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TDWI Upside - Where Data Means Business

BI Trends Developers Need to Know

How smart developers can help their enterprises get the most value from data.

Business has always run on information. That concept is not new. What is new is the impact massive data sets and computing power have had on how efficiently businesses can manipulate their information into actionable insights.

For Further Reading:

The Rise of the Data Curator

Career Switch Q&A: Negotiating the Path to Data Engineer or Scientist

Self-Service BI: Barriers, Benefits, and Best Practices

In less than a decade, business intelligence (BI) has radically transformed the way business gets done. From finance, sales, and marketing to production, logistics, and customer service, literally every business function across the enterprise has been transformed by data harnessed to do more work. took a look at the seven most in-demand tech jobs for 2018 and three BI-related positions topped the list, with average salaries starting at $83,000/year and climbing quickly north from there.

What's more, the heavy lifting of deep data analysis is moving to the cloud and the Internet of Things continues to collect billions of data points every day from connected devices, apps, and services.

Gartner refers to this as the intelligent digital mesh: "the entwining of people, devices, content, and services." It's a brave new world, and BI developers can play a crucial role in helping businesses make sense of it all.

Whether you live in a world of code or KPIs, here are four key trends for BI developers to watch in the upcoming year.

1. Continued demand for more powerful machine learning scripts to augment and improve analytics

A recent Capgemini study found that three in four organizations that implemented AI and machine learning saw sales of new products and services increase by at least 10 percent.

Demand for increasingly sophisticated scripts and machine learning will continue as users want to dig deeper into data. Properly implemented, machine learning can identify patterns earlier in the business cycle, enabling the organization to make decisions in anticipation of where consumers are going rather than where they are right now.

To put it simply, machine learning lets the machines do what they do best: chew through data and pick out the patterns so humans can spend more time determining how to use what they've learned.

2. Data curation is more important than ever

Garbage in, garbage out. As in so many other fields, GIGO is a foundational truth in business intelligence. Active curation must remain an active part of any organization's data policies.

As machine learning scripts become more complex and self-teaching, known-good data is crucial for its ability to generate accurate and actionable insights that drive business decisions. Furthermore, data quality is directly related to consistency and age. Thoughtful data curation ensures that data is clean and consistent as it flows into various repositories.

Data curation is also key to maintaining the "reputation" of an organization's data among its users. Users need to know that the insights they're seeing are based on a data set that's as complete as possible and statistically valid.

BI developers can play a crucial role in data curation by combining knowledge of the organization's business objectives with an intimate familiarity with the data, including any known blind spots.

3. The rising need for data engineers' know-how

There's never been a bigger demand for data engineers who understand the needs of the end user and can grasp the why behind their users' data needs. That's because they're crucial to maximizing their companies' return on investment in data.

Data engineers bring a potent trio of business, statistics, and coding skills together in designing and building the BI systems that drive businesses. They play a pivotal role in putting the organization's data to work by building the tools users need to leverage it. As a result, data engineers are in huge demand.

As of Q1 2018, Datanami found nearly 200,000 data engineer jobs posted on and Indeed alone, with most salaries starting at $100,000 or more annually.

4. Data mentoring is the next step in cultivating a more data-literate workforce

As a developer, you're in a unique position to teach your workforce to access and use data more productively. Data mentoring can put a developer's expertise to work by supporting users throughout their data journey. Put another way, mentoring teaches the person to fish.

Effective data mentors work hands-on with their users to help them discover how their data --from business-critical tools such as CRM, marketing automation, and ERP --"works" and how the data's various dimensions are related. Ultimately, users can leverage this newfound understanding to better inform paths of inquiry that lead to breakthrough uses of data.

A Final Word

There's no question about it. Data is both king and currency. A seemingly limitless supply of data is both the raw material and the fuel for businesses of every size and shape, but the data isn't going to use itself. To bring it to life and realize its real value, the inert stuff of data must be transformed into business intelligence. Smart developers will make that happen.

About the Author

Web Webster is a writer for, covering technology, marketing, education, and healthcare for companies across the U.S. You can contact the author via email.

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