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Data Science and ML

Top Six Data Quality Fixes to Maximize AI Potential

From medicine to manufacturing, AI has a significant presence across industries. The potential to improve systems with AI is limitless. That said, AI tools are only as useful as the data they work with. AI takes the data presented to it at face value and generates results accordingly. When based on poor-quality data, the results can have very serious consequences. 

Let’s say a customer applied for home insurance. The customer lives in an upmarket part of the city. However, the bank’s database has an incorrect address on file. It shows him living in an undeveloped suburb. This affects the premium calculated by AI models and may drive the customer to take his business elsewhere. In the healthcare and legal sector, the repercussions of running AI models with poor-quality data could influence life-and-death decisions. 

Today, collecting data is easy. A recent survey found that 82% of the respondents were prepared to share their data. There are other data sources as well – social media, IoT devices, external feeds and so on. The challenge lies in ensuring that the data used to train AI models can be relied on to meet high-quality standards. 

  1. Tackling data inaccuracies and inconsistencies

Having multiple data sources has its pros and cons. While you do get access to more data, this data may be shared in diverse formats and structures. Left unaddressed, this can create inaccuracies and inconsistencies. Let’s say a doctor recorded a patient’s temperature in Celsius degrees but the AI model is trained to use Fahrenheit. The result can be disastrous. 

The first step to overcoming this hurdle is to settle on a single format, unit, structure and so on, for all data. You cannot simply assume that all data coming in from external sources will meet your data formats. 

Hence, implementing a data validation step before data is added to the database is the second step. Before any data is added to the database, it must be verified and validated to be accurate and complete and checked to be structured according to your chosen data format. 

      2. De-duplicating data

On average, 8-10% of records in a database are duplicates. While having copies of data may seem trivial, it can inflate datasets, skew insights and reduce efficiency. It increases the risk of making bad decisions. In turn, this affects the confidence a company has in its data and data-driven decision making. 

Maintaining duplicate records in a database can also put the company at risk of violating data governance and privacy regulations. 

Fighting duplication requires regular data checks. Data governance practices that take proactive measures toward preventing duplication need to be implemented. All incoming data must be checked against existing data. In addition, existing data must also be compared to other existing records to remove redundant entries and merge incomplete records where required. 

    3. Defining data to maximize insights

When data is not properly defined, there’s a higher risk of it being misinterpreted. Let’s say inventory levels for a product are listed as ’10’. Without a proper definition, it is difficult to assess whether it refers to individual retail units or crates. This ambiguity affects the inventory manager’s ability to maintain the right stock level. 

Hence it is imperative for all data fields to be correctly labelled with standardized formats. Data hierarchies must also be clearly established to optimize the use of available data. 

    4. Ensuring data accessibility

For data to be useful, it must be accessible. When departments maintain individual databases, they risk creating data siloes. Siloed data leads to discrepancies and inconsistencies. This makes it harder to understand customer needs, identify trends and spot opportunities. 47% of marketer respondents to a study listed siloed data as the biggest hurdle to uncovering insights from their databases. 

To keep this from happening. Organizations must maintain a centralized database. Unifying data from different departments and centralizing its management makes it easier to implement quality control measures and facilitates integration. It gives the organization a more complete picture and the ability to create 360-degree customer profiles. 

    5. Maintaining data security

Data collected by an organization is valuable not only for them but also for hackers and fraudsters. A data breach can severely impact the organization’s operations and reputation. It could also snowball into substantial legal penalties as well as lost customer trust. 

Data security is very closely linked to data quality. An inefficient check on incoming data can allow hackers to infiltrate into a database by impersonating another customer. Hence, it is important to implement robust encryption techniques and audit data thoroughly. While databases should be centralized to prevent duplication, access must be controlled. The data governance team must also stay up to date with evolving data protection regulations and security protocols. 

    6. Fighting data decay

Like anything else, data has a lifespan. Products are discontinued, customers change their addresses, and so on. When these changes occur, a certain section of data decays. On average, data decays at the rate of 30% each year. Like duplicate data, decayed data does not serve a positive purpose and only inflates the database to skew analytics. 

Fighting data decay requires regular validation checks and audits. The same data validation tests used to assess incoming data must be run over existing records to make sure that it is still accurate and relevant. Data found to be outdated must be purged from the system. 

Summing it up

AI has the potential to give your business a competitive edge. But, its ability to do so depends largely on the quality of data fed into the AI models. Poor data leads to unreliable predictions, and poor decisions. Hence, it isn’t just about adopting new technology but improving the quality of data you work with. 

To achieve this, businesses today need to focus on building a data literate culture and addressing data quality issues. Data quality must be seen as a responsibility shared by the IT team and data users. Putting systems in place today can help you achieve your full potential. 

The post Top Six Data Quality Fixes to Maximize AI Potential appeared first on Datafloq.

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