Data analytics is no longer only for large corporations. Now, a business intelligence manager can deliver numerous benefits to enterprises of any scale.
Organizations are reaching new heights of business intelligence through leveraging data analysis services. Through effective analysis, companies can uncover valuable intelligence that enables management to make strategic, evidence-based decisions. With enhanced business intelligence, companies are increasing productivity and profit. However, despite the clear advantages, advanced analytics has yet to be universally adopted. Therefore, when it comes to maximizing the value of data, business intelligence managers face daily challenges. Here, we explore five obstacles a business intelligence manager needs to overcome to encourage the wider organization to embrace data.
1. Advanced analytics require capital investment
Often, cost will deter a business from implementing a business intelligence initiative. This is particularly the case for small businesses that may be reluctant to focus their capital investments on unfamiliar territory. However, with the arrival of SSBI (self-service business intelligence) platforms, analytic capabilities are more accessible to small-scale and medium-sized enterprises. With this technology, a business intelligence manager can easily access data and clearly visualize findings for a comparatively low cost.
2. Limited resources for training
Although an organization might have the IT infrastructure and software necessary for advanced analytics, a company may lack the resources to train staff in data literacy. Once again, this is an issue that particularly affects small businesses. The absence of readily available, affordable data training means that business intelligence managers have to work harder to communicate their findings. However, it is often a challenge for the colleagues acquainting themselves with an interface as opposed to the information being excessively difficult to interpret.
3. Unstructured data requires management
New technology is producing swathes of unstructured data. Produced by images, video, and enormous amounts of unstructured textual data, these datasets harbor a wealth of intelligence. Therefore, an organization’s business intelligence manager needs to develop a strategy to process and manage this data. Once an effective protocol is in place, the data team can work towards capturing and interpreting diverse datasets on-the-fly.
4. Define goals to deliver results
Often, a business intelligence manager is under tremendous pressure to deliver results. Management wants to see evidence that their investment in analytics is immediately profitable. However, rushed solutions are rarely successful. Therefore, business intelligence managers need to create a clear, defined strategy that addresses each business goal individually. Once the management makes sure that data projects are sequential and well-structured, the business intelligence manager can work towards delivering tangible results.
5. Ambiguous business intelligence impact
If management initiates a business intelligence project, often C-levels are not entirely familiar with what to expect. Therefore, the benefits that advanced analytics generates are not immediately obvious. Therefore, the business intelligence manager will need to build a legible framework of prerequisites and objectives in order to clearly illustrate the advantages analytics is delivering.
A simple solution a business intelligence manager can deploy
These common issues may cause organizations to lose faith in the value of advanced analytics. Although a data project may be challenging at the outset, the benefits will make the investment worthwhile. In order to continually drive a business intelligence initiative forward, the business intelligence manager should emphasize that advanced analytics seeks to improve business processes. Therefore, colleagues will see the work less as an IT-focused undertaking and recognize data’s wider value. With a solid, goal-oriented data strategy, businesses can begin to harness the power of data to drive productivity and profit.
