This current period of human history is known as the digital age, and for good reason. Digital information is becoming ever-more prevalent, and as technology expands to fill larger roles in the lives of individuals and organizations, the amount of raw, available data is expanding exponentially.
In fact, about 90% of all data in the world has been created within the last two years. At the same time, reports suggest that about 2.5 exabytes of digital data (which is equal to 2.5 billion gigabytes), was generated on a daily basis in 2012. And with new data sources being created and discovered every day, there can be little doubt that those numbers are continuing to grow.
Businesses have discovered that although this data may be in no short supply, it is nonetheless extremely valuable. Data captured from computer portals, customer interactions, purchase records, websites, and an almost-limitless variety of other sources can be organized, analyzed, and employed to help inform business decisions to a much higher degree than has ever before been available.
But while the data itself is certainly important, it is even more essential for businesses to take a closer look at how that captured data is managed. To do this, businesses should be relying on business intelligence (BI).
Business intelligence is a process by which advanced technology is used to analyze and refine large amounts of captured data in order to extract reliable conclusions that can then be used by business leaders and decision makers to make better informed business decisions. BI is not a single, specific tool, but rather it encompasses a variety of hardware, software, applications, and techniques of different types.
Although BI may sound like simply another name for business analytics (BA), the two concepts actually have several distinct differences. Simply put, BI focuses on what has happened in the past, as well as the present, rather than focusing on the future, in order to create a proper reactive response. BA, on the other hand, is a concept that uses statistical analysis, data mining, and quantitative analysis to identify and review past business trends, while using that information to predict future trends, and creating proactive solutions to potential future issues. In order to get the most value out of the captured data, both business intelligence and analytics need to be employed. And in order for that to work, there are certain features that a superior business intelligence tool may employ.
While storing data can be useful, if it isn’t analyzed and put to use, then it’s really nothing more than a waste of virtual space. And while this may seem obvious, it’s something that many organizations have difficulty with. In fact, only about 1 percent of the world’s data is ever actually analyzed. The rest of that data either isn’t captured at all, or is captured and stored without being exploited. BI allows for in-depth, focused analysis, which has the potential to turn useless stores of data, into an invaluable resource.
To properly use business intelligence software to analyze data requires a strategy. Here are four data analysis strategies to consider:
These strategies are not mutually exclusive. Likewise, organizations shouldn’t have to change their structure to accommodate a new BI solution. The best BI software can be customized to fit the needs of the users. This means faster adoption, lower training costs, and fewer disruptions associated with having to redefine or reinvent existing processes. By finding the right mix of strategies in order to illuminate different areas of a business, and by customizing their software solutions to fit the specifics of the business in question, leaders and other decision makers can get the most value out of their collected data. Of course, for any of this to be possible, users need to be able to accurately visualize the data being shared.
When it comes to business intelligence, the concerns often have much less to do with being able to capture or mine enough valuable data. Instead, organizations are faced with the problems of having too much data to be able to accurately visualize it at a glance. Business intelligence software that provides easy-to-understand dashboards and other visualization elements solve this problem, and are integral to the user’s analysis process.
However, even visually-rich and easy-to-follow dashboards become less valuable when the data being shared isn’t current, which is why so many BI tools rely on real-time processing of data. Unlike batch processing, which processes data at regular intervals—such a weekly or monthly—resulting in ‘batches’ of analytics data, real-time processing means that data is collected, analyzed, and reported constantly, so that the data being accessed can never be considered ‘out-of-date.’ This allows businesses to identify and address concerns as they arise, rather than having to wait for batches of data to be processed. And, by automating certain processes within the BI strategy, users can give their BI programs the ability to discover hidden insights entirely on their own.
Machine learning refers to the ability of certain softwares and systems to be able to use analysis, self-training, observation, and experience to adapt to be able to process data and discover valuable information in ways that they were not specifically designed for. In short, machine learning allows programs to modify themselves to improve performance. This ability can be extremely useful when it comes to business intelligence. As more and more data is acquired, organized, analyzed, and acted upon, the overall effectiveness of the system increases. And, given the massive amounts of data being pushed through the process, the rate of improvement in machine-learning capable BI systems can be staggering. However, even the most intelligent programs need to be supported by well-trained users, which is why effective employee training is so essential.
There is something of a contradiction when it comes to certain BI software solutions, namely that the software must be advanced and complex enough to be able to handle massive amounts of data, while also being simple enough for users to adopt it without having to put too much effort into training. And while machine learning and automation have come a long way in the past few decades, they haven’t come far enough that they are able to operate entirely independent of human users. As businesses adopt ever more complex BI solutions, it is important for them to remember that no matter who it is that will be working directly with the programs, those users will require effective training.
Thankfully, many BI programs include in-depth training and tutorial programs, which aid in improving user adoption rates, without sacrificing effectiveness. BI software providers might also offer helpful online resources, or direct help via call centers or chat programs. When committing to a BI solution, it’s important to also commit to proper training by taking advantage of these resources. Business leaders can further encourage employees to become familiar with BI software, by providing opportunities to explore the programs and tools, create their own analytical plans, and work with hypothetical ‘practice’ data. As with anything else, practice is often the key to fully and effectively utilizing BI software solutions.
Approximately 89% of all American businesses are investing in data and data analytics processes including through business intelligence programs. With so much data being captured and refined, we may soon see a very definite segregation between those businesses that choose to take advantage of the benefits of BI, and those that do not.