In the past several years, the world has experienced massive data growth, while businesses have deployed modern analytics and content management solutions to capitalize on these much higher volumes of information. In many ways, leaders' eyes have seemingly shifted to large-scale projects, and some are concerned that the quality of data is beginning to lose ground to quantitative reasoning, leaving significant room for error.
Reporting analytics, big data and other tools used to turn massive volumes of unstructured and structured information into actionable insights in a timely fashion are no doubt capable of handling higher quantities, but do not completely exonerate managers and IT departments from sifting through and identifying the types of files with the most value. Rather, companies that focus on the collection, generation and analysis of lower volumes of high-quality information will tend to enjoy more fluidity within their strategies, as well as enhanced outcomes.
Now, it is important to note that the highest volumes of quality data will tend to yield the most prolific results, but most organizations are still in the fledgling stages of modern analytics use, and need to get their feet wet before scaling the projects. Experts, professionals and analysts in a wealth of industries and regions have started to speak out regarding the importance of focusing on information quality rather than quantity in the earlier stages of modern content management solution utilization.
In many instances, businesses will simply try to do too much too soon, and this will inherently hemorrhage the strategy and lead to even more complexity than the already challenging issues that arise when new analytics programs are implemented. Sometimes the best place to begin looking for evidence of the need for quality-centric content management and analytics is in the industries that have to oblige the most acute best practices.
Getting it right
To make matters more complex, big data is not the only thing challenging IT departments today, as small data is also proving to be relatively problematic given its novelty and demanding requirements. Genetic Engineering and Biotechnology News recently reported that pharmaceutical companies and others appear to be struggling to reconcile small data projects and move past the difficult aspects of these initiatives, which then makes it more challenging to scale up and use a greater volume of information.
According to the news provider, laboratories that have turned their focus toward small data projects and worked to perfect their methodologies in this arena stand to improve decision-making and initiative outcomes as a result, and this also generally involves a commitment to re-use of insights. For clarity, sound content management strategies will not allow information to slip through the cracks or be expunged until every ounce of value has been extracted and capitalized on.
The source pointed out that the one-and-done mentality many firms are succumbing to in their data management and analytics programs will inherently lead to waste and inefficiencies, whereas businesses that strive to build out more long-term strategies will have more to gain. This is not the easiest type of approach or strategy to create, but it is often going to bring back high enough returns to make the investment of budget and resources worthwhile.
Furthermore, Genetic Engineering and Biotechnology News noted that standards and more multi-dimensional instrumentation and data formats will be necessary to help broaden the scope of firms that are getting small and big data initiatives right. After all, this is still highly novel and uncharted territory for the majority of organizations, and guidance that can bring newcomers up to speed will be invaluable for the reporting analytics, content management and big data markets in the near future.
Begin with reporting analytics
Reporting analytics solutions are becoming more popular as companies work to boost the speed with which they can reconcile record-keeping requirements, respond to compliance-related demands and streamline the responsibilities of their employees. As these tools automate some of the front-end processes that lead to reports and fuel content management platforms, they serve as an exceptional entry point for those firms that want to start a more robust analytics program slowly and surely.
When data-targeted automation solutions are deployed more patiently and with direct, specific purposes, the chances of the firm losing sight of the importance of information quality will be inherently lower, leading to better results down the road. Slow and steady will always win the race in this regard, and decision-makers should consider how the deployment of advanced solutions will be received by those responsible for using the tools.
In the end, many companies will benefit from leveraging the support and guidance of a proven automation software service provider, as this will reduce some of the strain that would otherwise be placed on analysts and IT, leading to stronger return on investment.
