Four Challenges On Your Way To Data Accessibility

Data is currently at the heart of everything we do; it is the resource on which businesses thrive. Maximizing your full business potential greatly depends on how much data you can access and harness to foster business operations, and the speed at which you can do so. Data mining and interpretation are quickly becoming necessary skills in the business world, as they can help companies reach their revenue targets more quickly.

Even though data seems to be readily available at large scales, accessing this data poses challenges to many businesses. Therefore most businesses have to find ways to break through the challenges of data accessibility in order to harness its value.

This article will outline the common challenges that businesses face in achieving data accessibility.

What Is Data Accessibility?

Data accessibility is a term that describes how easy it is for employees in an organization to leverage available data to foster productivity and provide business value. Improving data accessibility involves removing obstacles that might limit access to the organization’s stored data, thereby improving workplace efficiency.

A 2021research by NewVantage Partners on 85 Fortune 1000 and industry-leading companies in retail, financial services, life sciences, and healthcare demonstrated that even very large players are still struggling with utilizing data to drive innovation, despite making investments greater than $50M. A few key statistics shed light on this gap:

• Only 48.5% are driving innovation with data
• Only 41.2% are competing on analytics
• Only 39.3% are managing data as a business asset
• Only 30.0% have a well-articulated data strategy for their company
• Only 29.2% are experiencing transformation business outcomes
• Only 24.4% have forged a data culture
• Only 24.0% have created a data-driven organization.

Main Barriers to Data Accessibility

Many companies spend a lot of money on data integration and creating analytics methods for their businesses but later find that their investments do not yield the expected results. One of the main reasons they face this gap is that data does not reach the intended audience. Also, inaccessible data leads companies to draw false conclusions based on limited data sources.

We are gradually shifting away from relying on IT solution data providers (such as DBAs) who spend an enormous amount of time making datasets available to users and moving towards having multiple data users such as analysts and business managers who provide better delivery. Cloud-based data has a crucial effect in reducing technical difficulties and creating more robust data exploration.

Leaving the burden of unraveling complex data to the employees without offering them help can harm data accessibility. Data analytics can only be successfully implemented if the organization supports all its employees. Top-level managers must include lower-level staff when making data-driven decisions and should not expect them to automatically figure out any complex data that is thrown at them.

What Are the Common Challenges in Data Accessibility?

Today, with ever-growing data sources, advancing tools and technologies, and the growing sophistication of business users, the potential to unlock value in data-driven insights is higher than ever. Yet most companies still fail to connect data investments to business insights and results because they use outdated methods of data mastery, lack automated workflows, or even have budget constraints.

Data Processing Operational Challenges

Enabling more users, especially those who are less technical, to access data may be challenging. For example, safeguards should be placed so that users do not ingest too much data or consume too many resources. Because more people are accessing data, there is more need to transform data and move it from one location to another.

Users may also face difficulty when employing new data analysis methods, even if they possess technical skills. Many people have a hard time adjusting to change and may be confused when shifting from traditional methods to newer ones. Organizations should take time to highlight the importance of new analytics methods to enhance performance and productivity. Employees should familiarize themselves with utilizing data insights for decision-making rather than wasting time on unnecessary data collection and reporting.

Budget is another factor that might pose a threat to data accessibility. Getting approval for a robust analytics system or a new data store may be a challenge.

Difficulty Accessing Data Securely

Immense amounts of data are being stored and processed daily, which may be overwhelming. When data is rapidly created, transformed, processed, and stored, it is challenging to keep data access secure.

For starters, the data stakeholders (especially data governance and security) need to understand what data out of the data stored is sensitive data. The answer to this question may not be straightforward, as data is a moving target.

In addition, security policies need to be created (or reformed) to apply for a situation where there is higher data accessibility. For example, analysts may need to be able to access customers‘ data, but in an anonymized way, for example by applyingdynamic data masking.

When data is a major operation for the organization and is accessed by many people, proper DataSecOps practices need to be applied, to make sure that security is part of the data operation and is not an afterthought.

Compliance Challenges with Sensitive Data Access

Complying with various privacy laws can pose a threat to data analytics. For example, processing sensitive data under GDPR, CCPA, HIPAA, and other privacy and security regulations requires anonymization, setting security policies, and using up precious data engineering time. These processes may further necessitate setting requirements, implementing them on different data platforms, and sometimes also writing specific views or spanning up new ETL/ELT processes.

In other words, it is harder to answer audit requirements such as ”’what sensitive data do I have’ , and ”’who is accessing it’ , and keeping such requirements should be part of the move to better data accessibility in an organization.

Knowledge Gaps

It is one thing to have a modern data analytics system and another to have employees with the required skill sets to use it. Many organizations lack the talent to skillfully analyze data and implement it towards better decision-making. To fill this talent gap, organizations might have to target hiring only fully competent employees with proven track records in data analysis. Alternatively, another good way to tackle this problem is to train existing employees on how to use data management systems.

Companies should also consider transitioning to data analysis systems that are easy to use, such as cloud-based BI tools that are straightforward yet pack an analytics punch. A complex system promising many possible benefits is pointless if no one can use it. Before purchasing new data analysis tools and systems, organizations should conduct market research to determine user-friendliness and ensure a balance between efficiency and results of the product.

In many cases, internal training can boost the ability of users to make value out of data, and data teams may provide such internal training.

Conclusion

Big Data is undoubtedly a powerful moving force in business transformation. Digital spaces are inundated with data begging to be properly processed and utilized to improve our quality of life. Forward-thinking companies must strategically position themselves to take advantage of this data-driven era to facilitate more efficient processes.

However, more data comes with more challenges than businesses must consistently seek to solve. Organizations must foresee the many possible intricacies that can emerge from data access and find ways to resolve them. When new data is uncovered every day, organizations

Ben is Chief Scientist for Satori, streamlining access to sensitive data.