CEOs and corporate boards want to see big data deliver results in company revenues, operations, and financial performance. The C-suite’s desire to get these results faster than ever before is causing many big data leaders to feel more pressure in four specific areas.
1: Data as an asset
In a 2014 Wall Street Journal article, Gartner analyst Douglas Laney observed that, “It’s flummoxing that companies have better accounting for their office furniture than their information assets…. You can’t manage what you don’t measure.” And in that same WSJ article, Leonard Nakamura, an economist at the Federal Reserve Bank of Philadelphia, estimated that corporate holdings of data and other intangible assets could be worth more than $8 trillion.
Data isn’t on corporate balance sheets yet, but as companies begin to evolve P&L functions where data is packaged and resold to others, it could be. Even for companies with no current plans to commercialize data, the data under management could be considered an untapped source of future business spinoffs and revenue generation, so companies have to derive ways to value it. This squarely places pressures on IT leaders and other data stewards to understand what this accumulation of data consists of and how best to mine it.
SEE: Data’s new home: Your company’s balance sheet (TechRepublic)
2: Confusion on which tools to use and how to use them
There are so many types of data manipulation, extraction, cleaning, normalization, etc., tools that it is hard for organizations to decide which ones are the best in class for their environments. Companies are also trying to figure out how to actually use these tools.
“A great example is a company using Internet of Things (IoT) sensors to monitor machine operations and health in a production facility,” said Shawn Rogers, chief research officer at Dell Statistica, a predictive analytics supplier. “If a sensor emits data once per second, this can be as much as 86,400 times per day. Do you really need to capture all of this communication to obtain a good reading of machine health and interactions?”
3: The belief that anyone can do big data analytics
More affordable analytics tools and cloud-based solutions are making analytics available to small companies as well as to large enterprises; these tools provide layers of abstraction between the highly technical work of accessing and manipulating data and the more graphic tools that enable users with only a little understanding of IT mechanics to use them.
Employees are used to unbridled data access through browsers, search engines, and social media, and many of them want to be included in the analytics process—even if they don’t have big data project experience.
These forces all come together to exert additional pressure on IT, which must provide data stewardship, governance, and security as data is spread across all levels of employees in various departments.
SEE: Hiring kit: Microsoft Power BI developer (Tech Pro Research)
4: Corporate sentiment that analytics is a must to stay competitive
Performing data analytics used to be considered a luxury among companies—something only “smart” people did—but now more companies are starting to look at the data they have accumulated as an asset. The fear of using analytics has disappeared. “Instead, what companies are asking themselves is, ‘How can I do a better job with data?'” said Dell’s Rogers.
The bottom line
In order for big data initiatives to be successful and deliver on time, project leaders must have clear goals, and users must have the necessary tools for solving analytics problems—as well as training on how to use those tools.