Successful tool and data discovery

The three most common pitfalls in data discovery are:  

1. data availability 

2. data quality  

3. data representativeness  

In other words, the most frequent challenge is underestimating the efforts required for data preparation. While classification algorithms like machine learning and clustering often receive significant attention, practical applications typically revolve around the availability, quality, and representativeness of the data. 

Data discovery is a critical driver of insights in the data-driven world. However, companies often encounter challenges that can slow their progress. 

Setting Clear Goals for Data Discovery and Classification 

Organizations often fall victim to 'paralysis by analysis' in their data science endeavors. This phenomenon involves getting too caught up in collecting, cleaning, and centralizing data without a clear strategy for deriving value from it. It's crucial to shift from an obsession with data to a focus on transforming information into knowledge. 

One of the most common pitfalls in data discovery and classification is the lack of clear goals from the outset. Organizations often prioritize data collection without considering the specific business decisions they aim to influence. This can lead to wasted time and effort. Before embarking on data discovery and classification, it's essential to define clear goals for what the data will help you achieve. 

• Smart Data Cleanup: While you can't control the data you receive, you can employ intelligent data cleanup techniques to enhance the quality of messy data. Utilize smart tools that leverage machine learning to improve loosely structured data. 

• Flexible Classification: Allow for some flexibility in classification to avoid inadvertently excluding valuable matches due to typos and errors. Implement techniques like fuzzy logic, phonetic spellings, and regular expression formatting to ensure you don't overlook what you're searching for. 

• Leveraging User Queries: In today's search-based environment, insights can be gained from user queries and their usage patterns. Enhance the search functionality by combining effective logging with machine learning to continually refine and optimize search results. This synergy can provide valuable insights and improve the overall user experience. 

The Crucial Role of Data Discovery in Data Security 

Data discovery plays a pivotal role in shaping robust data security strategies. While data analysis is the forte of many professionals, data technology tools can pose challenges, often requiring coding skills that might not be part of a researcher's skill set. 

Data is a diverse entity, arriving in various formats from different sources. The most valuable insights emerge when we connect and analyze these disparate data sets. Yet, this is a complex and time-consuming task, as data systems don't always communicate effectively. 

Current data management platforms, often relied upon for handling vast data volumes, come with their own set of limitations. Manual analysis becomes nearly impossible, making it risky to invest time in exploration when you're unsure about what you're seeking. It's also difficult to predict the potential benefits of discovery. 

One of the core issues is that often, we don't recognize what we're looking for until we stumble upon it. During data discovery and classification, the importance of certain data may only become apparent after the fact. By then, we might be using a system ill-equipped to unlock the full value of our newfound data. 

Challenges in Data Discovery and Data Classification 

Data exploration and data classification present their own sets of challenges, from dealing with missing data to handling formatting mismatches.  

• Scattered Data Sources: Organizations commonly store data in various platforms and tools, making it challenging to locate and manage these resources efficiently. 

• Manual Data Logging: Manually logging data sources is a tedious and error-prone task, often resulting in an incomplete or outdated inventory of tools. 

• Data Quality and Completeness: Ensuring data quality and completeness across different tools and sources can be a persistent challenge. 

• Privacy Regulations: Organizations must stay compliant with evolving privacy regulations, but tracking data and tools to ensure compliance can be complex and labor-intensive. 

The Mindset Challenges in Data Discovery and Classification 

A critical challenge in data discovery and classification lies in mindset. First, for the sake of simplicity and efficiency, intelligent individuals, including many in the field of data science, often begin their work with a predefined answer in mind. This includes identifying the most suitable model, relevant data elements, and the anticipated outcome of their efforts. 

Second, analysts occasionally tackle the wrong problem but craft an exceptional and elegant solution. Frequently, their exploratory efforts concentrate on isolated sections or components of a larger issue. Data scientists tend to be more engrossed in modeling and may not fully grasp the interconnections between different business functions. Consequently, business professionals, who may lack comprehensive knowledge of the data, make assumptions about the problem without genuine exploratory analysis. This can lead analysts down a path to address these assumed issues. 

Third, there's the challenge of analysts remaining fixed in their frame of reference. To uncover new insights, it's imperative to alter the approach to data and reevaluate scenarios. When the same perspective, often aligning with the analyst's or their business partner's biases, is consistently chosen, it typically leads to expected outcomes. To avoid this, consider these actions: 

i) Change your frame of reference while exploring the data during the discovery phase. 

ii) Challenge your own biases as you extract insights. 

iii) Begin without a preconceived solution in mind and scrutinize the problem you're addressing in the broader context of the business. 

Tackling the Scale of Data in Data Discovery 

We're dealing with vast amounts of data, including petabytes and even terabytes that organizations may not be aware of. This doesn't even consider the continuous influx of new data generated each day. The sheer quantity of data at an organization's disposal makes it extremely challenging to discover and classify it effectively. 

Today, we have the capability to implement AI-driven auto-classification systems. These systems are trained on a limited subset of accurately identified data, making them a feasible solution. Machine learning tools represent the sole means by which organizations can make significant progress in this regard. While it's not flawless, it's a process that continually improves as the machine becomes more adept at identifying documents specific to the organization. 

Taking these steps now puts companies in a favorable position to harness a larger portion of their data in the future. This, in turn, provides cleaner data for upcoming predictive AI-powered analytics and decision-making processes. 

Simplifying Data & Tool Discovery and Compliance with Kertos 

Kertos is tailored to simplify data management and streamline compliance without the need for laborious processes. We place a strong emphasis on providing organizations with clear and dependable insights, while also offering an efficient approach to address the challenges posed by data tools and privacy regulations. 

Our system is characterized by intelligence and adaptability, ensuring you can effortlessly stay in compliance with evolving privacy regulations. What's more, Kertos seamlessly integrates into your existing systems and processes, seamlessly aligning with your organization's unique needs. 

We understand the importance of precision, completeness, and user-friendly compliance practices. That's why we provide organizations with comprehensive insights, enabling informed decision-making while upholding data privacy. 

The beauty of Kertos lies in its straightforward implementation, requiring no advanced technical knowledge. Our user-friendly, no-code platform simplifies integration, yielding significant results with minimal effort. 

With Kertos, our mission is to offer a practical and efficient solution to data privacy and compliance, accessible to organizations of all sizes. We aim to help you confidently navigate the complexities of data tools and regulations, ensuring data security while enhancing operational ease. 

Conclusion 
 

Successful data discovery in today's data-driven world requires addressing common challenges related to data availability, quality, and representativeness, and one key solution to streamline this process is the incorporation of Kertos—a user-friendly, no-code platform that simplifies data management and compliance, providing clear insights and enhancing operational ease.