The volume of available data continues to grow exponentially, making big data bigger than ever. To survive the age of big data, companies must join the data analytics revolution.
According to a recent report from the McKinsey Global Institute, the potential and opportunities of big data keeps growing. It’s clear that a few companies are keeping up with the accelerating pace of change, as these are integrating data analytics into their operations and strategies – but it’s far from the norm.
Despite that the data analytics revolution has started to gain momentum, the McKinsey report states that many companies have yet to exploit the full value of data analytics, as “most companies are capturing only a fraction of the potential value from data and analytics”.
Remarkably, an article from the Harvard Business Review recently stated that one of the biggest obstacles to incorporating data-driven insights into business processes is plain old access to the data. Typically, the barriers of data silos make data hard to access because data is stored in systems that operate in isolation from others.
The consequences of data silos
Silos of isolated data simply make data analytics impossible because silos, by nature, segregate and disperse data material. There are several problems related to data silos – the four largest complications are reviewed in the following.
By having multiple, redundant data sources the bigger picture of a situation becomes blurred. Consequently, decisions are based on gut instinct and inadequate data and employees may reach different conclusions about the same phenomena.
Duplicated data copies
When content is stored within several data sources, multiple copies of the same content appear. This causes complications in finding the original or most up-to-date version of the content.
Data sources have different search capacities that may provide very different search results. Furthermore, when data is scattered across sources it’s impossible to conduct an enterprise-wide search of content.
Disparate code base
Data sources are often incompatible, and trying to connect data silos without a common underlying code base will never be successful. Thus, with different underlying data schemas sources won’t be able to communicate with the same metadata or classifications.
For companies that want to thrive in the age of big data, breaking down data silos becomes an essential component for success.
Integration breaks down data silos
The McKinsey study states that solving the problem of siloed data is a prerequisite to capturing any value of data analytics at all and for competing in a data-driven world, stating, “the first step in creating value from data and analytics is accessing all the information that is relevant to a given problem”.
In this connection, the authors of the report state that by constantly integrating and combining data from multiple sources, companies have potential to generate new insights, as “massive data integration capabilities can break down organizational silos”.
But integration is not only about integrating external data sources – it’s also about integrating internal data sources, as the McKinsey report states that, “most organizations could also benefit from the ability to draw on new types of data from a range of external sources and combine it with their internal data”.
As more and more companies are realizing the advances of being data-driven, more companies are realizing the importance of their own internal data as well. However, sharing data across internal boundaries has shown not to be easy and thus the need for better integration of internal data has become essential when building the foundation of a data-driven company.
With an iPaas, organizations get a flexible integration infrastructure that manages internal data capabilities as these grow. By integrating internal data sources, companies can easily and quickly combine siloed and scattered data to gain insights that allow them to base decisions on evidence.
Do you want to connect your data for competitive advantages?
Integrate your internal data sources into the Cluedin platform to bring all of your company’s knowledge to the fingertips of your employees. Let Cluedin’s machine learning capabilities connect, enrich, and process the information in ways that add intelligence to the simplest of data.
Contact our integration specialists to learn how Cluedin can help your company become data-driven.