Top Trends in Analytics for 2019
In less than a decade, data has become the primary mover and shaker for businesses. From using data analytics to drastically improve customer experiences to applying machine learning algorithms to decrypt data sets no human could understand, data plays an integral role in modern organizations. As 2020 and a new decade of data innovations approach, let’s take a moment to reflect on the top trends in analytics for 2019.
The Data Lake has Dried Up
Did I surprise you? Is it Time to Drain the Data Lake?
For much of the 2010s, companies invested heaving in big data and data lakes. But significant barrier to analytics was figuring out howto analyze the vast amounts of data stored in data lakes. The inefficacy of data lakes resulted in up to 73% of enterprise data going unanalyzed at most organizations. Even though vendors such as Datameer offered unique tools to clean and turn data lakes into actionable information, some companies still chose manual, coding intensive methods of digging through all the data in their data lake.
The two leading Hadoop vendors – Cloudera and Hortonworks – completed their $5.2 billion merger in January, and the combined entity focused less on data lakes. The third major Hadoop vendor, MapR, was acquired by HPE. Hadoop in the cloud continued modest growth, but leading cloud platforms Amazon Web Services and Microsoft Azure saw their general analytics and cloud data warehousing businesses growth even faster.
In the end, the openness and sophistication of the data lake created architectures that were too complex to operate and manage. Companies hired large teams of data engineers with specialized skills, making it difficult to gain value from their data lakes. The successful ones such as Vivint employed tools like Datameer and were able to obtain the value they sought.
Machine Learning and AI exploded
For years, experts have predicted that machine learning and AI would be the next frontier for data analytics. 2019 saw those predictions come to fruition. But not always in a good way.
Machine learning algorithms are capable of combing through data selecting and analyzing valuable data with shocking accuracy. AI can then turn that data into not only actionable insights, but also predictions about how it will evolve.
However, both methods must be used carefully. AI in particular has raised concerns, as AI-created algorithms draw increasing criticism. At YouTube, for example, LGBTQ+ content creators are suing the company, claiming YouTube’s AI-based algorithm demonetized their content for no reason. Moreover, they’ve provided evidence to back up those claims.
Don’t be afraid to take advantage of machine learning and AI at your organization, but ensure you put the proper governance and auditing in place, and be prepared to explain it.
Enhanced Data Literacy is Necessary
It’s almost impossible to find a department or team within a large organization that doesn’t use data. Take sales and marketing, for example – big data has fundamentally restructured how these departments function in about 50% of companies.
As a result of data’s ubiquitous nature in the modern workplace, more and more employees are being asked to rely on data to make decisions.
Unfortunately, not all of those employees are data literate. For this reason, organizations have been pouring investments into not only creating easy-to-understand analytics insights, but also educating employees on data analysis. As companies continue finding new ways to improve operations and enhance strategies using data, the need for data-literate employees will only increase, so don’t expect to see this trend go anywhere anytime soon.
Sixty-one percent of companies have failed to construct comprehensive, long-term plans to incorporate data into their operations, such as providing employees with data literacy training. The sooner your organization focuses on incorporating data in a holistic way, the more you’ll outpace competitors in your industry.
In 2020, expect to see companies taking advantage of data analytics in new and inventive ways, especially through technologies such as machine learning and AI. Look for a blog in January where we will make our predictions on the cool trends in analytics for 2020.
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