As companies are increasingly recognizing, data is the new currency in business. Enterprises harness the power of their data and apply it to improve daily operations in many ways. But the more forward-thinking businesses are taking it a step further by using data to drive innovation and disrupt their industries. In this way, companies are moving toward a data-inspired future.
Companies that want to compete and win in a data-driven economy have to find a way to leverage the power of their data and extract maximum value. But that can be a challenge as the volume of data grows and new sources of information come online in a multitude of formats, threatening data overload. Data overload, if not carefully prepared for and mitigated, could prevent an organization from enjoying any of the benefits of a data-driven economy.
There are five potential challenges associated with data overload that an organization needs to be prepared to encounter:
1) Analysis paralysis from too much information and too many sources: There is already an enormous influx of data streaming into the enterprise from multiple sources, but it’s set to increase exponentially. Analysts predict that the Internet of Things (IoT) will comprise 200 billion connected devices by 2020. When IoT information is combined with other data sources, including cloud applications and social media, the volume of information can quickly overwhelm businesses.
It is a well documented fact that too many options can prove paralyzing for consumers, and businesses aren’t immune from that phenomenon. Without a solution that enables them to effectively handle the influx of information and harmonize data from multiple sources, businesses will face disrupted data workflows.
2) Silos created by fragmented data solutions: Big data works when companies can glean insights from a unified data pool. But too often, businesses face data fragmentation. They work with a range of big data tools that each address one part of the operation, including functions like data storage, cleansing, API management, data visualization, and more.
This piecemeal approach to data management results in multiple silos, which make governance and compliance incredibly challenging. Meanwhile data quality, security, and visibility decrease while expenses and inefficiency increase.
3) Data generation and resource disadvantages for small and mid-sized businesses: Big data is expensive; it requires an investment in resources to generate, process, and store all that information. Large companies like big box retailers have the cash and infrastructure to make big data work for them — they have assets like cameras, consumer apps, and point-of-sale software to generate and make sense of data so that they can continuously improve the customer experience.
But small and mid-sized businesses typically don’t have the resources to monitor, influence, and predict customer behavior. And even those that do usually do not have a sufficiently large customer base to generate macro-level insights.
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