning. AI can also enrich your data by:
- Extracting hidden patterns and correlations: Uncover hidden relationships between variables, like water temperature and feed efficiency, or disease outbreaks and environmental factors.
- Predicting future trends: AI models can forecast fish growth, disease outbreaks, and even tell you what products to use for treatments and when, allowing you to make data-driven decisions for proactive management.
- Integrating external data sources: Integrate and interlace your data to construct a comprehensive perspective of your operations (or products), enabling you to consistently monitor and manage in a personalized manner.
By actively tackling stale data, you can unlock valuable insights that can improve feed efficiency, optimize production cycles, minimize environmental impact, and ultimately drive profitability in your aquaculture operations. Stale data doesn't have to be a dead end. With the right approach, it can be transformed into a powerful tool for success.
Discover the full potential of Manolin's Data Intelligence platform and learn how you can clean and enrich your data to streamline your success in 2024.