Don't let the new, shiny data trends distract you

We’ve all heard of the old saying ‘Don’t run before you can walk’. Well, it applies to your data challenges as well.

It’s easy to get distracted by exciting new data integration tools and novel ways of working. But how do these shiny trends complement your business goals? Fixing the business-critical issues at the core of your data challenges is where you’ll find real value. And you often don’t need the shiniest tech to do it—there’s no point building a hovercar to get around a pothole.

So, let’s explore some of the trends that shouldn’t steal your attention and the issues you should be focusing on.

3 trends that might be distracting you

Trends arrive quickly, steal the limelight, and then shrink into the background. They’ll entertain you for a short while, but they won’t have a long-term impact on your data goals.

Some distracting trends could include:

1. Adaptive AI. AI continues to dominate the tech world. Adaptive AI systems speed up your decision making by adapting quickly to changes in your data sources. But, if your data isn’t ready for decision-making, incorporating AI isn’t going to bring much value.
2. Predictive analysis. Using historical data, machine learning helps you plan for the future. Predictive analysis is great if you have high quality past data available to you. But if your data sets are full of errors and unstandardized fields, you’re only going to end up with mismatched predictions.
3. Analytics in edge computing. Edge computing is where you process data as close to the originating source as possible. Expanding to the edge can increase your resilience and reliability by keeping your data streams flowing even when channels are running slowly. But, like the other entries on this list, if there are fundamental issues with your data, it doesn’t matter how close you are to where it came from.

What do you really need to focus on?

Improving your data quality

“Without clean data, or clean enough data, your data science is worthless.” — Michael Stonebraker, adjunct professor, MIT

In the end, it always comes back to data quality. If you experience issues with your data analytics, data quality should be your first touchpoint. After all, what you get out is only as good as what you put in. If your data quality is poor, your insights will be unreliable, inconsistent, and inaccurate.

There are eight dimensions of data quality, including criteria like accuracy, consistency and relevancy. Together, they determine the health of your data and how fit for purpose it is.

The business risks of poor data quality include:

• Reduced efficiency. If your teams need to manually fix data errors, this slows them down while they waste time making corrections.
• Inaccurate analyses. If your data is inconsistent, your analyses will be too.
• Lost revenue. Poor quality data costs organizations an average $12.9 million each year by wasting time and producing unreliable insights.

Validating your data quality is a detailed and thorough process. But small steps can make a big difference to your business intelligence. Using a validation tool helps you move quickly along the path to rapid data integrations. It increases your efficiency by taking away the need for manual corrections.

Addressing data engineering shortages

The number of data engineering roles grew by 50 percent in 2019, making it the fastest-growing job in the tech industry. It’s not surprising. Organizations like yours are dealing with increasing data volumes, so they need experts to build the architecture to contain it.

The problem is, the experts just aren’t there. In fact, there are only 2.5 candidates for every data engineering job posting on LinkedIn, which is some of the lowest figures for any technical role.

There are a few reasons for this:

• The unique skillset. Data engineers need a special set of skills to do their job, and they’re not easy to learn. Aside from mastering data feeds and databases, data engineers need impeccable knowledge of programming languages and infrastructure principles.
• Lack of education. There aren’t many university majors out there specializing in data engineering. Data science is often just a module or two in a broader course, but engineering barely sees the light of day. This means fewer students specialize in engineering.
• Low salaries. Sadly, the value a data engineer brings to a company isn’t always recognized in the salary. This dissuades talented individuals from pursuing data engineering as a career path.

So, what can you do about it? Well, you may want to look into new technology that allows less-experienced data teams to deal with complex issues competently. Powerful data platforms with automation capabilities—such as CloverDX—free up your experienced developers to work on your organization’s difficult data problems. It also provides the tooling to reduce the amount of repetitive work you need to do day to day, making the most of the talent you have in your data teams.

Increasing the speed of business insights

Real-time data helps you identify opportunities and act on them quickly. It also boosts the visibility of your data across your company, helping you flag issues quickly.

So, with that in mind, here are some ways you can speed up your business insights:

• Break down silo mentality. Silos ‘waste resources, kill productivity, and jeopardize the achievement of goals,’ according to Patrick Lencioni’s book. Breaking down silos in your organization frees your data and gets it into the hands of the people that need it.
• Use the right tools. If you use manual scripts, you’ll slow down your data insights and limit your business intelligence. Using a customizable, flexible data platform centralizes your data. It also allows you to create scalable workflows that are easy to manage.
• Optimize your data architecture. Is your architecture as efficient as it could be? Optimizing your architecture speeds up the time it takes to output actionable insights. Shifting to modern solutions and following best practices can help you with this.

Focus on what matters

‘All that glitters is not gold’ goes the proverb, and that resonates here too. Exciting new data trends may seem glamorous on the surface… But how much value will they actually bring to your organization? What problems are they going to solve?

If—like many other companies—you’re struggling with big data challenges, take a moment to reflect on what might actually be causing them. You might find the simplest solutions are the most effective.

Not sure where to start with your data problems? Contact us today to see how we can help.