Data is more technical and trickier than a simple metaphor. Businesses need predictions that are more measurable, usable and data-led. And in that spirit, here are my predictions for data in 2018.

Code-dependence is yesterday’s news. Data science will break free of its shackles

Algorithms are a commodity, but enterprises are under increased pressure to turn analysis into insights and insights into action—and in the current data science landscape, too many analytic models are never deployed.

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To address this challenge in 2018, there will be increased adoption of common frameworks for encoding, managing and deploying Machine Learning and analytic processes. The value of data science will become less about the code itself and more about the application of techniques.

As analytics becomes more pervasive in organisations, and the number of data sources and statistical languages (R, Python, Scala, etc.) continues to expand and evolve, there’ll be the need for a common, code-agnostic platform where LOB analysts and data scientists alike can preserve existing work and build new analytics going forward. Whether the user is no-code, low-code or code-friendly, this common platform will be key to making analytic models and applications easier to deploy for anyone in the enterprise.

Excuse me, I’m a data lake. Line of business analysts won’t be able to ignore what is lurking in my depths

To date, most analysts have not taken advantage of the vast amount of unstructured resources like clickstream data, IoT data, log data, etc. that have flooded into their data lakes—largely because it’s so hard to do so.

But truthfully, analysts aren’t doing their job if they leave this data untouched. It’s widely understood that many data lakes are underperforming assets—people don’t know what’s in there, how to access it, or how to create insights from the data.

>See also: Big data delivering business value with intelligence and analytics

This reality will change in 2018, as more CDOs and enterprises want better ROI for their data lakes. Analysts will begin replacing “brute force” tools like Excel and SQL and embracing more programmatic techniques as well as technologies that allow them to discover and get more value out of the data, such as data cataloguing.

The key will be the ability to extract information from the data lakes and combine that with information from other sources. It will be the insight gained from real-time sources blended with the deeper data in the data lakes that will lead to a surge of interest in the data lake.

The CDO is a real person that comes of age

The chief data officer is beginning to be recognised as the lynchpin for tackling one of the most important problems in enterprises today – driving value from data. Often with a budget of less than $10 million, one of the biggest challenges and opportunities for CDOs is making the much touted self-service opportunity a reality by bringing corporate data assets closer to line-of-business users.

>See also: The top 5 trends for digital transformation in 2018

In 2018, the CDOs that work to strike a balance between a centralised function and capabilities embedded in LOB will ultimately land the larger budgets. Success will come to CDOs who put greater focus on agile platforms and methodologies that allow resources, skills and functionality to shift rapidly between CoE and LOB.

2018

Let’s see what comes true next year. And if necessary, businesses will take that data, refine, and remodel, because people are living in the data age.

Anyone who loves data, and the thrill of solving, will discover that 2018 will be, hands-down, the year for you. Self-service platforms have never been easier to use, and a lot of the terrible mundanity of the grind of preparing data can be done automatically now. Let’s make 2018 our year.

Sourced by Jay Bourland, senior vice president Engineering at Alteryx