The last few years have seen an astronomical increase in the amount of data being created, stored, and shared. According to the IDC, 64.2 zettabytes of data were created or replicated in 2020 largely due to the dramatic increase in the number of people staying home for work, school, and entertainment. The firm also projects the amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage.

Yet, despite more data being created than ever before, the advancement of data analytics to make real use of this data has plateaued. Three main challenges drive this: 

• Concern for data privacy
• Accuracy of data analytics insights
• Cost of enriched data for next-gen insights

Here, we’ll look at why each of these challenges pose a hurdle to the advancement of data analytics, and we’ll discuss what can be done to overcome it. 

Manage Data Privacy Concerns with a Governance Strategy 

Data privacy is a key issue experts face when striving for improved data analytics. It’s also one that companies must prioritize to maintain and build customer trust and prevent legal and ethical issues. That said, having a clear policy and procedure for how any kind of data is used in analytics is critical, especially for personally identifiable information (PII) data. This includes things such as home and email addresses, phone numbers, and device IDs. With the volume of PII data seeing a 41% increase from the onset of the pandemic, the importance of data privacy has in conjunction increased and needs to be addressed to further data analytical capabilities.

The reason companies don’t have an organized privacy strategy for handling PII data is often because they don’t know where to start or are fearful of the legal and ethical expertise required to build this strategy. As a result, companies are forced to pause data enrichment processes and don’t benefit from high-quality, data-driven insights achieved through advanced data analytics. To overcome this, companies must work to understand how to govern and deploy their own data across the organization. By defining clear rules and policies for how PII can and cannot be used, and by creating data and processes internally that are compliant with the organization’s ethical and legal policies, business leaders can have confidence in utilizing this data without violating privacy concerns. Companies can then spend more time modeling, enriching, and understanding data and in turn improve data insights, analytics, and outcomes based on this incredibly valuable data. 

The question of whether best-practice companies will manage data privacy concerns by implementing data governance is not if, but when. The sooner companies overcome this barrier, the sooner they’ll have access to improved data insights and their competitive advantages.

Achieve Accurate Data Insights Through Automated Data Management Processes

Data integrity is the new imperative. Recent studies show business executives do not trust the integrity of their data nor the analytical insights produced from that data. In fact, a recent study found that data executives generally only trust an analytical insight that matches their expectation of that insight.

Managing data through manual processes and disparate systems are two of the many major reasons why companies still struggle to achieve trustworthy, meaningful data insights. To ensure accurate data insights that executives can rely on, companies must prioritize data automation tools that can help build data sets of high integrity and traceability. This involves producing accurate, complete, and contextual data sets for use and reuse across the organization. Businesses that combine data governance with investment in integrating data from many sources, profiling this data, and enriching it with third-party data will see results in the quality and accuracy of their data insights.  

In addition to building data that is accurate, complete, and contextual, ensuring data traceability is also critical to guarantee the insights’ accuracy and prevent incorrect insights in the future. This means each data set’s origin and each step it takes through every process is traceable within its data management system. By understanding their entire data supply chain, and employing data governance, companies can trust the insights pulled from their software and have documentation to prove it. Businesses implementing these strategies will see an invaluable return on investment due to the vast amount of time available to confidently focus on forwarding their data analytics, instead of working backward to understand where their insights came from.

Reduce Data Enrichment Costs to Unleash Next-Gen Insights

With the sheer amount of data in our world growing exponentially, investing in data enrichment throughout the entire organization is critical. Data enrichment allows companies to manage multiple data sets seamlessly and extract more business value from their data. However, the cost of third-party data, the tools and time needed to implement that data, and the need to hire specialized staff to manage the data all act as barriers to effectively deploy data enrichment. With the right strategies, these barriers can easily be overcome. 

It starts with decreasing the costs of implementing the data. By fine-tuning data sets, software, and processes to work together, data leaders can create faster timelines to data-driven business value. As a result of building a seamless process of interoperability, the need for specialized staff also decreases, as the data systems can better function with less manual intervention. Overcoming these barriers allows companies to spend more time applying the data to solve their business problems – unlocking data-driven business value – instead of consuming scarce resources preparing the data alone.

In sum, overcoming the barriers to achieving advanced data analytics is not an easy task, and can be overwhelming. However, the pay-off of implementing capabilities to meet the challenges is well worth the effort. Investing in data governance with privacy policies and procedures, improving analytics by building accurate and trustworthy data sets, and reducing the costs of data enrichment are three key strategies that set companies up for success. By building data processes that prioritize and achieve data integrity, leaders can spend more time advancing their data analytics and making informed, data-driven business decisions, instead of worrying about the accuracy of their data insights.