Business Intelligence (BI) – the ability to garner actionable, data-driven insights into the working of an enterprise – is revolutionizing how companies make decisions. It is not only making companies more efficient, it is enhancing the bottom line. In fact, business intelligence is such a major initiative for enterprises across the world that Gartner predicts the worldwide BI market will reach nearly $17 billion in 2016.
However, while data analytics and BI can yield better operational management, higher efficiency better processes, and help enterprises remain competitive or meet missions, BI tools are not always easy to implement. Here are three tips to consider when launching your BI platform:
Beware of Data Quality
You may still be able to make lemonade with rotten lemons, but I guarantee it won’t taste very good and may even make you sick.
Just like lemons for lemonade, for BI to be successful, you need to ensure your data is high quality. Otherwise, it won’t do its job. Lack of a data set selection, for example, can often limit users from receiving the proper data they desire or even having a comprehensive view of what their data is doing, which can be overwhelming. Frequently, collecting data via an integrator, such as a real-time sensor-based software, can result in data that isn’t “clean” as is can contain errors, anomalies or other unforeseen complications. Quality control is the first step toward implementing BI solutions for your enterprise and is especially important in critical infrastructure and government applications where data anomalies could have national security or safety implications.
Set Governance Rules
The amount of data generated today is staggering. To successfully leverage your data for BI, you need to ensure that you keep control of it in real time. Nothing is worse than drowning in data inundation. When considering adopting BI, an important step is defining how the data you gather will be processed, where it will go, how it will be stored, and many other data governance policies.
Tooling, for example, is a combination of processes and governance that works together to provide a single point of reference. This helps define and manage critical data across the enterprise, which also helps users get analysis faster. The reliability of the information extracted allows users to obtain good data that they can trust, allowing them to see correlations in the data to investigate.
Another benefit includes the “socialization” of data as a key performance indicator. By socializing performance, members not only have a company-wide view of what is going on, there’s an opportunity for organizations to set a new standard or best practice to help raise performance in all areas of the business. This is a concept that continues to provide advantages such as sustainability.
In the end, data analytics and BI tools are continuously changing. The next generation of analytics tools is leading users on a path of data discovery to help find more insights from their information. It is valuable for public and private sector entities to invest more time and resources into modern tools and personnel training. Then, individuals have an opportunity to report findings of performance across the enterprise to make key organizational decisions that will eventually define best practices.
(About the author: Steve Sarnecki is vice president of federal and public sector at OSIsoft)
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