In a previous column, we discussed why such a wide gap exists between companies that have digital capabilities and those that have an actual high digital IQ.
Simply having next-generation software and tools isn’t enough. Organizations that share access to information broadly across their user base are fostering data democracies, in which a self-service business intelligence (BI) and analytics environment is created to cultivate the digital IQ of every user.
In addition, the ability to visualize data is forging new paths in the enterprise, enabling information to be consumed by users throughout an organization. But to fully realize the power of this discipline, business leaders must first understand the foundation behind data visualizations.
Visualizing data – that is, using compelling images and graphics to gain deeper understanding of your data – simplifies, speeds, and improves the decision-making process. Stemming from the need to comprehend complex data that arrives from a variety of sources and in a variety of formats, a new crop of standalone data discovery tools has taken root in the field of traditional BI vendors. The tools’ popularity lies in their simplicity and their ability to make raw data look shiny and pretty. But there’s more to data discovery and visualization than just good looks.
Applying technical and aesthetic skills through these tools to create pleasing visuals may be impressive, but the question for enterprises is whether those visuals serve an informational purpose. Are they actually helping people do their jobs better, thereby raising the digital IQ of the company?
This raises an important point: Visualizations aren’t created just to be stared at. They represent data that should be a stepping stone to a decision or a cause to act.
Today’s enterprises are dealing with a wealth of information, from disparate sources, in increasingly greater volumes. The variety of sources makes it more difficult for companies to capture, process, and analyze data. This means metadata, which tells the organization where, when, how, and why the information was collected in the first place, also should be a component of a data solution. Additionally, a large percentage of the data is unstructured, which requires special data ingestion and visualization capabilities to glean insight from such data.
When organizations seek a data discovery solution, they should evaluate tools based on their ability to offer enterprise-grade features such as data quality, security and encryption, version control, and auditing. But be warned—visualizations are a component of some of the leading-edge data discovery solutions on the market today, but not all visualization tools include essential data quality, security, and auditing features. Nor do all tools offer the ability to quickly move from finding insights to deploying applications based on these insights to improve decision- making or field operations. Operationalizing insights is the secret ingredient for success.
Regarding data quality, information must be reliable in order to trust the analytics and resulting visualizations. However, many data discovery tools erroneously assume that the enterprise’s data is clean and lack any built-in functionality to ensure the accuracy of the underlying information. Enterprises that rely on these standalone tools emphasizing visualization over data quality run the risk of creating an environment in which their decisions are based on untrustworthy or unsubstantiated information. Uncertainty behind data visualizations and rogue dashboards can prevent an organization from arriving at the ultimate goal, which is a single view of the truth as told by the data.
The best platforms for data discovery have integrated data quality and master data management capabilities to ensure the quality of the underlying information on which the visualizations are based. Such tools also enable organizations to employ dynamic applications such as predictive analytics, complex ad hoc queries, social analytics, enterprise search, performance management, and others.
These are a few examples of how BI and analytics technology have evolved. Data discovery can permit information to be consumed by all types of users in an organization more immediately and with less ambiguity by making complex data sets simple. To enable the success of this endeavor, however, it’s critical that organizations understand the role of data quality. The responsibility to choose the correct data discovery solution lies with the enterprise and how it treats its most valuable asset—its own data.
Rado Kotorov is the Chief Innovation Officer of Information Builders.
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