7 trends shaping data today

1. Machine learning
Leveraging big data to better understand the inputs and context of historical data—and subsequently inform the analysis of more real-time data—will empower businesses to make decisions pre-event rather than post-event.

Machine learning won't be replacing the jobs of data scientists quite yet. Though Gartner recently predicted that over 40% of data science tasks will be automated by 2020. 

2. Log analytics
The ability to leverage real-time and/or streaming data is fundamentally dependent on the quality of the data pipeline. Businesses need an acute and holistic monitoring and alerting infrastructure that handles data security and integrity throughout their systems.

3. Specialization of job roles
As digital transformation turns nearly every organization into a technology organization, the ubiquity and increasing importance of data means that companies will need to adapt in two broad, overarching ways:

- By implementing tools specific to a discrete stage of the analytics solution delivery.

- By hiring highly skilled data experts versed in those stages and tools.

With specialization comes increased demand: IBM projects jobs for data and analytics professionals will grow from 364,000 openings to 2,720,000 by 2020.

4. Increased valuation of data assets
Decreasing expenses through operational efficiency is the top data initiative underway among Fortune 1000 leadership, according to a recent survey by NewVantage partners. The growth of data streaming platforms like Kafka and Amazon Kinesis will allow organizations to surface analytics even faster. The ability to tap into more real-time data means organizations can potentially speed up the monetization of their data.

5. Built-in data prep and governance in analytics tools
Business intelligence (BI) and analytics tools such as Tableau continue to enhance features like data certification that allow skilled practitioners to quickly achieve desired business deliverables, including providing layers of abstraction on top of previously-complex extract, transform, and load (ETL) and data management processes. The result: more accessible metadata and documentation for non-technical business users, who can then tackle analytics tasks on their own.

6. Elasticity and fluidity in analytics infrastructure
The ability to dynamically evaluate the load on a server or node, and then leverage cloud storage to mitigate performance issues will deliver invaluable advantage. How can you better your infrastructure? Check out thoughts from Pluralsight's Head of Analytics Engineering Architecture and Tableau Zen Master Mike Roberts on elasticity and fluidity. 

7. Data humanism
Data humanism is the process of fleshing out the unique and personal nature of data, mostly through data visualization. Turning big data into small data is a primary goal. Embracing the complexity and nuance of large datasets will allow us to humanize the data we collect and re-focus our efforts on data quality, rather than quantity.