Over the past several years, we’ve seen significant advances in data analytics tools– increasingly aided by artificial intelligence (AI) and machine learning – to extract value from mountains of corporate data. This is good, especially considering the opportunities that the future holds.
By 2023, IDC estimates, digital data storage capacity worldwide will reach 11.7 zettabytes, or the equivalent of 11.7 trillion gigabytes.
While it’s true that only a portion of this global data resource will be relevant to any given company, the types and volumes of data that are relevant are also on a steep upward trajectory. The days in which a company only needed to collect and analyze data directly tied to its own operations and markets are already long gone.
The reason? Analyzing only traditional data sources simply can’t unearth the treasure trove of insights and opportunities hidden across the broad digital data universe. Fully tapping that potential requires not just analytics, but also synthesis – the blending of data from different industries, disciplines, and sources.
In essence, companies need to look beyond the “big data” on which they’ve been focusing to see the “wide data” that is now within their reach. Wide data includes not just structured data residing in corporate databases but also unstructured data in documents, email, videos, audio recordings and more.
Beyond such corporate sources, data may come from government agencies, social media networks, news feeds, and dozens of other data generators. One of the most difficult challenges for companies, in fact, is identifying which subset of the multitude of data sources available holds the most valuable information relevant to their business operations and decision making.
There’s little doubt that there is value to be mined. By 2025, 20% of revenue growth will come from “white space” offerings that combine digital services from previously unlinked industries.
Clearly, organizations need tools that not only analyze data, but help collect, clean, and standardize data from a wide range of sources. That need is challenging enough, but solving it won’t fully unlock the potential value of both big and wide data. A comprehensive solution also must be able to address the “last mile” – getting meaningful data into the hands of each employee who needs it.
Such “democratization” of data analytics requires companies to break away from old organizational hierarchies in which only a handful of data scientists and other specialists controlled this powerful capability. Making this shift, however, requires data analytics technologies and their outputs are easily accessible – and understandable – to business users.
To that end, there’s one more element that comes into play when it comes to fully exploiting big and wide data: the employees themselves. In an upcoming post, we’ll explore how data-averse employees can negatively impact your organization and, conversely, how boosting the data literacy of your workforce can pay big dividends.
For information about how Qlik can help your organization analyze and synthesize relevant data from across the expanding digital universe, visit https://www.qlik.com/us/products/why-qlik-is-different