As businesses transform into data-driven enterprises, data technologies and strategies need to start delivering value. Here are six data analytics trends to watch in the months ahead.

Data lakes will need to demonstrate business value or die

Data has been accumulating in the enterprise at a torrid pace for years. The internet of things (IoT) will only accelerate the creation of data as data sources move from web to mobile to machines.

For many enterprises, buoyed by technologies like Apache Hadoop, the answer was to create data lakes — enterprise-wide data management platforms for storing all of an organization’s data in native formats.

But while they have proven successful for storing massive quantities of data, gaining actionable insights from that data has proven difficult.

To survive 2018, data lakes will have to start proving their business value, says Ken Hoang, VP at data catalog specialist Alation.

Langley Eide, CSO of data analytics specialist Alteryx, predicts that 2018 will see analysts replacing "brute force" tools like Excel and SQL with more programmatic techniques and technologies, like data cataloging, to discover and get more value out of the data.

The CDO will come of age

As part of this new push to get better insights from data, Eide also predicts the CDO (Chief Digital Officer) role will come into its own in 2018.

"Data is essentially the new oil, and the CDO is beginning to be recognized as the linchpin for tackling one of the most important problems in enterprises today: driving value from data,"

Eide believes CDOs that enable resources, skills, and functionality to shift rapidly between centers of excellence and LOB will find the most success. For this, Eide says, agile platforms and methodologies are key.

Rise of the data curator?

Tomer Shiran, CEO and co-founder of analytics startup Dremio, predicts that enterprises will see the need for a new role: the data curator.

The data curator, Shiran says, sits between data consumers (analysts and data scientists who use tools like Tableau and Python to answer important questions with data) and data engineers (the people who move and transform data between systems using scripting languages, Spark, Hive, and MapReduce). To be successful, data curators must understand the meaning of the data as well as the technologies that are applied to the data.

Data governance strategies will be key themes for all C-level executives

The EU's General Data Protection Regulation (GDPR) applies directly in all EU member states, and it radically changes how companies must seek consent to collect and process the data of EU citizens.

Even though GDPR fines are potentially massive — the administrative fines can be up to 20 million Euros or 4 percent of annual global turnover, whichever is highest — many enterprises, particularly in the U.S., are not prepared.

To enforce GDPR properly requires the C-suite be informed, prepared, and communicative with all facets of their organization, says Scott Gnau, CTO of Hortonworks. Organizations will need a better handle on the overall governance of their data assets. But large breaches, like the Equifax breach that came to light in 2017, means they will struggle to balance providing self-service access to data for employees while protecting that same data from prospective threats.

"A key goal should be developing a system that balances democratization of data, access, self-service analytics, and regulation," Gnau adds.

The proliferation of metadata management continues

It's not just the GDPR, of course. The data deluge keeps growing, and governments around the world are imposing new regulations as a result. Within organizations, teams have much greater access to data than ever before. This all adds up to increased importance of data governance, along with data quality, data integration and metadata management.

Extracting meaningful insights and increasing operational efficacy will require flexible, integrated tools that allow users to quickly ingest, prepare, analyze and govern data, Williams says. Metadata management, in particular, will be essential to supporting data governance, regulatory compliance, and data management demands in enterprise data environments.

Predictive analytics helps improve data quality

As data projects move into production, data quality is increasingly a concern. This is especially true as IoT opens the floodgates further. Infogix says 2018 will see organizations turning to machine learning algorithms to enhance data quality anomaly detection. By using historical patterns to predict future data quality outcomes, businesses can dynamically detect anomalies that might otherwise have gone unnoticed or might only have been found much later through manual intervention.