Frequently Asked Questions

What is data cleansing and why is it important?

Data cleansing is a process that detects and corrects inconsistencies and inaccuracies in your data. It's essential for maintaining the reliability and precision of your data, eliminating duplicates, verifying data accuracy, adopting standardised formats, and ensuring consistency. Properly cleansed data can be leveraged to its full potential, facilitating sound business decisions.

How does data enrichment improve my existing data?

Data enrichment enhances your existing data by adding more relevant and substantial information. This might involve including additional product attributes, categorising data, enhancing the data's completeness, and providing more comprehensive descriptions. The enriched data provides a more complete picture and enables more accurate analysis and insights.
 

What is the purpose of data comparison?

Data comparison involves assessing distinct sets of data to identify similarities, discrepancies, trends, or anomalies. By comparing data, you can pinpoint potential issues, refine processes, and understand market fluctuations. It's a powerful tool for making informed business decisions.

What does your consulting service entail?

Our consulting service provides personalised strategies to meet your unique needs. Our experienced team of data consultants works closely with your organisation, understanding the specifics of your data landscape. This allows us to identify problem areas and propose effective data management solutions.
 

How do I know if my business needs your services?

If your business relies on data for decision-making, our services can be instrumental in improving the quality and usefulness of your data. Whether you're facing issues with data accuracy, need more insightful data for analysis, or require assistance with data management, our services can provide the solutions you need.

How do you ensure the privacy and security of our data?

We take data security and privacy very seriously. We follow rigorous procedures and standards to ensure that your data remains secure and confidential throughout our data cleansing, enrichment, comparison, and consulting processes. We are compliant with all relevant data protection laws and regulations.
 

What functionalities does your SaaS platform offer?

Our SaaS platform provides a range of functionalities including data cleansing, data enrichment, data comparison, and more, all within a user-friendly interface. This allows you to manage and optimise your data directly and in real time.

Who can use your SaaS platform?

Any business that relies on data to drive decisions and processes can use our SaaS platform. It's designed to be intuitive and user-friendly, meaning you don't need extensive technical skills to take advantage of its features.
 

How do we start working with AICA?

Getting started with AICA is simple. You can reach out to us through the 'Contact Us' page on our website. Our team will get in touch with you to understand your needs and guide you on the next steps.

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WHAT CAUSES DIRTY PRODUCT DATA?

Dirty product data is inevitable in any organisation, and becomes more problematic over time due to manual input error, employee turnover, siloed departments and product updates.

Dirty Product Data Can Be Caused By:

  • Human Error -Data entry is often done manually and where there's a human touch, usually errors seep in. Typos, misinterpretations or omissions can lead to inconsistencies and inaccuracies in your data.
  • Duplications - Duplicate entries present themselves during data collection or merging processes, leading to confusion and inaccuracies.
  • Outdated Information - Data can quickly become outdated or obsolete, particularly in dynamic industries. If data isn't regularly updated, your databases may contain irrelevant or expired information.
  • Inconsistent Formats - Without standardised data entry formats, the same piece of information can be represented in several ways, causing inconsistencies.
  • Incomplete Data - Sometimes, all required information isn't captured at the point of entry, leading to gaps in your data.
  • Integration Errors - When merging data from different sources or systems such as your PIM or MDM, mismatches or errors can occur, causing data corruption.
 

The 1-10-100 Rule

At its core, the 1-10-100 rule represents the cost implications of data management at different stages.

Here's how it works:

  • 1: This represents the cost of preventing bad data from entering the system. It can entail proactive measures such as data validation checks, investing in a data quality tool, or creating robust data management protocols.
  • 10: This depicts the cost of correcting the data once it's entered the system but before it affects other processes. These costs could involve time spent troubleshooting, manual data corrections, or reprocessing data.
  • 100: This is the cost of dealing with the consequences of bad data that wasn't caught early enough. This includes indirect costs like poor decision-making based on inaccurate data, loss of customer trust due to data errors, and the resources spent rectifying these issues.

The key takeaway? It's significantly cheaper to prevent dirty data at the onset than to correct or deal with its consequences later.
 
The 1-10-100 Rule, Cost Implications of Data Management, 1: Cost of Preventing Bad Data, Proactive measures like data validation checks, data quality tools, and management protocols, 10: Cost of Correcting Data, Troubleshooting, manual data corrections, and reprocessing data, 100: Cost of Dealing with Consequences, Indirect costs like poor decision-making, loss of customer trust, and resources spent rectifying issues, Key Takeaway: Cheaper to Prevent Dirty Data Early, Illustration of the 1-10-100 rule explaining the cost implications of data management at different stages

What Are Data Anomalies?

Product data anomalies refer to inconsistencies, inaccuracies or unexpected variations within product-related information. This could encompass discrepancies in manufacturing specifications, inconsistent data in product catalogues, variations in quality control data or mismatches in inventory records. Such anomalies often arise from manual data entry errors, discrepancies in data received from different departments or even variations in data from suppliers.

Data Anomalies are:

  • Operational Hiccups: Inaccurate data can lead to manufacturing errors, incorrect procurement decisions, or inefficient inventory management, leading to operational inefficiencies.
  • Financial Implications: Discrepancies in product data can lead to budgeting and forecasting errors, potentially resulting in financial losses or increased costs.
  • Supply Chain Disruptions: Inconsistent data regarding product specifications or inventory can cause disruptions in the supply chain, affecting relationships with suppliers and potentially causing delays.
  • Quality Control Issues: Anomalies in quality control data can result in inferior products reaching the market, which can damage a company's reputation and trustworthiness.
  • Compliance Risks: Especially in industries with strict regulations, anomalies can lead to non-compliance issues, attracting penalties and legal ramifications.
  • Sales and Marketing Challenges: Inaccurate product specifications or details can misguide sales and marketing strategies, leading to ineffective campaigns or miscommunication to potential clients.
  • Decision-making Hurdles: For management, data anomalies can distort the understanding of business health and performance, leading to misguided strategic decisions.

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