Innovative Data Solutions: What Companies Should Be Doing

4 Challenges of Data Management Modernization —and How to Solve Them

September 28, 2022
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Are you getting the most out of your data? If not, it might be time for a data management platform upgrade. In this blog, we’ll explore four common challenges you may face when modernizing—and how you can address these issues proactively.

In theory, having robust data management would allow businesses to expand more consistently and capture visitors more effectively. In practice, however, companies don’t actually utilize the data they and their partners generate to their fullest potential. 

VP and Principal Analyst at Forrester Mike Gualtieri highlights that “on average, between 60% and 73% of all data within an enterprise goes unused for analytics.” 

There’s a wide range of reasons why all this collected data goes unused—here at Hakkoda, we’ve done the number-crunching. According to our research, complexity generally tops the list for reasons why data goes discarded. What data organizations do retain is often overwhelming and bound up in legacy procedures and systems, further hamstringing use. 

The key to making all this valuable data work for you is a modern data management platform. However, implementing one from scratch comes with its own challenges. What should you be on the lookout for? And how can you solve for these potential problems proactively?

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Management Challenge 1: Rapidly Increasing Data Volumes

Collecting data is easy. But turning that raw data into workable intelligence is hard and expensive work. 

Forecasts suggest that globally, 97 ZB of data will be created, captured, copied, and consumed by 2022—skyrocketing further to 181 ZB by 2025! This is good news for companies looking to leverage data for innovation. The problem, however, is our capacity to collect data has nearly always outpaced the ability to process it. Exacerbating this discrepancy is the proliferation of data silos across any given organization—a phenomenon known as data sprawl

Data sprawl occurs when individual initiatives copy and export data across various systems. In our research, we’ve found that 97% of data leaders have multiple business intelligence (BI) and reporting apps—one in four of which have more than 10 apps. Data sprawl results in duplicate data sets, which further increases the amount of information companies must store.

The more your data is fragmented across your organization, the more challenging managing the costs become. For example, business units may cover data reporting costs while IT manages data architecture costs. The lack of transparency across business units comes with a steep price tag. According to McKinsey, for an institution with $5 billion in operating costs, data management expenses can run above $250 million.

To keep costs manageable, companies must create a data management strategy that accounts for storage, access, and usage at every level.

Management Challenge 2: Inefficient Data Governance Framework

Once you’ve collected and organized your data, you also have to govern your architecture correctly. This may seem obvious—but businesses often run into a host of challenges when trying to establish good governance frameworks.

Certain industries may face tighter regulations than others—for example, healthcare data must adhere to HIPAA guidance, in addition to general governance rules. But all businesses need to understand how to properly monitor and police data for both their customers and their own safety. This includes understanding what to report, who owns the responsibility for reporting, and where to find the information. 

Certain data management modernization challenges can make governance even more complicated. For example, traditional data governance tools built for data warehousing prove more of an obstacle than an assist in today’s environment. Data can be sourced from anywhere—and as such, siloing this information creates more problems than it solves. Companies need data governance tools that work with these new frameworks.

To combat these problems, organizations must establish a coherent, limber data governance framework that centers on ever-updating compliance standards and guidelines. Keeping these guidelines at the forefront of your efforts will ensure proper data handling at all levels of the organization. These guidelines also help prevent data redundancy and abandoned business data analytics assets.

A data governance framework built for the modern moment will center around cloud technology. In an increasingly decentralized data ecosystem, cloud computing-based providers like Snowflake give users global access and security that fit modern governance frameworks.

Management Challenge 3: Legacy Systems and Processes

Legacy systems can be vital to business workflows. However, once they outlast their usefulness, they become more of a hindrance than a help. 

Thirty-eight percent of business leaders surveyed in our report indicate legacy technology causes inefficiencies in their data management programs. These systems often rely on unsupported software, crash frequently, and are incompatible with newer technology. 

For example, many cloud and other SaaS solutions are incompatible with older systems. Legacy data is also usually organized on databases and in files of various formats; this forces teams to write reams of custom code to modernize the system.

To address this challenge, companies must choose a specialty database type and stick to it. A standardized, cohesive system is the only way to assure that data is consistently accessible.

Management Challenge 4: IT Talent Attrition

Data-driven companies rely on data analysts, data architects, and data scientists to drive analytics processes. Thankfully, business leaders seem to know this. Our research shows that 41% of leaders say data analysts provide the most value to their business—with data architects close behind at 35%

But these specialists can be hard to find and recruit. When talent leaves a company, it significantly diminishes its ability to keep its platforms dynamic and competitive.

So why does tech talent leave? And how can you mitigate the churn?

Although money certainly plays a factor, employees these days are looking for more control over their work environment. Perks such as a flexible schedule, challenging work, and growth opportunities are important to all workers. 

Additionally, tech workers no longer feel compelled to come into an office five days a week. As a result, many are reevaluating their relationships with their companies. According to Gartner research, tech workers are 10% less likely to stay in their current positions when compared to non-IT employees. Managed data service providers like Hakkoda can fill in the gaps or provide capacity when resources or skills are scarce.

Modernize Your Data Management With Hakkoda

Companies need to spot data management hurdles and quickly solve them in order to maximize the benefit of a data-rich business ecosystem. To keep at the top of your game—without wasting time and resources—it helps to have a savvy data management partner like Hakkoda by your side.

Hakkoda is an Elite Snowflake systems integrator and managed service provider that helps enterprises get their data house in order—and keep it that way. We run a subscription model, providing scalable teams of Snowflake-certified data engineers, architects, machine learning, and app dev experts to help companies navigate their data innovation journey. 

Are you ready to make your data work for you? Reach out to Hakkoda today!

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