20
Jan

What Trends in Data and Business Intelligence Mean for Your Hiring

15-Second Overview:

  • Several data and BI initiatives are being adopted at increasing rates
  • These include data quality management, automation, edge computing, and more
  • A bigger need for talent in these areas amplifies the skills shortage and forces companies to adapt their hiring strategies

There are certain topics in the IT industry that are always making waves. Big data and business intelligence initiatives are two areas that business leaders are especially interested in, given the sheer potential for improvements they can bring to any company. Let’s take a look at what’s trending in these areas along with what it all means for hiring in your business.

Data Quality Management Takes the Spotlight
It’s estimated that poor data quality costs businesses millions of dollars each year, yet 60% do not even measure the financial impact of low-quality data. As organizations begin experimenting with data analytics and business intelligence measures, they might have trouble trusting the outcomes. Naturally, sound business decisions can only be based on accurate information.Organizations are understanding data quality better, proven by the fact that DQM (Data Quality Management) didn’t even exist as a term until recently. Companies are now adopting a number of proactive and reactive best practices to ensure that their data is correct. They set aside time to focus on validity. They identify issues and go back to discover how they occurred in order to prevent future ones. They set clearly defined employee roles and expectations so that everyone knows what part they play in data quality. In short, companies are rapidly investing in the tools and people that can guarantee them accurate results.

Automation Increases
Even as businesses continue to build trust in data and business intelligence, they are seeing the power of these initiatives like never before. It’s no longer conceptual or theoretical; many companies have already produced at least some actionable insights that positively impacted their business decisions. Subsequently, they are increasingly looking to automation. After all, why have someone run an analysis and report if a software program can do it automatically at the end of each week, month, or quarter? Automation programs have increased in accuracy to the point where adoption is an easy decision.
In fact, data scientists say that they spend 80% of their time on tedious tasks that can be automated, like data preparation and selecting algorithms. As these key individuals focus on facilitating automation rather than repetitive tasks, they will find more time to draw out the deepest insights. That will further accelerate data analytics success and BI adoption.

Edge Computing Emerges
By 2025, 80% of traditional data centers will reside in the cloud. It’s natural for big data, business intelligence, and the cloud to go hand in hand. But as technology advances, people are using the cloud differently and more efficiently.
The more data that is housed offsite, the more that businesses will notice the need to process certain data and run critical operations closer to home. Running everything in the cloud can start to slow down processes with large amounts of data transmitted back and forth. Simply put, edge computing processes data at the edge of a cloud’s network, effectively shortening the digital distance between a company’s computers and the task at hand. By operating closer to the actual sources of data, latency is decreased while all the same cloud benefits remain.

Employee Buy-In Lags Behind
Regardless of the industry, initiative, or technology in question, there is always one wildcard that can make or break a company’s goals: employees. Shifting a culture’s perspective can be the biggest challenge when implementing new data analytics and BI procedures. Most people just aren’t receptive to change, and that means they can delay or disrupt the data processes that companies have worked so hard to create.
It’s not that employees don’t get it – they know that drawing insights out of data can provide game-changing information for businesses – it’s just that they aren’t completely bought-in to their role in the big picture. Perhaps some saw data initiatives fail at a previous company. Others may just see a new task to perform that falls outside their normal responsibilities. Whatever the case, organizations are seeking ways to better connect an individual’s role to the end results of data and BI projects.