6 Terrifying Big Data Challenges (and How CDOs Can Solve Them)

Today's business data demands are higher than ever. More sources of information, bigger data warehouses and greater expectations from both business units and customers for instant results mean IT personnel and data scientists are under more pressure to deliver.

That's why more firms are recognizing the importance of having the right people in place to manage this, and at the head of this is the Chief Data Officer (CDO). In previous years, this may have been a luxury only the biggest and most data-intensive enterprises could have afforded, but today, it's increasingly a key part of any analytics team.

The growing role of the CDO

According to research from Strategy&, just 6% of firms globally had a CDO in 2015. However, by 2022, this had grown to more than one in five (21%). The percentage was even higher in Europe (26%), Latin America (27%) and North America (34%).

It also noted that companies in consumer-centric industries are more likely to have a CDO to manage their big data analytics strategy. Insurance providers were the most likely to employ these executives, with nearly half of firms (46%) having these employees. This was followed by banking (42%) and media and entertainment companies (40%).

However, making use of customer data to better serve consumers isn’t the only role big data analytics will have to play. Strategy&, for instance, noted that being able to keep close track of supply chains will be increasingly important, especially if firms are to weather disruptions such as climate change or future pandemics.

Despite this, many production-focused sectors, such as manufacturers, mining firms and oil and gas providers, still lack CDOs, which may make them less able to react to changing supply chain issues.

6 big data challenges CDOs will have to deal with

While having a CDO will be a vital first step for many companies, these professionals are likely to find they have a lot on their plate, especially those in companies with no strong history of using data at scale.

There will be a range of common challenges in big data analytics that, if not addressed effectively, can cause a big data strategy to fail. Here are a few that any CDO needs to be aware of, and how to solve them.

1. Understanding the technology

The first step will be to ensure that everyone actually understands what big data analytics entails and accepts the insights it delivers. If employees don't see the value of the efforts or have faith in its outcomes, they'll be reluctant to put it into practice. To address these issues, a good change management strategy is essential, to ensure users are educated about what the technology can do and have people advocating for it within the business.

2. Improving data quality

If you feed poor-quality data into your big data analytics system, you'll get inaccurate and misleading results - or garbage in, garbage out, as it's known. This doesn't just mean inaccurate data. Incomplete, duplicated or inconsistently-formatted information can all contribute to lower visibility into what's really going on and poor final recommendations.

As such, running effective data cleansing processes before raw data gets added to data warehouses or lakes is a must. This may add time and costs to the process, but it's vital if you want to enjoy a strong return on investment for your analytics platform.

3. Choosing the right big data tools

There are a wide range of tools and platforms available for managing big data analytics - so much so that selecting the right one can be a confusing and potentially costly challenge. Do you use Hadoop or Apache Spark to run your analytics? Which data storage platform is right for you? Get it wrong and you'll struggle to get results from a system that doesn't meet your needs.

This is where turning to outside consultants can be worth the expense, as they can provide an objective assessment of your situation and what will fit into your existing solutions.

4. A lack of skilled professionals

Big data analytics is still a relatively new field, which means skilled and experienced professionals are hard to come by. This is reflected in the age of CDOs themselves. According to M&A Executive Search, the average age of a typical C-Suite member is 56, whereas the majority of CDOs are between 25 and 34, indicating how this is still an emerging role. Hiring professionals with the right skills can quickly become very costly, so it pays to have internal programs to train existing staff to improve their data science skills.

5. Ensuring data is protected

Data governance and protection is a vital consideration for analytics platforms. This isn't just about keeping it safe from hackers and unauthorized users. You also need to consider how data is used. For instance, you'll have certain customer data that you won't be allowed to use in your analytics due to privacy regulations. You therefore need systems in place to identify and filter these parameters out of your solutions, as well as a comprehensive security solution.

6. Keeping costs under control

Big data can be an expensive business. Hardware and data center costs, ongoing software maintenance, the salaries and other perks you'll need to attract the best talent all add up. However, there are ways you can minimize these expenses. Cloud-based tools, for example, ensure you're not overpaying for more capacity than you need, while also making it easy to scale up on demand should the need arise.